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Fix demo
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@ -3,8 +3,7 @@ import numpy as np
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import argparse
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backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019,
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cv.dnn.DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, cv.dnn.DNN_BACKEND_OPENCV)
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backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV)
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targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
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parser = argparse.ArgumentParser(description='Use this script to run human parsing using JPPNet',
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@ -14,7 +13,6 @@ parser.add_argument('--model', '-m', required=True, help='Path to pb model.')
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parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
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help="Choose one of computation backends: "
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"%d: automatically (by default), "
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"%d: Halide language (http://halide-lang.org/), "
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"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"%d: OpenCV implementation" % backends)
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parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
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@ -23,6 +21,7 @@ parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU,
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'%d: OpenCL, '
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'%d: OpenCL fp16 (half-float precision), '
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'%d: VPU' % targets)
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args, _ = parser.parse_known_args()
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# To get pre-trained model download https://drive.google.com/file/d/1BFVXgeln-bek8TCbRjN6utPAgRE0LJZg/view
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# For correct convert .meta to .pb model download original repository https://github.com/Engineering-Course/LIP_JPPNet
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@ -165,7 +164,6 @@ def parse_human(image_path, model_path, backend, target):
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if __name__ == '__main__':
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args, _ = parser.parse_known_args()
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output = parse_human(args.input, args.model, args.backend, args.target)
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winName = 'Deep learning human parsing in OpenCV'
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cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
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