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191 lines
7.8 KiB
Python
191 lines
7.8 KiB
Python
#!/usr/bin/env python
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'''
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You can download the converted pb model from https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
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or convert the model yourself.
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Follow these steps if you want to convert the original model yourself:
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To get original .meta 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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Change script evaluate_parsing_JPPNet-s2.py for human parsing
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1. Remove preprocessing to create image_batch_origin:
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with tf.name_scope("create_inputs"):
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...
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Add
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image_batch_origin = tf.placeholder(tf.float32, shape=(2, None, None, 3), name='input')
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2. Create input
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image = cv2.imread(path/to/image)
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image_rev = np.flip(image, axis=1)
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input = np.stack([image, image_rev], axis=0)
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3. Hardcode image_h and image_w shapes to determine output shapes.
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We use default INPUT_SIZE = (384, 384) from evaluate_parsing_JPPNet-s2.py.
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parsing_out1 = tf.reduce_mean(tf.stack([tf.image.resize_images(parsing_out1_100, INPUT_SIZE),
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tf.image.resize_images(parsing_out1_075, INPUT_SIZE),
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tf.image.resize_images(parsing_out1_125, INPUT_SIZE)]), axis=0)
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Do similarly with parsing_out2, parsing_out3
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4. Remove postprocessing. Last net operation:
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raw_output = tf.reduce_mean(tf.stack([parsing_out1, parsing_out2, parsing_out3]), axis=0)
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Change:
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parsing_ = sess.run(raw_output, feed_dict={'input:0': input})
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5. To save model after sess.run(...) add:
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input_graph_def = tf.get_default_graph().as_graph_def()
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output_node = "Mean_3"
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output_graph_def = tf.graph_util.convert_variables_to_constants(sess, input_graph_def, output_node)
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output_graph = "LIP_JPPNet.pb"
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with tf.gfile.GFile(output_graph, "wb") as f:
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f.write(output_graph_def.SerializeToString())'
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'''
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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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cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
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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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cv.dnn.DNN_TARGET_HDDL, cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
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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',
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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, "
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"%d: VKCOM, "
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"%d: CUDA"% 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, '
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'%d: Vulkan, '
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'%d: CUDA, '
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'%d: CUDA fp16 (half-float preprocess)' % 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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