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‎common.py

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import sys
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import os
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import cv2 as cv
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def add_argument(zoo, parser, name, help, required=False, default=None, type=None, action=None, nargs=None):
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if len(sys.argv) <= 1:
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return
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modelName = sys.argv[1]
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if os.path.isfile(zoo):
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fs = cv.FileStorage(zoo, cv.FILE_STORAGE_READ)
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node = fs.getNode(modelName)
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if not node.empty():
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value = node.getNode(name)
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if not value.empty():
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if value.isReal():
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default = value.real()
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elif value.isString():
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default = value.string()
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elif value.isInt():
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default = int(value.real())
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elif value.isSeq():
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default = []
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for i in range(value.size()):
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v = value.at(i)
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if v.isInt():
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default.append(int(v.real()))
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elif v.isReal():
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default.append(v.real())
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else:
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print('Unexpected value format')
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exit(0)
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else:
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print('Unexpected field format')
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exit(0)
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required = False
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if action == 'store_true':
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default = 1 if default == 'true' else (0 if default == 'false' else default)
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assert(default is None or default == 0 or default == 1)
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parser.add_argument('--' + name, required=required, help=help, default=bool(default),
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action=action)
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else:
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parser.add_argument('--' + name, required=required, help=help, default=default,
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action=action, nargs=nargs, type=type)
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def add_preproc_args(zoo, parser, sample):
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aliases = []
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if os.path.isfile(zoo):
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fs = cv.FileStorage(zoo, cv.FILE_STORAGE_READ)
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root = fs.root()
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for name in root.keys():
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model = root.getNode(name)
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if model.getNode('sample').string() == sample:
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aliases.append(name)
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parser.add_argument('alias', nargs='?', choices=aliases,
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help='An alias name of model to extract preprocessing parameters from models.yml file.')
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add_argument(zoo, parser, 'model', required=True,
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help='Path to a binary file of model contains trained weights. '
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'It could be a file with extensions .caffemodel (Caffe), '
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'.pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet), .bin (OpenVINO)')
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add_argument(zoo, parser, 'config',
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help='Path to a text file of model contains network configuration. '
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'It could be a file with extensions .prototxt (Caffe), .pbtxt or .config (TensorFlow), .cfg (Darknet), .xml (OpenVINO)')
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add_argument(zoo, parser, 'mean', nargs='+', type=float, default=[0, 0, 0],
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help='Preprocess input image by subtracting mean values. '
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'Mean values should be in BGR order.')
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add_argument(zoo, parser, 'scale', type=float, default=1.0,
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help='Preprocess input image by multiplying on a scale factor.')
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add_argument(zoo, parser, 'width', type=int,
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help='Preprocess input image by resizing to a specific width.')
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add_argument(zoo, parser, 'height', type=int,
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help='Preprocess input image by resizing to a specific height.')
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add_argument(zoo, parser, 'rgb', action='store_true',
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help='Indicate that model works with RGB input images instead BGR ones.')
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add_argument(zoo, parser, 'classes',
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help='Optional path to a text file with names of classes to label detected objects.')
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def findFile(filename):
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if filename:
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if os.path.exists(filename):
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return filename
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fpath = cv.samples.findFile(filename, False)
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if fpath:
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return fpath
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samplesDataDir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
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'..',
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'data',
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'dnn')
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if os.path.exists(os.path.join(samplesDataDir, filename)):
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return os.path.join(samplesDataDir, filename)
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for path in ['OPENCV_DNN_TEST_DATA_PATH', 'OPENCV_TEST_DATA_PATH']:
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try:
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extraPath = os.environ[path]
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absPath = os.path.join(extraPath, 'dnn', filename)
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if os.path.exists(absPath):
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return absPath
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except KeyError:
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pass
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print('File ' + filename + ' not found! Please specify a path to '
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'/opencv_extra/testdata in OPENCV_DNN_TEST_DATA_PATH environment '
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'variable or pass a full path to model.')
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exit(0)

‎human_parsing.py

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import argparse
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import os.path
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import numpy as np
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import cv2 as cv
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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, cv.dnn.DNN_TARGET_HDDL)
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def preprocess(image):
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"""
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Create 4-dimensional blob from image and flip image
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:param image: input image
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"""
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image_rev = np.flip(image, axis=1)
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input = cv.dnn.blobFromImages([image, image_rev], mean=(104.00698793, 116.66876762, 122.67891434))
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return input
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def run_net(input, model_path, backend, target):
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"""
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Read network and infer model
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:param model_path: path to JPPNet model
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:param backend: computation backend
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:param target: computation device
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"""
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net = cv.dnn.readNet(model_path)
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net.setPreferableBackend(backend)
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net.setPreferableTarget(target)
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net.setInput(input)
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out = net.forward()
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return out
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def postprocess(out, input_shape):
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"""
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Create a grayscale human segmentation
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:param out: network output
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:param input_shape: input image width and height
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"""
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# LIP classes
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# 0 Background
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# 1 Hat
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# 2 Hair
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# 3 Glove
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# 4 Sunglasses
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# 5 UpperClothes
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# 6 Dress
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# 7 Coat
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# 8 Socks
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# 9 Pants
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# 10 Jumpsuits
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# 11 Scarf
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# 12 Skirt
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# 13 Face
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# 14 LeftArm
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# 15 RightArm
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# 16 LeftLeg
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# 17 RightLeg
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# 18 LeftShoe
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# 19 RightShoe
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head_output, tail_output = np.split(out, indices_or_sections=[1], axis=0)
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head_output = head_output.squeeze(0)
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tail_output = tail_output.squeeze(0)
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head_output = np.stack([cv.resize(img, dsize=input_shape) for img in head_output[:, ...]])
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tail_output = np.stack([cv.resize(img, dsize=input_shape) for img in tail_output[:, ...]])
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tail_list = np.split(tail_output, indices_or_sections=list(range(1, 20)), axis=0)
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tail_list = [arr.squeeze(0) for arr in tail_list]
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tail_list_rev = [tail_list[i] for i in range(14)]
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tail_list_rev.extend([tail_list[15], tail_list[14], tail_list[17], tail_list[16], tail_list[19], tail_list[18]])
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tail_output_rev = np.stack(tail_list_rev, axis=0)
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tail_output_rev = np.flip(tail_output_rev, axis=2)
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raw_output_all = np.mean(np.stack([head_output, tail_output_rev], axis=0), axis=0, keepdims=True)
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raw_output_all = np.argmax(raw_output_all, axis=1)
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raw_output_all = raw_output_all.transpose(1, 2, 0)
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return raw_output_all
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def decode_labels(gray_image):
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"""
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Colorize image according to labels
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:param gray_image: grayscale human segmentation result
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"""
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height, width, _ = gray_image.shape
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colors = [(0, 0, 0), (128, 0, 0), (255, 0, 0), (0, 85, 0), (170, 0, 51), (255, 85, 0),
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(0, 0, 85), (0, 119, 221), (85, 85, 0), (0, 85, 85), (85, 51, 0), (52, 86, 128),
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(0, 128, 0), (0, 0, 255), (51, 170, 221), (0, 255, 255),(85, 255, 170),
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(170, 255, 85), (255, 255, 0), (255, 170, 0)]
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segm = np.stack([colors[idx] for idx in gray_image.flatten()])
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segm = segm.reshape(height, width, 3).astype(np.uint8)
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segm = cv.cvtColor(segm, cv.COLOR_BGR2RGB)
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return segm
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def parse_human(image, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
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"""
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Prepare input for execution, run net and postprocess output to parse human.
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:param image: input image
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:param model_path: path to JPPNet model
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:param backend: name of computation backend
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:param target: name of computation target
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"""
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input = preprocess(image)
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input_h, input_w = input.shape[2:]
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output = run_net(input, model_path, backend, target)
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grayscale_out = postprocess(output, (input_w, input_h))
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segmentation = decode_labels(grayscale_out)
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return segmentation
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Use this script to run human parsing using JPPNet', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--input', '-i', required=True, help='Path to input image.')
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parser.add_argument('--model', '-m', default='lip_jppnet_384.pb', 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: 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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help='Choose one of target computation devices: '
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'%d: CPU target (by default), '
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'%d: OpenCL, '
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'%d: OpenCL fp16 (half-float precision), '
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'%d: NCS2 VPU, '
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'%d: HDDL VPU' % targets)
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args, _ = parser.parse_known_args()
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if not os.path.isfile(args.model):
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raise OSError("Model not exist")
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image = cv.imread(args.input)
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output = parse_human(image, 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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cv.imshow(winName, output)
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cv.waitKey()

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