122 lines
6.4 KiB
Python
122 lines
6.4 KiB
Python
# To use Inference Engine backend, specify location of plugins:
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# source /opt/intel/computer_vision_sdk/bin/setupvars.sh
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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, HAND) 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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parser.add_argument('--scale', default=0.003922, type=float, help='Scale for blob.')
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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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elif 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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elif args.dataset == 'HAND':
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BODY_PARTS = { "Wrist": 0,
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"ThumbMetacarpal": 1, "ThumbProximal": 2, "ThumbMiddle": 3, "ThumbDistal": 4,
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"IndexFingerMetacarpal": 5, "IndexFingerProximal": 6, "IndexFingerMiddle": 7, "IndexFingerDistal": 8,
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"MiddleFingerMetacarpal": 9, "MiddleFingerProximal": 10, "MiddleFingerMiddle": 11, "MiddleFingerDistal": 12,
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"RingFingerMetacarpal": 13, "RingFingerProximal": 14, "RingFingerMiddle": 15, "RingFingerDistal": 16,
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"LittleFingerMetacarpal": 17, "LittleFingerProximal": 18, "LittleFingerMiddle": 19, "LittleFingerDistal": 20,
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}
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POSE_PAIRS = [ ["Wrist", "ThumbMetacarpal"], ["ThumbMetacarpal", "ThumbProximal"],
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["ThumbProximal", "ThumbMiddle"], ["ThumbMiddle", "ThumbDistal"],
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["Wrist", "IndexFingerMetacarpal"], ["IndexFingerMetacarpal", "IndexFingerProximal"],
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["IndexFingerProximal", "IndexFingerMiddle"], ["IndexFingerMiddle", "IndexFingerDistal"],
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["Wrist", "MiddleFingerMetacarpal"], ["MiddleFingerMetacarpal", "MiddleFingerProximal"],
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["MiddleFingerProximal", "MiddleFingerMiddle"], ["MiddleFingerMiddle", "MiddleFingerDistal"],
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["Wrist", "RingFingerMetacarpal"], ["RingFingerMetacarpal", "RingFingerProximal"],
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["RingFingerProximal", "RingFingerMiddle"], ["RingFingerMiddle", "RingFingerDistal"],
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["Wrist", "LittleFingerMetacarpal"], ["LittleFingerMetacarpal", "LittleFingerProximal"],
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["LittleFingerProximal", "LittleFingerMiddle"], ["LittleFingerMiddle", "LittleFingerDistal"] ]
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else:
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raise(Exception("you need to specify either 'COCO', 'MPI', or 'Hand' in args.dataset"))
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inWidth = args.width
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inHeight = args.height
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inScale = args.scale
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net = cv.dnn.readNet(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, inScale, (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 corresponding 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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