From 6147b0b72b3eb2300c0b258ac46c944633b0a8ef Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Fri, 30 Jun 2023 11:51:59 +0200 Subject: [PATCH 01/28] initial script for dataset generation --- script/dataset_generator/example_config.json | 17 +++++ .../generate_hdf5_dataset.py | 65 ++++++++++++++++++ script/dataset_generator/output/0.hdf5 | Bin 0 -> 212309 bytes 3 files changed, 82 insertions(+) create mode 100644 script/dataset_generator/example_config.json create mode 100644 script/dataset_generator/generate_hdf5_dataset.py create mode 100644 script/dataset_generator/output/0.hdf5 diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json new file mode 100644 index 0000000000..ccbfc58450 --- /dev/null +++ b/script/dataset_generator/example_config.json @@ -0,0 +1,17 @@ +{ + "camera": { + "px": 500, + "py": 500, + "u0": 320, + "v0": 240, + "h": 480, + "w": 640, + "randomize_params_percent": 10.0 + }, + + "dataset": { + "object_poses": true, + "camera_poses": true, + + } +} \ No newline at end of file diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py new file mode 100644 index 0000000000..6dc68c538e --- /dev/null +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -0,0 +1,65 @@ +import blenderproc as bproc +''' +Script to generate a synthetic dataset, stored in hdf5 format. +This dataset can contain: + - RGB images + - Object poses + - Segmentation maps + - Bounding boxes + - Depth map + - Normal map + +To run, blenderproc and other things should be installed (virtual environment recommended): +$ conda activate dataset_generation +$ pip install blenderproc numpy +$ blenderproc quickstart # verify that it works, install minor dependencies + + +''' +import numpy as np +import argparse +import json + +class Config: + def __init__(self, config_path): + self.config_path = config_path + self.parser = argparse.ArgumentParser(prefix_chars='--') + self.parser.add_argument('config', type=str, required=False, help='Path to the JSON config file') + self.parser.add_argument('height', type=int, required=False, help='Height of the generated images') + self.parser.add_argument('width', type=int, required=False, help='Width of the generated images') + self.parser.add_argument('px', type=float, required=False, help='Camera parameter: ratio between pixel width and focal length') + self.parser.add_argument('py', type=float, required=False, help='Camera parameter: ratio between pixel height and focal length') + self.parser.add_argument('u0', type=float, required=False, help='Camera parameter: x-axis location of the principal point, in pixels') + self.parser.add_argument('v0', type=float, required=False, help='Camera parameter: y-axis location of the principal point, in pixels') + self.parser.add_argument('camera-randomization-range', type=float, required=False, help='Amount of randomization applied to a camera\'s intrinsics parameters, as a percentage of the base values') + + + + + + + # with open() + + +if __name__ == '__main__': + + bproc.init() + + # Create a simple object: + obj = bproc.object.create_primitive("MONKEY") + + # Create a point light next to it + light = bproc.types.Light() + light.set_location([2, -2, 0]) + light.set_energy(300) + + # Set the camera to be in front of the object + cam_pose = bproc.math.build_transformation_mat([0, -5, 0], [np.pi / 2, 0, 0]) + bproc.camera.add_camera_pose(cam_pose) + + # Render the scene + data = bproc.renderer.render() + + # Write the rendering into an hdf5 file + bproc.writer.write_hdf5("output/", data) + diff --git a/script/dataset_generator/output/0.hdf5 b/script/dataset_generator/output/0.hdf5 new file mode 100644 index 0000000000000000000000000000000000000000..d3dda5544f84c9601fb4ef6d8008ed5cabe522eb GIT binary patch literal 212309 zcmeFZWmFx})9#78y9U?b76|U{?(XjH?(Xgm!8N$MyL)ga5M+`+$-C~&yty-L&9}L? zS)AHk{oB>m`|N(srur;GMFfOkp|GF;er*sC03ZMe|7retdHcO7asvOy^=JRjah=}{ 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z&Ea&+1<#Z!R|`M%5rQI`k@?96jN7I*EUgx;^s^wwRpyaR)6}e&AtEOlhGDl^A;jb3 zkp!2ty|T($_-J}N+u>!pu?x9Pye3Uw%jZ#k;%KOwG^b3G@OUofeZJWjq`H? zzsSvIzq#5j6$BXOLW$+7EvxeR@Gy^ay=<48X0s_-N)Yhzcv(r#BCebAr=Q+d1;43V zMfCpNTUDyDpB_IRB=QhKlc^@p^1?s}-r>{Jkz)*qnx#Bb-``)qx?Yu;$R)quU7I2U zKi};(`%Qx!rQ#ma`Poj^wRxs89tC$u0b_*JpD#EEkKmFRyIwNR5`1&BlagNEyy#Bn z<3sm+9@b?YDHc`PpF6O2Ro0&lk2x1^7#>cSkB9T+dHDF{*qwVlyWAMUG$=!C zCFcns0pRngt4y}uE{QQ8;eTZAyOy$dBlenderproc has been developed and is an extremely useful and flexible tool to generate realistic scenes from Python. - -In this tutorial, we will install blenderproc and use it to generate various simple but varied scenes containing an object of interest. We will then be able to train a YoloV7 with this data, for later use on real data. - - -\section dnn_synthetic_install Requirements - - -\section dnn_synthetic_next Next steps - - -*/ diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox new file mode 100644 index 0000000000..7547ad7e8a --- /dev/null +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -0,0 +1,180 @@ +/** + +\page tutorial-synthetic-blenderproc Tutorial: Generating synthetic data for deep learning with Blenderproc +\tableofcontents + +\section dnn_synthetic_intro Introduction + +In this tutorial, we will show how to generate synthetic data that can be used to train a neural network, thanks to blenderproc. + +Most of the (manual) work when training a neural network resides in acquiring and labelling data. This process can be slow, tedious and error prone. +A solution to avoid this step is to use synthetic data, generated by a simulator/computer program. This approach comes with multiple advantages: +- Data acquisition is fast +- It is easy to acquire accurate ground truth labels +- Variations in the training data can be easily added + +There are however, some drawbacks: +- More knowledge of the scene is required: in the case of detection, we require a 3D model of the object, which is not the case for true images +- A difference between simulated and real data can be apparent and negatively impact network performance (this is called the Sim2Real gap) + +The latter point is heavily dependent on the quality of the generated images and the more realistic the images, the better the expected results. + +Blender, using ray tracing, can generate realistic images. To perform data generation, Blenderproc has been developed and is an extremely useful and flexible tool to generate realistic scenes from Python code. + +Along with RGB images, Blenderproc can generate different labels or inputs: +- Depth map +- Normals +- Semantic segmentation +- Instance segmentation +- Bounding box +- Optical flow (not provided in our generation script) + +In this tutorial, we will install blenderproc and use it to generate simple but varied scenes containing objects of interest. +We provide a simple, object-centric generation script that should suffice in many cases. +However, since Blenderproc is easy to use, with many examples included in the documentation, readapting this script to your needs should be easy. + +\section dnn_synthetic_install Requirements + +First, you should start by installing blenderproc. First, start by creating a new conda environment to avoid potential conflicts with other Python packages. +\code{.sh} +$ conda create --name blenderproc python=3.10 pip +$ conda activate blenderproc +\endcode +\note Our generation script has been tested with blenderproc 2.5.0 (with Blender 3.3 under the hood) and python 3.10. + +You can then run the Blenderproc sample example with: +\code{.sh} +(blenderproc) $ blenderproc quickstart +\endcode +This may take some time, as Blenderproc downloads its own version of Blender and sets up its own environment. This setup will only be performed once. + +Once Blenderproc is done, you can check its output with: +\code{.sh} +(blenderproc) $ blenderproc vis hdf5 output/0.hdf5 +\endcode + +Blenderproc stores its output in HDF5 file format. Each HDF5 **may** contain the RGB image, along with depth, normals, and other modalities. + +For the simulator to provide useful data, we should obtain a set of realistic textures (thus helping close the Sim2Real gap). +Thankfully, Blenderproc provides a helpful script to download a dataset of materials from cc0textures.com, containing more than 1500 high resolution materials. +To download the materials, run +\code{.sh} +(blenderproc) $ blenderproc download cc_textures path/to/folder/where/to/save/materials +\endcode +\warning Because the materials are in high definition, downloading the full dataset may take a large amount of disk space (30+ GB). If this is too much for you, you can safely delete some of the materials or stop the script after it has acquired enough materials. While using a small number of materials can be useful when performing quick tests, using the full set should be preferred as variety helps when transferring your deep learning model to real world data. + + +\section dnn_synthetic_script Running the object-centric generation script + +We will now run the generation script. +The script places a random set of objects in a simple cubic room, with added distractors. Materials of the walls and distractors are randomized. + +The basic algorithm is: +\verbatim +For each scene: + Choose N target objects from the provided models + Add noise to the N objects (material properties, size, geometry) + Generate a scene: + - compute s = length of the larget diagonal of the axis-aligned bounding box of the largest object + - set room_size = s * random_scale_factor + - Create the ground, walls and ceiling of the room (with size room_size) and select a random material for each of them + - Add random distractors, sampled from spheres, cubes, cylinders and monkey heads (Suzanne) + For each distractor: + - Sample a random position and orientation in the room + - Select a random material from cc0 + - Add noise to PBR + - Potentially add displacement + - Potentially set distractor as emissive (emitting light) + - Add random lights, either point lights or spots + For each light: + - Sample a random intensity and position + - Sample a random color + - If the light is a spot, orient it so that it focuses on a target object + If simulating physics: + - Simulate physics for a fixed time and set final object poses + Remove objects that left the room (Physics collisions) + Sample camera poses: + For each sample to generate: + Do while camera pose is not correct: + - Select a target object + - Select a point of interest in the bounding box of the object + - Sample a random camera location in a clamped ball around the object + - Camera position is set to have a minimum/maximum distance to the point of interest that is dependent on the object size + - Set camera orientation to look at the point of interest, with a random rotation around the optical axis + - Camera pose is correct if target object is visible and camera does not clip through an object or a wall + - Call blender rendering + - Save data in HDF5 format + - If required, compute occlusion-aware bounding boxes + - If required, save object pose in camera frame +\endverbatim +Many randomization parameters can be modified to alter the rendering, as explained in \ref dnn_input_configuration. + +With this simple approach, we can obtain images such as: +\image html misc/blenderproc_rgb_example.png + + +\subsection dnn_input_objects 3D model format +To use this data generation tool, you should first provide the 3D models. +You can provide multiple models, which will be sampled randomly during generation. + +The models should be contained in a folder as such: +\verbatim +- models + - objectA + - model.obj + - model.mtl + - texture.png + - objectB + - another_model.obj + - another_model.mtl +\endverbatim + +When setting up the configuration file in \ref dnn_input_configuration, "models_path" should point to the root folder, models. +Each subfolder should contain a single object, in `.obj` format (with potential materials and textures). Each object will be considered as having its own class, the class name being the name of the subfolder (e.g., objectA or objectB). +The class indices start with 1, and are sorted alphabetically depending on the name of the class (e.g., objectA = 1, objectB = 2). + +\subsection dnn_input_configuration Generation configuration +Configuring the dataset generation is done through a JSON file. An example configuration file can be seen below: +\include example_config.json + +The general parameters are: + + + + + + + + + + + + + + + + + + + + + + +
NameType, possible valuesDescription
numpy_seedIntSeed for numpy's random functions. Allows for reproducible results.
blenderproc_seedStringSeed for Blenderproc. Allows for reproducible results.
models_pathStringPath to the folder containing the models. See \ref dnn_input_objects
cc_textures_pathStringPath to the folder containing the CC0 materials.
+ + + + +\section dnn_output Using and parsing the generation output + +\subsection dnn_output_yolov7 Using detections to train a YoloV7 + +\subsection dnn_output_custom_parsing Parsing HDF5 with a custom script + +link tuto blenderproc + + +\section dnn_synthetic_next Next steps + + +*/ diff --git a/doc/tutorial/tutorial-users.dox b/doc/tutorial/tutorial-users.dox index b70e38db58..c39e616dbe 100644 --- a/doc/tutorial/tutorial-users.dox +++ b/doc/tutorial/tutorial-users.dox @@ -170,6 +170,7 @@ This page introduces the user to other tools that may be useful. in one thread and display these images in an other thread. - \subpage tutorial-pcl-viewer
This tutorial explains how to use a threaded PCL-based point cloud visualizer. - \subpage tutorial-json
This tutorial explains how to read and save data in the portable JSON format. It focuses on saving the data generated by a visual servoing experiment and exporting it to Python in order to generate plots. +- \subpage tutorial-synthetic-blenderproc
This tutorial shows you how to easily generate synthetic data from the 3D model of an object and obtain various modalities. This data can then be used to train a neural network for your own task. */ /*! \page tutorial_munkres Munkres Assignment Algorithm diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index f2852183fd..9beeef79d6 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -11,11 +11,12 @@ "randomize_params_percent": 5.0 }, "rendering": { - "max_num_samples": 64 + "max_num_samples": 32, + "denoiser": null }, "scene": { "room_size_multiplier_min": 2.0, - "room_size_multiplier_max": 3.0, + "room_size_multiplier_max": 10.0, "simulate_physics": true, "max_num_textures": 50, "distractors": { @@ -23,18 +24,21 @@ "max_count": 50, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, - "displacement_max_amount": 0.01, - "pbr_noise": 0.5 + "displacement_max_amount": 0.0, + "pbr_noise": 0.5, + "emissive_prob": 0.1, + "emissive_min_strength": 5.0, + "emissive_max_strength": 25.0 }, "lights": { "min_count": 3, "max_count": 6, "min_intensity": 50, - "max_intensity": 300 + "max_intensity": 200 }, "objects": { - "min_count": 3, - "max_count": 10, + "min_count": 5, + "max_count": 5, "multiple_occurences": true, "scale_noise": 0.2, "displacement_max_amount": 0.0, @@ -45,8 +49,8 @@ }, "dataset": { "scenes_per_run": 1, - "num_scenes": 100, - "images_per_scene": 50, + "num_scenes": 50, + "images_per_scene": 20, "pose": true, "depth": false, "normals": false, @@ -57,8 +61,8 @@ "min_visibility_percentage": 0.3, "points_sampling_occlusion": 100 }, - "save_path": "output" + "save_path": "output_cube" }, - "models_path": "../megapose-models", - "cc_textures_path": "../cc0" + "models_path": "/home/sfelton/megapose-cube", + "cc_textures_path": "/home/sfelton/cc0" } \ No newline at end of file diff --git a/script/dataset_generator/generate_scene.py b/script/dataset_generator/generate_scene.py index 6cd9cdc5eb..c9eb6715ae 100644 --- a/script/dataset_generator/generate_scene.py +++ b/script/dataset_generator/generate_scene.py @@ -27,7 +27,8 @@ import time from blenderproc.python.types import MeshObjectUtility, EntityUtility - +from blenderproc.python.renderer import RendererUtility +import bpy def bounding_box_2d_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, camTworld: np.ndarray) -> Tuple[List[float], float, np.ndarray]: ''' @@ -35,7 +36,7 @@ def bounding_box_2d_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, returns A tuple containing: [xmin, ymin, xmax, ymax] in pixels the proportion of visible vertices (that are not behind the camera, i.e., negative Z) - The 2D points, in meters in camera space, in ViSP/OpenCV frame + The 2D points, in normalized units in camera space, in ViSP/OpenCV frame ''' worldTobj = homogeneous_no_scaling(object) obj = object.blender_obj @@ -59,12 +60,19 @@ def bounding_box_2d_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, return [mins[0], mins[1], maxes[0], maxes[1]], len(visible_points) / len(points_cam), visible_points_m_2d def homogeneous_inverse(aTb): + ''' + Inverse of a 4x4 homogeneous transformation matrix + ''' bTa = aTb.copy() bTa[:3, :3] = bTa[:3, :3].T bTa[:3, 3] = -bTa[:3, :3] @ bTa[:3, 3] return bTa def homogeneous_no_scaling(object: bproc.types.MeshObject): + ''' + Get the homogeneous transformation of an object, but without potential scaling + object.local2world() may contain scaling factors. + ''' localTworld = np.eye(4) localTworld[:3, :3] = object.get_rotation_mat() localTworld[:3, 3] = object.get_location() @@ -72,17 +80,23 @@ def homogeneous_no_scaling(object: bproc.types.MeshObject): def convert_to_visp_frame(aTb): ''' + Blender uses a frame that is different to ViSP's. + Blender: +X = Right, +Y = Up, +Z = Behind Same as converting to an OpenCV frame ''' return bproc.math.change_source_coordinate_frame_of_transformation_matrix(aTb, ["X", "-Y", "-Z"]) def convert_points_to_visp_frame(points: np.ndarray): + ''' + Convert a set of points to ViSP coordinate frame + ''' + points = points.copy() points[:, 1:3] = -points[:, 1:3] return points def point_in_bounding_box(object: bproc.types.MeshObject, bb_scale=1.0): ''' - Sample a random point in the bounding box of an object. + Sample a random point in the AXIS-ALIGNED bounding box of an object. the bounding box can be scaled with bb_scale if we wish to sample a point "near" the true bounding box Returned coordinates are in world frame ''' @@ -110,16 +124,20 @@ def object_bb_length(object: bproc.types.MeshObject) -> float: return size def randomize_pbr(objects: List[bproc.types.MeshObject], noise): + ''' + Randomize the physical properties of objects. + Objects should have the Principled BSDF for changes to be applied. + + ''' # set shading and physics properties and randomize PBR materials for j, obj in enumerate(objects): - rand_angle = np.random.uniform(30, 90) - obj.set_shading_mode('auto', rand_angle) - mat = obj.get_materials()[0] + # rand_angle = np.random.uniform(30, 90) + # obj.set_shading_mode('auto', rand_angle) + for mat in obj.get_materials(): keys = ["Roughness", "Specular", "Metallic"] for k in keys: base_value = mat.get_principled_shader_value(k) new_value = None - import bpy if isinstance(base_value, bpy.types.NodeSocketColor): new_value = base_value.default_value + np.concatenate((np.random.uniform(-noise, noise, size=3), [1.0])) #base_value.default_value = new_value @@ -132,7 +150,6 @@ def randomize_pbr(objects: List[bproc.types.MeshObject], noise): def add_displacement(objects: List[bproc.types.MeshObject], max_displacement_strength=0.05): for obj in objects: obj.add_uv_mapping("cylinder") - # Create a random procedural texture noise_models = ["CLOUDS", "DISTORTED_NOISE", "MAGIC", "MARBLE", "MUSGRAVE", "NOISE", "STUCCI", "VORONOI", "WOOD"] @@ -174,10 +191,13 @@ def __init__(self, config_path: Path, scene_index: int, scene_count: int): os.environ['BLENDER_PROC_RANDOM_SEED'] = str(blender_seed_int * (scene_index + 1)) self.objects = None self.classes = None - - def init(self): + ''' + Initialize the scene generation. + Load the target objects and the materials. + Setup the rendering + ''' print('Loading objects...') if self.objects is not None: for k in self.objects: @@ -185,11 +205,11 @@ def init(self): self.objects, self.classes = self.load_objects() self.save_class_file() print('Preloading CC0 textures...') - self.cc_textures = bproc.loader.load_ccmaterials(self.json_config['cc_textures_path'], preload=True) + self.cc_textures = bproc.loader.load_ccmaterials(self.json_config['cc_textures_path']) self.cc_textures = np.random.choice(self.cc_textures, size=self.json_config['scene']['max_num_textures']) self.setup_renderer() - def load_objects(self) -> Dict[str, bproc.types.MeshObject]: + def load_objects(self) -> Tuple[Dict[str, bproc.types.MeshObject], Dict[str, int]]: ''' Load the objects in the models directory The directory should have the following structure: @@ -198,10 +218,12 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: ----- model.obj --- obj_2_name/ ----- model.obj - Returns a dict where keys are the model names - (from the containing folder name of each object, - in the example case: 'obj_1_name' and 'obj_2_name') - and the values are the loaded model + Returns + - a dict where keys are the model names + (from the containing folder name of each object, + in the example case: 'obj_1_name' and 'obj_2_name') + and the values are the loaded model + - A dict where keys are model names and values are the class indices (from 1 to N for N objects) ''' models_dict = {} class_dict = {} @@ -233,7 +255,14 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: class_dict[model_name] = cls cls += 1 return models_dict, class_dict + def save_class_file(self): + ''' + Save the class for the dataset. + Write the classes to a text file. + Each line contain + This method only changes something if the class file does not exist or the generated scene is the first one. + ''' save_path = Path(self.json_config['dataset']['save_path']).absolute() cls_file = save_path / 'classes.txt' if self.scene_index == 0 or not cls_file.exists(): @@ -242,12 +271,14 @@ def save_class_file(self): cls_indices = sorted(cls_to_model_name.keys()) lines = [] for i in cls_indices: - lines.append(cls_to_model_name[i]) + lines.append(cls_to_model_name[i] + '\n') print(f'Writing classes to file: \n {lines}') f.writelines(lines) + def setup_renderer(self): depth = itemgetter('depth')(self.json_config['dataset']) bproc.renderer.set_max_amount_of_samples(self.json_config['rendering']['max_num_samples']) + RendererUtility.set_denoiser(self.json_config['rendering']['denoiser']) if depth: bproc.renderer.enable_depth_output(activate_antialiasing=False) # if normals: @@ -255,7 +286,9 @@ def setup_renderer(self): def render(self) -> Dict: ''' - + Call blender to render the frames. + Camera poses should already be sampled, and the scene created. + This returns a dictionary where each key (e.g "colors" for RGB rendering) contains a list of numpy arrays (1 numpy array per frame) ''' normals, segmentation = itemgetter('normals', 'segmentation')(self.json_config['dataset']) if normals: # Ugly, but doesn't work if it isn't called every render @@ -267,6 +300,12 @@ def render(self) -> Dict: return data def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dict): + ''' + Save the data to HDF5 format (one file per frame). + In addition, this function will compute the object information (bounding box, pose, class) if required. + + path: Path to the folder in which to save the HDF5 files + ''' json_dataset = self.json_config['dataset'] out_object_pose, out_bounding_box = itemgetter('pose', 'detection')(json_dataset) @@ -350,18 +389,17 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic continue object_data['bounding_box'] = [mins[0], mins[1], size[0], size[1]] - objects_data.append(object_data) + frames_data.append(objects_data) + data['object_data'] = frames_data bproc.writer.write_hdf5(str(path.absolute()), data, append_to_existing_output=False) - - def set_camera_intrinsics(self) -> None: ''' Set camera intrinsics from config. - Randomized depending on randomize_params_percent. This does not impact image resolution + Randomized depending on randomize_params_percent. This does not impact image resolution. ''' px, py, u0, v0, h, w , r = itemgetter('px', 'py', 'u0', 'v0', 'h', 'w', 'randomize_params_percent')(self.json_config['camera']) r = r / 100.0 @@ -374,11 +412,20 @@ def set_camera_intrinsics(self) -> None: bproc.camera.set_intrinsics_from_K_matrix(K, w, h) - def create_distractors(self, scene_size, other_objects) -> List[bproc.types.MeshObject]: + def create_distractors(self, scene_size: float, other_objects: List[bproc.types.MeshObject]) -> List[bproc.types.MeshObject]: + ''' + Add simple objects to the scene. + These objects have no class (no bounding box computed and does not appear in segmentation) + They are meant to add variation to the scene + Their position, texture and shape are randomized. + Their size is also randomized, in a range that is dependent on the scale of the room + The 3D models are simple primitives (cube, sphere, cone, etc.) and they are distorted through the displacement_max_amount_params + ''' json_distractors = self.json_config['scene']['distractors'] min_count, max_count = itemgetter('min_count', 'max_count')(json_distractors) min_size, max_size = itemgetter('min_size_rel_scene', 'max_size_rel_scene')(json_distractors) displacement_strength, pbr_noise = itemgetter('displacement_max_amount', 'pbr_noise')(json_distractors) + emissive_proba, emissive_min_strength, emissive_max_strength = itemgetter(*['emissive_' + s for s in ['prob', 'min_strength', 'max_strength']])(json_distractors) count = np.random.randint(min_count, max_count + 1) if count == 0: @@ -403,9 +450,25 @@ def sample_pose(obj: bproc.types.MeshObject): randomize_pbr(distractors, pbr_noise) if displacement_strength > 0.0: add_displacement(distractors, displacement_strength) + if emissive_proba > 0.0: + for distractor in distractors: + is_emissive = np.random.uniform(0.0, 1.0) < emissive_proba + if is_emissive: + for material in distractor.get_materials(): + emission_strength = np.random.uniform(emissive_min_strength, emissive_max_strength) + material.make_emissive(emission_strength) return distractors - def create_lights(self, scene_size, target_objects) -> List[bproc.types.Light]: + def create_lights(self, scene_size: float, target_objects: List[bproc.types.MeshObject]) -> List[bproc.types.Light]: + ''' + Add lights to the environment. + Sample n lights between min_count and max_count: + For each light: + - Randomly choose whether it is a point light (uniform lighting) or a spot (lighting in cone, focused on random target object) + - Randomly set its intensity between min_intensity and max_intensity + - Randomly set its position + - Randomly set its color + ''' light_json = self.json_config['scene']['lights'] min_count, max_count = itemgetter('min_count', 'max_count')(light_json) min_intensity, max_intensity = itemgetter('min_intensity', 'max_intensity')(light_json) @@ -428,6 +491,7 @@ def basic_light_sampling(light: bproc.types.Light) -> None: for _ in range(spot_light_count): light = bproc.types.Light('SPOT') basic_light_sampling(light) + # Point towards a target object looked_at_obj = np.random.choice(len(target_objects)) looked_at_obj: bproc.types.MeshObject = target_objects[looked_at_obj] poi = point_in_bounding_box(looked_at_obj) @@ -438,6 +502,15 @@ def basic_light_sampling(light: bproc.types.Light) -> None: return lights def create_target_objects(self) -> List[bproc.types.MeshObject]: + ''' + Sample targets objects (3D models that are provided by the user). These target objects have a class and their bounding box and pose may be computed. + N objects are sampled, between min_count and max_count. + If multiple_occurences is true, then a single object may appear multiple times in scene (and in the images) + For each object: + - Randomly set its pose in the room + - Randomly change its scale if scale_noise > 0.0 + Note that the target models should already be loaded before calling this function + ''' json_objects = self.json_config['scene']['objects'] min_count, max_count, replace = itemgetter('min_count', 'max_count', 'multiple_occurences')(json_objects) scale_noise, displacement_amount, pbr_noise = itemgetter('scale_noise', 'displacement_max_amount', 'pbr_noise')(json_objects) @@ -446,6 +519,7 @@ def create_target_objects(self) -> List[bproc.types.MeshObject]: if not replace: max_count = min(max_count, len(object_keys)) + min_count = min(min_count, max_count) count = np.random.randint(min_count, max_count + 1) selected_key_indices = np.random.choice(len(object_keys), size=count, replace=replace) @@ -466,6 +540,10 @@ def create_target_objects(self) -> List[bproc.types.MeshObject]: return objects def create_room(self, size: float) -> List[bproc.types.MeshObject]: + ''' + Create a basic square room, with size size. + The materials of the walls are randomly sampled + ''' ground = bproc.object.create_primitive('PLANE') ground.set_location([0, 0, -size / 2]) ground.set_scale([size / 2, size / 2, 1]) @@ -493,11 +571,15 @@ def create_room(self, size: float) -> List[bproc.types.MeshObject]: def create_scene(self): ''' Create a basic scene, a square room. - The size of the room is dependent on the size of the biggest object multiplied by a user supplied param - Each wall has a random texture, sampled from cc0 materials - Distractors are added randomly in the room - Random lights are also placed + The size of the room is dependent on the size of the biggest object multiplied by a user supplied param. + Each wall has a random texture, sampled from cc0 materials. + Distractors are added randomly in the room. + Random lights are also placed. + If simulate physics is true, then objects are dropped on the ground. + When using simulate physics, some objects may leave the room (through weird collisions): they are deleted. + ''' + objects = [] room_size = 0.0 room_size_multiplier_min, room_size_multiplier_max = itemgetter('room_size_multiplier_min', 'room_size_multiplier_max')(self.json_config['scene']) @@ -528,7 +610,6 @@ def sample_pose(obj: bproc.types.MeshObject): distractors = self.create_distractors(size, room_objects + objects) - if simulate_physics: for object in objects + distractors: object.enable_rigidbody(True) @@ -552,11 +633,19 @@ def filter_objects_outside_room(objects: List[bproc.types.MeshObject]) -> List[b objects = filter_objects_outside_room(objects) distractors = filter_objects_outside_room(distractors) - lights = self.create_lights(size, objects) - - return Scene(size, objects, distractors, room_objects, lights) + lights = self.create_lights(size, objects) # do this after potential physics sampling so that spots are correctly oriented + scene = Scene(size, objects, distractors, room_objects, lights) + return scene def sample_camera_poses(self, scene: Scene) -> None: + ''' + Sample camera poses in a scene. + Camera are sampled as such: + - Select a target object: choose a point of interest in its bounding box + - Choose a camera position in a "hollow ball": the position should be at at least (cam_min_dist_rel * object_size) meters from the object + and at at most (cam_max_dist_rel * object_size). Note that the object size is computed from the largest diagonal of the axis aligned bounding box and is thus overestimated. + - Fix camera orientation so that it points towards the point of interest + ''' dataset_config = self.json_config['dataset'] images_per_scene = dataset_config['images_per_scene'] cam_min_dist, cam_max_dist = itemgetter('cam_min_dist_rel', 'cam_max_dist_rel')(self.json_config['scene']['objects']) @@ -570,17 +659,19 @@ def sample_camera_poses(self, scene: Scene) -> None: object_size = object_bb_length(object) poi = point_in_bounding_box(object, bb_scale=1) tries_distance = 0 + # To fight the potential bias of having many more far camera than near cameras, Generate multiple hypothesis for a single distance before rejecting it. + # This gives more chance to near camera locations, that have a higher risk of occlusion. distance = np.random.uniform(cam_min_dist, cam_max_dist) while not good and tries_distance < 100: location = bproc.sampler.sphere(object.get_location(), distance * object_size, 'SURFACE') rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) - import bpy + cam = bpy.context.scene.camera.data clip_start = cam.clip_start focal_length = cam.lens / 1000.0 cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) - if object in bproc.camera.visible_objects(cam2world_matrix, 20) and bproc.camera.perform_obstacle_in_view_check(cam2world_matrix, {'min': focal_length + clip_start}, bvh): + if object in bproc.camera.visible_objects(cam2world_matrix, 10) and bproc.camera.perform_obstacle_in_view_check(cam2world_matrix, {'min': focal_length + clip_start}, bvh): good = True else: good = False @@ -591,29 +682,38 @@ def sample_camera_poses(self, scene: Scene) -> None: bproc.camera.add_camera_pose(cam2world_matrix) def run(self): + ''' + Generate the scenes and save the results. + ''' + # import cProfile, pstats, io + # from pstats import SortKey + # pr = cProfile.Profile() + # pr.enable() + save_path = Path(self.json_config['dataset']['save_path']) save_path.mkdir(exist_ok=True) self.init() - import bpy for i in range(self.scene_count): bproc.utility.reset_keyframes() self.set_camera_intrinsics() scene = self.create_scene() - bproc.loader.load_ccmaterials(self.json_config['cc_textures_path'], fill_used_empty_materials=True) + while len(scene.target_objects) == 0: + scene.cleanup() + scene = self.create_scene() self.sample_camera_poses(scene) - bpy.context.scene.render.use_persistent_data = True + # bpy.context.scene.render.use_persistent_data = False data = self.render() path = save_path / str(self.scene_index + i) path.mkdir(exist_ok=True) self.save_data(path, scene.target_objects, data) scene.cleanup() - - - - - - -from blenderproc.python.renderer import RendererUtility + # # ... do something ... + # pr.disable() + # s = io.StringIO() + # sortby = SortKey.CUMULATIVE + # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) + # ps.print_stats() + # print(s.getvalue()) if __name__ == '__main__': @@ -627,6 +727,7 @@ def run(self): # RendererUtility.set_render_devices(use_only_cpu=True) bproc.clean_up(clean_up_camera=True) bproc.init() # Works if you have a GPU + generator.run() From fd83c485c348a1563f68ebeac57ad9f9492bd0c3 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Wed, 19 Jul 2023 17:13:06 +0200 Subject: [PATCH 17/28] More documentation on configuration file --- doc/config-doxygen.in | 5 +- .../misc/tutorial-synthetic-blenderproc.dox | 392 +++++++++++++++++- script/dataset_generator/export_for_yolov7.py | 3 - 3 files changed, 393 insertions(+), 7 deletions(-) diff --git a/doc/config-doxygen.in b/doc/config-doxygen.in index 3a3d9ea664..b2e49cdc13 100644 --- a/doc/config-doxygen.in +++ b/doc/config-doxygen.in @@ -314,7 +314,7 @@ OPTIMIZE_OUTPUT_SLICE = NO # Note that for custom extensions you also need to set FILE_PATTERNS otherwise # the files are not read by doxygen. -EXTENSION_MAPPING = +EXTENSION_MAPPING = json=JavaScript # If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments # according to the Markdown format, which allows for more readable @@ -857,7 +857,8 @@ FILE_PATTERNS = *.h \ *.cpp \ *.doc \ *.dox \ - *.mm + *.mm \ + *.json # The RECURSIVE tag can be used to specify whether or not subdirectories should # be searched for input files as well. diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox index 7547ad7e8a..237c845896 100644 --- a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -53,7 +53,7 @@ Once Blenderproc is done, you can check its output with: (blenderproc) $ blenderproc vis hdf5 output/0.hdf5 \endcode -Blenderproc stores its output in HDF5 file format. Each HDF5 **may** contain the RGB image, along with depth, normals, and other modalities. +Blenderproc stores its output in HDF5 file format. Each HDF5 **may** contain the RGB image, along with depth, normals, and other modalities. For the simulator to provide useful data, we should obtain a set of realistic textures (thus helping close the Sim2Real gap). Thankfully, Blenderproc provides a helpful script to download a dataset of materials from cc0textures.com, containing more than 1500 high resolution materials. @@ -66,9 +66,12 @@ To download the materials, run \section dnn_synthetic_script Running the object-centric generation script -We will now run the generation script. +We will now run the generation script. The script places a random set of objects in a simple cubic room, with added distractors. Materials of the walls and distractors are randomized. +This script and an example configuration file can be found in the `script/dataset_generator` folder of your ViSP source directory. + + The basic algorithm is: \verbatim For each scene: @@ -109,6 +112,8 @@ For each scene: \endverbatim Many randomization parameters can be modified to alter the rendering, as explained in \ref dnn_input_configuration. + + With this simple approach, we can obtain images such as: \image html misc/blenderproc_rgb_example.png @@ -162,7 +167,390 @@ The general parameters are: +You can also control some of the rendering parameters. This will impact the rendering time and the quality of the generated RGB images. +These parameters are located in the "rendering" field. + + + + + + + + + + + + +
NameType, possible valuesDescription
max_num_samplesInt > 0Number of rays per pixels. A lower number results in noisier images (especially in scenes with large variations).
denoiserOne of [null, "INTEL", "OPTIX"]Which denoiser to use after performing ray tracing. null indicates that no denoiser is used. "OPTIX" requires a compatible Nvidia GPU. + Using a denoiser allows to obtain a clean image, with a low number of rays per pixels.
+ +You can also modify the camera's intrinsic parameters. The camera uses an undistorted perspective projection model. For more information on camera parameters, see vpCameraParameters. +These parameters are found in the "camera" field of the configuration. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameType, possible valuesDescription
pxfloatSee vpCameraParameters
pyfloatSee vpCameraParameters
v0floatSee vpCameraParameters
u0floatSee vpCameraParameters
hIntHeight of the generated images.
wIntWidth of the generated images.
randomize_params_percentfloat, [0, 1) + Controls the randomization of the camera parameters \f$p_x, p_y, u_0, v_0\f$. If randomize_params_percent > 0, then, each time a scene is created the intrinsics are perturbed around the given values. + For example, if this parameters is equal to 10 and \f$p_x = 500\f$, then the used \f$p_x\f$ when generating images will be in the range [450, 550]. +
+ + +To customize the scene, you can change the parameters in the "scene" field: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameType, possible valuesDescription
room_size_multiplier_minfloat > 1.0, < room_size_multiplier_max + Minimum room size as a factor of the biggest sampled target object. The room is cubic. + The size of the biggest object is the length of the largest diagonal of its axis-aligned bounding box. This tends to overestimate the size of the object. + If the size of the biggest object is 0.5m, room_size_multiplier_max = 2 and room_size_multiplier_max = 4, then the room's size will be randomly sampled to be between 1m and 2m. +
room_size_multiplier_maxfloat > room_size_multiplier_min + Minimum room size as a factor of the biggest sampled target object. The room is cubic. + The size of the biggest object is the length of the largest diagonal of its axis-aligned bounding box. This tends to overestimate the size of the object. + If the size of the biggest object is 0.5m, room_size_multiplier_max = 2 and room_size_multiplier_max = 4, then the room's size will be randomly sampled to be between 1m and 2m. +
simulate_physicsBooleanWhether to simulate physics. If false, then objects will be floating across the room. If true, then objects will fall to the ground.
max_num_texturesInt > 0Max number of textures per blenderproc run. If scenes_per_run is 1, max_num_textures = 50 and the number of distractors is more than 50, then the 50 textures will be used across all distractors (and walls). In this case, new materials will be sampled for each scene.
distractorsDictionarySee below
lightsDictionarySee below
objectsDictionarySee below
+ +Distractors are small, simple objects that are added along with the target objects to create some variations and occlusions. +To modify their properties, you can change the "distractors" field of the scene + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameType, possible valuesDescription
min_countInt >= 0, < max_count + Minimum number of distractors to place in the room. +
max_countInt > min_count + Maximum number of distractors to place in the room. +
min_size_rel_scenefloat > 0.0 + Minimum size of the distractors, relative to the room size. + If the room size is 0.5m and min_size_rel_scene = 0.1, then the minimum size of distractor will be 0.05m. + Scale is applied independently on each axis. +
max_size_rel_scenefloat < 1.0, > min_size_rel_scene + maximum size of the distractors, relative to the room size. + If the room size is 0.5m and max_size_rel_scene = 0.2, then the maximum size of distractor will be 0.1m. + Scale is applied independently on each axis. +
displacement_max_amountfloat >= 0.0 + Amount of displacement to apply to distractors. + Displacement subdivides the mesh and displaces each of the distractor's vertices according to a random noise pattern. + This option greatly slows down rendering: set it to 0 if needed. +
pbr_noisefloat >= 0.0 + Amount of noise to add to the material properties of the distractors. + These properties include the specularity, the "metallicness" and the roughness of the material, according to Blender's principled BSDF. +
emissive_probfloat >= 0.0 , <= 1.0 + Probability that a distractor becomes a light source: its surface emits light. Set to more than 0 to add more light variations and shadows. +
emissive_min_strengthfloat >= 0.0, < emissive_max_strength + Minimum emission strength for a distractor that emits lights. In Watts/m². +
emissive_max_strengthfloat > emissive_min_strength + Maxmimum emission strength for a distractor that emits lights. In Watts/m². +
+ +To change the lighting behaviour, see the options below: + + + + + + + + + + + + + + + + + + + + + + +
NameType, possible valuesDescription
min_countInt >= 0, < max_count + Minimum number of lights in the scene. +
max_countInt > min_count + Maximum number of lights in the scene +
min_intensityfloat > 0.0, < max_intensity + Minimum intensity of a light. In Watts. +
max_intensityfloat > min_intensity + Maximum intensity of a light. In Watts. +
+To change the sampling behaviour of target objects, see the properties below: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameType, possible valuesDescription
min_countInt >= 0, < max_count + Minimum number of target objects in the scene. +
max_countInt > min_count + Maximum number of target objects in the scene. +
multiple_occurencesBoolean + Whether a single object can appear multiple times in the same scene (sampling with replacement). +
scale_noisefloat >= 0.0 + Object size noise. if scale_noise > 0.0, the object is scaled uniformly on all axes (it does not appear stretched) +
displacement_max_amountfloat >= 0.0 + Amount of displacement to apply to target objects. + Displacement subdivides the mesh and displaces each of the distractor's vertices according to a random noise pattern. + This option greatly slows down rendering: set it to 0 if needed. + Note that this is in absolute units: and does not vary depending on the size of the object. +
pbr_noisefloat >= 0.0 + Amount of noise to add to the material properties of the target objects. + These properties include the specularity, the "metallicness" and the roughness of the material, according to Blender's principled BSDF. +
cam_min_dist_relfloat >= 0.0, < cam_max_dist_rel + Minimum distance of the camera to the point of interest of the object when sampling camera poses. This is expressed in terms of the size of the target object. + If the target object has a size of 0.5m and cam_min_dist_rel = 1.5, then the closest possible camera will be at 0.75m away from the point of interest. +
cam_max_dist_relfloat >= cam_min_dist_rel + Maximum distance of the camera to the point of interest of the object when sampling camera poses. This is expressed in terms of the size of the target object. + If the target object has a size of 0.5m and cam_max_dist_rel = 2.0, then the farthest possible camera will be 1m away from the point of interest. +
+ +Finally, we can customize the dataset that we will generate from the given scenes. +This includes the number of scenes, images, and what information to save. + +All the data will be stored in HDF5 format, which can then be unpacked later. + +To customize the dataset, modify the options in the "dataset" field: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameType, possible valuesDescription
save_pathStringPath to the folder that will contain the final dataset. This folder will contain one folder per scene, and each sample of a scene will be its own HDF5 file.
scenes_per_runInt > 0 + Number of scenes to generate per blenderproc run. Between blenderproc runs, Blender is restarted in order to avoid memory issues. +
num_scenesInt > 0 + Total number of scenes to generate. Generating many scenes will add more diversity to the dataset as object placement, materials and lighting are randomized once per scene. +
images_per_sceneInt > 0 + Number of images to generate per scene. The total number of samples in the dataset will be num_scenes * images_per_scene. +
poseBoolean + Whether to save the pose of target objects that are visible in the camera. The pose of the objects are expressed in the camera frame as an homogeneous matrix \f$^{c}\mathbf{T}_{o}\f$ +
depthBoolean + Whether to save the depth buffer associated to the RGB image. Same size as the RGB image. +
normalsBoolean + Whether to save the normal map associated to the RGB image. Same size as the RGB image. + The normals are 3D unit vectors, expressed in the camera frame. +
segmentationBoolean + Whether to save the segmentation maps (by class and by instance). + Segmentation by class only contains the target objects (class >= 1). + Segmentation by instance includes every visible object. +
detectionBoolean + Whether to save the bounding box detections. In this case, bounding boxes are not computed from the segmentation map (also possible with Blenderproc), but rather in way such that occlusion does not influence the final bounding box. + The detections can be filtered with the parameters in "detection_params". +
detection_params:min_size_size_pxInt > 0 + Minimum side length of a detection for it to be considered as valid. Used to filter really far or small objects, for which detection would be hard. +
detection_params:min_visibilityfloat [0.0, 1.0] + Percentage of the object that must be visible for a detection to be considered as valid. The visibility score is computed as such: + First, the vertices of the mesh that are behind the camera are filtered. Then, the vertices that are outside of the camera's field of view are filtered. Then, we randomly sample "detection_params:points_sampling_occlusion" points to test whether the object is occluded (test done through ray casting). + If too many points are filtered, then the object is considered as not visible and detection is invalid. +
+ + +\section dnn_run_script Running the script to generate data + +\code{.sh} +(blenderproc) $ python +\endcode \section dnn_output Using and parsing the generation output diff --git a/script/dataset_generator/export_for_yolov7.py b/script/dataset_generator/export_for_yolov7.py index d2f00fa3af..813f4c9d97 100644 --- a/script/dataset_generator/export_for_yolov7.py +++ b/script/dataset_generator/export_for_yolov7.py @@ -52,9 +52,6 @@ def export_split(hdf5_paths: List[Path], images_export_path: Path, labels_export for cls in detection_data.keys(): print(f'\tClass: {cls}\t Detection count: {detection_data[cls]}') - - - if __name__ == '__main__': parser = argparse.ArgumentParser('Convert an HDF5 dataset generated with Blenderproc to a YoloV7-ready format') parser.add_argument('--input', type=str, required=True, help='''Path to the HDF5 dataset. 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z9@lP-IyjjcHUpfy2l3OdR8!Icu)ULP8nD}5-N)=TW^P#R?3B!R*Fvw@#0l(E9a1>7 z?}G=WNZdmhv3g*!^nmn|8pN3|!0O{c@yjUW1^X`t&u-}#^iT1E0R zbw9?Jqj=FYbqm?PJ>Oobs^7TrbQf^r>P-QMYLMirfxJA}3Xp5nLe@DC+Dc-GMm@TI z?P-C2adw)d+iNuIe~bmX7sXZqlb!|+J_DqsPRG zdr(`z?W!S>4O3k_f+TyE2XJd7-x4D`Cn^NF<2*?9T!vAu6681I>7p|;@x_Ww%0a3& z4_Zs2(dWc2!(KuJq~+6Kt9!e47MO5afeNP?IZEJAPfzt-uI>EyD4}+DJ_Cs!Y7k|S zlL_Re#XE2?GEqzt)P?!_zIOf`a{58d%NameType, possible valuesDescription px - float + Float See vpCameraParameters py - float + Float See vpCameraParameters v0 - float + Float See vpCameraParameters u0 - float + Float See vpCameraParameters @@ -220,7 +221,7 @@ These parameters are found in the "camera" field of the configuration. randomize_params_percent - float, [0, 1) + Float, [0, 1) Controls the randomization of the camera parameters \f$p_x, p_y, u_0, v_0\f$. If randomize_params_percent > 0, then, each time a scene is created the intrinsics are perturbed around the given values. For example, if this parameters is equal to 10 and \f$p_x = 500\f$, then the used \f$p_x\f$ when generating images will be in the range [450, 550]. @@ -234,7 +235,7 @@ To customize the scene, you can change the parameters in the "scene" field: NameType, possible valuesDescription room_size_multiplier_min - float > 1.0, < room_size_multiplier_max + Float > 1.0, < room_size_multiplier_max Minimum room size as a factor of the biggest sampled target object. The room is cubic. The size of the biggest object is the length of the largest diagonal of its axis-aligned bounding box. This tends to overestimate the size of the object. @@ -243,7 +244,7 @@ To customize the scene, you can change the parameters in the "scene" field: room_size_multiplier_max - float > room_size_multiplier_min + Float > room_size_multiplier_min Minimum room size as a factor of the biggest sampled target object. The room is cubic. The size of the biggest object is the length of the largest diagonal of its axis-aligned bounding box. This tends to overestimate the size of the object. @@ -298,7 +299,7 @@ To modify their properties, you can change the "distractors" field of the scene min_size_rel_scene - float > 0.0 + Float > 0.0 Minimum size of the distractors, relative to the room size. If the room size is 0.5m and min_size_rel_scene = 0.1, then the minimum size of distractor will be 0.05m. @@ -307,7 +308,7 @@ To modify their properties, you can change the "distractors" field of the scene max_size_rel_scene - float < 1.0, > min_size_rel_scene + Float < 1.0, > min_size_rel_scene maximum size of the distractors, relative to the room size. If the room size is 0.5m and max_size_rel_scene = 0.2, then the maximum size of distractor will be 0.1m. @@ -316,7 +317,7 @@ To modify their properties, you can change the "distractors" field of the scene displacement_max_amount - float >= 0.0 + Float >= 0.0 Amount of displacement to apply to distractors. Displacement subdivides the mesh and displaces each of the distractor's vertices according to a random noise pattern. @@ -325,7 +326,7 @@ To modify their properties, you can change the "distractors" field of the scene pbr_noise - float >= 0.0 + Float >= 0.0 Amount of noise to add to the material properties of the distractors. These properties include the specularity, the "metallicness" and the roughness of the material, according to Blender's principled BSDF. @@ -333,21 +334,21 @@ To modify their properties, you can change the "distractors" field of the scene emissive_prob - float >= 0.0 , <= 1.0 + Float >= 0.0 , <= 1.0 Probability that a distractor becomes a light source: its surface emits light. Set to more than 0 to add more light variations and shadows. emissive_min_strength - float >= 0.0, < emissive_max_strength + Float >= 0.0, < emissive_max_strength Minimum emission strength for a distractor that emits lights. In Watts/m². emissive_max_strength - float > emissive_min_strength + Float > emissive_min_strength Maxmimum emission strength for a distractor that emits lights. In Watts/m². @@ -373,14 +374,14 @@ To change the lighting behaviour, see the options below: min_intensity - float > 0.0, < max_intensity + Float > 0.0, < max_intensity Minimum intensity of a light. In Watts. max_intensity - float > min_intensity + Float > min_intensity Maximum intensity of a light. In Watts. @@ -413,14 +414,14 @@ To change the sampling behaviour of target objects, see the properties below: scale_noise - float >= 0.0 + Float >= 0.0 Object size noise. if scale_noise > 0.0, the object is scaled uniformly on all axes (it does not appear stretched) displacement_max_amount - float >= 0.0 + Float >= 0.0 Amount of displacement to apply to target objects. Displacement subdivides the mesh and displaces each of the distractor's vertices according to a random noise pattern. @@ -430,7 +431,7 @@ To change the sampling behaviour of target objects, see the properties below: pbr_noise - float >= 0.0 + Float >= 0.0 Amount of noise to add to the material properties of the target objects. These properties include the specularity, the "metallicness" and the roughness of the material, according to Blender's principled BSDF. @@ -438,7 +439,7 @@ To change the sampling behaviour of target objects, see the properties below: cam_min_dist_rel - float >= 0.0, < cam_max_dist_rel + Float >= 0.0, < cam_max_dist_rel Minimum distance of the camera to the point of interest of the object when sampling camera poses. This is expressed in terms of the size of the target object. If the target object has a size of 0.5m and cam_min_dist_rel = 1.5, then the closest possible camera will be at 0.75m away from the point of interest. @@ -446,7 +447,7 @@ To change the sampling behaviour of target objects, see the properties below: cam_max_dist_rel - float >= cam_min_dist_rel + Float >= cam_min_dist_rel Maximum distance of the camera to the point of interest of the object when sampling camera poses. This is expressed in terms of the size of the target object. If the target object has a size of 0.5m and cam_max_dist_rel = 2.0, then the farthest possible camera will be 1m away from the point of interest. @@ -536,7 +537,7 @@ To customize the dataset, modify the options in the "dataset" field: detection_params:min_visibility - float [0.0, 1.0] + Float [0.0, 1.0] Percentage of the object that must be visible for a detection to be considered as valid. The visibility score is computed as such: First, the vertices of the mesh that are behind the camera are filtered. Then, the vertices that are outside of the camera's field of view are filtered. Then, we randomly sample "detection_params:points_sampling_occlusion" points to test whether the object is occluded (test done through ray casting). @@ -548,19 +549,150 @@ To customize the dataset, modify the options in the "dataset" field: \section dnn_run_script Running the script to generate data +Once you have configured the generation to your liking, navigate to the `script/dataset_generator` located in your ViSP source directory. + +You can then run the `generate_dataset.py` script as such \code{.sh} -(blenderproc) $ python +(blenderproc) user@machine:~/visp/script/dataset_generator $ python generate_dataset.py --config path/to/config.json \endcode +If all is well setup, then the dataset generation should start and run. + + +\warning If during generation, you encounter a message about invalid camera placement, try to make room_size_multiplier_min and room_size_multiplier_max larger, so that more space is available for object placement. + +To give an idea of generation time, generating 1000 images (with a resolution of 640 x 480) and detections of a single object, with a few added distractors, takes around 30mins on a Quadro RTX 6000. + +Once generation is finished, you are ready to leverage the data to train your neural network. \section dnn_output Using and parsing the generation output -\subsection dnn_output_yolov7 Using detections to train a YoloV7 +The dataset generated by Blender is located in the "dataset:save_path" path that you specified in your JSON configuration file. + +The dataset has the following structure +\verbatim +- dataset + - 0 + - 0.hdf5 + - 1.hdf5 + - ... + - 1 + - 0.hdf5 + - 1.hdf5 + - ... + - ... + - classes.txt +\endverbatim + +There is one subfolder per scene, and each HDF5 file is a single sample that may contain RGB, depth, normals, etc. + +You can visualise the generated images (RGB, depth, normals, segmentation maps) by running +\code{.sh} +(blenderproc) $ blenderproc vis hdf5 path/to/your/output/0/*.hdf5 +\endcode + +You will then see something that looks like this, depending on the outputs that you chose in the configuration file: +\image html blenderproc_viz.png + +where you can replace the "0" in the path with another number, which corresponds to the generated scene index. +You can also specify the path to a single HDF5 file to view one sample at a time. + +\subsection dnn_output_yolov7 Using detections to finetune a YoloV7 + +\code{.sh} +(blenderproc) $ python export_for_yolov7.py --input path/to/dataset --output path/to/yolodataset --train-split +\endcode + + \subsection dnn_output_custom_parsing Parsing HDF5 with a custom script -link tuto blenderproc +In Python, an HDF5 file can be read like a dictionary. + +With the following script, you can inspect the content of an HDF5 file: +\code{.py} +from pathlib import Path +import numpy as np +import h5py +import json + +if __name__ == '__main__': + dataset_path = Path('output') + for scene_folder in dataset_path.iterdir(): + if not scene_folder.is_dir(): + continue + print(f'Reading scene {scene_folder.name}') + for sample in scene_folder.iterdir(): + if not sample.name.endswith('.hdf5'): + continue + print(f'\tReading sample {sample.name}') + with h5py.File(sample) as f: + # Image data + keys = ['colors', 'depth', 'normals', 'class_segmaps', 'instance_segmaps'] + for key in keys: + if key in f: + data = np.array(f[key]) + print(f'\t\t{key}: shape = {data.shape}, type = {data.dtype}') + # Json objects + if 'instance_attribute_maps' in f: + print('\t\tMapping between instance and class:') + text = np.array(f['instance_attribute_maps']).tobytes() + json_rep = json.loads(text) + print(json_rep) + print() + + if 'object_data' in f: + text = np.array(f['object_data']).tobytes() + json_rep = json.loads(text) + + for object_idx, object_data in enumerate(json_rep): + print(f'\t\tObject {object_idx} data: ') + for obj_k in object_data.keys(): + print(f'\t\t\t{obj_k} = {object_data[obj_k]}') + print() +\endcode + +Running on a custom dataset with all outputs enabled (see \ref dnn_input_configuration) we obtain the following output: +\verbatim +... +Reading scene 4 + Reading sample 0.hdf5 + colors: shape = (480, 640, 3), type = uint8 + depth: shape = (480, 640), type = float32 + normals: shape = (480, 640, 3), type = float32 + class_segmaps: shape = (480, 640), type = uint8 + instance_segmaps: shape = (480, 640), type = uint8 + Mapping between instance and class: + [{'idx': 5, 'category_id': 2}, {'idx': 7, 'category_id': 3}, {'idx': 10, 'category_id': 0}, {'idx': 11, 'category_id': 0}, + {'idx': 14, 'category_id': 0}, {'idx': 18, 'category_id': 0}, {'idx': 22, 'category_id': 0}, {'idx': 32, 'category_id': 0}, + {'idx': 37, 'category_id': 0}] + + Object 0 data: + class = 2 + name = cube.001 + cTo = [[-0.9067991971969604, -0.12124679982662201, 0.40374961495399475, 0.06561152129493264], + [-0.399792343378067, 0.5511342883110046, -0.7324047088623047, 0.31461843930247957], + [-0.1337185800075531, -0.8255603313446045, -0.548241913318634, 0.01648345626311576], + [0.0, 0.0, 0.0, 1.0]] + bounding_box = [138.9861569133729, 254.62236838239235, 100.20349472431002, 107.79005297420909] + + Object 1 data: + class = 3 + name = dragon.002 + cTo = [[-0.9067991971969604, -0.12124679982662201, 0.40374961495399475, 0.06561152129493264], + [-0.399792343378067, 0.5511342883110046, -0.7324047088623047, 0.31461843930247957], + [-0.1337185800075531, -0.8255603313446045, -0.548241913318634, 0.01648345626311576], + [0.0, 0.0, 0.0, 1.0]] + bounding_box = [314.54058632091767, 202.86607727119753, 70.64506150004479, 90.54480823214647] +... +\endverbatim + +- Both depth and normals are represented as floating points, conserving accuracy. +- The object data is represented as a JSON document. Which you can directly save or reparse to save only the information of interest. +- Object poses are expressed in the camera frame and are represented as homogeneous matrix. +- Bounding boxes coordinates are in pixels, and the values are [x_min, y_min, width, height] +You can modify this script to export the dataset to another format, has it was done in \ref dnn_output_yolov7. \section dnn_synthetic_next Next steps diff --git a/doc/tutorial/tracking/tutorial-tracking-megapose-object-export.dox b/doc/tutorial/tracking/tutorial-tracking-megapose-object-export.dox index ac5a9ac2f2..c1ba3ac87b 100644 --- a/doc/tutorial/tracking/tutorial-tracking-megapose-object-export.dox +++ b/doc/tutorial/tracking/tutorial-tracking-megapose-object-export.dox @@ -259,9 +259,9 @@ When using with MegaPose, you may encounter multiple issues: - MegaPose relies on Panda3D to perform 3D rendering. Using gltf (or their binary version, glb) files requires a different rendering pipeline which is not the default choice for megapose - Prefer an export to .obj -\section megapose_model_testing Testing your model - -To test whether your model is correct and can be used for pose estimation or tracking, you should follow \ref tutorial-tracking-megapose and run the sample program. +\section megapose_model_testing Next steps +- To test whether your model is correct and can be used for pose estimation or tracking, you should follow \ref tutorial-tracking-megapose and run the sample program. +- Since Megapose requires a way to detect the object, you can train a neural network to do so entirely from synthetic data by using the 3D model that you just obtained. The process is detailed in \ref tutorial-synthetic-blenderproc */ \ No newline at end of file diff --git a/doc/tutorial/tracking/tutorial-tracking-megapose.dox b/doc/tutorial/tracking/tutorial-tracking-megapose.dox index 3507971a11..af7a53abb9 100644 --- a/doc/tutorial/tracking/tutorial-tracking-megapose.dox +++ b/doc/tutorial/tracking/tutorial-tracking-megapose.dox @@ -303,6 +303,6 @@ You may wish to save the results. You can do so by serializing to JSON, as expla This program can run with new 3D models, as MegaPose does require retraining. To adapt this script to your problem, you will require multiple things: - The intrinsic parameters of your camera. To calibrate your camera, see \ref tutorial-calibration-intrinsic - The 3D model of your object. See \ref tutorial-megapose-model -- Optionally (but recommended), an automated way to detect the object. You can for instance train a deep neural network and use it in ViSP, as explained in \ref tutorial-detection-dnn. +- Optionally (but recommended), an automated way to detect the object. You can for instance train a deep neural network and use it in ViSP, as explained in \ref tutorial-detection-dnn. Since you already have your 3D model, you can use Blender to generate a synthetic dataset and train a detection network without manually annotating images. This process is explained in \ref tutorial-synthetic-blenderproc */ diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 9beeef79d6..73f60a782f 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -50,11 +50,11 @@ "dataset": { "scenes_per_run": 1, "num_scenes": 50, - "images_per_scene": 20, + "images_per_scene": 1, "pose": true, - "depth": false, - "normals": false, - "segmentation": false, + "depth": true, + "normals": true, + "segmentation": true, "detection": true, "detection_params": { "min_side_size_px": 10, @@ -63,6 +63,6 @@ }, "save_path": "output_cube" }, - "models_path": "/home/sfelton/megapose-cube", + "models_path": "/home/sfelton/megapose-models", "cc_textures_path": "/home/sfelton/cc0" -} \ No newline at end of file +} From 77622c51808429cc41b40038c14f2a3a320a9197 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Thu, 20 Jul 2023 17:51:33 +0200 Subject: [PATCH 19/28] cleanup json config, documentation for yolo training --- .../misc/tutorial-synthetic-blenderproc.dox | 66 ++++++++++++++++--- script/dataset_generator/example_config.json | 42 ++++++------ 2 files changed, 79 insertions(+), 29 deletions(-) diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox index b10a0daaad..f81d7b89ba 100644 --- a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -530,7 +530,7 @@ To customize the dataset, modify the options in the "dataset" field: detection_params:min_size_size_px - Int > 0 + Int >= 0 Minimum side length of a detection for it to be considered as valid. Used to filter really far or small objects, for which detection would be hard. @@ -589,25 +589,75 @@ There is one subfolder per scene, and each HDF5 file is a single sample that may You can visualise the generated images (RGB, depth, normals, segmentation maps) by running \code{.sh} (blenderproc) $ blenderproc vis hdf5 path/to/your/output/0/*.hdf5 -\endcode +\endcode You will then see something that looks like this, depending on the outputs that you chose in the configuration file: \image html blenderproc_viz.png -where you can replace the "0" in the path with another number, which corresponds to the generated scene index. +where you can replace the "0" in the path with another number, which corresponds to the generated scene index. You can also specify the path to a single HDF5 file to view one sample at a time. \subsection dnn_output_yolov7 Using detections to finetune a YoloV7 +To help, we provide a script that reformats a blenderproc dataset to the format expected by YoloV7 +This script can be run like this: \code{.sh} -(blenderproc) $ python export_for_yolov7.py --input path/to/dataset --output path/to/yolodataset --train-split +(blenderproc) $ python export_for_yolov7.py --input path/to/dataset --output path/to/yolodataset --train-split 0.8 \endcode +here "--input" indicates the path to the location of the blenderproc dataset, while "--output" points to the folder where the dataset in the format that YoloV7 expects will be saved. "--train-split" is an argument that indicates how much of the dataset is kept for training. A value of 0.8 indicates that 80% of the dataset is used for training, while 20% is used for validation. The split is performed randomly across all scenes (a scene may be visible in both train and validation sets). + +Once the script has run, the folder "path/to/yolodataset" should be created and contain the dataset as expected by YoloV7. +This folder contains a "dataset.yml" file, which will be used when training a YoloV7. It contains: +the following: + +``` +names: +- esa +nc: 1 +train: /local/sfelton/yolov7_esa_dataset/images/train +val: /local/sfelton/yolov7_esa_dataset/images/val +``` +where nc is the number of class, "names" are the class names, and "train" and "val" are the paths to the dataset splits. + +To start training a YoloV7, you should download the repository and install the required dependencies. Again, we will create a conda environment. You can also use a docker container, as explained in the documentation. We also download the pretrained yolo model, that we will finetune on our own dataset. +``` +~ $ git clone https://github.com/WongKinYiu/yolov7.git +~ $ cd yolov7 +~/yolov7 $ conda create --name yolov7 pip +~/yolov7 $ conda activate yolov7 +(yolov7) ~/yolov7 $ pip install -r requirements.txt +(yolov7) ~/yolov7 $ wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt +``` + +To fine-tune a YoloV7, we should create two new files: the network configuration and the hyperparameters. We will reuse the ones provided for the tiny model. +``` +CFG=cfg/training/yolov7-tiny-custom.yaml +cp cfg/training/yolov7-tiny.yaml $CFG + +HYP=data/hyp.scratch.custom.yaml +cp data/hyp.scratch.tiny.yaml $HYP +``` +Next open the new cfg file, and modify the number of classes (set "nc" from 80 to the number classes you have in your dataset) + +You can also modify the hyperparameters file to add more augmentation during training. + +To fine-tune the YoloV7-tiny on our data, run +``` +YOLO_DATASET=/path/to/dataset +IMG_SIZE=640 +YOLO_NAME=blenderproc-tiny +python train.py --workers 8 --device 0 --batch-size 64 --data "${YOLO_DATASET}/dataset.yaml" --img $IMG_SIZE $IMG_SIZE --cfg $CFG --weights yolov7-tiny.pt --name $YOLO_NAME --hyp $HYP +``` + +Note that if your run finetuning multiple times, then a number will be appended to the name that you have given to your model. + +If you run out of memory during training, change the batch size to a lower value. \subsection dnn_output_custom_parsing Parsing HDF5 with a custom script -In Python, an HDF5 file can be read like a dictionary. +In Python, an HDF5 file can be read like a dictionary. With the following script, you can inspect the content of an HDF5 file: \code{.py} @@ -644,7 +694,7 @@ if __name__ == '__main__': if 'object_data' in f: text = np.array(f['object_data']).tobytes() json_rep = json.loads(text) - + for object_idx, object_data in enumerate(json_rep): print(f'\t\tObject {object_idx} data: ') for obj_k in object_data.keys(): @@ -670,8 +720,8 @@ Reading scene 4 Object 0 data: class = 2 name = cube.001 - cTo = [[-0.9067991971969604, -0.12124679982662201, 0.40374961495399475, 0.06561152129493264], - [-0.399792343378067, 0.5511342883110046, -0.7324047088623047, 0.31461843930247957], + cTo = [[-0.9067991971969604, -0.12124679982662201, 0.40374961495399475, 0.06561152129493264], + [-0.399792343378067, 0.5511342883110046, -0.7324047088623047, 0.31461843930247957], [-0.1337185800075531, -0.8255603313446045, -0.548241913318634, 0.01648345626311576], [0.0, 0.0, 0.0, 1.0]] bounding_box = [138.9861569133729, 254.62236838239235, 100.20349472431002, 107.79005297420909] diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 73f60a782f..ba1aba3af6 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -1,6 +1,8 @@ { "numpy_seed": 19, "blenderproc_seed": "69", + "models_path": "../megapose-esa", + "cc_textures_path": "../cc0", "camera": { "px": 600, "py": 600, @@ -11,24 +13,24 @@ "randomize_params_percent": 5.0 }, "rendering": { - "max_num_samples": 32, - "denoiser": null + "max_num_samples": 64, + "denoiser": "OPTIX" }, "scene": { - "room_size_multiplier_min": 2.0, + "room_size_multiplier_min": 4.0, "room_size_multiplier_max": 10.0, "simulate_physics": true, "max_num_textures": 50, "distractors": { "min_count": 20, - "max_count": 50, - "min_size_rel_scene": 0.01, + "max_count": 100, + "min_size_rel_scene": 0.02, "max_size_rel_scene": 0.05, "displacement_max_amount": 0.0, "pbr_noise": 0.5, - "emissive_prob": 0.1, - "emissive_min_strength": 5.0, - "emissive_max_strength": 25.0 + "emissive_prob": 0.0, + "emissive_min_strength": 1.0, + "emissive_max_strength": 5.0 }, "lights": { "min_count": 3, @@ -37,32 +39,30 @@ "max_intensity": 200 }, "objects": { - "min_count": 5, - "max_count": 5, + "min_count": 1, + "max_count": 1, "multiple_occurences": true, "scale_noise": 0.2, "displacement_max_amount": 0.0, "pbr_noise": 0.3, "cam_min_dist_rel": 1.0, - "cam_max_dist_rel": 5.0 + "cam_max_dist_rel": 6.0 } }, "dataset": { + "save_path": "output_esa_v2", "scenes_per_run": 1, - "num_scenes": 50, - "images_per_scene": 1, + "num_scenes": 100, + "images_per_scene": 20, "pose": true, - "depth": true, - "normals": true, - "segmentation": true, + "depth": false, + "normals": false, + "segmentation": false, "detection": true, "detection_params": { "min_side_size_px": 10, "min_visibility_percentage": 0.3, "points_sampling_occlusion": 100 - }, - "save_path": "output_cube" - }, - "models_path": "/home/sfelton/megapose-models", - "cc_textures_path": "/home/sfelton/cc0" + } + } } From 2175f734cbd6d76eb8f041bf61b8571663a287a4 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Wed, 9 Aug 2023 13:16:05 +0200 Subject: [PATCH 20/28] Fix typo --- doc/tutorial/misc/tutorial-synthetic-blenderproc.dox | 2 +- script/dataset_generator/example_config.json | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox index f81d7b89ba..012c978e8b 100644 --- a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -650,7 +650,7 @@ YOLO_NAME=blenderproc-tiny python train.py --workers 8 --device 0 --batch-size 64 --data "${YOLO_DATASET}/dataset.yaml" --img $IMG_SIZE $IMG_SIZE --cfg $CFG --weights yolov7-tiny.pt --name $YOLO_NAME --hyp $HYP ``` -Note that if your run finetuning multiple times, then a number will be appended to the name that you have given to your model. +Note that if your run fine-tuning multiple times, then a number will be appended to the name that you have given to your model. If you run out of memory during training, change the batch size to a lower value. diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index ba1aba3af6..0a7da7ecf6 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -13,7 +13,7 @@ "randomize_params_percent": 5.0 }, "rendering": { - "max_num_samples": 64, + "max_num_samples": 32, "denoiser": "OPTIX" }, "scene": { @@ -29,7 +29,7 @@ "displacement_max_amount": 0.0, "pbr_noise": 0.5, "emissive_prob": 0.0, - "emissive_min_strength": 1.0, + "emissive_min_strength": 2.0, "emissive_max_strength": 5.0 }, "lights": { @@ -40,13 +40,13 @@ }, "objects": { "min_count": 1, - "max_count": 1, + "max_count": 3, "multiple_occurences": true, "scale_noise": 0.2, "displacement_max_amount": 0.0, "pbr_noise": 0.3, "cam_min_dist_rel": 1.0, - "cam_max_dist_rel": 6.0 + "cam_max_dist_rel": 3.0 } }, "dataset": { From 68512d30e08a2f253248939a2b5342ea9feb41d6 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Fri, 11 Aug 2023 18:19:31 +0200 Subject: [PATCH 21/28] Add empty scene generation, fix bb vis script when image has no object --- script/dataset_generator/example_config.json | 9 +++++---- script/dataset_generator/generate_scene.py | 19 +++++++++++++++++-- script/dataset_generator/viz_bb.py | 13 +++++++------ 3 files changed, 29 insertions(+), 12 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 73f60a782f..57a1561f30 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -51,17 +51,18 @@ "scenes_per_run": 1, "num_scenes": 50, "images_per_scene": 1, + "empty_images_per_scene": 1, "pose": true, - "depth": true, - "normals": true, - "segmentation": true, + "depth": false, + "normals": false, + "segmentation": false, "detection": true, "detection_params": { "min_side_size_px": 10, "min_visibility_percentage": 0.3, "points_sampling_occlusion": 100 }, - "save_path": "output_cube" + "save_path": "output" }, "models_path": "/home/sfelton/megapose-models", "cc_textures_path": "/home/sfelton/cc0" diff --git a/script/dataset_generator/generate_scene.py b/script/dataset_generator/generate_scene.py index c9eb6715ae..7816ec790e 100644 --- a/script/dataset_generator/generate_scene.py +++ b/script/dataset_generator/generate_scene.py @@ -680,7 +680,22 @@ def sample_camera_poses(self, scene: Scene) -> None: if tries >= 1000: print('Warning! Could not correctly place camera, results may be wrong') bproc.camera.add_camera_pose(cam2world_matrix) - + def add_empty_images(self, count: int, scene: Scene): + ''' + Add empty images to the rendering queue. The camera poses for the empty images are sampled from already existing poses (where the target object should be visible) + The number of empty images cannot thus be more than the number of images with target objects present. + ''' + num_frames = bproc.utility.num_frames() + assert count <= num_frames, 'Number of empty images cannot be greater than the number of images with objects' + empty_frame_chosen_indices = np.random.choice(num_frames, size=count, replace=False) + for object in scene.target_objects: + location = object.get_location() + for i in range(num_frames): + object.set_location(location, frame=i) + for i, f in enumerate(empty_frame_chosen_indices): + bproc.camera.add_camera_pose(bproc.camera.get_camera_pose(f)) + for object in scene.target_objects: + object.set_location([10000.0, 10000.0, 10000.0], frame=num_frames + i) def run(self): ''' Generate the scenes and save the results. @@ -701,13 +716,13 @@ def run(self): scene.cleanup() scene = self.create_scene() self.sample_camera_poses(scene) + self.add_empty_images(self.json_config['dataset']['empty_images_per_scene'], scene) # bpy.context.scene.render.use_persistent_data = False data = self.render() path = save_path / str(self.scene_index + i) path.mkdir(exist_ok=True) self.save_data(path, scene.target_objects, data) scene.cleanup() - # # ... do something ... # pr.disable() # s = io.StringIO() # sortby = SortKey.CUMULATIVE diff --git a/script/dataset_generator/viz_bb.py b/script/dataset_generator/viz_bb.py index 8163f6ac6d..ff1f2eca97 100644 --- a/script/dataset_generator/viz_bb.py +++ b/script/dataset_generator/viz_bb.py @@ -12,12 +12,13 @@ def show(file: h5py.File): rgb = np.array(file['colors']) text = np.array(f["object_data"]).tostring() print(text) - object_data = json.loads(text) - for object in object_data: - if 'bounding_box' in object: - bb = object['bounding_box'] - rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor='r', facecolor='none') - plt.gca().add_patch(rect) + if len(text) > 0: + object_data = json.loads(text) + for object in object_data: + if 'bounding_box' in object: + bb = object['bounding_box'] + rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor='r', facecolor='none') + plt.gca().add_patch(rect) plt.imshow(rgb) plt.show() From 0d7adce55fe471f8bb6551ab962bad1ec761510c Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Fri, 11 Aug 2023 18:28:24 +0200 Subject: [PATCH 22/28] documentation for empty scenes --- doc/tutorial/misc/tutorial-synthetic-blenderproc.dox | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox index b10a0daaad..acde75f7c8 100644 --- a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -486,7 +486,14 @@ To customize the dataset, modify the options in the "dataset" field: images_per_scene Int > 0 - Number of images to generate per scene. The total number of samples in the dataset will be num_scenes * images_per_scene. + Number of images to generate per scene. The total number of samples in the dataset will be num_scenes * (images_per_scene + empty_images_per_scene). + + + + empty_images_per_scene + Int >= 0, <= images_per_scene + + Number of images without target objects to generate per scene. The camera poses for these images are sampled from the poses used to generate images with target objects. Thus, the only difference will be that the objects are not present, the rest of the scene is left untouched. From 70cc2ed5736fc1ecd9c9858ea62751378b3d56f9 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Thu, 17 Aug 2023 14:06:57 +0200 Subject: [PATCH 23/28] Add custom distractors to dataset generator --- .../misc/tutorial-synthetic-blenderproc.dox | 1 + script/dataset_generator/example_config.json | 28 +++---- script/dataset_generator/generate_scene.py | 76 ++++++++++++++++--- 3 files changed, 83 insertions(+), 22 deletions(-) diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox index acde75f7c8..99007c617c 100644 --- a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -39,6 +39,7 @@ First, you should start by installing blenderproc. First, start by creating a ne \code{.sh} $ conda create --name blenderproc python=3.10 pip $ conda activate blenderproc +$ pip install blenderproc \endcode \note Our generation script has been tested with blenderproc 2.5.0 (with Blender 3.3 under the hood) and python 3.10. diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 57a1561f30..4582844393 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -1,6 +1,8 @@ { "numpy_seed": 19, "blenderproc_seed": "69", + "models_path": "/home/sfelton/megapose-cube", + "cc_textures_path": "/home/sfelton/cc0", "camera": { "px": 600, "py": 600, @@ -11,19 +13,21 @@ "randomize_params_percent": 5.0 }, "rendering": { - "max_num_samples": 32, + "max_num_samples": 64, "denoiser": null }, "scene": { - "room_size_multiplier_min": 2.0, - "room_size_multiplier_max": 10.0, + "room_size_multiplier_min": 3.0, + "room_size_multiplier_max": 8.0, "simulate_physics": true, "max_num_textures": 50, "distractors": { - "min_count": 20, + "min_count": 10, "max_count": 50, - "min_size_rel_scene": 0.01, - "max_size_rel_scene": 0.05, + "min_size_rel_scene": 0.05, + "max_size_rel_scene": 0.1, + "custom_distractors": "/home/sfelton/megapose-distractors", + "custom_distractor_proba": 0.5, "displacement_max_amount": 0.0, "pbr_noise": 0.5, "emissive_prob": 0.1, @@ -37,7 +41,7 @@ "max_intensity": 200 }, "objects": { - "min_count": 5, + "min_count": 2, "max_count": 5, "multiple_occurences": true, "scale_noise": 0.2, @@ -50,8 +54,8 @@ "dataset": { "scenes_per_run": 1, "num_scenes": 50, - "images_per_scene": 1, - "empty_images_per_scene": 1, + "images_per_scene": 10, + "empty_images_per_scene": 2, "pose": true, "depth": false, "normals": false, @@ -62,8 +66,6 @@ "min_visibility_percentage": 0.3, "points_sampling_occlusion": 100 }, - "save_path": "output" - }, - "models_path": "/home/sfelton/megapose-models", - "cc_textures_path": "/home/sfelton/cc0" + "save_path": "output_cube" + } } diff --git a/script/dataset_generator/generate_scene.py b/script/dataset_generator/generate_scene.py index 7816ec790e..53690d797f 100644 --- a/script/dataset_generator/generate_scene.py +++ b/script/dataset_generator/generate_scene.py @@ -68,14 +68,14 @@ def homogeneous_inverse(aTb): bTa[:3, 3] = -bTa[:3, :3] @ bTa[:3, 3] return bTa -def homogeneous_no_scaling(object: bproc.types.MeshObject): +def homogeneous_no_scaling(object: bproc.types.MeshObject, frame=None): ''' Get the homogeneous transformation of an object, but without potential scaling object.local2world() may contain scaling factors. ''' localTworld = np.eye(4) - localTworld[:3, :3] = object.get_rotation_mat() - localTworld[:3, 3] = object.get_location() + localTworld[:3, :3] = object.get_rotation_mat(frame) + localTworld[:3, 3] = object.get_location(frame) return localTworld def convert_to_visp_frame(aTb): @@ -307,6 +307,7 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic path: Path to the folder in which to save the HDF5 files ''' json_dataset = self.json_config['dataset'] + num_frames_objects = json_dataset['images_per_scene'] out_object_pose, out_bounding_box = itemgetter('pose', 'detection')(json_dataset) keys = [ @@ -329,6 +330,9 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic visible_objects = bproc.camera.visible_objects(worldTcam, min(width, height) // min_side_px) objects_data = [] + if frame >= num_frames_objects: + frames_data.append({}) + continue for object in objects: if object not in visible_objects: continue @@ -337,7 +341,7 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic 'name': object.get_name() } if out_object_pose: - worldTobj = homogeneous_no_scaling(object) + worldTobj = homogeneous_no_scaling(object, frame) camTobj = camTworld @ worldTobj object_data['cTo'] = convert_to_visp_frame(camTobj) if out_bounding_box: @@ -411,8 +415,65 @@ def set_camera_intrinsics(self) -> None: ] bproc.camera.set_intrinsics_from_K_matrix(K, w, h) + def create_distractors(self, scene_size: float) -> List[bproc.types.MeshObject]: + ''' + Add distractor objects + ''' + json_distractors = self.json_config['scene']['distractors'] + min_count, max_count = itemgetter('min_count', 'max_count')(json_distractors) + + custom_distractors_path, custom_distractors_proba = itemgetter('custom_distractors', 'custom_distractor_proba')(json_distractors) + count = np.random.randint(min_count, max_count + 1) + if custom_distractors_path is not None: + custom_count = np.sum(np.random.choice(2, size=count, replace=True, p=[1 - custom_distractors_proba, custom_distractors_proba])) + else: + custom_count = 0 + simple_count = count - custom_count + return self.create_simple_distractors(scene_size, simple_count) + self.create_custom_distractors(scene_size, custom_count) + + def create_custom_distractors(self, scene_size, count: int) -> List[bproc.types.MeshObject]: + json_distractors = self.json_config['scene']['distractors'] + min_size, max_size, pbr_noise = itemgetter('min_size_rel_scene', 'max_size_rel_scene', 'pbr_noise')(json_distractors) + custom_distractors_path = itemgetter('custom_distractors')(json_distractors) + if count == 0: + return [] + distractor_files = [] + assert Path(custom_distractors_path).exists(), f'Custom distractors folder {custom_distractors_path} does not exist' + for file in Path(custom_distractors_path).iterdir(): + if file.name.endswith(('.ply', '.obj')): + distractor_files.append(file) + assert len(distractor_files) > 0, f'Requested custom distractors from folder but {custom_distractors_path} did not find any!' + + chosen_files_list = np.random.choice(distractor_files, size=count, replace=True) + chosen_files_set = set(chosen_files_list) + path_to_object = {} + for path in chosen_files_set: + obj = bproc.loader.load_obj(str(path.absolute()))[0] + obj.set_location([100000.0, 100000.0, 10000.0]) + path_to_object[path] = obj + + chosen_distractors = [] + for path in chosen_files_list: + distractor = path_to_object[path].duplicate() + chosen_distractors.append(distractor) + object_size = object_bb_length(distractor) + rescale_size = np.random.uniform((scene_size * min_size) / object_size, (scene_size * max_size) / object_size) + distractor.set_scale([rescale_size, rescale_size, rescale_size]) + + def sample_pose(obj: bproc.types.MeshObject): + loc = np.random.uniform([-scene_size / 2, -scene_size / 2, -scene_size / 2], [scene_size / 2, scene_size / 2, scene_size / 2]) + obj.set_location(loc) + obj.set_rotation_euler(bproc.sampler.uniformSO3()) - def create_distractors(self, scene_size: float, other_objects: List[bproc.types.MeshObject]) -> List[bproc.types.MeshObject]: + bproc.object.sample_poses( + chosen_distractors, + sample_pose_func=sample_pose, + objects_to_check_collisions=None + ) + randomize_pbr(chosen_distractors, pbr_noise) + return chosen_distractors + + def create_simple_distractors(self, scene_size: float, count: int) -> List[bproc.types.MeshObject]: ''' Add simple objects to the scene. These objects have no class (no bounding box computed and does not appear in segmentation) @@ -422,12 +483,9 @@ def create_distractors(self, scene_size: float, other_objects: List[bproc.types. The 3D models are simple primitives (cube, sphere, cone, etc.) and they are distorted through the displacement_max_amount_params ''' json_distractors = self.json_config['scene']['distractors'] - min_count, max_count = itemgetter('min_count', 'max_count')(json_distractors) min_size, max_size = itemgetter('min_size_rel_scene', 'max_size_rel_scene')(json_distractors) displacement_strength, pbr_noise = itemgetter('displacement_max_amount', 'pbr_noise')(json_distractors) emissive_proba, emissive_min_strength, emissive_max_strength = itemgetter(*['emissive_' + s for s in ['prob', 'min_strength', 'max_strength']])(json_distractors) - - count = np.random.randint(min_count, max_count + 1) if count == 0: return [] def sample_pose(obj: bproc.types.MeshObject): @@ -608,7 +666,7 @@ def sample_pose(obj: bproc.types.MeshObject): for object in objects: object.persist_transformation_into_mesh() - distractors = self.create_distractors(size, room_objects + objects) + distractors = self.create_distractors(size) if simulate_physics: for object in objects + distractors: From 791056c662ea710b5d640f21d654c24865d80f6f Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Fri, 18 Aug 2023 16:53:38 +0200 Subject: [PATCH 24/28] running generation dataset for nerf reconstructed model --- .../misc/tutorial-synthetic-blenderproc.dox | 18 +++++++++++++++++- script/dataset_generator/example_config.json | 12 ++++++------ 2 files changed, 23 insertions(+), 7 deletions(-) diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox index 4c13db48af..374c90c9fa 100644 --- a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -280,7 +280,7 @@ To customize the scene, you can change the parameters in the "scene" field: -Distractors are small, simple objects that are added along with the target objects to create some variations and occlusions. +Distractors are small, simple objects that are added along with the target objects to create some variations and occlusions. You can also load custom objects as distractors. To modify their properties, you can change the "distractors" field of the scene @@ -298,6 +298,20 @@ To modify their properties, you can change the "distractors" field of the scene Maximum number of distractors to place in the room. + + + + + + + + + + @@ -754,5 +768,7 @@ You can modify this script to export the dataset to another format, has it was d \section dnn_synthetic_next Next steps +If you use this generator to train a detection network, you can combine it with Megapose to perform 6D pose estimation and tracking. See \ref tutorial-tracking-megapose + */ diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 95170a7eee..6fc3fce0a9 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -2,7 +2,7 @@ "numpy_seed": 19, "blenderproc_seed": "69", - "models_path": "../megapose-cube", + "models_path": "../megapose-esa", "cc_textures_path": "../cc0", "camera": { "px": 600, @@ -18,12 +18,12 @@ "denoiser": "OPTIX" }, "scene": { - "room_size_multiplier_min": 3.0, - "room_size_multiplier_max": 8.0, - "simulate_physics": true, + "room_size_multiplier_min": 5.0, + "room_size_multiplier_max": 10.0, + "simulate_physics": false, "max_num_textures": 50, "distractors": { - "min_count": 10, + "min_count": 20, "max_count": 50, "min_size_rel_scene": 0.05, "max_size_rel_scene": 0.1, @@ -53,7 +53,7 @@ } }, "dataset": { - "save_path": "output_cube", + "save_path": "output_esa_v3", "scenes_per_run": 1, "num_scenes": 100, "images_per_scene": 10, From 7b7d44b600b648819d33a3ddfe58685b0e2853e1 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Mon, 21 Aug 2023 15:01:07 +0200 Subject: [PATCH 25/28] Cleaning up non relevant comments, fix paths in example_config.json --- script/dataset_generator/example_config.json | 8 +-- script/dataset_generator/generate_scene.py | 57 ++++++++------------ 2 files changed, 25 insertions(+), 40 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 6fc3fce0a9..26f1602404 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -2,8 +2,8 @@ "numpy_seed": 19, "blenderproc_seed": "69", - "models_path": "../megapose-esa", - "cc_textures_path": "../cc0", + "models_path": "./megapose-models", + "cc_textures_path": "./cc0", "camera": { "px": 600, "py": 600, @@ -27,7 +27,7 @@ "max_count": 50, "min_size_rel_scene": 0.05, "max_size_rel_scene": 0.1, - "custom_distractors": "../megapose-distractors", + "custom_distractors": "./megapose-distractors", "custom_distractor_proba": 0.5, "displacement_max_amount": 0.0, "pbr_noise": 0.5, @@ -53,7 +53,7 @@ } }, "dataset": { - "save_path": "output_esa_v3", + "save_path": "./output_blenderproc", "scenes_per_run": 1, "num_scenes": 100, "images_per_scene": 10, diff --git a/script/dataset_generator/generate_scene.py b/script/dataset_generator/generate_scene.py index 53690d797f..c9b2eb1363 100644 --- a/script/dataset_generator/generate_scene.py +++ b/script/dataset_generator/generate_scene.py @@ -34,7 +34,7 @@ def bounding_box_2d_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, ''' Compute the 2D bounding from an object's vertices returns A tuple containing: - [xmin, ymin, xmax, ymax] in pixels + [xmin, ymin, xmax, ymax] in pixels the proportion of visible vertices (that are not behind the camera, i.e., negative Z) The 2D points, in normalized units in camera space, in ViSP/OpenCV frame ''' @@ -47,16 +47,16 @@ def bounding_box_2d_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, camTobj = camTworld @ worldTobj points_cam = camTobj @ np.concatenate((points, np.ones((len(points), 1))), axis=-1).T points_cam = convert_points_to_visp_frame((points_cam[:3] / points_cam[3, None]).T) - + visible_points = points_cam[points_cam[:, 2] > 0] visible_points_m_2d = visible_points[:, :2] / visible_points[:, 2, None] visible_points_px_2d = K @ np.concatenate((visible_points_m_2d, np.ones((len(visible_points_m_2d), 1))), axis=-1).T visible_points_px_2d = visible_points_px_2d.T[:, :2] / visible_points_px_2d.T[:, 2, None] - + mins = np.min(visible_points_px_2d, axis=0) assert len(mins) == 2 maxes = np.max(visible_points_px_2d, axis=0) - + return [mins[0], mins[1], maxes[0], maxes[1]], len(visible_points) / len(points_cam), visible_points_m_2d def homogeneous_inverse(aTb): @@ -218,7 +218,7 @@ def load_objects(self) -> Tuple[Dict[str, bproc.types.MeshObject], Dict[str, int ----- model.obj --- obj_2_name/ ----- model.obj - Returns + Returns - a dict where keys are the model names (from the containing folder name of each object, in the example case: 'obj_1_name' and 'obj_2_name') @@ -281,8 +281,7 @@ def setup_renderer(self): RendererUtility.set_denoiser(self.json_config['rendering']['denoiser']) if depth: bproc.renderer.enable_depth_output(activate_antialiasing=False) - # if normals: - # bproc.renderer.enable_normals_output() + def render(self) -> Dict: ''' @@ -291,7 +290,7 @@ def render(self) -> Dict: This returns a dictionary where each key (e.g "colors" for RGB rendering) contains a list of numpy arrays (1 numpy array per frame) ''' normals, segmentation = itemgetter('normals', 'segmentation')(self.json_config['dataset']) - if normals: # Ugly, but doesn't work if it isn't called every render + if normals: # Enabling normals should be done every at every render bproc.renderer.enable_normals_output() data = bproc.renderer.render() @@ -310,29 +309,26 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic num_frames_objects = json_dataset['images_per_scene'] out_object_pose, out_bounding_box = itemgetter('pose', 'detection')(json_dataset) - keys = [ - 'min_side_size_px', 'min_visibility_percentage', - 'points_sampling_occlusion' - ] + keys = ['min_side_size_px', 'min_visibility_percentage', 'points_sampling_occlusion'] min_side_px, min_visibility, points_sampling_occlusion = itemgetter(*keys)(json_dataset['detection_params']) - + width, height = itemgetter('w', 'h')(self.json_config['camera']) frames_data = [] - import time + for frame in range(bproc.utility.num_frames()): - worldTcam = bproc.camera.get_camera_pose(frame) camTworld = homogeneous_inverse(worldTcam) K = bproc.camera.get_intrinsics_as_K_matrix() - + minx, miny = (-K[0, 2]) / K[0, 0], (-K[1, 2]) / K[1, 1] maxx, maxy = (width -K[0, 2]) / K[0, 0], (height -K[1, 2]) / K[1, 1] visible_objects = bproc.camera.visible_objects(worldTcam, min(width, height) // min_side_px) objects_data = [] + # Stop when parsing frames with no objects: blenderproc API still detects objects even though they're not here for these frames if frame >= num_frames_objects: frames_data.append({}) - continue + continue for object in objects: if object not in visible_objects: continue @@ -345,7 +341,6 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic camTobj = camTworld @ worldTobj object_data['cTo'] = convert_to_visp_frame(camTobj) if out_bounding_box: - t = time.time() bb_corners, z_front_proportion, points_im = bounding_box_2d_from_vertices(object, K, camTworld) if z_front_proportion < min_visibility: continue @@ -363,18 +358,18 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic if size[0] < min_side_px or size[1] < min_side_px: continue - vis_points_in_image = points_im[(points_im[:, 0] > minx) & ((points_im[:, 0] < maxx)) & (points_im[:, 1] > miny) & ((points_im[:, 1] < maxy))] - base_visibility = z_front_proportion * (len(vis_points_in_image) / len(points_im)) + # Camera clipping removes too much of the object if base_visibility < min_visibility: continue + if points_sampling_occlusion > 0: point_count = len(vis_points_in_image) points = vis_points_in_image[np.random.choice(point_count, size=min(point_count, points_sampling_occlusion))] points = np.concatenate((points, np.ones((len(points), 1))), axis=-1) - + points_cam = convert_points_to_visp_frame(points) # Convert back to blender frame points_world = worldTcam @ np.concatenate((points_cam, np.ones((len(points_cam), 1))), axis=-1).T points_world = (points_world[:3] / points_world[3, None]).T @@ -421,7 +416,7 @@ def create_distractors(self, scene_size: float) -> List[bproc.types.MeshObject]: ''' json_distractors = self.json_config['scene']['distractors'] min_count, max_count = itemgetter('min_count', 'max_count')(json_distractors) - + custom_distractors_path, custom_distractors_proba = itemgetter('custom_distractors', 'custom_distractor_proba')(json_distractors) count = np.random.randint(min_count, max_count + 1) if custom_distractors_path is not None: @@ -475,7 +470,7 @@ def sample_pose(obj: bproc.types.MeshObject): def create_simple_distractors(self, scene_size: float, count: int) -> List[bproc.types.MeshObject]: ''' - Add simple objects to the scene. + Add simple objects to the scene. These objects have no class (no bounding box computed and does not appear in segmentation) They are meant to add variation to the scene Their position, texture and shape are randomized. @@ -700,7 +695,7 @@ def sample_camera_poses(self, scene: Scene) -> None: Sample camera poses in a scene. Camera are sampled as such: - Select a target object: choose a point of interest in its bounding box - - Choose a camera position in a "hollow ball": the position should be at at least (cam_min_dist_rel * object_size) meters from the object + - Choose a camera position in a "hollow ball": the position should be at at least (cam_min_dist_rel * object_size) meters from the object and at at most (cam_max_dist_rel * object_size). Note that the object size is computed from the largest diagonal of the axis aligned bounding box and is thus overestimated. - Fix camera orientation so that it points towards the point of interest ''' @@ -723,7 +718,7 @@ def sample_camera_poses(self, scene: Scene) -> None: while not good and tries_distance < 100: location = bproc.sampler.sphere(object.get_location(), distance * object_size, 'SURFACE') rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) - + cam = bpy.context.scene.camera.data clip_start = cam.clip_start focal_length = cam.lens / 1000.0 @@ -758,10 +753,6 @@ def run(self): ''' Generate the scenes and save the results. ''' - # import cProfile, pstats, io - # from pstats import SortKey - # pr = cProfile.Profile() - # pr.enable() save_path = Path(self.json_config['dataset']['save_path']) save_path.mkdir(exist_ok=True) @@ -781,12 +772,6 @@ def run(self): path.mkdir(exist_ok=True) self.save_data(path, scene.target_objects, data) scene.cleanup() - # pr.disable() - # s = io.StringIO() - # sortby = SortKey.CUMULATIVE - # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) - # ps.print_stats() - # print(s.getvalue()) if __name__ == '__main__': @@ -800,7 +785,7 @@ def run(self): # RendererUtility.set_render_devices(use_only_cpu=True) bproc.clean_up(clean_up_camera=True) bproc.init() # Works if you have a GPU - + generator.run() From dbab55da5df856f22c988051b3fd1831767746e7 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Mon, 21 Aug 2023 15:29:46 +0200 Subject: [PATCH 26/28] add megapose pipeline diagram in documentation --- .../tracking/megapose/megapose_pipeline.png | Bin 0 -> 222752 bytes 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w2E2$_?4J<*-_0_=1A!~d|1|c0bL+c65G)3tLgjrG2=Gf%Oir{ySkM3e18fE(qyPW_ literal 0 HcmV?d00001 diff --git a/doc/tutorial/tracking/tutorial-tracking-megapose.dox b/doc/tutorial/tracking/tutorial-tracking-megapose.dox index af7a53abb9..e3b1088ff5 100644 --- a/doc/tutorial/tracking/tutorial-tracking-megapose.dox +++ b/doc/tutorial/tracking/tutorial-tracking-megapose.dox @@ -28,19 +28,25 @@ It has however, several drawbacks: You may thus require a way to detect the object, such as an object detection neural network (available in ViSP with the class vpDetectorDNNOpenCV, see \ref tutorial-detection-dnn). For initial tests, the bounding box can also be provided by the user via click. +For the 3D model and detection inputs required by megapose, we provide tutorials to help you get setup. See \ref tutorial-megapose-model for the 3D model creation and \ref tutorial-synthetic-blenderproc to train a detection network. +With these tutorials and the tools presented therein, the work to use megapose can be almost fully automated as summed up in the figure below: + +\image html tutorial/tracking/megapose/megapose_pipeline.png + + The MegaPose integration in ViSP is based on a client-server model: - The client, that uses either vpMegaPose or vpMegaPoseTracker, is C++-based. It sends pose estimation requests to the server. -- The server is written in Python. It wraps around the MegaPose model. Each time a pose estimation is requested, the server reshapes the data and forwards it to MegaPose. - It then sends back the information to the client. +- The server is written in Python. It wraps around the MegaPose model. Each time a pose estimation is requested, the server reshapes the data and forwards it to MegaPose. + It then sends back the information to the client. -\note The computer running the server needs a GPU. The client can run on the same computer as the server. It can also run on another computer without a GPU. - As Ethernet is used to communicate between client and server, both computers must be accessible on the Ethernet network. +\note The computer running the server needs a GPU. The client can run on the same computer as the server. It can also run on another computer without a GPU. To obtain have a decent tracking speed, it is recommended to have both machines on the same network. This tutorial will explain how to install and run MegaPose and then demonstrate its usage with a simple object tracking application. + \section megapose_install Installation \subsection megapose_cpp_install Installing the client From 0ced02b8c667298a9b7a06a5414b2bf3d41e8f87 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Thu, 31 Aug 2023 18:18:11 +0200 Subject: [PATCH 27/28] Link youtube results video for detection from synthetic data --- doc/tutorial/misc/tutorial-synthetic-blenderproc.dox | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox index 374c90c9fa..d500887912 100644 --- a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -676,6 +676,16 @@ Note that if your run fine-tuning multiple times, then a number will be appended If you run out of memory during training, change the batch size to a lower value. +Here is an overview of the generated images and the resulting detections for a simple model (cube) and a NeRF-reconstructed one: +\htmlonly +

+ + +

+\endhtmlonly + + + \subsection dnn_output_custom_parsing Parsing HDF5 with a custom script From 949c9403dffb7a7e3ea346f6037ad639c26b8f7f Mon Sep 17 00:00:00 2001 From: Fabien Spindler Date: Fri, 1 Sep 2023 16:23:07 +0200 Subject: [PATCH 28/28] Minor typo changes --- ChangeLog.txt | 4 +++- doc/tutorial/misc/tutorial-synthetic-blenderproc.dox | 9 ++++----- .../tutorial-tracking-megapose-object-export.dox | 4 ++-- doc/tutorial/tracking/tutorial-tracking-megapose.dox | 4 ++-- doc/tutorial/tutorial-users.dox | 2 +- 5 files changed, 12 insertions(+), 11 deletions(-) diff --git a/ChangeLog.txt b/ChangeLog.txt index ef74c88a8b..8107757f26 100644 --- a/ChangeLog.txt +++ b/ChangeLog.txt @@ -66,8 +66,10 @@ ViSP 3.5.1 (under development) https://visp-doc.inria.fr/doxygen/visp-daily/tutorial-pcl-viewer.html . New tutorial: Tracking with MegaPose https://visp-doc.inria.fr/doxygen/visp-daily/tutorial-tracking-megapose.html - . New tutorial: Exporting a 3D model to MegaPose + . New tutorial: Exporting a 3D model to MegaPose after reconstruction with NeRF https://visp-doc.inria.fr/doxygen/visp-daily/tutorial-megapose-model.html + . New tutorial: Generating synthetic data for deep learning with Blenderproc + https://visp-doc.inria.fr/doxygen/visp-daily/tutorial-synthetic-blenderproc.html - Bug fixed . [#1042] testColorConversion.cpp segfault randomly . [#1045] CXX standard not propagated when using pkg-config or visp-config diff --git a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox index d500887912..23e2602aba 100644 --- a/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox +++ b/doc/tutorial/misc/tutorial-synthetic-blenderproc.dox @@ -225,7 +225,7 @@ These parameters are found in the "camera" field of the configuration.
NameType, possible valuesDescription
custom_distractorsstring, or null + If not null, path to a folder containing custom distractor objects, in .obj or .ply format. +
custom_distractor_probafloat, >= 0.0, <= 1.0 + If custom_distractors is not null, probability that a distractor is sampled from the user specified distractors. +
min_size_rel_scene Float > 0.0Float, [0, 1) Controls the randomization of the camera parameters \f$p_x, p_y, u_0, v_0\f$. If randomize_params_percent > 0, then, each time a scene is created the intrinsics are perturbed around the given values. - For example, if this parameters is equal to 10 and \f$p_x = 500\f$, then the used \f$p_x\f$ when generating images will be in the range [450, 550]. + For example, if this parameters is equal to 0.10 and \f$p_x = 500\f$, then the used \f$p_x\f$ when generating images will be in the range [450, 550].
@@ -621,7 +621,7 @@ You can also specify the path to a single HDF5 file to view one sample at a time \subsection dnn_output_yolov7 Using detections to finetune a YoloV7 -To help, we provide a script that reformats a blenderproc dataset to the format expected by YoloV7 +To help, we provide a script that reformats a blenderproc dataset to the format expected by YoloV7. This script can be run like this: \code{.sh} (blenderproc) $ python export_for_yolov7.py --input path/to/dataset --output path/to/yolodataset --train-split 0.8 @@ -774,11 +774,10 @@ Reading scene 4 - Object poses are expressed in the camera frame and are represented as homogeneous matrix. - Bounding boxes coordinates are in pixels, and the values are [x_min, y_min, width, height] -You can modify this script to export the dataset to another format, has it was done in \ref dnn_output_yolov7. +You can modify this script to export the dataset to another format, as it was done in \ref dnn_output_yolov7. \section dnn_synthetic_next Next steps -If you use this generator to train a detection network, you can combine it with Megapose to perform 6D pose estimation and tracking. See \ref tutorial-tracking-megapose - +If you use this generator to train a detection network, you can combine it with Megapose to perform 6D pose estimation and tracking. See \ref tutorial-tracking-megapose. */ diff --git a/doc/tutorial/tracking/tutorial-tracking-megapose-object-export.dox b/doc/tutorial/tracking/tutorial-tracking-megapose-object-export.dox index c1ba3ac87b..01ba376270 100644 --- a/doc/tutorial/tracking/tutorial-tracking-megapose-object-export.dox +++ b/doc/tutorial/tracking/tutorial-tracking-megapose-object-export.dox @@ -1,6 +1,6 @@ /** -\page tutorial-megapose-model Tutorial: Exporting a 3D model to MegaPose +\page tutorial-megapose-model Tutorial: Exporting a 3D model to MegaPose after reconstruction with NeRF \tableofcontents This tutorial follows \ref tutorial-tracking-megapose and details how to export your 3D model for usage with MegaPose. @@ -262,6 +262,6 @@ When using with MegaPose, you may encounter multiple issues: \section megapose_model_testing Next steps - To test whether your model is correct and can be used for pose estimation or tracking, you should follow \ref tutorial-tracking-megapose and run the sample program. -- Since Megapose requires a way to detect the object, you can train a neural network to do so entirely from synthetic data by using the 3D model that you just obtained. The process is detailed in \ref tutorial-synthetic-blenderproc +- Since Megapose requires a way to detect the object, you can train a neural network to do so entirely from synthetic data by using the 3D model that you just obtained. The process is detailed in \ref tutorial-synthetic-blenderproc. */ \ No newline at end of file diff --git a/doc/tutorial/tracking/tutorial-tracking-megapose.dox b/doc/tutorial/tracking/tutorial-tracking-megapose.dox index e3b1088ff5..8911afb7dd 100644 --- a/doc/tutorial/tracking/tutorial-tracking-megapose.dox +++ b/doc/tutorial/tracking/tutorial-tracking-megapose.dox @@ -304,11 +304,11 @@ Once MegaPose has been called, we can display the results in the image. We plot: We have walked through the code of a single object tracking with MegaPose. You may wish to save the results. You can do so by serializing to JSON, as explained in \ref tutorial-json -\subsection megapose_adaptation Adapting this tutorial for your use case +\section megapose_adaptation Adapting this tutorial for your use case This program can run with new 3D models, as MegaPose does require retraining. To adapt this script to your problem, you will require multiple things: - The intrinsic parameters of your camera. To calibrate your camera, see \ref tutorial-calibration-intrinsic - The 3D model of your object. See \ref tutorial-megapose-model -- Optionally (but recommended), an automated way to detect the object. You can for instance train a deep neural network and use it in ViSP, as explained in \ref tutorial-detection-dnn. Since you already have your 3D model, you can use Blender to generate a synthetic dataset and train a detection network without manually annotating images. This process is explained in \ref tutorial-synthetic-blenderproc +- Optionally (but recommended), an automated way to detect the object. You can for instance train a deep neural network and use it in ViSP, as explained in \ref tutorial-detection-dnn. Since you already have your 3D model, you can use Blender to generate a synthetic dataset and train a detection network without manually annotating images. This process is explained in \ref tutorial-synthetic-blenderproc. */ diff --git a/doc/tutorial/tutorial-users.dox b/doc/tutorial/tutorial-users.dox index c39e616dbe..23977b9628 100644 --- a/doc/tutorial/tutorial-users.dox +++ b/doc/tutorial/tutorial-users.dox @@ -99,7 +99,7 @@ This page introduces the user to the way to track objects in images. - \subpage tutorial-tracking-mb-generic-rgbd-Blender
This tutorial shows how to use Blender to generate simulated data to test the model-based tracker. - \subpage tutorial-tracking-tt
This tutorial focuses on template trackers based on image registration approaches. - \subpage tutorial-tracking-megapose
This tutorial shows how to use megapose, a deep learning pose estimation solution, to perform single object tracking. -- \subpage tutorial-megapose-model
This tutorial shows how to prepare your 3D model for usage in megapose. +- \subpage tutorial-megapose-model
This tutorial shows how to prepare your 3D model for usage in megapose after reconstruction with Neural Radiance Fields (NeRF). */

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From 271223d241d23c411c5ebf7a82a8ad43bd3ec468 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Mon, 3 Jul 2023 00:45:40 +0200 Subject: [PATCH 03/28] Dataset generation: light placement, distractors, objects --- script/dataset_generator/example_config.json | 64 ++- .../generate_hdf5_dataset.py | 364 +++++++++++++++--- 2 files changed, 349 insertions(+), 79 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index d15c2d7657..613d112d4c 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -1,22 +1,50 @@ { - "camera": { - "px": 600, - "py": 600, - "u0": 320, - "v0": 240, - "h": 480, - "w": 640, - "randomize_params_percent": 5.0 + "numpy_seed": 1994, + "blenderproc_seed": "69", + "camera": { + "px": 600, + "py": 600, + "u0": 320, + "v0": 240, + "h": 480, + "w": 640, + "randomize_params_percent": 5.0 + }, + "rendering": { + "max_num_samples": 64 + }, + "scene": { + "room_size_multiplier": 2.5, + "distractors": { + "min_count": 256, + "max_count": 257, + "min_size_rel_scene": 0.01, + "max_size_rel_scene": 0.05, + "displacement_max_amount": 0.1 }, - - "dataset": { - "object_poses": true, - "depth": false, - "normals": false, - "segmentation": false, - "detection": true + "lights": { + "min_count": 4, + "max_count": 8, + "min_intensity": 100, + "max_intensity": 1000 }, - "models_path": "/home/sfelton/megapose-models", - "cc_textures_path": "/home/sfelton/cc0", - "output_path": "output" + "objects": { + "min_count": 1, + "max_count": 10, + "multiple_occurences": true, + "scale_noise": 0.2, + "displacement_max_amount": 0.001, + "pbr_noise": true + } + }, + "dataset": { + "object_poses": true, + "depth": true, + "normals": true, + "segmentation": true, + "detection": true + }, + "models_path": "/home/sfelton/models", + "cc_textures_path": "/home/sfelton/cc0", + "output_path": "output" } \ No newline at end of file diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py index cfa04f3553..78f839ed16 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -20,40 +20,157 @@ ''' TODO: - Random objects selection: max number of objects, location, rotation, sample_on_surface tuto - - Random cc0 - Random distractors - - Random light + - Random : color and orientation for spot ''' +import os import numpy as np import argparse import json from operator import itemgetter -from typing import Dict +from pathlib import Path +from typing import Callable, Dict, List + +from blenderproc.python.types import MeshObjectUtility, EntityUtility + +def homogeneous_inverse(aTb): + bTa = aTb.copy() + bTa[:3, :3] = bTa[:3, :3].T + bTa[:3, 3] = -bTa[:3, :3] @ bTa[:3, 3] + return bTa +def homogeneous_no_scaling(object: bproc.types.MeshObject): + worldTlocal = np.eye(4) + worldTlocal[:3, :3] = object.get_rotation_mat() + worldTlocal[:3, 3] = object.get_location() + return worldTlocal + +def point_in_bounding_box(object: bproc.types.MeshObject, bb_scale=1.0): + ''' + Sample a random point in the bounding box of an object. + the bounding box can be scaled with bb_scale if we wish to sample a point "near" the true bounding box + Returned coordinates are in world frame + ''' + + bb = object.get_bound_box() * bb_scale # 8 x 3 + worldTlocal = homogeneous_no_scaling(object) # local2world includes scaling + localTworld = homogeneous_inverse(worldTlocal) + # go to object space to sample with axis aligned bb, since sampling in world space would be harder + bb_local = localTworld @ np.concatenate((bb, np.ones((8, 1))), axis=-1).T + bb_local = bb_local.T[:, :3] # 8 X 3 + mins, maxes = np.min(bb_local, axis=0), np.max(bb_local, axis=0) + point_local = np.zeros(3) + for i in range(3): + point_local[i] = np.random.uniform(mins[i], maxes[i]) + point = worldTlocal @ np.concatenate((point_local, np.ones(1)))[:, None] + point = point[:3, 0] + return point + + + + +def randomize_pbr(objects): + # set shading and physics properties and randomize PBR materials + for j, obj in enumerate(objects): + obj.set_shading_mode('auto') + mat = obj.get_materials()[0] + mat.set_principled_shader_value("Roughness", np.random.uniform(0, 1.0)) + mat.set_principled_shader_value("Specular", np.random.uniform(0, 1.0)) + mat.set_principled_shader_value("Metallic", np.random.uniform(0, 1.0)) + +def add_displacement(objects, max_displacement_strength=0.05): + for obj in objects: + obj.add_uv_mapping("cylinder") + + # Create a random procedural texture + noise_models = ["CLOUDS", "DISTORTED_NOISE", "MAGIC", "MARBLE", + "MUSGRAVE", "NOISE", "STUCCI", "VORONOI", "WOOD"] + texture = bproc.material.create_procedural_texture(noise_models[np.random.choice(len(noise_models))]) + # Displace the vertices of the object based on that random texture + obj.add_displace_modifier( + texture=texture, + strength=np.random.uniform(0, max_displacement_strength), + subdiv_level=np.random.randint(1, 3), + ) -class Config: +class Generator: def __init__(self, config_path): self.config_path = config_path with open(self.config_path, 'r') as json_config_file: self.json_config = json.load(json_config_file) print(self.json_config) + os.environ['BLENDER_PROC_RANDOM_SEED'] = self.json_config['blenderproc_seed'] - self.objects = self.get_objects() + def init(self): + np.random.seed(self.json_config['numpy_seed']) + self.objects, self.classes = self.load_objects() self.cc_textures = bproc.loader.load_ccmaterials(self.json_config['cc_textures_path']) - def get_objects(self) -> Dict: - models_path = self.json_config['models_path'] - - - def setup_renderer(self) -> None: + def load_objects(self) -> Dict[str, bproc.types.MeshObject]: + ''' + Load the objects in the models directory + The directory should have the following structure: + - models/ + --- obj_1_name/ + ----- model.obj + --- obj_2_name/ + ----- model.obj + Returns a dict where keys are the model names + (from the containing folder name of each object, + in the example case: 'obj_1_name' and 'obj_2_name') + and the values are the loaded model + ''' + models_dict = {} + class_dict = {} + models_path = Path(self.json_config['models_path']).absolute() + cls = 1 + assert models_path.exists() and models_path.is_dir(), f'Models path {models_path} must exist and be a directory' + for model_dir in models_path.iterdir(): + if not model_dir.is_dir(): + continue + model_name = model_dir.name + for content in model_dir.iterdir(): + if not content.is_file(): + continue + load_fn: Callable[[str], List[bproc.types.MeshObject]] = None + if content.name.endswith('.obj') or content.name.endswith('.ply'): + load_fn = bproc.loader.load_obj + if load_fn is not None: + print(models_dict.keys()) + assert model_name not in models_dict, f'A folder should contain a single object, but {content} contains multiple objects (.blend or .obj)' + models = load_fn(str(content.absolute())) + assert len(models) > 0, f'Loaded an empty file: {content}' + model = models[0] + if len(models) > 1: + model.join_with_other_objects(models[1:]) + model.set_cp('category_id', cls) + model.hide() # Hide by default + + models_dict[model_name] = model + class_dict[model_name] = cls + cls += 1 + + return models_dict, class_dict + + def render(self) -> Dict: + ''' + Setup rendering: enable depth, normal and segmentation output + ''' depth, normals, segmentation = itemgetter('depth', 'normals', 'segmentation')(self.json_config['dataset']) - + bproc.renderer.set_max_amount_of_samples(self.json_config['rendering']['max_num_samples']) if depth: bproc.renderer.enable_depth_output(activate_antialiasing=False) if normals: bproc.renderer.enable_normals_output() + # if segmentation: + # bproc.renderer.enable_segmentation_output(map_by=['category_id', 'instance_id'], default_values={'category_id': None, 'instance_id': None}) + data = bproc.renderer.render() if segmentation: - bproc.renderer.enable_segmentation_output() + data.update(bproc.renderer.render_segmap(map_by=["instance", "class"])) + return data + + def save_data(self, objects: List[bproc.types.MeshObject], data: Dict): + pass def set_camera_intrinsics(self) -> None: ''' @@ -71,39 +188,157 @@ def set_camera_intrinsics(self) -> None: bproc.camera.set_intrinsics_from_K_matrix(K, w, h) -def create_room(size, textures): - ground = bproc.object.create_primitive('PLANE') - ground.set_location([0, 0, 0]) - ground.set_scale([size / 2, size / 2, 1]) - - objects = [ground] - wall_data = [ - {'loc': [size / 2, 0, size / 2], 'rot': [0, np.pi / 2, 0]}, - {'loc': [-size / 2, 0, size / 2], 'rot': [0, np.pi / 2, 0]}, - {'loc': [0, size / 2, size / 2], 'rot': [np.pi / 2, 0, 0]}, - {'loc': [0, -size / 2, size / 2], 'rot': [np.pi / 2, 0, 0]}, - ] - for w_data in wall_data: - wall = bproc.object.create_primitive('PLANE') - wall.set_location(w_data['loc']) - wall.set_rotation_euler(w_data['rot']) - wall.set_scale([size / 2, size / 2, 1]) - objects.append(wall) - - for obj in objects: - random_texture = np.random.choice(textures) - obj.replace_materials(random_texture) - - - return objects - - - - - - + def create_distractors(self, scene_size, other_objects) -> List[bproc.types.MeshObject]: + json_distractors = self.json_config['scene']['distractors'] + min_count, max_count = itemgetter('min_count', 'max_count')(json_distractors) + min_size, max_size = itemgetter('min_size_rel_scene', 'max_size_rel_scene')(json_distractors) + displacement_strength = itemgetter('displacement_max_amount')(json_distractors) + + count = np.random.randint(min_count, max_count + 1) + def sample_pose(obj: bproc.types.MeshObject): + loc = np.random.uniform([-scene_size / 2, -scene_size / 2, -scene_size / 2], [scene_size / 2, scene_size / 2, scene_size / 2]) + obj.set_location(loc) + obj.set_scale(np.random.uniform(scene_size * min_size, scene_size * max_size, size=3)) + obj.set_rotation_euler(bproc.sampler.uniformSO3()) + distractor_type = np.random.choice(['CUBE', 'CYLINDER', 'CONE', 'PLANE', 'SPHERE', 'MONKEY'], size=count, replace=True) + distractors = [bproc.object.create_primitive(distractor_type[c]) for c in range(count)] + + bproc.object.sample_poses( + distractors, + sample_pose_func=sample_pose, + objects_to_check_collisions=None + ) + + for obj in distractors: + random_texture = np.random.choice(self.cc_textures) + obj.replace_materials(random_texture) + randomize_pbr(distractors) + add_displacement(distractors, displacement_strength) + return distractors + + def create_lights(self, scene_size, count, target_objects) -> List[bproc.types.Light]: + light_json = self.json_config['scene']['lights'] + min_count, max_count = itemgetter('min_count', 'max_count')(light_json) + min_intensity, max_intensity = itemgetter('min_intensity', 'max_intensity')(light_json) + + count = np.random.randint(min_count, max_count + 1) + + point_light_count = np.sum(np.random.choice(2, size=count, replace=True)) + spot_light_count = count - point_light_count + + lights = [] + for l in range(point_light_count): + light = bproc.types.Light('POINT') + loc = np.random.uniform([-scene_size / 2, -scene_size / 2, -scene_size / 2], [scene_size / 2, scene_size / 2, scene_size / 2]) + light.set_location(loc) + light.set_energy(np.random.uniform(min_intensity, max_intensity)) + light.set_color(np.random.uniform(0.5, 1.0, size=3)) + lights.append(light) + for l in range(spot_light_count): + light = bproc.types.Light('SPOT') + loc = np.random.uniform([-scene_size / 2, -scene_size / 2, -scene_size / 2], [scene_size / 2, scene_size / 2, scene_size / 2]) + light.set_location(loc) + light.set_energy(np.random.uniform(min_intensity, max_intensity)) + light.set_color(np.random.uniform(0.5, 1.0, size=3)) + + looked_at_obj = np.random.choice(len(target_objects)) + looked_at_obj = target_objects[looked_at_obj] + poi = point_in_bounding_box(looked_at_obj) + R = bproc.camera.rotation_from_forward_vec(poi - light.get_location()) + light.set_rotation_mat(R) + lights.append(light) + return lights + + def create_target_objects(self) -> List[bproc.types.MeshObject]: + json_objects = self.json_config['scene']['objects'] + min_count, max_count, replace = itemgetter('min_count', 'max_count', 'multiple_occurences')(json_objects) + scale_noise, displacement_amount, pbr_noise = itemgetter('scale_noise', 'displacement_max_amount', 'pbr_noise')(json_objects) + + object_keys = list(self.objects.keys()) + + if not replace: + max_count = min(max_count, len(object_keys)) + count = np.random.randint(min_count, max_count + 1) + selected_key_indices = np.random.choice(len(object_keys), size=count, replace=replace) + + objects: List[bproc.types.MeshObject] = [self.objects[object_keys[index]].duplicate() for index in selected_key_indices] + for object in objects: + object.hide(False) + if scale_noise > 0.0: + random_scale = np.random.uniform(-scale_noise, scale_noise) + 1.0 + object.set_scale([random_scale, random_scale, random_scale]) # Uniform scaling + + if displacement_amount > 0.0: + add_displacement(objects, displacement_amount) + if pbr_noise: + randomize_pbr(objects) + + return objects + + def create_scene(self): + ''' + Create a basic scene, a square room. + The size of the room is dependent on the size of the biggest object multiplied by a user supplied param + Each wall has a random texture, sampled from cc0 materials + Distractors are added randomly in the room + Random lights are also placed + ''' -from blenderproc.python.renderer import RendererUtility + room_size = 0.0 + room_size_multiplier = self.json_config['scene']['room_size_multiplier'] + + assert room_size_multiplier >= 1.0, 'Room size multiplier should be more than one' + objects = self.create_target_objects() + + for object in objects: + print() + bb = object.get_bound_box() + print(object.get_name(), object.get_scale(), bb) + for corner_index in range(len(bb) - 1): + dists = np.linalg.norm(bb[corner_index+1:] - bb[corner_index], axis=-1, ord=2) + print(dists) + room_size = max(np.max(dists), room_size) + print(object.get_location()) + print('room size = ', room_size) + size = room_size * room_size_multiplier + + ground = bproc.object.create_primitive('PLANE') + ground.set_location([0, 0, -size / 2]) + ground.set_scale([size / 2, size / 2, 1]) + room_objects = [ground] + wall_data = [ + {'loc': [size / 2, 0, 0], 'rot': [0, np.pi / 2, 0]}, + {'loc': [-size / 2, 0, 0], 'rot': [0, np.pi / 2, 0]}, + {'loc': [0, size / 2, 0], 'rot': [np.pi / 2, 0, 0]}, + {'loc': [0, -size / 2, 0], 'rot': [np.pi / 2, 0, 0]}, + ] + for w_data in wall_data: + wall = bproc.object.create_primitive('PLANE') + wall.set_location(w_data['loc']) + wall.set_rotation_euler(w_data['rot']) + wall.set_scale([size / 2, size / 2, 1]) + room_objects.append(wall) + + for obj in room_objects: + random_texture = np.random.choice(self.cc_textures) + obj.replace_materials(random_texture) + randomize_pbr(room_objects) + + def sample_pose(obj: bproc.types.MeshObject): + loc = np.random.uniform([-size / 2, -size / 2, -size / 2], [size / 2, size / 2, size / 2]) + obj.set_location(loc) + obj.set_rotation_euler(bproc.sampler.uniformSO3()) + bproc.object.sample_poses( + objects, + sample_pose_func=sample_pose, + objects_to_check_collisions=None + ) + + distractors = self.create_distractors(size, room_objects + objects) # TODO: place real objects + lights = self.create_lights(size, 5, objects) + return objects, room_objects, distractors, lights + +# from blenderproc.python.renderer import RendererUtility if __name__ == '__main__': @@ -111,32 +346,39 @@ def create_room(size, textures): parser.add_argument('--config', required=True, type=str, help='Path to the JSON configuration file for the dataset generation script') args = parser.parse_args() - - - RendererUtility.set_render_devices(use_only_cpu=True) - bproc.clean_up(clean_up_camera=True) - config = Config(args.config) - # bproc.init() # Works if you have a GPU - config.set_camera_intrinsics() + # RendererUtility.set_render_devices(use_only_cpu=True) + # bproc.clean_up(clean_up_camera=True) + generator = Generator(args.config) + bproc.init() # Works if you have a GPU + generator.init() + generator.set_camera_intrinsics() # Create a simple object: obj = bproc.object.create_primitive("MONKEY") + obj.set_scale([0.01, 0.01, 0.01]) + add_displacement([obj]) - # Create a point light next to it - light = bproc.types.Light() - light.set_location([2, -2, 0]) - light.set_energy(1000) # Set the camera to be in front of the object - cam_pose = bproc.math.build_transformation_mat([0, -5, 1], [np.pi / 2, 0, 0]) - bproc.camera.add_camera_pose(cam_pose) - create_room(10.0, config.cc_textures) + poi = bproc.object.compute_poi([obj]) + location = np.random.uniform([-1, -1, -1], [1,1,1]) + rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) + cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) + bproc.camera.add_camera_pose(cam2world_matrix) + generator.create_scene() # Render the scene - config.setup_renderer() + data = generator.render() + print(data) - data = bproc.renderer.render() + #seg_data = bproc.renderer.render_segmap(map_by=["instance", "class", "name"]) # Write the rendering into an hdf5 file bproc.writer.write_hdf5("output/", data) - + # bproc.writer.write_coco_annotations('output/coco', + # instance_segmaps=seg_data["instance_segmaps"], + # instance_attribute_maps=seg_data["instance_attribute_maps"], + # colors=data["colors"], + # color_file_format="PNG") + + From 9a7d32f02c4aa424284e385ac53a92ddd2569729 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Mon, 3 Jul 2023 18:04:20 +0200 Subject: [PATCH 04/28] Dataset generator: basic camera sampling, modify pbr noise --- script/dataset_generator/example_config.json | 29 +-- .../generate_hdf5_dataset.py | 196 ++++++++++-------- 2 files changed, 130 insertions(+), 95 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 613d112d4c..1a4a704884 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -1,5 +1,5 @@ { - "numpy_seed": 1994, + "numpy_seed": 19, "blenderproc_seed": "69", "camera": { "px": 600, @@ -14,19 +14,20 @@ "max_num_samples": 64 }, "scene": { - "room_size_multiplier": 2.5, + "room_size_multiplier": 1.5, "distractors": { - "min_count": 256, - "max_count": 257, + "min_count": 20, + "max_count": 50, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, - "displacement_max_amount": 0.1 + "displacement_max_amount": 0.1, + "pbr_noise": 0.5 }, "lights": { "min_count": 4, "max_count": 8, "min_intensity": 100, - "max_intensity": 1000 + "max_intensity": 500 }, "objects": { "min_count": 1, @@ -34,17 +35,19 @@ "multiple_occurences": true, "scale_noise": 0.2, "displacement_max_amount": 0.001, - "pbr_noise": true + "pbr_noise": 0.2 } }, "dataset": { + "num_scenes": 2, + "images_per_scene": 2, "object_poses": true, - "depth": true, + "depth": false, "normals": true, - "segmentation": true, - "detection": true + "segmentation": false, + "detection": true, + "save_path": "output" }, - "models_path": "/home/sfelton/models", - "cc_textures_path": "/home/sfelton/cc0", - "output_path": "output" + "models_path": "/home/sfelton/megapose-models", + "cc_textures_path": "/home/sfelton/cc0" } \ No newline at end of file diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py index 78f839ed16..9de76aa61c 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -50,7 +50,7 @@ def point_in_bounding_box(object: bproc.types.MeshObject, bb_scale=1.0): the bounding box can be scaled with bb_scale if we wish to sample a point "near" the true bounding box Returned coordinates are in world frame ''' - + bb = object.get_bound_box() * bb_scale # 8 x 3 worldTlocal = homogeneous_no_scaling(object) # local2world includes scaling localTworld = homogeneous_inverse(worldTlocal) @@ -65,18 +65,26 @@ def point_in_bounding_box(object: bproc.types.MeshObject, bb_scale=1.0): point = point[:3, 0] return point - - -def randomize_pbr(objects): +def randomize_pbr(objects: List[bproc.types.MeshObject], noise): # set shading and physics properties and randomize PBR materials for j, obj in enumerate(objects): obj.set_shading_mode('auto') - mat = obj.get_materials()[0] - mat.set_principled_shader_value("Roughness", np.random.uniform(0, 1.0)) - mat.set_principled_shader_value("Specular", np.random.uniform(0, 1.0)) - mat.set_principled_shader_value("Metallic", np.random.uniform(0, 1.0)) - + mat = obj.get_materials()[0] + keys = ["Roughness", "Specular", "Metallic"] + for k in keys: + base_value = mat.get_principled_shader_value(k) + new_value = None + import bpy + if isinstance(base_value, bpy.types.NodeSocketColor): + new_value = base_value.default_value + np.concatenate((np.random.uniform(-noise, noise, size=3), [1.0])) + #base_value.default_value = new_value + new_value = base_value + elif isinstance(base_value, float): + new_value = max(0.0, min(1.0, base_value + np.random.uniform(-noise, noise))) + if new_value is not None: + mat.set_principled_shader_value(k, new_value) + def add_displacement(objects, max_displacement_strength=0.05): for obj in objects: obj.add_uv_mapping("cylinder") @@ -93,6 +101,22 @@ def add_displacement(objects, max_displacement_strength=0.05): ) +class Scene: + def __init__(self, size: float, target_objects: List[bproc.types.MeshObject], + distractors: List[bproc.types.MeshObject], room_objects: List[bproc.types.MeshObject], lights: List[bproc.types.MeshObject]): + self.size = size + self.target_objects = target_objects + self.distractors = distractors + self.room_objects = room_objects + self.lights = lights + + def cleanup(self): + objects = self.target_objects + self.distractors + self.room_objects + for object in objects: + object.delete(True) + for light in self.lights: + light.delete(True) + class Generator: def __init__(self, config_path): self.config_path = config_path @@ -105,6 +129,7 @@ def init(self): np.random.seed(self.json_config['numpy_seed']) self.objects, self.classes = self.load_objects() self.cc_textures = bproc.loader.load_ccmaterials(self.json_config['cc_textures_path']) + self.setup_renderer() def load_objects(self) -> Dict[str, bproc.types.MeshObject]: ''' @@ -115,8 +140,8 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: ----- model.obj --- obj_2_name/ ----- model.obj - Returns a dict where keys are the model names - (from the containing folder name of each object, + Returns a dict where keys are the model names + (from the containing folder name of each object, in the example case: 'obj_1_name' and 'obj_2_name') and the values are the loaded model ''' @@ -136,7 +161,6 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: if content.name.endswith('.obj') or content.name.endswith('.ply'): load_fn = bproc.loader.load_obj if load_fn is not None: - print(models_dict.keys()) assert model_name not in models_dict, f'A folder should contain a single object, but {content} contains multiple objects (.blend or .obj)' models = load_fn(str(content.absolute())) assert len(models) > 0, f'Loaded an empty file: {content}' @@ -145,30 +169,34 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: model.join_with_other_objects(models[1:]) model.set_cp('category_id', cls) model.hide() # Hide by default - models_dict[model_name] = model class_dict[model_name] = cls cls += 1 return models_dict, class_dict - def render(self) -> Dict: - ''' - Setup rendering: enable depth, normal and segmentation output - ''' - depth, normals, segmentation = itemgetter('depth', 'normals', 'segmentation')(self.json_config['dataset']) + def setup_renderer(self): + depth, normals = itemgetter('depth', 'normals')(self.json_config['dataset']) bproc.renderer.set_max_amount_of_samples(self.json_config['rendering']['max_num_samples']) if depth: bproc.renderer.enable_depth_output(activate_antialiasing=False) - if normals: + # if normals: + # bproc.renderer.enable_normals_output() + + def render(self) -> Dict: + ''' + + ''' + normals, segmentation = itemgetter('normals', 'segmentation')(self.json_config['dataset']) + if normals: # Ugly, but doesn't work if it isn't called every render bproc.renderer.enable_normals_output() - # if segmentation: - # bproc.renderer.enable_segmentation_output(map_by=['category_id', 'instance_id'], default_values={'category_id': None, 'instance_id': None}) data = bproc.renderer.render() + print(data.keys()) + print(len(data['normals'])) if segmentation: data.update(bproc.renderer.render_segmap(map_by=["instance", "class"])) return data - + def save_data(self, objects: List[bproc.types.MeshObject], data: Dict): pass @@ -192,8 +220,8 @@ def create_distractors(self, scene_size, other_objects) -> List[bproc.types.Mesh json_distractors = self.json_config['scene']['distractors'] min_count, max_count = itemgetter('min_count', 'max_count')(json_distractors) min_size, max_size = itemgetter('min_size_rel_scene', 'max_size_rel_scene')(json_distractors) - displacement_strength = itemgetter('displacement_max_amount')(json_distractors) - + displacement_strength, pbr_noise = itemgetter('displacement_max_amount', 'pbr_noise')(json_distractors) + count = np.random.randint(min_count, max_count + 1) def sample_pose(obj: bproc.types.MeshObject): loc = np.random.uniform([-scene_size / 2, -scene_size / 2, -scene_size / 2], [scene_size / 2, scene_size / 2, scene_size / 2]) @@ -202,7 +230,7 @@ def sample_pose(obj: bproc.types.MeshObject): obj.set_rotation_euler(bproc.sampler.uniformSO3()) distractor_type = np.random.choice(['CUBE', 'CYLINDER', 'CONE', 'PLANE', 'SPHERE', 'MONKEY'], size=count, replace=True) distractors = [bproc.object.create_primitive(distractor_type[c]) for c in range(count)] - + bproc.object.sample_poses( distractors, sample_pose_func=sample_pose, @@ -212,35 +240,34 @@ def sample_pose(obj: bproc.types.MeshObject): for obj in distractors: random_texture = np.random.choice(self.cc_textures) obj.replace_materials(random_texture) - randomize_pbr(distractors) + randomize_pbr(distractors, pbr_noise) add_displacement(distractors, displacement_strength) return distractors - - def create_lights(self, scene_size, count, target_objects) -> List[bproc.types.Light]: + + def create_lights(self, scene_size, target_objects) -> List[bproc.types.Light]: light_json = self.json_config['scene']['lights'] min_count, max_count = itemgetter('min_count', 'max_count')(light_json) min_intensity, max_intensity = itemgetter('min_intensity', 'max_intensity')(light_json) count = np.random.randint(min_count, max_count + 1) - + point_light_count = np.sum(np.random.choice(2, size=count, replace=True)) spot_light_count = count - point_light_count - + lights = [] - for l in range(point_light_count): - light = bproc.types.Light('POINT') + def basic_light_sampling(light: bproc.types.Light) -> None: loc = np.random.uniform([-scene_size / 2, -scene_size / 2, -scene_size / 2], [scene_size / 2, scene_size / 2, scene_size / 2]) light.set_location(loc) light.set_energy(np.random.uniform(min_intensity, max_intensity)) light.set_color(np.random.uniform(0.5, 1.0, size=3)) + + for _ in range(point_light_count): + light = bproc.types.Light('POINT') + basic_light_sampling(light) lights.append(light) - for l in range(spot_light_count): + for _ in range(spot_light_count): light = bproc.types.Light('SPOT') - loc = np.random.uniform([-scene_size / 2, -scene_size / 2, -scene_size / 2], [scene_size / 2, scene_size / 2, scene_size / 2]) - light.set_location(loc) - light.set_energy(np.random.uniform(min_intensity, max_intensity)) - light.set_color(np.random.uniform(0.5, 1.0, size=3)) - + basic_light_sampling(light) looked_at_obj = np.random.choice(len(target_objects)) looked_at_obj = target_objects[looked_at_obj] poi = point_in_bounding_box(looked_at_obj) @@ -248,19 +275,19 @@ def create_lights(self, scene_size, count, target_objects) -> List[bproc.types.L light.set_rotation_mat(R) lights.append(light) return lights - + def create_target_objects(self) -> List[bproc.types.MeshObject]: json_objects = self.json_config['scene']['objects'] min_count, max_count, replace = itemgetter('min_count', 'max_count', 'multiple_occurences')(json_objects) scale_noise, displacement_amount, pbr_noise = itemgetter('scale_noise', 'displacement_max_amount', 'pbr_noise')(json_objects) - + object_keys = list(self.objects.keys()) if not replace: max_count = min(max_count, len(object_keys)) count = np.random.randint(min_count, max_count + 1) selected_key_indices = np.random.choice(len(object_keys), size=count, replace=replace) - + objects: List[bproc.types.MeshObject] = [self.objects[object_keys[index]].duplicate() for index in selected_key_indices] for object in objects: object.hide(False) @@ -270,9 +297,9 @@ def create_target_objects(self) -> List[bproc.types.MeshObject]: if displacement_amount > 0.0: add_displacement(objects, displacement_amount) - if pbr_noise: - randomize_pbr(objects) - + if pbr_noise > 0.0: + randomize_pbr(objects, pbr_noise) + return objects def create_scene(self): @@ -286,20 +313,15 @@ def create_scene(self): room_size = 0.0 room_size_multiplier = self.json_config['scene']['room_size_multiplier'] - + assert room_size_multiplier >= 1.0, 'Room size multiplier should be more than one' objects = self.create_target_objects() for object in objects: - print() bb = object.get_bound_box() - print(object.get_name(), object.get_scale(), bb) for corner_index in range(len(bb) - 1): dists = np.linalg.norm(bb[corner_index+1:] - bb[corner_index], axis=-1, ord=2) - print(dists) room_size = max(np.max(dists), room_size) - print(object.get_location()) - print('room size = ', room_size) size = room_size * room_size_multiplier ground = bproc.object.create_primitive('PLANE') @@ -322,7 +344,7 @@ def create_scene(self): for obj in room_objects: random_texture = np.random.choice(self.cc_textures) obj.replace_materials(random_texture) - randomize_pbr(room_objects) + #randomize_pbr(room_objects) def sample_pose(obj: bproc.types.MeshObject): loc = np.random.uniform([-size / 2, -size / 2, -size / 2], [size / 2, size / 2, size / 2]) @@ -334,11 +356,40 @@ def sample_pose(obj: bproc.types.MeshObject): objects_to_check_collisions=None ) - distractors = self.create_distractors(size, room_objects + objects) # TODO: place real objects - lights = self.create_lights(size, 5, objects) - return objects, room_objects, distractors, lights - -# from blenderproc.python.renderer import RendererUtility + distractors = self.create_distractors(size, room_objects + objects) + lights = self.create_lights(size, objects) + return Scene(size, objects, distractors, room_objects, lights) + + def sample_camera_poses(self, scene: Scene) -> None: + dataset_config = self.json_config['dataset'] + images_per_scene = dataset_config['images_per_scene'] + hs = scene.size / 2 + for i in range(images_per_scene): + poi = bproc.object.compute_poi(scene.target_objects) + location = np.random.uniform([-hs, -hs, -hs], [hs,hs,hs]) + rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) + cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) + bproc.camera.add_camera_pose(cam2world_matrix) + + def run(self): + num_scenes = self.json_config['dataset']['num_scenes'] + save_path = Path(self.json_config['dataset']['save_path']) + save_path.mkdir(exist_ok=True) + for scene_idx in range(num_scenes): + bproc.utility.reset_keyframes() + self.set_camera_intrinsics() + scene = self.create_scene() + self.sample_camera_poses(scene) + data = self.render() + path = save_path / str(scene_idx) + path.mkdir(exist_ok=True) + bproc.writer.write_hdf5(str(path.absolute()), data, append_to_existing_output=True) + scene.cleanup() + + + + +from blenderproc.python.renderer import RendererUtility if __name__ == '__main__': @@ -346,39 +397,20 @@ def sample_pose(obj: bproc.types.MeshObject): parser.add_argument('--config', required=True, type=str, help='Path to the JSON configuration file for the dataset generation script') args = parser.parse_args() - # RendererUtility.set_render_devices(use_only_cpu=True) - # bproc.clean_up(clean_up_camera=True) generator = Generator(args.config) - bproc.init() # Works if you have a GPU - generator.init() - generator.set_camera_intrinsics() - # Create a simple object: - obj = bproc.object.create_primitive("MONKEY") - obj.set_scale([0.01, 0.01, 0.01]) - add_displacement([obj]) - + RendererUtility.set_render_devices(use_only_cpu=True) + bproc.clean_up(clean_up_camera=True) - # Set the camera to be in front of the object - poi = bproc.object.compute_poi([obj]) - location = np.random.uniform([-1, -1, -1], [1,1,1]) - rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) - cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) - bproc.camera.add_camera_pose(cam2world_matrix) - generator.create_scene() - - # Render the scene - - data = generator.render() - print(data) + # bproc.init() # Works if you have a GPU + generator.init() + generator.run() - #seg_data = bproc.renderer.render_segmap(map_by=["instance", "class", "name"]) # Write the rendering into an hdf5 file - bproc.writer.write_hdf5("output/", data) # bproc.writer.write_coco_annotations('output/coco', # instance_segmaps=seg_data["instance_segmaps"], # instance_attribute_maps=seg_data["instance_attribute_maps"], # colors=data["colors"], # color_file_format="PNG") - - + + From ef62c2e7a93a33b2deb34934f98da30c3ae2dbfe Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Tue, 4 Jul 2023 00:29:30 +0200 Subject: [PATCH 05/28] Added ceilling, debugging some stuff --- script/dataset_generator/example_config.json | 6 +++--- script/dataset_generator/generate_hdf5_dataset.py | 9 +++++---- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 1a4a704884..b73e871bda 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -16,8 +16,8 @@ "scene": { "room_size_multiplier": 1.5, "distractors": { - "min_count": 20, - "max_count": 50, + "min_count": 5, + "max_count": 5, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, "displacement_max_amount": 0.1, @@ -39,7 +39,7 @@ } }, "dataset": { - "num_scenes": 2, + "num_scenes": 5, "images_per_scene": 2, "object_poses": true, "depth": false, diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py index 9de76aa61c..b903caf82a 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -333,6 +333,7 @@ def create_scene(self): {'loc': [-size / 2, 0, 0], 'rot': [0, np.pi / 2, 0]}, {'loc': [0, size / 2, 0], 'rot': [np.pi / 2, 0, 0]}, {'loc': [0, -size / 2, 0], 'rot': [np.pi / 2, 0, 0]}, + {'loc': [0, 0, size / 2], 'rot': [0.0, 0, 0]}, ] for w_data in wall_data: wall = bproc.object.create_primitive('PLANE') @@ -344,7 +345,7 @@ def create_scene(self): for obj in room_objects: random_texture = np.random.choice(self.cc_textures) obj.replace_materials(random_texture) - #randomize_pbr(room_objects) + randomize_pbr(room_objects, 0.2) def sample_pose(obj: bproc.types.MeshObject): loc = np.random.uniform([-size / 2, -size / 2, -size / 2], [size / 2, size / 2, size / 2]) @@ -398,10 +399,10 @@ def run(self): args = parser.parse_args() generator = Generator(args.config) - RendererUtility.set_render_devices(use_only_cpu=True) - bproc.clean_up(clean_up_camera=True) + # RendererUtility.set_render_devices(use_only_cpu=True) + # bproc.clean_up(clean_up_camera=True) - # bproc.init() # Works if you have a GPU + bproc.init() # Works if you have a GPU generator.init() generator.run() From 2e5202c8f9fa13e1901ae4490acd53544b4f8594 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Tue, 4 Jul 2023 01:26:14 +0200 Subject: [PATCH 06/28] rework camera sampling --- script/dataset_generator/example_config.json | 14 ++++---- .../generate_hdf5_dataset.py | 34 +++++++++++++------ 2 files changed, 31 insertions(+), 17 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index b73e871bda..022c71288a 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -14,9 +14,9 @@ "max_num_samples": 64 }, "scene": { - "room_size_multiplier": 1.5, + "room_size_multiplier": 1.0, "distractors": { - "min_count": 5, + "min_count": 1, "max_count": 5, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, @@ -24,18 +24,20 @@ "pbr_noise": 0.5 }, "lights": { - "min_count": 4, + "min_count": 2, "max_count": 8, "min_intensity": 100, - "max_intensity": 500 + "max_intensity": 300 }, "objects": { "min_count": 1, "max_count": 10, "multiple_occurences": true, "scale_noise": 0.2, - "displacement_max_amount": 0.001, - "pbr_noise": 0.2 + "displacement_max_amount": 0.01, + "pbr_noise": 0.2, + "cam_min_dist_rel": 0.8, + "cam_max_dist_rel": 5.0 } }, "dataset": { diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py index b903caf82a..5922f6b3ba 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -65,6 +65,13 @@ def point_in_bounding_box(object: bproc.types.MeshObject, bb_scale=1.0): point = point[:3, 0] return point +def object_bb_length(object: bproc.types.MeshObject) -> float: + size = 0.0 + bb = object.get_bound_box() + for corner_index in range(len(bb) - 1): + dists = np.linalg.norm(bb[corner_index+1:] - bb[corner_index], axis=-1, ord=2) + size = max(np.max(dists), size) + return size def randomize_pbr(objects: List[bproc.types.MeshObject], noise): # set shading and physics properties and randomize PBR materials @@ -85,7 +92,7 @@ def randomize_pbr(objects: List[bproc.types.MeshObject], noise): if new_value is not None: mat.set_principled_shader_value(k, new_value) -def add_displacement(objects, max_displacement_strength=0.05): +def add_displacement(objects: List[bproc.types.MeshObject], max_displacement_strength=0.05): for obj in objects: obj.add_uv_mapping("cylinder") @@ -97,7 +104,7 @@ def add_displacement(objects, max_displacement_strength=0.05): obj.add_displace_modifier( texture=texture, strength=np.random.uniform(0, max_displacement_strength), - subdiv_level=np.random.randint(1, 3), + subdiv_level=1, ) @@ -318,10 +325,7 @@ def create_scene(self): objects = self.create_target_objects() for object in objects: - bb = object.get_bound_box() - for corner_index in range(len(bb) - 1): - dists = np.linalg.norm(bb[corner_index+1:] - bb[corner_index], axis=-1, ord=2) - room_size = max(np.max(dists), room_size) + room_size = max(object_bb_length(object), room_size) size = room_size * room_size_multiplier ground = bproc.object.create_primitive('PLANE') @@ -364,12 +368,18 @@ def sample_pose(obj: bproc.types.MeshObject): def sample_camera_poses(self, scene: Scene) -> None: dataset_config = self.json_config['dataset'] images_per_scene = dataset_config['images_per_scene'] + cam_min_dist, cam_max_dist = itemgetter('cam_min_dist_rel', 'cam_max_dist_rel')(self.json_config['scene']['objects']) hs = scene.size / 2 for i in range(images_per_scene): - poi = bproc.object.compute_poi(scene.target_objects) - location = np.random.uniform([-hs, -hs, -hs], [hs,hs,hs]) - rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) - cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) + good = False + while not good: + object = scene.target_objects[np.random.choice(len(scene.target_objects))] + object_size = object_bb_length(object) + poi = point_in_bounding_box(object, bb_scale=1) + location = bproc.sampler.sphere(object.get_location(), np.random.uniform(cam_min_dist, cam_max_dist) * object_size, 'SURFACE') + rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) + cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) + good = object in bproc.camera.visible_objects(cam2world_matrix) bproc.camera.add_camera_pose(cam2world_matrix) def run(self): @@ -384,8 +394,10 @@ def run(self): data = self.render() path = save_path / str(scene_idx) path.mkdir(exist_ok=True) - bproc.writer.write_hdf5(str(path.absolute()), data, append_to_existing_output=True) + bproc.writer.write_hdf5(str(path.absolute()), data, append_to_existing_output=False) scene.cleanup() + del scene + del data From e564ef78d042560754d491ef96d5d5f6b13b2c70 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Tue, 4 Jul 2023 17:28:25 +0200 Subject: [PATCH 07/28] physics positioning --- script/dataset_generator/example_config.json | 17 +++++----- .../generate_hdf5_dataset.py | 34 +++++++++++++++---- 2 files changed, 36 insertions(+), 15 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 022c71288a..13cd194cb7 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -15,9 +15,10 @@ }, "scene": { "room_size_multiplier": 1.0, + "simulate_physics": true, "distractors": { - "min_count": 1, - "max_count": 5, + "min_count": 25, + "max_count": 200, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, "displacement_max_amount": 0.1, @@ -26,26 +27,26 @@ "lights": { "min_count": 2, "max_count": 8, - "min_intensity": 100, + "min_intensity": 50, "max_intensity": 300 }, "objects": { "min_count": 1, - "max_count": 10, + "max_count": 50, "multiple_occurences": true, "scale_noise": 0.2, - "displacement_max_amount": 0.01, + "displacement_max_amount": 0.005, "pbr_noise": 0.2, - "cam_min_dist_rel": 0.8, + "cam_min_dist_rel": 1.0, "cam_max_dist_rel": 5.0 } }, "dataset": { - "num_scenes": 5, + "num_scenes": 1, "images_per_scene": 2, "object_poses": true, "depth": false, - "normals": true, + "normals": false, "segmentation": false, "detection": true, "save_path": "output" diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py index 5922f6b3ba..fc0d2cb66a 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -198,14 +198,23 @@ def render(self) -> Dict: if normals: # Ugly, but doesn't work if it isn't called every render bproc.renderer.enable_normals_output() data = bproc.renderer.render() - print(data.keys()) - print(len(data['normals'])) + if segmentation: data.update(bproc.renderer.render_segmap(map_by=["instance", "class"])) return data - def save_data(self, objects: List[bproc.types.MeshObject], data: Dict): - pass + def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dict): + bb_data = [] + + for frame in bproc.utility.num_frames(): + worldTcam = bproc.camera.get_camera_pose(frame) + visible_objects = bproc.camera.visible_objects(worldTcam) + for object in objects: + if object not in visible_objects: + continue + for k in data.keys(): + print(f'{k}, data = {data[k]}') + def set_camera_intrinsics(self) -> None: ''' @@ -320,6 +329,7 @@ def create_scene(self): room_size = 0.0 room_size_multiplier = self.json_config['scene']['room_size_multiplier'] + simulate_physics = self.json_config['scene']['simulate_physics'] assert room_size_multiplier >= 1.0, 'Room size multiplier should be more than one' objects = self.create_target_objects() @@ -363,6 +373,15 @@ def sample_pose(obj: bproc.types.MeshObject): distractors = self.create_distractors(size, room_objects + objects) lights = self.create_lights(size, objects) + + if simulate_physics: + for object in objects + distractors: + object.enable_rigidbody(True) + for object in room_objects: + object.enable_rigidbody(False) + + bproc.object.simulate_physics_and_fix_final_poses(min_simulation_time=3, max_simulation_time=3.5, check_object_interval=1) + return Scene(size, objects, distractors, room_objects, lights) def sample_camera_poses(self, scene: Scene) -> None: @@ -394,6 +413,7 @@ def run(self): data = self.render() path = save_path / str(scene_idx) path.mkdir(exist_ok=True) + self.save_data(path, scene.target_objects, data) bproc.writer.write_hdf5(str(path.absolute()), data, append_to_existing_output=False) scene.cleanup() del scene @@ -411,10 +431,10 @@ def run(self): args = parser.parse_args() generator = Generator(args.config) - # RendererUtility.set_render_devices(use_only_cpu=True) - # bproc.clean_up(clean_up_camera=True) + RendererUtility.set_render_devices(use_only_cpu=True) + bproc.clean_up(clean_up_camera=True) - bproc.init() # Works if you have a GPU + # bproc.init() # Works if you have a GPU generator.init() generator.run() From c63e25172651e0413b51ea190e2c626d33b7e991 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Thu, 6 Jul 2023 01:25:16 +0200 Subject: [PATCH 08/28] computing bounding box wip, fixing camera sampling clipping in objects --- script/dataset_generator/example_config.json | 24 ++- .../generate_hdf5_dataset.py | 170 +++++++++++++----- 2 files changed, 145 insertions(+), 49 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 13cd194cb7..ba0eaa46b2 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -14,14 +14,15 @@ "max_num_samples": 64 }, "scene": { - "room_size_multiplier": 1.0, + "room_size_multiplier_min": 2.0, + "room_size_multiplier_max": 5.0, "simulate_physics": true, "distractors": { - "min_count": 25, - "max_count": 200, + "min_count": 5, + "max_count": 20, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, - "displacement_max_amount": 0.1, + "displacement_max_amount": 0.0, "pbr_noise": 0.5 }, "lights": { @@ -32,23 +33,28 @@ }, "objects": { "min_count": 1, - "max_count": 50, + "max_count": 10, "multiple_occurences": true, "scale_noise": 0.2, - "displacement_max_amount": 0.005, + "displacement_max_amount": 0.0, "pbr_noise": 0.2, "cam_min_dist_rel": 1.0, "cam_max_dist_rel": 5.0 } }, "dataset": { - "num_scenes": 1, - "images_per_scene": 2, - "object_poses": true, + "num_scenes": 5, + "images_per_scene": 10, + "pose": true, "depth": false, "normals": false, "segmentation": false, "detection": true, + "detection_params": { + "filter_when_object_mostly_behind": true, + "min_side_size_px": 10, + "min_visibility_percentage": 0.3 + }, "save_path": "output" }, "models_path": "/home/sfelton/megapose-models", diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py index fc0d2cb66a..83b5c3544b 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -39,10 +39,21 @@ def homogeneous_inverse(aTb): bTa[:3, 3] = -bTa[:3, :3] @ bTa[:3, 3] return bTa def homogeneous_no_scaling(object: bproc.types.MeshObject): - worldTlocal = np.eye(4) - worldTlocal[:3, :3] = object.get_rotation_mat() - worldTlocal[:3, 3] = object.get_location() - return worldTlocal + localTworld = np.eye(4) + localTworld[:3, :3] = object.get_rotation_mat() + localTworld[:3, 3] = object.get_location() + return localTworld + +def convert_to_visp_frame(aTb): + ''' + Same as converting to an OpenCV frame + ''' + return bproc.math.change_source_coordinate_frame_of_transformation_matrix(aTb, ["X", "-Y", "-Z"]) +def convert_points_to_visp_frame(points: np.ndarray): + new_points = points.copy() + for i in range(len(new_points)): + new_points[i] = bproc.math.change_coordinate_frame_of_point(new_points[i], ["X", "-Y", "-Z"]) + return new_points def point_in_bounding_box(object: bproc.types.MeshObject, bb_scale=1.0): ''' @@ -76,7 +87,8 @@ def object_bb_length(object: bproc.types.MeshObject) -> float: def randomize_pbr(objects: List[bproc.types.MeshObject], noise): # set shading and physics properties and randomize PBR materials for j, obj in enumerate(objects): - obj.set_shading_mode('auto') + rand_angle = np.random.uniform(30, 90) + obj.set_shading_mode('auto', rand_angle) mat = obj.get_materials()[0] keys = ["Roughness", "Specular", "Metallic"] for k in keys: @@ -131,6 +143,7 @@ def __init__(self, config_path): self.json_config = json.load(json_config_file) print(self.json_config) os.environ['BLENDER_PROC_RANDOM_SEED'] = self.json_config['blenderproc_seed'] + def init(self): np.random.seed(self.json_config['numpy_seed']) @@ -204,16 +217,62 @@ def render(self) -> Dict: return data def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dict): - bb_data = [] + json_dataset = self.json_config['dataset'] - for frame in bproc.utility.num_frames(): + out_object_pose, out_bounding_box = itemgetter('pose', 'detection')(json_dataset) + filter_z, min_side_px, min_visibility = itemgetter('filter_when_object_mostly_behind', 'min_side_size_px', 'min_visibility_percentage')(json_dataset['detection_params']) + frames_data = [] + + for frame in range(bproc.utility.num_frames()): worldTcam = bproc.camera.get_camera_pose(frame) + camTworld = homogeneous_inverse(worldTcam) + K = bproc.camera.get_intrinsics_as_K_matrix() visible_objects = bproc.camera.visible_objects(worldTcam) + objects_data = [] for object in objects: if object not in visible_objects: continue - for k in data.keys(): - print(f'{k}, data = {data[k]}') + print(object.get_all_cps()) + object_data = { + 'class': object.get_cp('category_id', frame), + 'name': object.get_name() + } + if out_object_pose: + worldTobj = homogeneous_no_scaling(object) + camTobj = camTworld @ worldTobj + object_data['cTo'] = convert_to_visp_frame(camTobj) + if out_bounding_box: + # Get object bounding box in world, project in camera frame + bb3d_world = object.get_bound_box() + bb3d_cam = camTworld @ np.concatenate((bb3d_world, np.ones((8, 1))), axis=-1).T + bb3d_cam = convert_points_to_visp_frame(bb3d_cam.T[:, :3]) # 8 X 3 + points_behind = bb3d_cam[bb3d_cam[:, 2] < 0] + # More than half the points of the bounding box are behind the camera, object will be visible because it is close, but probably not recognizable + if filter_z and len(points_behind) > 4: + continue + visible_bb3d_points = bb3d_cam[bb3d_cam[:, 2] > 0] + print(visible_bb3d_points.shape) + print(bb3d_cam) + # Project points that are in front of the camera in the image + visible_points_m_2d = visible_bb3d_points[:, :2] / visible_bb3d_points[:, 2, None] + visible_points_px_2d = K @ np.concatenate((visible_points_m_2d, np.ones((len(visible_points_m_2d), 1))), axis=-1).T + visible_points_px_2d = visible_points_px_2d.T[:, :2] + + mins = np.min(visible_points_px_2d, axis=0) + maxes = np.max(visible_points_px_2d, axis=0) + + + + print(f'x, y = ({mins[0]}, {mins[1]}), size = ({maxes[0] - mins[0]}, {maxes[1] - mins[1]})') + + # object_data['bounding_box'] = ... + + + objects_data.append(object_data) + frames_data.append(objects_data) + data['object_data'] = frames_data + bproc.writer.write_hdf5(str(path.absolute()), data, append_to_existing_output=False) + def set_camera_intrinsics(self) -> None: @@ -257,7 +316,8 @@ def sample_pose(obj: bproc.types.MeshObject): random_texture = np.random.choice(self.cc_textures) obj.replace_materials(random_texture) randomize_pbr(distractors, pbr_noise) - add_displacement(distractors, displacement_strength) + if displacement_strength > 0.0: + add_displacement(distractors, displacement_strength) return distractors def create_lights(self, scene_size, target_objects) -> List[bproc.types.Light]: @@ -266,7 +326,6 @@ def create_lights(self, scene_size, target_objects) -> List[bproc.types.Light]: min_intensity, max_intensity = itemgetter('min_intensity', 'max_intensity')(light_json) count = np.random.randint(min_count, max_count + 1) - point_light_count = np.sum(np.random.choice(2, size=count, replace=True)) spot_light_count = count - point_light_count @@ -285,11 +344,13 @@ def basic_light_sampling(light: bproc.types.Light) -> None: light = bproc.types.Light('SPOT') basic_light_sampling(light) looked_at_obj = np.random.choice(len(target_objects)) - looked_at_obj = target_objects[looked_at_obj] + looked_at_obj: bproc.types.MeshObject = target_objects[looked_at_obj] + print(looked_at_obj, looked_at_obj.get_local2world_mat()) poi = point_in_bounding_box(looked_at_obj) R = bproc.camera.rotation_from_forward_vec(poi - light.get_location()) light.set_rotation_mat(R) lights.append(light) + return lights def create_target_objects(self) -> List[bproc.types.MeshObject]: @@ -317,27 +378,8 @@ def create_target_objects(self) -> List[bproc.types.MeshObject]: randomize_pbr(objects, pbr_noise) return objects - - def create_scene(self): - ''' - Create a basic scene, a square room. - The size of the room is dependent on the size of the biggest object multiplied by a user supplied param - Each wall has a random texture, sampled from cc0 materials - Distractors are added randomly in the room - Random lights are also placed - ''' - - room_size = 0.0 - room_size_multiplier = self.json_config['scene']['room_size_multiplier'] - simulate_physics = self.json_config['scene']['simulate_physics'] - - assert room_size_multiplier >= 1.0, 'Room size multiplier should be more than one' - objects = self.create_target_objects() - - for object in objects: - room_size = max(object_bb_length(object), room_size) - size = room_size * room_size_multiplier - + + def create_room(self, size: float) -> List[bproc.types.MeshObject]: ground = bproc.object.create_primitive('PLANE') ground.set_location([0, 0, -size / 2]) ground.set_scale([size / 2, size / 2, 1]) @@ -360,6 +402,29 @@ def create_scene(self): random_texture = np.random.choice(self.cc_textures) obj.replace_materials(random_texture) randomize_pbr(room_objects, 0.2) + return room_objects + + def create_scene(self): + ''' + Create a basic scene, a square room. + The size of the room is dependent on the size of the biggest object multiplied by a user supplied param + Each wall has a random texture, sampled from cc0 materials + Distractors are added randomly in the room + Random lights are also placed + ''' + + room_size = 0.0 + room_size_multiplier_min, room_size_multiplier_max = itemgetter('room_size_multiplier_min', 'room_size_multiplier_max')(self.json_config['scene']) + simulate_physics = self.json_config['scene']['simulate_physics'] + + assert room_size_multiplier_min >= 1.0, 'Room size multiplier should be more than one' + objects = self.create_target_objects() + + for object in objects: + room_size = max(object_bb_length(object), room_size) + size = room_size * np.random.uniform(room_size_multiplier_min, room_size_multiplier_max) + + room_objects = self.create_room(size) def sample_pose(obj: bproc.types.MeshObject): loc = np.random.uniform([-size / 2, -size / 2, -size / 2], [size / 2, size / 2, size / 2]) @@ -372,24 +437,41 @@ def sample_pose(obj: bproc.types.MeshObject): ) distractors = self.create_distractors(size, room_objects + objects) - lights = self.create_lights(size, objects) + if simulate_physics: for object in objects + distractors: object.enable_rigidbody(True) for object in room_objects: - object.enable_rigidbody(False) + object.enable_rigidbody(False, collision_shape='BOX') bproc.object.simulate_physics_and_fix_final_poses(min_simulation_time=3, max_simulation_time=3.5, check_object_interval=1) - return Scene(size, objects, distractors, room_objects, lights) + def filter_objects_outside_room(objects: List[bproc.types.MeshObject]) -> List[bproc.types.MeshObject]: + inside = [] + outside = [] + is_inside = lambda loc: np.all(np.logical_and(loc < size / 2, loc > -size / 2)) + for object in objects: + inside.append(object) if is_inside(object.get_location()) else outside.append(object) + for object in outside: + object.delete() + + print(f'Filtered {len(objects) - len(inside)} objects') + return inside + + objects = filter_objects_outside_room(objects) + distractors = filter_objects_outside_room(distractors) + lights = self.create_lights(size, objects) + + return Scene(size, objects, distractors, room_objects, lights) def sample_camera_poses(self, scene: Scene) -> None: dataset_config = self.json_config['dataset'] images_per_scene = dataset_config['images_per_scene'] cam_min_dist, cam_max_dist = itemgetter('cam_min_dist_rel', 'cam_max_dist_rel')(self.json_config['scene']['objects']) hs = scene.size / 2 - for i in range(images_per_scene): + bvh = bproc.object.create_bvh_tree_multi_objects(scene.target_objects + scene.distractors + scene.room_objects) + for frame in range(images_per_scene): good = False while not good: object = scene.target_objects[np.random.choice(len(scene.target_objects))] @@ -397,8 +479,16 @@ def sample_camera_poses(self, scene: Scene) -> None: poi = point_in_bounding_box(object, bb_scale=1) location = bproc.sampler.sphere(object.get_location(), np.random.uniform(cam_min_dist, cam_max_dist) * object_size, 'SURFACE') rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) + import bpy + cam = bpy.context.scene.camera.data + clip_start = cam.clip_start + focal_length = cam.lens / 1000.0 + cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) - good = object in bproc.camera.visible_objects(cam2world_matrix) + if object in bproc.camera.visible_objects(cam2world_matrix) and bproc.camera.perform_obstacle_in_view_check(cam2world_matrix, {'min': focal_length + clip_start}, bvh): + good = True + else: + good = False bproc.camera.add_camera_pose(cam2world_matrix) def run(self): @@ -414,7 +504,7 @@ def run(self): path = save_path / str(scene_idx) path.mkdir(exist_ok=True) self.save_data(path, scene.target_objects, data) - bproc.writer.write_hdf5(str(path.absolute()), data, append_to_existing_output=False) + scene.cleanup() del scene del data @@ -431,7 +521,7 @@ def run(self): args = parser.parse_args() generator = Generator(args.config) - RendererUtility.set_render_devices(use_only_cpu=True) + # RendererUtility.set_render_devices(use_only_cpu=True) bproc.clean_up(clean_up_camera=True) # bproc.init() # Works if you have a GPU From d9f47348a90dbe9e912c5cbb8ab0ba2ee5af9851 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Thu, 6 Jul 2023 18:11:11 +0200 Subject: [PATCH 09/28] Exporting correct bounding boxes! visualization script provided --- script/dataset_generator/example_config.json | 8 +- .../generate_hdf5_dataset.py | 148 ++++++++++++++---- script/dataset_generator/viz_bb.py | 32 ++++ 3 files changed, 156 insertions(+), 32 deletions(-) create mode 100644 script/dataset_generator/viz_bb.py diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index ba0eaa46b2..4c800c8145 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -14,9 +14,9 @@ "max_num_samples": 64 }, "scene": { - "room_size_multiplier_min": 2.0, + "room_size_multiplier_min": 4.0, "room_size_multiplier_max": 5.0, - "simulate_physics": true, + "simulate_physics": false, "distractors": { "min_count": 5, "max_count": 20, @@ -33,9 +33,9 @@ }, "objects": { "min_count": 1, - "max_count": 10, + "max_count": 1, "multiple_occurences": true, - "scale_noise": 0.2, + "scale_noise": 0.0, "displacement_max_amount": 0.0, "pbr_noise": 0.2, "cam_min_dist_rel": 1.0, diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py index 83b5c3544b..80d79997cd 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -17,12 +17,6 @@ ''' -''' -TODO: - - Random objects selection: max number of objects, location, rotation, sample_on_surface tuto - - Random distractors - - Random : color and orientation for spot -''' import os import numpy as np import argparse @@ -33,6 +27,84 @@ from blenderproc.python.types import MeshObjectUtility, EntityUtility +def get_obb(object: bproc.types.MeshObject) -> np.ndarray: + ''' + Get oriented bounding box in world space + Adapted from + https://blender.stackexchange.com/questions/261070/python-how-to-make-box-out-of-selected-verts-bounding-box-in-local-space/261076#261076 + ''' + obj = object.blender_obj + verts = [v.co for v in obj.data.vertices] + points = np.asarray(verts) + print(points.shape) + # means = np.mean(points, axis=1) + + cov = np.cov(points, y = None,rowvar = 0,bias = 1) + + v, vect = np.linalg.eig(cov) + + tvect = np.transpose(vect) + points_r = np.dot(points, np.linalg.inv(tvect)) + + co_min = np.min(points_r, axis=0) + co_max = np.max(points_r, axis=0) + + xmin, xmax = co_min[0], co_max[0] + ymin, ymax = co_min[1], co_max[1] + zmin, zmax = co_min[2], co_max[2] + + xdif = (xmax - xmin) * 0.5 + ydif = (ymax - ymin) * 0.5 + zdif = (zmax - zmin) * 0.5 + + cx = xmin + xdif + cy = ymin + ydif + cz = zmin + zdif + + corners = np.array([ + [cx - xdif, cy - ydif, cz - zdif], + [cx - xdif, cy + ydif, cz - zdif], + [cx + xdif, cy + ydif, cz - zdif], + [cx + xdif, cy - ydif, cz - zdif], + [cx - xdif, cy - ydif, cz + zdif], + [cx - xdif, cy + ydif, cz + zdif], + [cx + xdif, cy + ydif, cz + zdif], + [cx + xdif, cy - ydif, cz + zdif], + ]) + + corners = np.dot(corners, tvect) + worldTlocal = object.get_local2world_mat() + corners = worldTlocal @ np.concatenate((corners, np.ones((len(corners), 1))), axis=-1).T + corners = corners.T[:, :3] / corners.T[:, 3, None] + + return corners + +def bounding_box_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, camTworld: np.ndarray, width: int, height: int) -> np.ndarray: + worldTobj = homogeneous_no_scaling(object) + obj = object.blender_obj + verts = verts = [v.co for v in obj.data.vertices] + points = np.asarray(verts) + camTobj = camTworld @ worldTobj + points_cam = camTobj @ np.concatenate((points, np.ones((len(points), 1))), axis=-1).T + points_cam = convert_points_to_visp_frame((points_cam[:3] / points_cam[3, None]).T) + + visible_points = points_cam[points_cam[:, 2] > 0] + visible_points_m_2d = visible_points[:, :2] / visible_points[:, 2, None] + visible_points_px_2d = K @ np.concatenate((visible_points_m_2d, np.ones((len(visible_points_m_2d), 1))), axis=-1).T + visible_points_px_2d = visible_points_px_2d.T[:, :2] / visible_points_px_2d.T[:, 2, None] + # print(f'OBB = {get_obb(object)}, AABB = {object.get_bound_box()}') + mins = np.min(visible_points_px_2d, axis=0) + assert len(mins) == 2 + maxes = np.max(visible_points_px_2d, axis=0) + original_size = maxes - mins + + mins = np.maximum(mins, [0, 0]) + maxes = np.minimum(maxes, [width, height]) + size = maxes - mins + return [mins[0], mins[1], size[0], size[1]] + + + def homogeneous_inverse(aTb): bTa = aTb.copy() bTa[:3, :3] = bTa[:3, :3].T @@ -143,7 +215,7 @@ def __init__(self, config_path): self.json_config = json.load(json_config_file) print(self.json_config) os.environ['BLENDER_PROC_RANDOM_SEED'] = self.json_config['blenderproc_seed'] - + def init(self): np.random.seed(self.json_config['numpy_seed']) @@ -188,6 +260,7 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: if len(models) > 1: model.join_with_other_objects(models[1:]) model.set_cp('category_id', cls) + model.set_location([-1000.0, -1000.0, -1000.0]) # Avoid warning about collisions with other objects in the scene model.hide() # Hide by default models_dict[model_name] = model class_dict[model_name] = cls @@ -196,7 +269,7 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: return models_dict, class_dict def setup_renderer(self): - depth, normals = itemgetter('depth', 'normals')(self.json_config['dataset']) + depth = itemgetter('depth')(self.json_config['dataset']) bproc.renderer.set_max_amount_of_samples(self.json_config['rendering']['max_num_samples']) if depth: bproc.renderer.enable_depth_output(activate_antialiasing=False) @@ -221,58 +294,61 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic out_object_pose, out_bounding_box = itemgetter('pose', 'detection')(json_dataset) filter_z, min_side_px, min_visibility = itemgetter('filter_when_object_mostly_behind', 'min_side_size_px', 'min_visibility_percentage')(json_dataset['detection_params']) + width, height = itemgetter('w', 'h')(self.json_config['camera']) frames_data = [] for frame in range(bproc.utility.num_frames()): worldTcam = bproc.camera.get_camera_pose(frame) camTworld = homogeneous_inverse(worldTcam) K = bproc.camera.get_intrinsics_as_K_matrix() - visible_objects = bproc.camera.visible_objects(worldTcam) + visible_objects = bproc.camera.visible_objects(worldTcam, 20) objects_data = [] for object in objects: if object not in visible_objects: continue - print(object.get_all_cps()) object_data = { 'class': object.get_cp('category_id', frame), - 'name': object.get_name() + 'name': object.get_name() } if out_object_pose: worldTobj = homogeneous_no_scaling(object) camTobj = camTworld @ worldTobj object_data['cTo'] = convert_to_visp_frame(camTobj) if out_bounding_box: - # Get object bounding box in world, project in camera frame - bb3d_world = object.get_bound_box() + # Get object oriented bounding box in world, project in camera frame + bb3d_world = get_obb(object) bb3d_cam = camTworld @ np.concatenate((bb3d_world, np.ones((8, 1))), axis=-1).T - bb3d_cam = convert_points_to_visp_frame(bb3d_cam.T[:, :3]) # 8 X 3 + bb3d_cam = convert_points_to_visp_frame(bb3d_cam.T[:, :3] / bb3d_cam.T[:, 3, None]) # 8 X 3 points_behind = bb3d_cam[bb3d_cam[:, 2] < 0] # More than half the points of the bounding box are behind the camera, object will be visible because it is close, but probably not recognizable if filter_z and len(points_behind) > 4: continue visible_bb3d_points = bb3d_cam[bb3d_cam[:, 2] > 0] - print(visible_bb3d_points.shape) - print(bb3d_cam) # Project points that are in front of the camera in the image visible_points_m_2d = visible_bb3d_points[:, :2] / visible_bb3d_points[:, 2, None] visible_points_px_2d = K @ np.concatenate((visible_points_m_2d, np.ones((len(visible_points_m_2d), 1))), axis=-1).T - visible_points_px_2d = visible_points_px_2d.T[:, :2] - + visible_points_px_2d = visible_points_px_2d.T[:, :2] / visible_points_px_2d.T[:, 2, None] + # print(f'OBB = {get_obb(object)}, AABB = {object.get_bound_box()}') mins = np.min(visible_points_px_2d, axis=0) + assert len(mins) == 2 maxes = np.max(visible_points_px_2d, axis=0) + original_size = maxes - mins + mins = np.maximum(mins, [0, 0]) + maxes = np.minimum(maxes, [width, height]) + size = maxes - mins - + bb = bounding_box_from_vertices(object, K, camTworld, width, height) print(f'x, y = ({mins[0]}, {mins[1]}), size = ({maxes[0] - mins[0]}, {maxes[1] - mins[1]})') - # object_data['bounding_box'] = ... - + object_data['bounding_box'] = bb + objects_data.append(object_data) frames_data.append(objects_data) data['object_data'] = frames_data bproc.writer.write_hdf5(str(path.absolute()), data, append_to_existing_output=False) - + def set_camera_intrinsics(self) -> None: @@ -371,6 +447,8 @@ def create_target_objects(self) -> List[bproc.types.MeshObject]: if scale_noise > 0.0: random_scale = np.random.uniform(-scale_noise, scale_noise) + 1.0 object.set_scale([random_scale, random_scale, random_scale]) # Uniform scaling + object.set_location([0.0, 0.0, 0.0]) + object.persist_transformation_into_mesh() if displacement_amount > 0.0: add_displacement(objects, displacement_amount) @@ -378,7 +456,7 @@ def create_target_objects(self) -> List[bproc.types.MeshObject]: randomize_pbr(objects, pbr_noise) return objects - + def create_room(self, size: float) -> List[bproc.types.MeshObject]: ground = bproc.object.create_primitive('PLANE') ground.set_location([0, 0, -size / 2]) @@ -430,12 +508,21 @@ def sample_pose(obj: bproc.types.MeshObject): loc = np.random.uniform([-size / 2, -size / 2, -size / 2], [size / 2, size / 2, size / 2]) obj.set_location(loc) obj.set_rotation_euler(bproc.sampler.uniformSO3()) + bproc.object.sample_poses( objects, sample_pose_func=sample_pose, objects_to_check_collisions=None ) + for object in objects: + object.persist_transformation_into_mesh() + obb = get_obb(object) + for point in obb: + s = bproc.object.create_primitive('SPHERE') + s.set_location(point) + s.set_scale([0.05 * object_bb_length(object) for _ in range(3)]) + distractors = self.create_distractors(size, room_objects + objects) @@ -446,7 +533,8 @@ def sample_pose(obj: bproc.types.MeshObject): object.enable_rigidbody(False, collision_shape='BOX') bproc.object.simulate_physics_and_fix_final_poses(min_simulation_time=3, max_simulation_time=3.5, check_object_interval=1) - + for object in objects: + object.persist_transformation_into_mesh() def filter_objects_outside_room(objects: List[bproc.types.MeshObject]) -> List[bproc.types.MeshObject]: inside = [] outside = [] @@ -458,7 +546,7 @@ def filter_objects_outside_room(objects: List[bproc.types.MeshObject]) -> List[b print(f'Filtered {len(objects) - len(inside)} objects') return inside - + objects = filter_objects_outside_room(objects) distractors = filter_objects_outside_room(distractors) lights = self.create_lights(size, objects) @@ -472,8 +560,9 @@ def sample_camera_poses(self, scene: Scene) -> None: hs = scene.size / 2 bvh = bproc.object.create_bvh_tree_multi_objects(scene.target_objects + scene.distractors + scene.room_objects) for frame in range(images_per_scene): + tries = 0 good = False - while not good: + while not good and tries < 1000: object = scene.target_objects[np.random.choice(len(scene.target_objects))] object_size = object_bb_length(object) poi = point_in_bounding_box(object, bb_scale=1) @@ -483,12 +572,15 @@ def sample_camera_poses(self, scene: Scene) -> None: cam = bpy.context.scene.camera.data clip_start = cam.clip_start focal_length = cam.lens / 1000.0 - + cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) if object in bproc.camera.visible_objects(cam2world_matrix) and bproc.camera.perform_obstacle_in_view_check(cam2world_matrix, {'min': focal_length + clip_start}, bvh): good = True else: good = False + tries += 1 + if tries >= 1000: + print('Warning! Could not correctly place camera, results may be wrong') bproc.camera.add_camera_pose(cam2world_matrix) def run(self): @@ -504,7 +596,7 @@ def run(self): path = save_path / str(scene_idx) path.mkdir(exist_ok=True) self.save_data(path, scene.target_objects, data) - + scene.cleanup() del scene del data diff --git a/script/dataset_generator/viz_bb.py b/script/dataset_generator/viz_bb.py new file mode 100644 index 0000000000..92f4368242 --- /dev/null +++ b/script/dataset_generator/viz_bb.py @@ -0,0 +1,32 @@ +import h5py +from pathlib import Path +import numpy as np +import json +import matplotlib.pyplot as plt +import matplotlib.patches as patches + +import argparse + +def show(file: h5py.File): + fig = plt.figure() + rgb = np.array(file['colors']) + text = np.array(f["object_data"]).tostring() + object_data = json.loads(text) + for object in object_data: + bb = object['bounding_box'] + rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor='r', facecolor='none') + plt.gca().add_patch(rect) + plt.imshow(rgb) + plt.show() + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--path', type=str, help='Path to a folder containing hdf5 files') + args = parser.parse_args() + folder = Path(args.path) + assert folder.exists() + for file in folder.iterdir(): + if file.name.endswith('.hdf5'): + with h5py.File(str(file)) as f: + print(f'Visualizing file {f}') + show(f) From 3674efaf8e598f4325f69504e62e20ae6b72e994 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Sun, 9 Jul 2023 23:07:20 +0200 Subject: [PATCH 10/28] Initial version before switching to windows for testing --- script/dataset_generator/example_config.json | 29 +-- script/dataset_generator/generate_dataset.py | 55 ++++++ ...rate_hdf5_dataset.py => generate_scene.py} | 184 ++++++------------ 3 files changed, 131 insertions(+), 137 deletions(-) create mode 100644 script/dataset_generator/generate_dataset.py rename script/dataset_generator/{generate_hdf5_dataset.py => generate_scene.py} (77%) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 4c800c8145..cb24bc7b77 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -14,28 +14,29 @@ "max_num_samples": 64 }, "scene": { - "room_size_multiplier_min": 4.0, + "room_size_multiplier_min": 2.0, "room_size_multiplier_max": 5.0, - "simulate_physics": false, + "simulate_physics": true, + "max_num_textures": 5, "distractors": { - "min_count": 5, - "max_count": 20, + "min_count": 50, + "max_count": 100, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, "displacement_max_amount": 0.0, "pbr_noise": 0.5 }, "lights": { - "min_count": 2, - "max_count": 8, + "min_count": 3, + "max_count": 6, "min_intensity": 50, "max_intensity": 300 }, "objects": { - "min_count": 1, - "max_count": 1, + "min_count": 10, + "max_count": 20, "multiple_occurences": true, - "scale_noise": 0.0, + "scale_noise": 0.2, "displacement_max_amount": 0.0, "pbr_noise": 0.2, "cam_min_dist_rel": 1.0, @@ -43,20 +44,20 @@ } }, "dataset": { - "num_scenes": 5, - "images_per_scene": 10, + "num_scenes": 100, + "images_per_scene": 50, "pose": true, "depth": false, "normals": false, "segmentation": false, "detection": true, "detection_params": { - "filter_when_object_mostly_behind": true, + "min_proportion_object_vertices_in_front": 0.5, "min_side_size_px": 10, - "min_visibility_percentage": 0.3 + "min_visibility_percentage": 0.2 }, "save_path": "output" }, "models_path": "/home/sfelton/megapose-models", - "cc_textures_path": "/home/sfelton/cc0" + "cc_textures_path": "/home/sfelton/cc0_full" } \ No newline at end of file diff --git a/script/dataset_generator/generate_dataset.py b/script/dataset_generator/generate_dataset.py new file mode 100644 index 0000000000..b62f3b481b --- /dev/null +++ b/script/dataset_generator/generate_dataset.py @@ -0,0 +1,55 @@ + +''' +Script to generate a synthetic dataset, stored in hdf5 format. +This dataset can contain: + - RGB images + - Object poses + - Segmentation maps + - Bounding boxes + - Depth map + - Normal map + +To run, blenderproc and other things should be installed (virtual environment recommended): +$ conda activate dataset_generation +$ pip install blenderproc numpy +$ blenderproc quickstart # verify that it works, install minor dependencies + + +''' + +import os +import numpy as np +import argparse +import json +from pathlib import Path +import subprocess + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument('--config', required=True, type=str, help='Path to the JSON configuration file for the dataset generation script') + args = parser.parse_args() + config_path = Path(args.config) + assert config_path.exists(), f'Config file {config_path} does not exist' + with open(config_path, 'r') as json_config_file: + json_config = json.load(json_config_file) + + num_scenes = json_config['dataset']['num_scenes'] + print(__file__) + scene_generator = (Path(__file__).parent / 'generate_scene.py').absolute() + import time + for i in range(num_scenes): + args = [ + 'blenderproc', 'run', str(scene_generator), + '--config', str(config_path.absolute()), + '--scene-index', str(i) + ] + t = time.time() + result = subprocess.run(args) + print(f'Generating scene {i} took {time.time() - t}s') + if result.returncode != 0: + raise RuntimeError(f'Generation of scene {i} failed!') + + + diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_scene.py similarity index 77% rename from script/dataset_generator/generate_hdf5_dataset.py rename to script/dataset_generator/generate_scene.py index 80d79997cd..d48164efbb 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_scene.py @@ -23,63 +23,16 @@ import json from operator import itemgetter from pathlib import Path -from typing import Callable, Dict, List +from typing import Callable, Dict, List, Tuple from blenderproc.python.types import MeshObjectUtility, EntityUtility -def get_obb(object: bproc.types.MeshObject) -> np.ndarray: - ''' - Get oriented bounding box in world space - Adapted from - https://blender.stackexchange.com/questions/261070/python-how-to-make-box-out-of-selected-verts-bounding-box-in-local-space/261076#261076 - ''' - obj = object.blender_obj - verts = [v.co for v in obj.data.vertices] - points = np.asarray(verts) - print(points.shape) - # means = np.mean(points, axis=1) - - cov = np.cov(points, y = None,rowvar = 0,bias = 1) - - v, vect = np.linalg.eig(cov) - - tvect = np.transpose(vect) - points_r = np.dot(points, np.linalg.inv(tvect)) - - co_min = np.min(points_r, axis=0) - co_max = np.max(points_r, axis=0) - - xmin, xmax = co_min[0], co_max[0] - ymin, ymax = co_min[1], co_max[1] - zmin, zmax = co_min[2], co_max[2] - xdif = (xmax - xmin) * 0.5 - ydif = (ymax - ymin) * 0.5 - zdif = (zmax - zmin) * 0.5 - - cx = xmin + xdif - cy = ymin + ydif - cz = zmin + zdif - - corners = np.array([ - [cx - xdif, cy - ydif, cz - zdif], - [cx - xdif, cy + ydif, cz - zdif], - [cx + xdif, cy + ydif, cz - zdif], - [cx + xdif, cy - ydif, cz - zdif], - [cx - xdif, cy - ydif, cz + zdif], - [cx - xdif, cy + ydif, cz + zdif], - [cx + xdif, cy + ydif, cz + zdif], - [cx + xdif, cy - ydif, cz + zdif], - ]) - - corners = np.dot(corners, tvect) - worldTlocal = object.get_local2world_mat() - corners = worldTlocal @ np.concatenate((corners, np.ones((len(corners), 1))), axis=-1).T - corners = corners.T[:, :3] / corners.T[:, 3, None] - - return corners - -def bounding_box_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, camTworld: np.ndarray, width: int, height: int) -> np.ndarray: +def bounding_box_2d_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, camTworld: np.ndarray) -> Tuple[List[float], float]: + ''' + Compute the 2D bounding from an object's vertices + returns A tuple containing [xmin, ymin, xmax, ymax] in pixels as well as the proportion of visible vertices (that are not behind the camera, i.e., negative Z) + ''' worldTobj = homogeneous_no_scaling(object) obj = object.blender_obj verts = verts = [v.co for v in obj.data.vertices] @@ -92,24 +45,18 @@ def bounding_box_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, ca visible_points_m_2d = visible_points[:, :2] / visible_points[:, 2, None] visible_points_px_2d = K @ np.concatenate((visible_points_m_2d, np.ones((len(visible_points_m_2d), 1))), axis=-1).T visible_points_px_2d = visible_points_px_2d.T[:, :2] / visible_points_px_2d.T[:, 2, None] - # print(f'OBB = {get_obb(object)}, AABB = {object.get_bound_box()}') mins = np.min(visible_points_px_2d, axis=0) assert len(mins) == 2 maxes = np.max(visible_points_px_2d, axis=0) - original_size = maxes - mins - - mins = np.maximum(mins, [0, 0]) - maxes = np.minimum(maxes, [width, height]) - size = maxes - mins - return [mins[0], mins[1], size[0], size[1]] - - + + return [mins[0], mins[1], maxes[0], maxes[1]], len(visible_points) / len(points_cam) def homogeneous_inverse(aTb): bTa = aTb.copy() bTa[:3, :3] = bTa[:3, :3].T bTa[:3, 3] = -bTa[:3, :3] @ bTa[:3, 3] return bTa + def homogeneous_no_scaling(object: bproc.types.MeshObject): localTworld = np.eye(4) localTworld[:3, :3] = object.get_rotation_mat() @@ -209,18 +156,29 @@ def cleanup(self): light.delete(True) class Generator: - def __init__(self, config_path): + def __init__(self, config_path: Path, scene_index: int): self.config_path = config_path with open(self.config_path, 'r') as json_config_file: self.json_config = json.load(json_config_file) print(self.json_config) - os.environ['BLENDER_PROC_RANDOM_SEED'] = self.json_config['blenderproc_seed'] + np.random.seed(self.json_config['numpy_seed'] * (scene_index + 1)) + self.scene_index = scene_index + blender_seed_int = int(self.json_config['blenderproc_seed']) + os.environ['BLENDER_PROC_RANDOM_SEED'] = str(blender_seed_int * (scene_index + 1)) + self.objects = None + self.classes = None + def init(self): - np.random.seed(self.json_config['numpy_seed']) + print('Loading objects...') + if self.objects is not None: + for k in self.objects: + self.objects[k].delete() self.objects, self.classes = self.load_objects() - self.cc_textures = bproc.loader.load_ccmaterials(self.json_config['cc_textures_path']) + print('Preloading CC0 textures...') + self.cc_textures = bproc.loader.load_ccmaterials(self.json_config['cc_textures_path'], preload=True) + self.cc_textures = np.random.choice(self.cc_textures, size=self.json_config['scene']['max_num_textures']) self.setup_renderer() def load_objects(self) -> Dict[str, bproc.types.MeshObject]: @@ -242,11 +200,11 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: models_path = Path(self.json_config['models_path']).absolute() cls = 1 assert models_path.exists() and models_path.is_dir(), f'Models path {models_path} must exist and be a directory' - for model_dir in models_path.iterdir(): + for model_dir in sorted(models_path.iterdir()): if not model_dir.is_dir(): continue model_name = model_dir.name - for content in model_dir.iterdir(): + for content in sorted(model_dir.iterdir()): if not content.is_file(): continue load_fn: Callable[[str], List[bproc.types.MeshObject]] = None @@ -265,7 +223,6 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: models_dict[model_name] = model class_dict[model_name] = cls cls += 1 - return models_dict, class_dict def setup_renderer(self): @@ -293,7 +250,7 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic json_dataset = self.json_config['dataset'] out_object_pose, out_bounding_box = itemgetter('pose', 'detection')(json_dataset) - filter_z, min_side_px, min_visibility = itemgetter('filter_when_object_mostly_behind', 'min_side_size_px', 'min_visibility_percentage')(json_dataset['detection_params']) + min_z_filter, min_side_px, min_visibility = itemgetter('min_proportion_object_vertices_in_front', 'min_side_size_px', 'min_visibility_percentage')(json_dataset['detection_params']) width, height = itemgetter('w', 'h')(self.json_config['camera']) frames_data = [] @@ -301,7 +258,7 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic worldTcam = bproc.camera.get_camera_pose(frame) camTworld = homogeneous_inverse(worldTcam) K = bproc.camera.get_intrinsics_as_K_matrix() - visible_objects = bproc.camera.visible_objects(worldTcam, 20) + visible_objects = bproc.camera.visible_objects(worldTcam, min(width, height) // min_side_px) objects_data = [] for object in objects: if object not in visible_objects: @@ -315,33 +272,27 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic camTobj = camTworld @ worldTobj object_data['cTo'] = convert_to_visp_frame(camTobj) if out_bounding_box: - # Get object oriented bounding box in world, project in camera frame - bb3d_world = get_obb(object) - bb3d_cam = camTworld @ np.concatenate((bb3d_world, np.ones((8, 1))), axis=-1).T - bb3d_cam = convert_points_to_visp_frame(bb3d_cam.T[:, :3] / bb3d_cam.T[:, 3, None]) # 8 X 3 - points_behind = bb3d_cam[bb3d_cam[:, 2] < 0] - # More than half the points of the bounding box are behind the camera, object will be visible because it is close, but probably not recognizable - if filter_z and len(points_behind) > 4: + bb_corners, z_front_proportion = bounding_box_2d_from_vertices(object, K, camTworld) + if z_front_proportion < min_z_filter: continue - visible_bb3d_points = bb3d_cam[bb3d_cam[:, 2] > 0] - # Project points that are in front of the camera in the image - visible_points_m_2d = visible_bb3d_points[:, :2] / visible_bb3d_points[:, 2, None] - visible_points_px_2d = K @ np.concatenate((visible_points_m_2d, np.ones((len(visible_points_m_2d), 1))), axis=-1).T - visible_points_px_2d = visible_points_px_2d.T[:, :2] / visible_points_px_2d.T[:, 2, None] - # print(f'OBB = {get_obb(object)}, AABB = {object.get_bound_box()}') - mins = np.min(visible_points_px_2d, axis=0) - assert len(mins) == 2 - maxes = np.max(visible_points_px_2d, axis=0) - original_size = maxes - mins + mins = np.array([bb_corners[0], bb_corners[1]]) + maxes = np.array([bb_corners[2], bb_corners[3]]) + original_size = maxes - mins + original_area = np.prod(original_size) mins = np.maximum(mins, [0, 0]) maxes = np.minimum(maxes, [width, height]) size = maxes - mins + area = np.prod(size) - bb = bounding_box_from_vertices(object, K, camTworld, width, height) - print(f'x, y = ({mins[0]}, {mins[1]}), size = ({maxes[0] - mins[0]}, {maxes[1] - mins[1]})') + # If clipping removes too much of the actual object, discard the detection. TODO: take into account occlusions + if area / original_area < min_visibility: + continue - object_data['bounding_box'] = bb + # Filter objects that are too small to be believably detectable + if size[0] < min_side_px or size[1] < min_side_px: + continue + object_data['bounding_box'] = [mins[0], mins[1], size[0], size[1]] objects_data.append(object_data) @@ -374,12 +325,14 @@ def create_distractors(self, scene_size, other_objects) -> List[bproc.types.Mesh displacement_strength, pbr_noise = itemgetter('displacement_max_amount', 'pbr_noise')(json_distractors) count = np.random.randint(min_count, max_count + 1) + if count == 0: + return [] def sample_pose(obj: bproc.types.MeshObject): loc = np.random.uniform([-scene_size / 2, -scene_size / 2, -scene_size / 2], [scene_size / 2, scene_size / 2, scene_size / 2]) obj.set_location(loc) obj.set_scale(np.random.uniform(scene_size * min_size, scene_size * max_size, size=3)) obj.set_rotation_euler(bproc.sampler.uniformSO3()) - distractor_type = np.random.choice(['CUBE', 'CYLINDER', 'CONE', 'PLANE', 'SPHERE', 'MONKEY'], size=count, replace=True) + distractor_type = np.random.choice(['CUBE', 'CYLINDER', 'CONE', 'SPHERE', 'MONKEY'], size=count, replace=True) distractors = [bproc.object.create_primitive(distractor_type[c]) for c in range(count)] bproc.object.sample_poses( @@ -517,11 +470,6 @@ def sample_pose(obj: bproc.types.MeshObject): for object in objects: object.persist_transformation_into_mesh() - obb = get_obb(object) - for point in obb: - s = bproc.object.create_primitive('SPHERE') - s.set_location(point) - s.set_scale([0.05 * object_bb_length(object) for _ in range(3)]) distractors = self.create_distractors(size, room_objects + objects) @@ -574,7 +522,7 @@ def sample_camera_poses(self, scene: Scene) -> None: focal_length = cam.lens / 1000.0 cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) - if object in bproc.camera.visible_objects(cam2world_matrix) and bproc.camera.perform_obstacle_in_view_check(cam2world_matrix, {'min': focal_length + clip_start}, bvh): + if object in bproc.camera.visible_objects(cam2world_matrix, 20) and bproc.camera.perform_obstacle_in_view_check(cam2world_matrix, {'min': focal_length + clip_start}, bvh): good = True else: good = False @@ -584,22 +532,21 @@ def sample_camera_poses(self, scene: Scene) -> None: bproc.camera.add_camera_pose(cam2world_matrix) def run(self): - num_scenes = self.json_config['dataset']['num_scenes'] save_path = Path(self.json_config['dataset']['save_path']) save_path.mkdir(exist_ok=True) - for scene_idx in range(num_scenes): - bproc.utility.reset_keyframes() - self.set_camera_intrinsics() - scene = self.create_scene() - self.sample_camera_poses(scene) - data = self.render() - path = save_path / str(scene_idx) - path.mkdir(exist_ok=True) - self.save_data(path, scene.target_objects, data) + + bproc.utility.reset_keyframes() + self.init() + self.set_camera_intrinsics() + scene = self.create_scene() + bproc.loader.load_ccmaterials(self.json_config['cc_textures_path'], fill_used_empty_materials=True) + self.sample_camera_poses(scene) + data = self.render() + path = save_path / str(self.scene_index) + path.mkdir(exist_ok=True) + self.save_data(path, scene.target_objects, data) - scene.cleanup() - del scene - del data + @@ -610,22 +557,13 @@ def run(self): parser = argparse.ArgumentParser() parser.add_argument('--config', required=True, type=str, help='Path to the JSON configuration file for the dataset generation script') + parser.add_argument('--scene-index', required=True, type=int, help='Index of the scene to generate') args = parser.parse_args() - generator = Generator(args.config) + generator = Generator(args.config, args.scene_index) # RendererUtility.set_render_devices(use_only_cpu=True) bproc.clean_up(clean_up_camera=True) - - # bproc.init() # Works if you have a GPU - generator.init() + bproc.init() # Works if you have a GPU generator.run() - # Write the rendering into an hdf5 file - # bproc.writer.write_coco_annotations('output/coco', - # instance_segmaps=seg_data["instance_segmaps"], - # instance_attribute_maps=seg_data["instance_attribute_maps"], - # colors=data["colors"], - # color_file_format="PNG") - - From 91089f831f40db0297a74e7945b4ed6bd34715d6 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Sun, 9 Jul 2023 23:08:02 +0200 Subject: [PATCH 11/28] Initial dataset generation --- script/dataset_generator/example_config.json | 29 ++-- script/dataset_generator/generate_dataset.py | 5 +- script/dataset_generator/generate_scene.py | 139 +++++++++++++------ script/dataset_generator/viz_bb.py | 8 +- 4 files changed, 119 insertions(+), 62 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index cb24bc7b77..16faa20faa 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -15,26 +15,26 @@ }, "scene": { "room_size_multiplier_min": 2.0, - "room_size_multiplier_max": 5.0, + "room_size_multiplier_max": 4.0, "simulate_physics": true, - "max_num_textures": 5, + "max_num_textures": 100, "distractors": { - "min_count": 50, - "max_count": 100, + "min_count": 100, + "max_count": 200, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, - "displacement_max_amount": 0.0, + "displacement_max_amount": 0.01, "pbr_noise": 0.5 }, "lights": { - "min_count": 3, + "min_count": 4, "max_count": 6, "min_intensity": 50, "max_intensity": 300 }, "objects": { - "min_count": 10, - "max_count": 20, + "min_count": 20, + "max_count": 30, "multiple_occurences": true, "scale_noise": 0.2, "displacement_max_amount": 0.0, @@ -44,20 +44,21 @@ } }, "dataset": { - "num_scenes": 100, - "images_per_scene": 50, + "scenes_per_run": 1, + "num_scenes": 50, + "images_per_scene": 100, "pose": true, "depth": false, "normals": false, "segmentation": false, "detection": true, "detection_params": { - "min_proportion_object_vertices_in_front": 0.5, "min_side_size_px": 10, - "min_visibility_percentage": 0.2 + "min_visibility_percentage": 0.3, + "points_sampling_occlusion": 100 }, "save_path": "output" }, - "models_path": "/home/sfelton/megapose-models", - "cc_textures_path": "/home/sfelton/cc0_full" + "models_path": "../megapose-models", + "cc_textures_path": "../cc0" } \ No newline at end of file diff --git a/script/dataset_generator/generate_dataset.py b/script/dataset_generator/generate_dataset.py index b62f3b481b..ccc7ba6b8d 100644 --- a/script/dataset_generator/generate_dataset.py +++ b/script/dataset_generator/generate_dataset.py @@ -36,6 +36,8 @@ json_config = json.load(json_config_file) num_scenes = json_config['dataset']['num_scenes'] + scenes_per_run = json_config['dataset']['scenes_per_run'] + print(__file__) scene_generator = (Path(__file__).parent / 'generate_scene.py').absolute() import time @@ -43,7 +45,8 @@ args = [ 'blenderproc', 'run', str(scene_generator), '--config', str(config_path.absolute()), - '--scene-index', str(i) + '--scene-index', str(i * scenes_per_run), + '--scene-count', str(scenes_per_run) ] t = time.time() result = subprocess.run(args) diff --git a/script/dataset_generator/generate_scene.py b/script/dataset_generator/generate_scene.py index d48164efbb..c524acf3e7 100644 --- a/script/dataset_generator/generate_scene.py +++ b/script/dataset_generator/generate_scene.py @@ -24,32 +24,39 @@ from operator import itemgetter from pathlib import Path from typing import Callable, Dict, List, Tuple +import time from blenderproc.python.types import MeshObjectUtility, EntityUtility -def bounding_box_2d_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, camTworld: np.ndarray) -> Tuple[List[float], float]: +def bounding_box_2d_from_vertices(object: bproc.types.MeshObject, K: np.ndarray, camTworld: np.ndarray) -> Tuple[List[float], float, np.ndarray]: ''' Compute the 2D bounding from an object's vertices - returns A tuple containing [xmin, ymin, xmax, ymax] in pixels as well as the proportion of visible vertices (that are not behind the camera, i.e., negative Z) + returns A tuple containing: + [xmin, ymin, xmax, ymax] in pixels + the proportion of visible vertices (that are not behind the camera, i.e., negative Z) + The 2D points, in meters in camera space, in ViSP/OpenCV frame ''' worldTobj = homogeneous_no_scaling(object) obj = object.blender_obj - verts = verts = [v.co for v in obj.data.vertices] - points = np.asarray(verts) + verts = np.ones(len(obj.data.vertices) * 3) + obj.data.vertices.foreach_get("co", verts) + points = verts.reshape(-1, 3) + # verts = [v.co for v in obj.data.vertices] camTobj = camTworld @ worldTobj points_cam = camTobj @ np.concatenate((points, np.ones((len(points), 1))), axis=-1).T points_cam = convert_points_to_visp_frame((points_cam[:3] / points_cam[3, None]).T) - + visible_points = points_cam[points_cam[:, 2] > 0] visible_points_m_2d = visible_points[:, :2] / visible_points[:, 2, None] visible_points_px_2d = K @ np.concatenate((visible_points_m_2d, np.ones((len(visible_points_m_2d), 1))), axis=-1).T visible_points_px_2d = visible_points_px_2d.T[:, :2] / visible_points_px_2d.T[:, 2, None] + mins = np.min(visible_points_px_2d, axis=0) assert len(mins) == 2 maxes = np.max(visible_points_px_2d, axis=0) - return [mins[0], mins[1], maxes[0], maxes[1]], len(visible_points) / len(points_cam) + return [mins[0], mins[1], maxes[0], maxes[1]], len(visible_points) / len(points_cam), visible_points_m_2d def homogeneous_inverse(aTb): bTa = aTb.copy() @@ -68,11 +75,10 @@ def convert_to_visp_frame(aTb): Same as converting to an OpenCV frame ''' return bproc.math.change_source_coordinate_frame_of_transformation_matrix(aTb, ["X", "-Y", "-Z"]) + def convert_points_to_visp_frame(points: np.ndarray): - new_points = points.copy() - for i in range(len(new_points)): - new_points[i] = bproc.math.change_coordinate_frame_of_point(new_points[i], ["X", "-Y", "-Z"]) - return new_points + points[:, 1:3] = -points[:, 1:3] + return points def point_in_bounding_box(object: bproc.types.MeshObject, bb_scale=1.0): ''' @@ -156,13 +162,14 @@ def cleanup(self): light.delete(True) class Generator: - def __init__(self, config_path: Path, scene_index: int): + def __init__(self, config_path: Path, scene_index: int, scene_count: int): self.config_path = config_path with open(self.config_path, 'r') as json_config_file: self.json_config = json.load(json_config_file) print(self.json_config) np.random.seed(self.json_config['numpy_seed'] * (scene_index + 1)) self.scene_index = scene_index + self.scene_count = scene_count blender_seed_int = int(self.json_config['blenderproc_seed']) os.environ['BLENDER_PROC_RANDOM_SEED'] = str(blender_seed_int * (scene_index + 1)) self.objects = None @@ -250,14 +257,24 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic json_dataset = self.json_config['dataset'] out_object_pose, out_bounding_box = itemgetter('pose', 'detection')(json_dataset) - min_z_filter, min_side_px, min_visibility = itemgetter('min_proportion_object_vertices_in_front', 'min_side_size_px', 'min_visibility_percentage')(json_dataset['detection_params']) + keys = [ + 'min_side_size_px', 'min_visibility_percentage', + 'points_sampling_occlusion' + ] + min_side_px, min_visibility, points_sampling_occlusion = itemgetter(*keys)(json_dataset['detection_params']) + width, height = itemgetter('w', 'h')(self.json_config['camera']) frames_data = [] - + import time for frame in range(bproc.utility.num_frames()): + worldTcam = bproc.camera.get_camera_pose(frame) camTworld = homogeneous_inverse(worldTcam) K = bproc.camera.get_intrinsics_as_K_matrix() + + minx, miny = (-K[0, 2]) / K[0, 0], (-K[1, 2]) / K[1, 1] + maxx, maxy = (width -K[0, 2]) / K[0, 0], (height -K[1, 2]) / K[1, 1] + visible_objects = bproc.camera.visible_objects(worldTcam, min(width, height) // min_side_px) objects_data = [] for object in objects: @@ -272,8 +289,9 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic camTobj = camTworld @ worldTobj object_data['cTo'] = convert_to_visp_frame(camTobj) if out_bounding_box: - bb_corners, z_front_proportion = bounding_box_2d_from_vertices(object, K, camTworld) - if z_front_proportion < min_z_filter: + t = time.time() + bb_corners, z_front_proportion, points_im = bounding_box_2d_from_vertices(object, K, camTworld) + if z_front_proportion < min_visibility: continue mins = np.array([bb_corners[0], bb_corners[1]]) maxes = np.array([bb_corners[2], bb_corners[3]]) @@ -285,13 +303,38 @@ def save_data(self, path: Path, objects: List[bproc.types.MeshObject], data: Dic size = maxes - mins area = np.prod(size) - # If clipping removes too much of the actual object, discard the detection. TODO: take into account occlusions - if area / original_area < min_visibility: - continue - # Filter objects that are too small to be believably detectable if size[0] < min_side_px or size[1] < min_side_px: continue + + + vis_points_in_image = points_im[(points_im[:, 0] > minx) & ((points_im[:, 0] < maxx)) & (points_im[:, 1] > miny) & ((points_im[:, 1] < maxy))] + + base_visibility = z_front_proportion * (len(vis_points_in_image) / len(points_im)) + # Camera clipping removes too much of the object + if base_visibility < min_visibility: + continue + if points_sampling_occlusion > 0: + point_count = len(vis_points_in_image) + points = vis_points_in_image[np.random.choice(point_count, size=min(point_count, points_sampling_occlusion))] + points = np.concatenate((points, np.ones((len(points), 1))), axis=-1) + + points_cam = convert_points_to_visp_frame(points) # Convert back to blender frame + points_world = worldTcam @ np.concatenate((points_cam, np.ones((len(points_cam), 1))), axis=-1).T + points_world = (points_world[:3] / points_world[3, None]).T + ray_directions = points_world - worldTcam[None, :3, 3] + hit_count = 0 + for ray in ray_directions: + hit_object = bproc.object.scene_ray_cast(worldTcam[:3, 3], ray)[-2] + if hit_object == object: + hit_count += 1 + + final_visibility = base_visibility * (hit_count / len(ray_directions)) + print(final_visibility) + # Including occlusions, the object is now not visible enough to be detectable + if final_visibility < min_visibility: + print(f'Filtered object {object.get_name()}, because of occlusions: {final_visibility}') + continue object_data['bounding_box'] = [mins[0], mins[1], size[0], size[1]] @@ -514,19 +557,23 @@ def sample_camera_poses(self, scene: Scene) -> None: object = scene.target_objects[np.random.choice(len(scene.target_objects))] object_size = object_bb_length(object) poi = point_in_bounding_box(object, bb_scale=1) - location = bproc.sampler.sphere(object.get_location(), np.random.uniform(cam_min_dist, cam_max_dist) * object_size, 'SURFACE') - rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) - import bpy - cam = bpy.context.scene.camera.data - clip_start = cam.clip_start - focal_length = cam.lens / 1000.0 - - cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) - if object in bproc.camera.visible_objects(cam2world_matrix, 20) and bproc.camera.perform_obstacle_in_view_check(cam2world_matrix, {'min': focal_length + clip_start}, bvh): - good = True - else: - good = False - tries += 1 + tries_distance = 0 + distance = np.random.uniform(cam_min_dist, cam_max_dist) + while not good and tries_distance < 100: + location = bproc.sampler.sphere(object.get_location(), distance * object_size, 'SURFACE') + rotation_matrix = bproc.camera.rotation_from_forward_vec(poi - location, inplane_rot=np.random.uniform(-0.7854, 0.7854)) + import bpy + cam = bpy.context.scene.camera.data + clip_start = cam.clip_start + focal_length = cam.lens / 1000.0 + + cam2world_matrix = bproc.math.build_transformation_mat(location, rotation_matrix) + if object in bproc.camera.visible_objects(cam2world_matrix, 20) and bproc.camera.perform_obstacle_in_view_check(cam2world_matrix, {'min': focal_length + clip_start}, bvh): + good = True + else: + good = False + tries += 1 + tries_distance += 1 if tries >= 1000: print('Warning! Could not correctly place camera, results may be wrong') bproc.camera.add_camera_pose(cam2world_matrix) @@ -534,17 +581,20 @@ def sample_camera_poses(self, scene: Scene) -> None: def run(self): save_path = Path(self.json_config['dataset']['save_path']) save_path.mkdir(exist_ok=True) - - bproc.utility.reset_keyframes() self.init() - self.set_camera_intrinsics() - scene = self.create_scene() - bproc.loader.load_ccmaterials(self.json_config['cc_textures_path'], fill_used_empty_materials=True) - self.sample_camera_poses(scene) - data = self.render() - path = save_path / str(self.scene_index) - path.mkdir(exist_ok=True) - self.save_data(path, scene.target_objects, data) + import bpy + for i in range(self.scene_count): + bproc.utility.reset_keyframes() + self.set_camera_intrinsics() + scene = self.create_scene() + bproc.loader.load_ccmaterials(self.json_config['cc_textures_path'], fill_used_empty_materials=True) + self.sample_camera_poses(scene) + bpy.context.scene.render.use_persistent_data = True + data = self.render() + path = save_path / str(self.scene_index + i) + path.mkdir(exist_ok=True) + self.save_data(path, scene.target_objects, data) + scene.cleanup() @@ -557,10 +607,11 @@ def run(self): parser = argparse.ArgumentParser() parser.add_argument('--config', required=True, type=str, help='Path to the JSON configuration file for the dataset generation script') - parser.add_argument('--scene-index', required=True, type=int, help='Index of the scene to generate') + parser.add_argument('--scene-index', required=True, type=int, help='Index of the first scene to generate') + parser.add_argument('--scene-count', required=True, type=int, help='Number of scenes to render') args = parser.parse_args() - generator = Generator(args.config, args.scene_index) + generator = Generator(args.config, args.scene_index, args.scene_count) # RendererUtility.set_render_devices(use_only_cpu=True) bproc.clean_up(clean_up_camera=True) bproc.init() # Works if you have a GPU diff --git a/script/dataset_generator/viz_bb.py b/script/dataset_generator/viz_bb.py index 92f4368242..8163f6ac6d 100644 --- a/script/dataset_generator/viz_bb.py +++ b/script/dataset_generator/viz_bb.py @@ -11,11 +11,13 @@ def show(file: h5py.File): fig = plt.figure() rgb = np.array(file['colors']) text = np.array(f["object_data"]).tostring() + print(text) object_data = json.loads(text) for object in object_data: - bb = object['bounding_box'] - rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor='r', facecolor='none') - plt.gca().add_patch(rect) + if 'bounding_box' in object: + bb = object['bounding_box'] + rect = patches.Rectangle((bb[0], bb[1]), bb[2], bb[3], linewidth=1, edgecolor='r', facecolor='none') + plt.gca().add_patch(rect) plt.imshow(rgb) plt.show() From 7f4bc77fd26d26ed56317d4ca807e7775cc352c3 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Mon, 10 Jul 2023 17:56:01 +0200 Subject: [PATCH 12/28] Initial version for the yolov7 dataset export script --- script/dataset_generator/export_for_yolov7.py | 123 ++++++++++++++++++ 1 file changed, 123 insertions(+) create mode 100644 script/dataset_generator/export_for_yolov7.py diff --git a/script/dataset_generator/export_for_yolov7.py b/script/dataset_generator/export_for_yolov7.py new file mode 100644 index 0000000000..4045fef64a --- /dev/null +++ b/script/dataset_generator/export_for_yolov7.py @@ -0,0 +1,123 @@ +''' +Script to export an hdf5 stored dataset to the format expected by a YoloV7. +This requires bounding boxes to be generated along with the RGB images. +This is done through the json config file. + +''' + +from pathlib import Path +import argparse +from typing import List +import h5py +from PIL import Image +import numpy as np +import json +import progressbar +import yaml + +def make_name(hdf5_path: Path): + hdf5_name = hdf5_path.name[:-len('.hdf5')] + return hdf5_path.parent.name + '_' + hdf5_name + +def export_split(hdf5_paths: List[Path], images_export_path: Path, labels_export_path: Path): + bar = progressbar.ProgressBar() + detection_data = {} + for hdf5_path in bar(hdf5_paths): + with h5py.File(str(hdf5_path)) as hdf5: + rgb = np.array(hdf5['colors']) + img = Image.fromarray(rgb) + base_name = make_name(hdf5_path) + img.save(images_export_path / (base_name + '.png')) + h, w = rgb.shape[:2] + text = np.array(hdf5['object_data']).tobytes() + with open(labels_export_path / (base_name + '.txt'), 'w') as label_file: + if len(text) > 0: + object_data = json.loads(text) + for object in object_data: + if 'bounding_box' in object: + bb = object['bounding_box'] + x, width = bb[0] / w, bb[2] / w + y, height = bb[1] / h, bb[3] / h + bb = [x, y, width, height] + cls = object['class'] + fmt = f'{cls - 1} {x} {y} {width} {height}\n' + label_file.write(fmt) + if cls not in detection_data: + detection_data[cls] = 0 + detection_data[cls] += 1 + + print('Detection summary:') + for cls in detection_data.keys(): + print(f'\tClass: {cls}\t Detection count: {detection_data[cls]}') + + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser('Convert an HDF5 dataset generated with Blenderproc to a YoloV7-ready format') + parser.add_argument('--input', type=str, required=True, help='''Path to the HDF5 dataset. It is the root folder of the dataset, + containing one subfolder for each generated scene''') + parser.add_argument('--output', type=str, required=True, help='''Where to export the dataset to YoloV7 format''') + parser.add_argument('--train-split', required=True, type=float, help='''Portion of the dataset to take for training. Between 0 and 1''') + + args = parser.parse_args() + + input_path = Path(args.input) + assert input_path.exists(), f'Path to the HDF5 dataset must be valid and exist, but got {input_path}' + + output_path = Path(args.output) + assert not output_path.exists() or len(list(output_path.iterdir())), f'The path to the exported folder should not exist, or should be empty' + output_path.mkdir(exist_ok=True) + + train_split = args.train_split + assert train_split > 0 and train_split < 1, 'Train split should be in range (0, 1)' + + + num_scenes = 0 + hdf5_paths = [] + print('Collecting hdf5 paths...') + for scene_dir in input_path.iterdir(): + if not scene_dir.is_dir(): + continue + for file in scene_dir.iterdir(): + if file.name.endswith('.hdf5'): + hdf5_paths.append(file.absolute()) + num_scenes += 1 + print(f'Found {num_scenes} scenes, for a total of {len(hdf5_paths)} images!') + train_count = int(len(hdf5_paths) * train_split) + val_count = len(hdf5_paths) - train_count + print(f'Splitting randomly: taking {train_count} images for test, {val_count} for validation') + train_hdf5, val_hdf5 = [], [] + train_indices = set(np.random.choice(len(hdf5_paths), size=train_count, replace=False)) + for i in range(len(hdf5_paths)): + train_hdf5.append(hdf5_paths[i]) if i in train_indices else val_hdf5.append(hdf5_paths[i]) + print(len(train_hdf5), len(val_hdf5)) + + images_path = output_path / 'images' + labels_path = output_path / 'labels' + images_train_path = images_path / 'train' + images_val_path = images_path / 'val' + labels_train_path = labels_path / 'train' + labels_val_path = labels_path / 'val' + + for path in [images_path, labels_path, images_train_path, images_val_path, labels_train_path, labels_val_path]: + path.mkdir() + + classes = [] + with open(input_path / 'classes.txt') as cls_file: + for line in cls_file.readlines(): + classes.append(line.strip()) + with open(output_path / 'dataset.yaml', 'w') as yaml_file: + data = { + 'train': str(images_train_path.absolute()), + 'val': str(images_val_path.absolute()), + 'nc': len(classes), + 'names': classes + } + yaml.dump(data, yaml_file) + + print('Generating train split...') + export_split(train_hdf5, images_train_path, labels_train_path) + print('Generating validation split...') + export_split(val_hdf5, images_val_path, labels_val_path) + From fe964ecbd2cdb8a0a83ddba889ac6820eb95539e Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Tue, 11 Jul 2023 01:50:51 +0200 Subject: [PATCH 13/28] Generating second dataset with more objects --- script/dataset_generator/example_config.json | 20 ++++++++++---------- script/dataset_generator/generate_scene.py | 16 ++++++++++++++-- 2 files changed, 24 insertions(+), 12 deletions(-) diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index 16faa20faa..f2852183fd 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -15,38 +15,38 @@ }, "scene": { "room_size_multiplier_min": 2.0, - "room_size_multiplier_max": 4.0, + "room_size_multiplier_max": 3.0, "simulate_physics": true, - "max_num_textures": 100, + "max_num_textures": 50, "distractors": { - "min_count": 100, - "max_count": 200, + "min_count": 20, + "max_count": 50, "min_size_rel_scene": 0.01, "max_size_rel_scene": 0.05, "displacement_max_amount": 0.01, "pbr_noise": 0.5 }, "lights": { - "min_count": 4, + "min_count": 3, "max_count": 6, "min_intensity": 50, "max_intensity": 300 }, "objects": { - "min_count": 20, - "max_count": 30, + "min_count": 3, + "max_count": 10, "multiple_occurences": true, "scale_noise": 0.2, "displacement_max_amount": 0.0, - "pbr_noise": 0.2, + "pbr_noise": 0.3, "cam_min_dist_rel": 1.0, "cam_max_dist_rel": 5.0 } }, "dataset": { "scenes_per_run": 1, - "num_scenes": 50, - "images_per_scene": 100, + "num_scenes": 100, + "images_per_scene": 50, "pose": true, "depth": false, "normals": false, diff --git a/script/dataset_generator/generate_scene.py b/script/dataset_generator/generate_scene.py index c524acf3e7..6cd9cdc5eb 100644 --- a/script/dataset_generator/generate_scene.py +++ b/script/dataset_generator/generate_scene.py @@ -183,6 +183,7 @@ def init(self): for k in self.objects: self.objects[k].delete() self.objects, self.classes = self.load_objects() + self.save_class_file() print('Preloading CC0 textures...') self.cc_textures = bproc.loader.load_ccmaterials(self.json_config['cc_textures_path'], preload=True) self.cc_textures = np.random.choice(self.cc_textures, size=self.json_config['scene']['max_num_textures']) @@ -227,11 +228,23 @@ def load_objects(self) -> Dict[str, bproc.types.MeshObject]: model.set_cp('category_id', cls) model.set_location([-1000.0, -1000.0, -1000.0]) # Avoid warning about collisions with other objects in the scene model.hide() # Hide by default + model.set_name(model_name) models_dict[model_name] = model class_dict[model_name] = cls cls += 1 return models_dict, class_dict - + def save_class_file(self): + save_path = Path(self.json_config['dataset']['save_path']).absolute() + cls_file = save_path / 'classes.txt' + if self.scene_index == 0 or not cls_file.exists(): + with open(cls_file, 'w') as f: + cls_to_model_name = {self.classes[k]: k for k in self.classes.keys()} + cls_indices = sorted(cls_to_model_name.keys()) + lines = [] + for i in cls_indices: + lines.append(cls_to_model_name[i]) + print(f'Writing classes to file: \n {lines}') + f.writelines(lines) def setup_renderer(self): depth = itemgetter('depth')(self.json_config['dataset']) bproc.renderer.set_max_amount_of_samples(self.json_config['rendering']['max_num_samples']) @@ -417,7 +430,6 @@ def basic_light_sampling(light: bproc.types.Light) -> None: basic_light_sampling(light) looked_at_obj = np.random.choice(len(target_objects)) looked_at_obj: bproc.types.MeshObject = target_objects[looked_at_obj] - print(looked_at_obj, looked_at_obj.get_local2world_mat()) poi = point_in_bounding_box(looked_at_obj) R = bproc.camera.rotation_from_forward_vec(poi - light.get_location()) light.set_rotation_mat(R) From 4ed492aa6f59ed40fba6013cd23fad38d3ec535c Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Wed, 12 Jul 2023 17:43:21 +0200 Subject: [PATCH 14/28] Fix for wrong bounding box export to Yolov7 format --- script/dataset_generator/export_for_yolov7.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/script/dataset_generator/export_for_yolov7.py b/script/dataset_generator/export_for_yolov7.py index 4045fef64a..d2f00fa3af 100644 --- a/script/dataset_generator/export_for_yolov7.py +++ b/script/dataset_generator/export_for_yolov7.py @@ -36,8 +36,10 @@ def export_split(hdf5_paths: List[Path], images_export_path: Path, labels_export for object in object_data: if 'bounding_box' in object: bb = object['bounding_box'] - x, width = bb[0] / w, bb[2] / w - y, height = bb[1] / h, bb[3] / h + # Yolo format: [x_center, y_center, width, height]. all coordinates normalized by image dimensions + x_center, y_center = bb[0] + bb[2] / 2, bb[1] + bb[3] / 2 + x, width = x_center / w, bb[2] / w + y, height = y_center / h, bb[3] / h bb = [x, y, width, height] cls = object['class'] fmt = f'{cls - 1} {x} {y} {width} {height}\n' From 9a95ce7e666519b1669bd2ed752f39377a8fdc55 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Tue, 18 Jul 2023 17:01:55 +0200 Subject: [PATCH 15/28] Starting documentation --- .../tutorial-detection-training-synthetic.dox | 33 +++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 doc/tutorial/detection_dnn/tutorial-detection-training-synthetic.dox diff --git a/doc/tutorial/detection_dnn/tutorial-detection-training-synthetic.dox b/doc/tutorial/detection_dnn/tutorial-detection-training-synthetic.dox new file mode 100644 index 0000000000..8a5641d700 --- /dev/null +++ b/doc/tutorial/detection_dnn/tutorial-detection-training-synthetic.dox @@ -0,0 +1,33 @@ +/** + +\page tutorial-detection-dnn-training-synthetic Tutorial: Training a detection network from a CAD model with Blenderproc +\tableofcontents + +\section dnn_synthetic_intro Introduction + +In this tutorial, we will show how to train a baseline detection neural network from a 3D model, thanks to blenderproc. + +Most of the (manual) work when training a neural network resides in acquiring and labelling data. This process can be slow, tedious and error prone. +A solution to avoid this step is to use synthetic data, generated by a simulator/computer program. This approach comes with multiple advantages: +- Data acquisition is fast +- It is easy to acquire accurate ground truth labels +- Variations in the training data can be easily added + +There are however, some drawbacks: +- More knowledge of the scene is required: in the case of detection, we require a 3D model of the object, which is not the case for true images +- A gap between simulated and real data can be apparent and negatively impact network performance (this is the Sim2Real gap) + +The latter point is heavily dependent on the quality of the generated images and the more realistic the images, the better the expected results. + +Blender, using ray tracing, can generate realistic images. To perform data generation, Blenderproc has been developed and is an extremely useful and flexible tool to generate realistic scenes from Python. + +In this tutorial, we will install blenderproc and use it to generate various simple but varied scenes containing an object of interest. We will then be able to train a YoloV7 with this data, for later use on real data. + + +\section dnn_synthetic_install Requirements + + +\section dnn_synthetic_next Next steps + + +*/ From a0bc4cd17f116cc9608b77b1214962ccba5784d9 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Wed, 19 Jul 2023 12:33:44 +0200 Subject: [PATCH 16/28] More documentation --- doc/config-doxygen.in | 2 + .../tutorial/misc/blenderproc_rgb_example.png | Bin 0 -> 1894257 bytes .../tutorial-detection-training-synthetic.dox | 33 --- .../misc/tutorial-synthetic-blenderproc.dox | 180 +++++++++++++++++ doc/tutorial/tutorial-users.dox | 1 + script/dataset_generator/example_config.json | 28 +-- script/dataset_generator/generate_scene.py | 189 ++++++++++++++---- 7 files changed, 344 insertions(+), 89 deletions(-) create mode 100644 doc/image/tutorial/misc/blenderproc_rgb_example.png delete mode 100644 doc/tutorial/detection_dnn/tutorial-detection-training-synthetic.dox create mode 100644 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z2x}49{@Zq_kBfn`px+w&F_d{(KcN_3n0eTVZS@zsLnS(IfQk~&HM!_F7QJcmPF|Ie z2BOVwRwf_TVtUzTZpvG9Wp!=Sq$(~C;k&x+oGr_#%1ST<1Ap`Fgu#0NJiDK^F65?qGI z0$wkQ@Xq9HE(XtFl4x}*As{pW761)^8h@uh%Y#4G|2F{`DErSPQq;tbTCx70jgF4~ z_l^eusz1j87--zjG5+rUoci}s`tQa+`+pGl2Z4VO_y>W15cmgy{{sjJnDc{8EJZEB i=0fJ8!h!-O!X|v?mV&}411u;a$Y&wIZz*6ZEbt#zhfT}? literal 0 HcmV?d00001 From 34e6b322656d7154d59e9a0664df878a71237a78 Mon Sep 17 00:00:00 2001 From: Samuel Felton Date: Fri, 30 Jun 2023 16:54:39 +0200 Subject: [PATCH 02/28] Creating a room, cc0 textures --- script/dataset_generator/example_config.json | 17 ++- .../generate_hdf5_dataset.py | 115 +++++++++++++++--- script/dataset_generator/output/0.hdf5 | Bin 212309 -> 0 bytes 3 files changed, 107 insertions(+), 25 deletions(-) delete mode 100644 script/dataset_generator/output/0.hdf5 diff --git a/script/dataset_generator/example_config.json b/script/dataset_generator/example_config.json index ccbfc58450..d15c2d7657 100644 --- a/script/dataset_generator/example_config.json +++ b/script/dataset_generator/example_config.json @@ -1,17 +1,22 @@ { "camera": { - "px": 500, - "py": 500, + "px": 600, + "py": 600, "u0": 320, "v0": 240, "h": 480, "w": 640, - "randomize_params_percent": 10.0 + "randomize_params_percent": 5.0 }, "dataset": { "object_poses": true, - "camera_poses": true, - - } + "depth": false, + "normals": false, + "segmentation": false, + "detection": true + }, + "models_path": "/home/sfelton/megapose-models", + "cc_textures_path": "/home/sfelton/cc0", + "output_path": "output" } \ No newline at end of file diff --git a/script/dataset_generator/generate_hdf5_dataset.py b/script/dataset_generator/generate_hdf5_dataset.py index 6dc68c538e..cfa04f3553 100644 --- a/script/dataset_generator/generate_hdf5_dataset.py +++ b/script/dataset_generator/generate_hdf5_dataset.py @@ -15,49 +15,126 @@ $ blenderproc quickstart # verify that it works, install minor dependencies +''' + +''' +TODO: + - Random objects selection: max number of objects, location, rotation, sample_on_surface tuto + - Random cc0 + - Random distractors + - Random light ''' import numpy as np import argparse import json +from operator import itemgetter +from typing import Dict + class Config: def __init__(self, config_path): self.config_path = config_path - self.parser = argparse.ArgumentParser(prefix_chars='--') - self.parser.add_argument('config', type=str, required=False, help='Path to the JSON config file') - self.parser.add_argument('height', type=int, required=False, help='Height of the generated images') - self.parser.add_argument('width', type=int, required=False, help='Width of the generated images') - self.parser.add_argument('px', type=float, required=False, help='Camera parameter: ratio between pixel width and focal length') - self.parser.add_argument('py', type=float, required=False, help='Camera parameter: ratio between pixel height and focal length') - self.parser.add_argument('u0', type=float, required=False, help='Camera parameter: x-axis location of the principal point, in pixels') - self.parser.add_argument('v0', type=float, required=False, help='Camera parameter: y-axis location of the principal point, in pixels') - self.parser.add_argument('camera-randomization-range', type=float, required=False, help='Amount of randomization applied to a camera\'s intrinsics parameters, as a percentage of the base values') - - - - - - - # with open() + with open(self.config_path, 'r') as json_config_file: + self.json_config = json.load(json_config_file) + print(self.json_config) + + self.objects = self.get_objects() + self.cc_textures = bproc.loader.load_ccmaterials(self.json_config['cc_textures_path']) + + def get_objects(self) -> Dict: + models_path = self.json_config['models_path'] + + + def setup_renderer(self) -> None: + depth, normals, segmentation = itemgetter('depth', 'normals', 'segmentation')(self.json_config['dataset']) + + if depth: + bproc.renderer.enable_depth_output(activate_antialiasing=False) + if normals: + bproc.renderer.enable_normals_output() + if segmentation: + bproc.renderer.enable_segmentation_output() + + def set_camera_intrinsics(self) -> None: + ''' + Set camera intrinsics from config. + Randomized depending on randomize_params_percent. This does not impact image resolution + ''' + px, py, u0, v0, h, w , r = itemgetter('px', 'py', 'u0', 'v0', 'h', 'w', 'randomize_params_percent')(self.json_config['camera']) + r = r / 100.0 + randomize = lambda x: x + np.random.uniform(-x * r, x * r) + K = [ + [randomize(px), 0, randomize(u0)], + [0, randomize(py), randomize(v0)], + [0, 0, 1], + ] + bproc.camera.set_intrinsics_from_K_matrix(K, w, h) + + +def create_room(size, textures): + ground = bproc.object.create_primitive('PLANE') + ground.set_location([0, 0, 0]) + ground.set_scale([size / 2, size / 2, 1]) + + objects = [ground] + wall_data = [ + {'loc': [size / 2, 0, size / 2], 'rot': [0, np.pi / 2, 0]}, + {'loc': [-size / 2, 0, size / 2], 'rot': [0, np.pi / 2, 0]}, + {'loc': [0, size / 2, size / 2], 'rot': [np.pi / 2, 0, 0]}, + {'loc': [0, -size / 2, size / 2], 'rot': [np.pi / 2, 0, 0]}, + ] + for w_data in wall_data: + wall = bproc.object.create_primitive('PLANE') + wall.set_location(w_data['loc']) + wall.set_rotation_euler(w_data['rot']) + wall.set_scale([size / 2, size / 2, 1]) + objects.append(wall) + for obj in objects: + random_texture = np.random.choice(textures) + obj.replace_materials(random_texture) + + + return objects + + + + + + + +from blenderproc.python.renderer import RendererUtility if __name__ == '__main__': - bproc.init() + parser = argparse.ArgumentParser() + parser.add_argument('--config', required=True, type=str, help='Path to the JSON configuration file for the dataset generation script') + args = parser.parse_args() + + + RendererUtility.set_render_devices(use_only_cpu=True) + bproc.clean_up(clean_up_camera=True) + config = Config(args.config) + # bproc.init() # Works if you have a GPU + config.set_camera_intrinsics() # Create a simple object: obj = bproc.object.create_primitive("MONKEY") # Create a point light next to it light = bproc.types.Light() light.set_location([2, -2, 0]) - light.set_energy(300) + light.set_energy(1000) # Set the camera to be in front of the object - cam_pose = bproc.math.build_transformation_mat([0, -5, 0], [np.pi / 2, 0, 0]) + cam_pose = bproc.math.build_transformation_mat([0, -5, 1], [np.pi / 2, 0, 0]) bproc.camera.add_camera_pose(cam_pose) + create_room(10.0, config.cc_textures) # Render the scene + + config.setup_renderer() + data = bproc.renderer.render() # Write the rendering into an hdf5 file diff --git a/script/dataset_generator/output/0.hdf5 b/script/dataset_generator/output/0.hdf5 deleted file mode 100644 index d3dda5544f84c9601fb4ef6d8008ed5cabe522eb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 212309 zcmeFZWmFx})9#78y9U?b76|U{?(XjH?(Xgm!8N$MyL)ga5M+`+$-C~&yty-L&9}L? zS)AHk{oB>m`|N(srur;GMFfOkp|GF;er*sC03ZMe|7retdHcO7asvOy^=JRjah=}{ zz~46Hw=DsBZvX=P`T+pI`0Yph9sh4mK~7K*;3xSf{vZ3h8vq^1dkf-^41eCI?;FHV?$-tk1mXhvpB8}6PxI^dSI^(Q zf3^U4e!cvE-sGjE1pt3u+#i*oX8x}${%>RdxnJ@B*VupC_y5n%UwQ`!K=*&syIaWr zcURqv_|6&qga04i`2T_0QRnNZwoJ(0@Gd*5`lT{ja@Cn)hb(Kls1% 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