Update experiments

Author: daniilpastukhov

gen2/gen2-age-gender/README.md gen2-age-gender/README.md

@@ -0,0 +1,37 @@
+from depthai_sdk import OakCamera, TwoStagePacket, AspectRatioResizeMode, Visualizer, TextPosition, BboxStyle
+import numpy as np
+import cv2
+
+with OakCamera() as oak:
+    color = oak.create_camera('color')
+
+    det = oak.create_nn('face-detection-retail-0004', color)
+    # AspectRatioResizeMode has to be CROP for 2-stage pipelines at the moment
+    det.config_nn(aspectRatioResizeMode=AspectRatioResizeMode.CROP)
+
+    age_gender = oak.create_nn('age-gender-recognition-retail-0013', input=det)
+    # age_gender.config_multistage_nn(show_cropped_frames=True) # For debugging
+
+    def cb(packet: TwoStagePacket, visualizer: Visualizer):
+        for det, rec in zip(packet.detections, packet.nnData):
+            age = int(float(np.squeeze(np.array(rec.getLayerFp16('age_conv3')))) * 100)
+            gender = np.squeeze(np.array(rec.getLayerFp16('prob')))
+            gender_str = "woman" if gender[0] > gender[1] else "man"
+
+            visualizer.add_text(f'{gender_str}\nage: {age}',
+                                bbox=(*det.top_left, *det.bottom_right),
+                                position=TextPosition.BOTTOM_RIGHT)
+
+        frame = visualizer.draw(packet.frame)
+        cv2.imshow('Age-gender estimation', frame)
+
+
+    # Visualize detections on the frame. Don't show the frame but send the packet
+    # to the callback function (where it will be displayed)
+    oak.visualize(age_gender, callback=cb).detections(fill_transparency=0.1)
+    oak.visualize(det.out.passthrough)
+    oak.visualize(age_gender.out.twostage_crops, scale=3.0)
+
+
+    # oak.show_graph() # Show pipeline graph, no need for now
+    oak.start(blocking=True)  # This call will block until the app is stopped (by pressing 'Q' button)

gen2/gen2-age-gender/README.zh-CN.md gen2-age-gender/README.zh-CN.md

@@ -0,0 +1 @@
+depthai-sdk==1.9.1

gen2-age-gender/main.py

@@ -95,6 +95,7 @@ def store_data(in_frame, detections):
     thread = None
     detections = []
 
+
     while True:
         # instead of get (blocking) used tryGet (nonblocking) which will return the available data or None otherwise
         in_rgb = q_rgb.tryGet()

gen2-age-gender/requirements.txt

@@ -1,63 +1,63 @@
-import numpy as np
-import cv2
-import depthai as dai
-
-SHAPE = 300
-
-p = dai.Pipeline()
-p.setOpenVINOVersion(dai.OpenVINO.VERSION_2021_4)
-
-camRgb = p.create(dai.node.ColorCamera)
-camRgb.setPreviewSize(SHAPE, SHAPE)
-camRgb.setInterleaved(False)
-camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
-
-left = p.create(dai.node.MonoCamera)
-left.setBoardSocket(dai.CameraBoardSocket.LEFT)
-left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
-
-# ImageManip for cropping (face detection NN requires input image of 300x300) and to change frame type
-manipLeft = p.create(dai.node.ImageManip)
-manipLeft.initialConfig.setResize(300, 300)
-# The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
-manipLeft.initialConfig.setFrameType(dai.RawImgFrame.Type.BGR888p)
-left.out.link(manipLeft.inputImage)
-
-right = p.create(dai.node.MonoCamera)
-right.setBoardSocket(dai.CameraBoardSocket.RIGHT)
-right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
-
-# ImageManip for cropping (face detection NN requires input image of 300x300) and to change frame type
-manipRight = p.create(dai.node.ImageManip)
-manipRight.initialConfig.setResize(300, 300)
-# The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
-manipRight.initialConfig.setFrameType(dai.RawImgFrame.Type.BGR888p)
-right.out.link(manipRight.inputImage)
-
-# NN that detects faces in the image
-nn = p.create(dai.node.NeuralNetwork)
-nn.setBlobPath("models/concat_openvino_2021.4_6shave.blob")
-nn.setNumInferenceThreads(2)
-
-manipLeft.out.link(nn.inputs['img1'])
-camRgb.preview.link(nn.inputs['img2'])
-manipRight.out.link(nn.inputs['img3'])
-
-# Send bouding box from the NN to the host via XLink
-nn_xout = p.create(dai.node.XLinkOut)
-nn_xout.setStreamName("nn")
-nn.out.link(nn_xout.input)
-
-# Pipeline is defined, now we can connect to the device
-with dai.Device(p) as device:
-    qNn = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
-    shape = (3, SHAPE, SHAPE * 3)
-
-    while True:
-        inNn = np.array(qNn.get().getData())
-        frame = inNn.view(np.float16).reshape(shape).transpose(1, 2, 0).astype(np.uint8).copy()
-
-        cv2.imshow("Concat", frame)
-
-        if cv2.waitKey(1) == ord('q'):
-            break
+import numpy as np
+import cv2
+import depthai as dai
+
+SHAPE = 300
+
+p = dai.Pipeline()
+p.setOpenVINOVersion(dai.OpenVINO.VERSION_2021_4)
+
+camRgb = p.create(dai.node.ColorCamera)
+camRgb.setPreviewSize(SHAPE, SHAPE)
+camRgb.setInterleaved(False)
+camRgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
+
+left = p.create(dai.node.MonoCamera)
+left.setBoardSocket(dai.CameraBoardSocket.LEFT)
+left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
+
+# ImageManip for cropping (face detection NN requires input image of 300x300) and to change frame type
+manipLeft = p.create(dai.node.ImageManip)
+manipLeft.initialConfig.setResize(300, 300)
+# The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
+manipLeft.initialConfig.setFrameType(dai.RawImgFrame.Type.BGR888p)
+left.out.link(manipLeft.inputImage)
+
+right = p.create(dai.node.MonoCamera)
+right.setBoardSocket(dai.CameraBoardSocket.RIGHT)
+right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
+
+# ImageManip for cropping (face detection NN requires input image of 300x300) and to change frame type
+manipRight = p.create(dai.node.ImageManip)
+manipRight.initialConfig.setResize(300, 300)
+# The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
+manipRight.initialConfig.setFrameType(dai.RawImgFrame.Type.BGR888p)
+right.out.link(manipRight.inputImage)
+
+# NN that detects faces in the image
+nn = p.create(dai.node.NeuralNetwork)
+nn.setBlobPath("models/concat_openvino_2021.4_6shave.blob")
+nn.setNumInferenceThreads(2)
+
+manipLeft.out.link(nn.inputs['img1'])
+camRgb.preview.link(nn.inputs['img2'])
+manipRight.out.link(nn.inputs['img3'])
+
+# Send bouding box from the NN to the host via XLink
+nn_xout = p.create(dai.node.XLinkOut)
+nn_xout.setStreamName("nn")
+nn.out.link(nn_xout.input)
+
+# Pipeline is defined, now we can connect to the device
+with dai.Device(p) as device:
+    qNn = device.getOutputQueue(name="nn", maxSize=4, blocking=False)
+    shape = (3, SHAPE, SHAPE * 3)
+
+    while True:
+        inNn = np.array(qNn.get().getData())
+        frame = inNn.view(np.float16).reshape(shape).transpose(1, 2, 0).astype(np.uint8).copy()
+
+        cv2.imshow("Concat", frame)
+
+        if cv2.waitKey(1) == ord('q'):
+            break

gen2/gen2-blur-faces/README.md gen2-blur-faces/README.md

@@ -14,7 +14,6 @@
 blob = dai.OpenVINO.Blob(blobconverter.from_zoo(name="deeplab_v3_mnv2_256x256", zoo_type="depthai", shaves=6))
 # for name,tensorInfo in blob.networkInputs.items(): print(name, tensorInfo.dims)
 INPUT_SHAPE = blob.networkInputs['Input'].dims[:2]
-print(INPUT_SHAPE)
 TARGET_SHAPE = (400,400)
 
 def decode_deeplabv3p(output_tensor):