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Open Source Computer Vision Library
https://opencv.org/
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100 lines
4.7 KiB
100 lines
4.7 KiB
# To use Inference Engine backend, specify location of plugins: |
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# export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH |
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import cv2 as cv |
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import numpy as np |
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import argparse |
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parser = argparse.ArgumentParser( |
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description='This script is used to demonstrate OpenPose human pose estimation network ' |
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'from https://github.com/CMU-Perceptual-Computing-Lab/openpose project using OpenCV. ' |
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'The sample and model are simplified and could be used for a single person on the frame.') |
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parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera') |
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parser.add_argument('--proto', help='Path to .prototxt') |
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parser.add_argument('--model', help='Path to .caffemodel') |
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parser.add_argument('--dataset', help='Specify what kind of model was trained. ' |
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'It could be (COCO, MPI) depends on dataset.') |
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parser.add_argument('--thr', default=0.1, type=float, help='Threshold value for pose parts heat map') |
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parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.') |
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parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.') |
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args = parser.parse_args() |
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if args.dataset == 'COCO': |
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BODY_PARTS = { "Nose": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4, |
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"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9, |
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"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "REye": 14, |
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"LEye": 15, "REar": 16, "LEar": 17, "Background": 18 } |
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POSE_PAIRS = [ ["Neck", "RShoulder"], ["Neck", "LShoulder"], ["RShoulder", "RElbow"], |
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["RElbow", "RWrist"], ["LShoulder", "LElbow"], ["LElbow", "LWrist"], |
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["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"], |
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["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"], |
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["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ] |
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else: |
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assert(args.dataset == 'MPI') |
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BODY_PARTS = { "Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4, |
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"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9, |
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"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14, |
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"Background": 15 } |
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POSE_PAIRS = [ ["Head", "Neck"], ["Neck", "RShoulder"], ["RShoulder", "RElbow"], |
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["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"], |
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["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"], |
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["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"] ] |
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inWidth = args.width |
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inHeight = args.height |
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net = cv.dnn.readNetFromCaffe(cv.samples.findFile(args.proto), cv.samples.findFile(args.model)) |
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cap = cv.VideoCapture(args.input if args.input else 0) |
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while cv.waitKey(1) < 0: |
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hasFrame, frame = cap.read() |
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if not hasFrame: |
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cv.waitKey() |
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break |
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frameWidth = frame.shape[1] |
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frameHeight = frame.shape[0] |
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inp = cv.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight), |
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(0, 0, 0), swapRB=False, crop=False) |
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net.setInput(inp) |
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out = net.forward() |
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assert(len(BODY_PARTS) == out.shape[1]) |
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points = [] |
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for i in range(len(BODY_PARTS)): |
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# Slice heatmap of corresponging body's part. |
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heatMap = out[0, i, :, :] |
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# Originally, we try to find all the local maximums. To simplify a sample |
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# we just find a global one. However only a single pose at the same time |
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# could be detected this way. |
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_, conf, _, point = cv.minMaxLoc(heatMap) |
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x = (frameWidth * point[0]) / out.shape[3] |
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y = (frameHeight * point[1]) / out.shape[2] |
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# Add a point if it's confidence is higher than threshold. |
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points.append((int(x), int(y)) if conf > args.thr else None) |
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for pair in POSE_PAIRS: |
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partFrom = pair[0] |
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partTo = pair[1] |
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assert(partFrom in BODY_PARTS) |
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assert(partTo in BODY_PARTS) |
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idFrom = BODY_PARTS[partFrom] |
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idTo = BODY_PARTS[partTo] |
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if points[idFrom] and points[idTo]: |
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cv.line(frame, points[idFrom], points[idTo], (0, 255, 0), 3) |
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cv.ellipse(frame, points[idFrom], (3, 3), 0, 0, 360, (0, 0, 255), cv.FILLED) |
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cv.ellipse(frame, points[idTo], (3, 3), 0, 0, 360, (0, 0, 255), cv.FILLED) |
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t, _ = net.getPerfProfile() |
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freq = cv.getTickFrequency() / 1000 |
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cv.putText(frame, '%.2fms' % (t / freq), (10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0)) |
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cv.imshow('OpenPose using OpenCV', frame)
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