生成关键点数据

scripts/data_gen/GeneratePNetLandmarkData.py

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+# coding: utf-8
+"""
+读取lfw net face 数据集
+生成正样本和负样本。
+正样本标签为：1
+负样本标签为：0
+部分人脸样本标签为：2
+landmark样本:-1
+综合标签为： img_path xmin ymin xmax ymax landmark[10] label
+
+注意：保存的图片缩放到了12*12，bbox的坐标也是相对于12*12的
+"""
+import sys
+sys.path.append(sys.path[0] + "/../")
+import os
+import numpy as np
+import random
+import cv2
+from pylab import plt
+from util.utility import *
+from util.Logger import Logger
+import time
+import lmdb
+if not os.path.exists("./log"):
+    os.mkdir("./log")
+log = Logger("./log/{}_{}.log".format(__file__.split('/')[-1],
+                                      time.strftime("%Y%m%d-%H%M%S"), time.localtime), level='debug').logger
+# 小于该人脸的就不要了
+# 太小的话，会有太多的误检
+MIN_FACE_SIZE = 40
+IOU_POS_THRES = 0.65
+IOU_NEG_THRES = 0.3
+IOU_PART_THRES = 0.4
+
+## 关键点样本个数
+landmark_samples = 20
+
+OUT_IMAGE_SIZE = 12
+
+root_dir = r'../../dataset/LFW_NET_FACE'
+root_dir = os.path.expanduser(root_dir)
+output_root_dir = r"../../dataset/train_faces_p"
+if not os.path.exists(output_root_dir):
+    os.mkdir(output_root_dir)
+output_landmark_dir = os.path.join(output_root_dir, "landmark")
+if not os.path.exists(output_landmark_dir):
+    os.mkdir(output_landmark_dir)
+
+LMDB_MAP_SIZE = 1099511627776
+env_landmark_image = lmdb.open(os.path.join(output_landmark_dir, "image_landmark"), map_size=LMDB_MAP_SIZE)
+env_landmark_label = lmdb.open(os.path.join(output_landmark_dir, "label_landmark"), map_size=LMDB_MAP_SIZE)
+global_idx_landmark = 0
+txn_landmark_image = env_landmark_image.begin(write=True)
+txn_landmark_label = env_landmark_label.begin(write=True)
+
+anno_file = os.path.join(root_dir, "trainImageList.txt")
+
+with open(anno_file, "r") as f:
+    inner_landmark_idx = 0
+    while True:
+        line = f.readline()
+        if not line:
+                break
+        line_split = line.split()
+        filename_split = line_split[0].split("\\")
+        filename = os.path.join(filename_split[0],filename_split[1])
+        img = cv2.imread(os.path.join(root_dir,filename))
+        if img is None:
+            log.warning("error to load image {}", filename)
+            continue
+        # 读取真值 bbox
+        H, W, C = img.shape
+        x = int(line_split[1])
+        x1 = int(line_split[2])
+        y = int(line_split[3])
+        y1 = int(line_split[4])
+        w = x1 - x
+        h = y1 - y
+        box = np.array([x, y, w, h])
+        for i in range(landmark_samples):
+            size = random.randrange(int(np.min((w,h)) * 0.8), int(np.ceil(1.25 * np.max((w,h)))))
+            dx = random.randrange(int(-w * 0.2), int(w * 0.2))
+            dy = random.randrange(int(-h * 0.2), int(h * 0.2))
+            nx = np.max((x + w / 2 + dx - size / 2), 0)
+            ny = np.max((y + h / 2 + dy - size / 2), 0)
+            nx = int(nx)
+            ny = int(ny)
+            if nx < 0:
+                nx = 0
+            if ny < 0:
+                ny = 0
+            if nx + size > W or ny + size > H:
+                continue
+            if size < OUT_IMAGE_SIZE / 2:
+                continue
+
+            #iou
+            crop_box = np.array([nx, ny, size, size])
+            iou = IOU(box, crop_box)
+            # log.info("{} {} {} {}".format(nx, ny, size, size))
+            crop = img[ny: ny+size, nx:nx+size]
+            out = cv2.resize(crop, (OUT_IMAGE_SIZE, OUT_IMAGE_SIZE))
+            scalor = float(size) / float(OUT_IMAGE_SIZE)
+            # ground truth 坐标变换
+            ## 变换到crop
+            ox = x - nx
+            oy = y - ny
+            ow = w
+            oh = h
+            # ## 变换到out
+            ox = ox / scalor
+            oy = oy / scalor
+            ow = ow / scalor
+            oh = oh / scalor
+            #关键点
+            landmarks = []
+            for i in range(5):
+                lx = int(float(line_split[5+i*2])) - nx
+                ly = int(float(line_split[5+i*2+1])) - ny
+                lx = lx / scalor
+                ly = ly / scalor
+                landmarks.append(lx)
+                landmarks.append(ly)
+            #
+            # # 这里保存成左上角点和右下角点
+            xmin = ox
+            ymin = oy
+            xmax = xmin + ow
+            ymax = ymin + oh
+            if iou > IOU_POS_THRES:
+                #### 正样本
+                #path_ = "/home/chengliu/MTCNN/mtcnn-pytorch/dataset/out_img/" + str(global_idx_landmark) + ".png"
+                #cv2.imwrite(path_,out)
+                label_list = [xmin, ymin, xmax, ymax] + landmarks + [-1]
+                label = np.array(label_list, dtype=np.float32)
+                txn_landmark_image.put("{}".format(global_idx_landmark).encode("ascii"), out.tostring())
+                txn_landmark_label.put("{}".format(global_idx_landmark).encode("ascii"), label.tostring())
+                global_idx_landmark += 1
+                inner_landmark_idx += 1
+            log.info("landmark num: {}".format(global_idx_landmark))
+            if inner_landmark_idx > 1000:
+                txn_landmark_image.commit()
+                txn_landmark_label.commit()
+                txn_landmark_image = env_landmark_image.begin(write=True)
+                txn_landmark_label = env_landmark_label.begin(write=True)
+                inner_landmark_idx = 0
+                log.info("now commit landmark lmdb")
+log.info("process done!")
+txn_landmark_image.commit()
+txn_landmark_label.commit()