# Specify the paths for the 2 filesprotoFile = "pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt"weightsFile = "pose/mpi/pose_iter_160000.caffemodel"# Read the network into Memorynet = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)
# Read imageframe = cv2.imread("image.jpg")# Specify the input image dimensionsinWidth = 368inHeight = 368# Prepare the frame to be fed to the networkinpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight), (0, 0, 0), swapRB=False, crop=False)# Set the prepared object as the input blob of the networknet.setInput(inpBlob)
H = out.shape[2]W = out.shape[3]# Empty list to store the detected keypointspoints = []for i in range(len()): # confidence map of corresponding body's part. probMap = output[0, i, :, :] # Find global maxima of the probMap. minVal, prob, minLoc, point = cv2.minMaxLoc(probMap) # Scale the point to fit on the original image x = (frameWidth * point[0]) / W y = (frameHeight * point[1]) / H if prob > threshold : cv2.circle(frame, (int(x), int(y)), 15, (0, 255, 255), thickness=-1, lineType=cv.FILLED) cv2.putText(frame, "{}".format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1.4, (0, 0, 255), 3, lineType=cv2.LINE_AA)# Add the point to the list if the probability is greater than the threshold points.append((int(x), int(y))) else : points.append(None)cv2.imshow("Output-Keypoints",frame)cv2.waitKey(0)cv2.destroyAllWindows()
步骤4：绘制骨架
由于我们已经绘制了关键点，因此我们现在只需将两对连接即可绘制骨架。
for pair in POSE_PAIRS: partA = pair[0] partB = pair[1] if points[partA] and points[partB]: cv2.line(frameCopy, points[partA], points[partB], (0, 255, 0), 3)
