OpenPose.py

其代码较为简单，模型（较小：7.8M）已经训练好在graph_opt.pb文件中,其中全部实现代码在openpose.py文件中，下面是实现代码及测试效果： # To use Inference Engine backend, specify location of plugins: # export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH import cv2 as cv import numpy as np import argparse parser = argparse.ArgumentParser() parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera') parser.add_argument('--thr', default=0.2, type=float, help='Threshold value for pose parts heat map') parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.') parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.') args = parser.parse_args() BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4, "LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9, "RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14, "LEye": 15, "REar": 16, "LEar": 17, "Background": 18 } POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"], ["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"], ["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"], ["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ] inWidth = args.width inHeight = args.height net = cv.dnn.readNetFromTensorflow("graph_opt.pb") cap = cv.VideoCapture(args.input if args.input else 0) while cv.waitKey(1) < 0: hasFrame, frame = cap.read() if not hasFrame: cv.waitKey() break frameWidth = frame.shape[1] frameHeight = frame.shape[0] net.setInput(cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight), (127.5, 127.5, 127.5), swapRB=True, crop=False)) out = net.forward() out = o