#!/usr/bin/env python ''' You can download the converted pb model from https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0 or convert the model yourself. Follow these steps if you want to convert the original model yourself: To get original .meta pre-trained model download https://drive.google.com/file/d/1BFVXgeln-bek8TCbRjN6utPAgRE0LJZg/view For correct convert .meta to .pb model download original repository https://github.com/Engineering-Course/LIP_JPPNet Change script evaluate_parsing_JPPNet-s2.py for human parsing 1. Remove preprocessing to create image_batch_origin: with tf.name_scope("create_inputs"): ... Add image_batch_origin = tf.placeholder(tf.float32, shape=(2, None, None, 3), name='input') 2. Create input image = cv2.imread(path/to/image) image_rev = np.flip(image, axis=1) input = np.stack([image, image_rev], axis=0) 3. Hardcode image_h and image_w shapes to determine output shapes. We use default INPUT_SIZE = (384, 384) from evaluate_parsing_JPPNet-s2.py. parsing_out1 = tf.reduce_mean(tf.stack([tf.image.resize_images(parsing_out1_100, INPUT_SIZE), tf.image.resize_images(parsing_out1_075, INPUT_SIZE), tf.image.resize_images(parsing_out1_125, INPUT_SIZE)]), axis=0) Do similarly with parsing_out2, parsing_out3 4. Remove postprocessing. Last net operation: raw_output = tf.reduce_mean(tf.stack([parsing_out1, parsing_out2, parsing_out3]), axis=0) Change: parsing_ = sess.run(raw_output, feed_dict={'input:0': input}) 5. To save model after sess.run(...) add: input_graph_def = tf.get_default_graph().as_graph_def() output_node = "Mean_3" output_graph_def = tf.graph_util.convert_variables_to_constants(sess, input_graph_def, output_node) output_graph = "LIP_JPPNet.pb" with tf.gfile.GFile(output_graph, "wb") as f: f.write(output_graph_def.SerializeToString())' ''' import argparse import os.path import numpy as np import cv2 as cv backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA) 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, cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16) def preprocess(image): """ Create 4-dimensional blob from image and flip image :param image: input image """ image_rev = np.flip(image, axis=1) input = cv.dnn.blobFromImages([image, image_rev], mean=(104.00698793, 116.66876762, 122.67891434)) return input def run_net(input, model_path, backend, target): """ Read network and infer model :param model_path: path to JPPNet model :param backend: computation backend :param target: computation device """ net = cv.dnn.readNet(model_path) net.setPreferableBackend(backend) net.setPreferableTarget(target) net.setInput(input) out = net.forward() return out def postprocess(out, input_shape): """ Create a grayscale human segmentation :param out: network output :param input_shape: input image width and height """ # LIP classes # 0 Background # 1 Hat # 2 Hair # 3 Glove # 4 Sunglasses # 5 UpperClothes # 6 Dress # 7 Coat # 8 Socks # 9 Pants # 10 Jumpsuits # 11 Scarf # 12 Skirt # 13 Face # 14 LeftArm # 15 RightArm # 16 LeftLeg # 17 RightLeg # 18 LeftShoe # 19 RightShoe head_output, tail_output = np.split(out, indices_or_sections=[1], axis=0) head_output = head_output.squeeze(0) tail_output = tail_output.squeeze(0) head_output = np.stack([cv.resize(img, dsize=input_shape) for img in head_output[:, ...]]) tail_output = np.stack([cv.resize(img, dsize=input_shape) for img in tail_output[:, ...]]) tail_list = np.split(tail_output, indices_or_sections=list(range(1, 20)), axis=0) tail_list = [arr.squeeze(0) for arr in tail_list] tail_list_rev = [tail_list[i] for i in range(14)] tail_list_rev.extend([tail_list[15], tail_list[14], tail_list[17], tail_list[16], tail_list[19], tail_list[18]]) tail_output_rev = np.stack(tail_list_rev, axis=0) tail_output_rev = np.flip(tail_output_rev, axis=2) raw_output_all = np.mean(np.stack([head_output, tail_output_rev], axis=0), axis=0, keepdims=True) raw_output_all = np.argmax(raw_output_all, axis=1) raw_output_all = raw_output_all.transpose(1, 2, 0) return raw_output_all def decode_labels(gray_image): """ Colorize image according to labels :param gray_image: grayscale human segmentation result """ height, width, _ = gray_image.shape colors = [(0, 0, 0), (128, 0, 0), (255, 0, 0), (0, 85, 0), (170, 0, 51), (255, 85, 0), (0, 0, 85), (0, 119, 221), (85, 85, 0), (0, 85, 85), (85, 51, 0), (52, 86, 128), (0, 128, 0), (0, 0, 255), (51, 170, 221), (0, 255, 255),(85, 255, 170), (170, 255, 85), (255, 255, 0), (255, 170, 0)] segm = np.stack([colors[idx] for idx in gray_image.flatten()]) segm = segm.reshape(height, width, 3).astype(np.uint8) segm = cv.cvtColor(segm, cv.COLOR_BGR2RGB) return segm def parse_human(image, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU): """ Prepare input for execution, run net and postprocess output to parse human. :param image: input image :param model_path: path to JPPNet model :param backend: name of computation backend :param target: name of computation target """ input = preprocess(image) input_h, input_w = input.shape[2:] output = run_net(input, model_path, backend, target) grayscale_out = postprocess(output, (input_w, input_h)) segmentation = decode_labels(grayscale_out) return segmentation if __name__ == '__main__': parser = argparse.ArgumentParser(description='Use this script to run human parsing using JPPNet', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--input', '-i', required=True, help='Path to input image.') parser.add_argument('--model', '-m', default='lip_jppnet_384.pb', help='Path to pb model.') parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int, help="Choose one of computation backends: " "%d: automatically (by default), " "%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), " "%d: OpenCV implementation, " "%d: VKCOM, " "%d: CUDA"% backends) parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int, help='Choose one of target computation devices: ' '%d: CPU target (by default), ' '%d: OpenCL, ' '%d: OpenCL fp16 (half-float precision), ' '%d: NCS2 VPU, ' '%d: HDDL VPU, ' '%d: Vulkan, ' '%d: CUDA, ' '%d: CUDA fp16 (half-float preprocess)' % targets) args, _ = parser.parse_known_args() if not os.path.isfile(args.model): raise OSError("Model not exist") image = cv.imread(args.input) output = parse_human(image, args.model, args.backend, args.target) winName = 'Deep learning human parsing in OpenCV' cv.namedWindow(winName, cv.WINDOW_AUTOSIZE) cv.imshow(winName, output) cv.waitKey()