mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
171 lines
6.9 KiB
171 lines
6.9 KiB
import cv2 as cv |
|
import numpy as np |
|
import argparse |
|
|
|
|
|
backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV) |
|
targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD) |
|
|
|
parser = argparse.ArgumentParser(description='Use this script to run human parsing using JPPNet', |
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter) |
|
parser.add_argument('--input', '-i', help='Path to input image. Skip this argument to capture frames from a camera.') |
|
parser.add_argument('--model', '-m', required=True, 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" % 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: VPU' % targets) |
|
args, _ = parser.parse_known_args() |
|
|
|
# To get 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()) |
|
|
|
|
|
def preprocess(image_path): |
|
""" |
|
Create 4-dimensional blob from image and flip image |
|
:param image_path: path to input image |
|
""" |
|
image = cv.imread(image_path) |
|
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_path, model_path, backend, target): |
|
""" |
|
Prepare input for execution, run net and postprocess output to parse human. |
|
:param image_path: path to input image |
|
:param model_path: path to JPPNet model |
|
:param backend: name of computation backend |
|
:param target: name of computation target |
|
""" |
|
input = preprocess(image_path) |
|
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__': |
|
output = parse_human(args.input, 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()
|
|
|