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d789cb459c
dnn: cleanup of halide backend for 5.x #24231 Merge with https://github.com/opencv/opencv_extra/pull/1092. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
472 lines
20 KiB
Python
472 lines
20 KiB
Python
#!/usr/bin/env python3
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'''
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You can download the Geometric Matching Module model from https://www.dropbox.com/s/tyhc73xa051grjp/cp_vton_gmm.onnx?dl=0
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You can download the Try-On Module model from https://www.dropbox.com/s/q2x97ve2h53j66k/cp_vton_tom.onnx?dl=0
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You can download the cloth segmentation model from https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
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You can find the OpenPose proto in opencv_extra/testdata/dnn/openpose_pose_coco.prototxt
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and get .caffemodel using opencv_extra/testdata/dnn/download_models.py
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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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from numpy import linalg
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from common import findFile
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from human_parsing import parse_human
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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, cv.dnn.DNN_TARGET_HDDL,
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cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
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parser = argparse.ArgumentParser(description='Use this script to run virtial try-on using CP-VTON',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--input_image', '-i', required=True, help='Path to image with person.')
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parser.add_argument('--input_cloth', '-c', required=True, help='Path to target cloth image')
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parser.add_argument('--gmm_model', '-gmm', default='cp_vton_gmm.onnx', help='Path to Geometric Matching Module .onnx model.')
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parser.add_argument('--tom_model', '-tom', default='cp_vton_tom.onnx', help='Path to Try-On Module .onnx model.')
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parser.add_argument('--segmentation_model', default='lip_jppnet_384.pb', help='Path to cloth segmentation .pb model.')
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parser.add_argument('--openpose_proto', default='openpose_pose_coco.prototxt', help='Path to OpenPose .prototxt model was trained on COCO dataset.')
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parser.add_argument('--openpose_model', default='openpose_pose_coco.caffemodel', help='Path to OpenPose .caffemodel model was trained on COCO dataset.')
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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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def get_pose_map(image, proto_path, model_path, backend, target, height=256, width=192):
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radius = 5
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inp = cv.dnn.blobFromImage(image, 1.0 / 255, (width, height))
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net = cv.dnn.readNet(proto_path, model_path)
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net.setPreferableBackend(backend)
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net.setPreferableTarget(target)
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net.setInput(inp)
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out = net.forward()
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threshold = 0.1
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_, out_c, out_h, out_w = out.shape
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pose_map = np.zeros((height, width, out_c - 1))
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# last label: Background
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for i in range(0, out.shape[1] - 1):
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heatMap = out[0, i, :, :]
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keypoint = np.full((height, width), -1)
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_, conf, _, point = cv.minMaxLoc(heatMap)
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x = width * point[0] // out_w
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y = height * point[1] // out_h
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if conf > threshold and x > 0 and y > 0:
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keypoint[y - radius:y + radius, x - radius:x + radius] = 1
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pose_map[:, :, i] = keypoint
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pose_map = pose_map.transpose(2, 0, 1)
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return pose_map
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class BilinearFilter(object):
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"""
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PIL bilinear resize implementation
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image = image.resize((image_width // 16, image_height // 16), Image.BILINEAR)
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"""
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def _precompute_coeffs(self, inSize, outSize):
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filterscale = max(1.0, inSize / outSize)
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ksize = int(np.ceil(filterscale)) * 2 + 1
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kk = np.zeros(shape=(outSize * ksize, ), dtype=np.float32)
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bounds = np.empty(shape=(outSize * 2, ), dtype=np.int32)
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centers = (np.arange(outSize) + 0.5) * filterscale + 0.5
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bounds[::2] = np.where(centers - filterscale < 0, 0, centers - filterscale)
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bounds[1::2] = np.where(centers + filterscale > inSize, inSize, centers + filterscale) - bounds[::2]
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xmins = bounds[::2] - centers + 1
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points = np.array([np.arange(row) + xmins[i] for i, row in enumerate(bounds[1::2])]) / filterscale
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for xx in range(0, outSize):
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point = points[xx]
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bilinear = np.where(point < 1.0, 1.0 - abs(point), 0.0)
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ww = np.sum(bilinear)
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kk[xx * ksize : xx * ksize + bilinear.size] = np.where(ww == 0.0, bilinear, bilinear / ww)
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return bounds, kk, ksize
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def _resample_horizontal(self, out, img, ksize, bounds, kk):
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for yy in range(0, out.shape[0]):
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for xx in range(0, out.shape[1]):
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xmin = bounds[xx * 2 + 0]
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xmax = bounds[xx * 2 + 1]
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k = kk[xx * ksize : xx * ksize + xmax]
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out[yy, xx] = np.round(np.sum(img[yy, xmin : xmin + xmax] * k))
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def _resample_vertical(self, out, img, ksize, bounds, kk):
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for yy in range(0, out.shape[0]):
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ymin = bounds[yy * 2 + 0]
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ymax = bounds[yy * 2 + 1]
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k = kk[yy * ksize: yy * ksize + ymax]
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out[yy] = np.round(np.sum(img[ymin : ymin + ymax, 0:out.shape[1]] * k[:, np.newaxis], axis=0))
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def imaging_resample(self, img, xsize, ysize):
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height, width = img.shape[0:2]
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bounds_horiz, kk_horiz, ksize_horiz = self._precompute_coeffs(width, xsize)
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bounds_vert, kk_vert, ksize_vert = self._precompute_coeffs(height, ysize)
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out_hor = np.empty((img.shape[0], xsize), dtype=np.uint8)
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self._resample_horizontal(out_hor, img, ksize_horiz, bounds_horiz, kk_horiz)
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out = np.empty((ysize, xsize), dtype=np.uint8)
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self._resample_vertical(out, out_hor, ksize_vert, bounds_vert, kk_vert)
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return out
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class CpVton(object):
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def __init__(self, gmm_model, tom_model, backend, target):
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super(CpVton, self).__init__()
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self.gmm_net = cv.dnn.readNet(gmm_model)
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self.tom_net = cv.dnn.readNet(tom_model)
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self.gmm_net.setPreferableBackend(backend)
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self.gmm_net.setPreferableTarget(target)
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self.tom_net.setPreferableBackend(backend)
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self.tom_net.setPreferableTarget(target)
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def prepare_agnostic(self, segm_image, input_image, pose_map, height=256, width=192):
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palette = {
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'Background' : (0, 0, 0),
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'Hat' : (128, 0, 0),
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'Hair' : (255, 0, 0),
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'Glove' : (0, 85, 0),
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'Sunglasses' : (170, 0, 51),
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'UpperClothes' : (255, 85, 0),
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'Dress' : (0, 0, 85),
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'Coat' : (0, 119, 221),
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'Socks' : (85, 85, 0),
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'Pants' : (0, 85, 85),
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'Jumpsuits' : (85, 51, 0),
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'Scarf' : (52, 86, 128),
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'Skirt' : (0, 128, 0),
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'Face' : (0, 0, 255),
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'Left-arm' : (51, 170, 221),
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'Right-arm' : (0, 255, 255),
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'Left-leg' : (85, 255, 170),
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'Right-leg' : (170, 255, 85),
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'Left-shoe' : (255, 255, 0),
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'Right-shoe' : (255, 170, 0)
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}
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color2label = {val: key for key, val in palette.items()}
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head_labels = ['Hat', 'Hair', 'Sunglasses', 'Face', 'Pants', 'Skirt']
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segm_image = cv.cvtColor(segm_image, cv.COLOR_BGR2RGB)
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phead = np.zeros((1, height, width), dtype=np.float32)
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pose_shape = np.zeros((height, width), dtype=np.uint8)
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for r in range(height):
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for c in range(width):
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pixel = tuple(segm_image[r, c])
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if tuple(pixel) in color2label:
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if color2label[pixel] in head_labels:
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phead[0, r, c] = 1
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if color2label[pixel] != 'Background':
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pose_shape[r, c] = 255
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input_image = cv.dnn.blobFromImage(input_image, 1.0 / 127.5, (width, height), mean=(127.5, 127.5, 127.5), swapRB=True)
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input_image = input_image.squeeze(0)
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img_head = input_image * phead - (1 - phead)
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downsample = BilinearFilter()
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down = downsample.imaging_resample(pose_shape, width // 16, height // 16)
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res_shape = cv.resize(down, (width, height), cv.INTER_LINEAR)
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res_shape = cv.dnn.blobFromImage(res_shape, 1.0 / 127.5, mean=(127.5, 127.5, 127.5), swapRB=True)
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res_shape = res_shape.squeeze(0)
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agnostic = np.concatenate((res_shape, img_head, pose_map), axis=0)
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agnostic = np.expand_dims(agnostic, axis=0)
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return agnostic.astype(np.float32)
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def get_warped_cloth(self, cloth_img, agnostic, height=256, width=192):
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cloth = cv.dnn.blobFromImage(cloth_img, 1.0 / 127.5, (width, height), mean=(127.5, 127.5, 127.5), swapRB=True)
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self.gmm_net.setInput(agnostic, "input.1")
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self.gmm_net.setInput(cloth, "input.18")
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theta = self.gmm_net.forward()
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grid = self._generate_grid(theta)
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warped_cloth = self._bilinear_sampler(cloth, grid).astype(np.float32)
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return warped_cloth
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def get_tryon(self, agnostic, warp_cloth):
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inp = np.concatenate([agnostic, warp_cloth], axis=1)
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self.tom_net.setInput(inp)
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out = self.tom_net.forward()
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p_rendered, m_composite = np.split(out, [3], axis=1)
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p_rendered = np.tanh(p_rendered)
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m_composite = 1 / (1 + np.exp(-m_composite))
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p_tryon = warp_cloth * m_composite + p_rendered * (1 - m_composite)
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rgb_p_tryon = cv.cvtColor(p_tryon.squeeze(0).transpose(1, 2, 0), cv.COLOR_BGR2RGB)
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rgb_p_tryon = (rgb_p_tryon + 1) / 2
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return rgb_p_tryon
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def _compute_L_inverse(self, X, Y):
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N = X.shape[0]
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Xmat = np.tile(X, (1, N))
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Ymat = np.tile(Y, (1, N))
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P_dist_squared = np.power(Xmat - Xmat.transpose(1, 0), 2) + np.power(Ymat - Ymat.transpose(1, 0), 2)
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P_dist_squared[P_dist_squared == 0] = 1
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K = np.multiply(P_dist_squared, np.log(P_dist_squared))
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O = np.ones([N, 1], dtype=np.float32)
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Z = np.zeros([3, 3], dtype=np.float32)
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P = np.concatenate([O, X, Y], axis=1)
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first = np.concatenate((K, P), axis=1)
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second = np.concatenate((P.transpose(1, 0), Z), axis=1)
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L = np.concatenate((first, second), axis=0)
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Li = linalg.inv(L)
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return Li
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def _prepare_to_transform(self, out_h=256, out_w=192, grid_size=5):
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grid_X, grid_Y = np.meshgrid(np.linspace(-1, 1, out_w), np.linspace(-1, 1, out_h))
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grid_X = np.expand_dims(np.expand_dims(grid_X, axis=0), axis=3)
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grid_Y = np.expand_dims(np.expand_dims(grid_Y, axis=0), axis=3)
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axis_coords = np.linspace(-1, 1, grid_size)
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N = grid_size ** 2
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P_Y, P_X = np.meshgrid(axis_coords, axis_coords)
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P_X = np.reshape(P_X,(-1, 1))
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P_Y = np.reshape(P_Y,(-1, 1))
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P_X = np.expand_dims(np.expand_dims(np.expand_dims(P_X, axis=2), axis=3), axis=4).transpose(4, 1, 2, 3, 0)
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P_Y = np.expand_dims(np.expand_dims(np.expand_dims(P_Y, axis=2), axis=3), axis=4).transpose(4, 1, 2, 3, 0)
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return grid_X, grid_Y, N, P_X, P_Y
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def _expand_torch(self, X, shape):
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if len(X.shape) != len(shape):
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return X.flatten().reshape(shape)
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else:
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axis = [1 if src == dst else dst for src, dst in zip(X.shape, shape)]
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return np.tile(X, axis)
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def _apply_transformation(self, theta, points, N, P_X, P_Y):
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if len(theta.shape) == 2:
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theta = np.expand_dims(np.expand_dims(theta, axis=2), axis=3)
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batch_size = theta.shape[0]
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P_X_base = np.copy(P_X)
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P_Y_base = np.copy(P_Y)
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Li = self._compute_L_inverse(np.reshape(P_X, (N, -1)), np.reshape(P_Y, (N, -1)))
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Li = np.expand_dims(Li, axis=0)
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# split theta into point coordinates
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Q_X = np.squeeze(theta[:, :N, :, :], axis=3)
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Q_Y = np.squeeze(theta[:, N:, :, :], axis=3)
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Q_X += self._expand_torch(P_X_base, Q_X.shape)
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Q_Y += self._expand_torch(P_Y_base, Q_Y.shape)
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points_b = points.shape[0]
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points_h = points.shape[1]
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points_w = points.shape[2]
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P_X = self._expand_torch(P_X, (1, points_h, points_w, 1, N))
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P_Y = self._expand_torch(P_Y, (1, points_h, points_w, 1, N))
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W_X = self._expand_torch(Li[:,:N,:N], (batch_size, N, N)) @ Q_X
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W_Y = self._expand_torch(Li[:,:N,:N], (batch_size, N, N)) @ Q_Y
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W_X = np.expand_dims(np.expand_dims(W_X, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
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W_X = np.repeat(W_X, points_h, axis=1)
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W_X = np.repeat(W_X, points_w, axis=2)
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W_Y = np.expand_dims(np.expand_dims(W_Y, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
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W_Y = np.repeat(W_Y, points_h, axis=1)
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W_Y = np.repeat(W_Y, points_w, axis=2)
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A_X = self._expand_torch(Li[:, N:, :N], (batch_size, 3, N)) @ Q_X
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A_Y = self._expand_torch(Li[:, N:, :N], (batch_size, 3, N)) @ Q_Y
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A_X = np.expand_dims(np.expand_dims(A_X, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
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A_X = np.repeat(A_X, points_h, axis=1)
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A_X = np.repeat(A_X, points_w, axis=2)
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A_Y = np.expand_dims(np.expand_dims(A_Y, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
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A_Y = np.repeat(A_Y, points_h, axis=1)
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A_Y = np.repeat(A_Y, points_w, axis=2)
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points_X_for_summation = np.expand_dims(np.expand_dims(points[:, :, :, 0], axis=3), axis=4)
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points_X_for_summation = self._expand_torch(points_X_for_summation, points[:, :, :, 0].shape + (1, N))
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points_Y_for_summation = np.expand_dims(np.expand_dims(points[:, :, :, 1], axis=3), axis=4)
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points_Y_for_summation = self._expand_torch(points_Y_for_summation, points[:, :, :, 0].shape + (1, N))
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if points_b == 1:
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delta_X = points_X_for_summation - P_X
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delta_Y = points_Y_for_summation - P_Y
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else:
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delta_X = points_X_for_summation - self._expand_torch(P_X, points_X_for_summation.shape)
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delta_Y = points_Y_for_summation - self._expand_torch(P_Y, points_Y_for_summation.shape)
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dist_squared = np.power(delta_X, 2) + np.power(delta_Y, 2)
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dist_squared[dist_squared == 0] = 1
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U = np.multiply(dist_squared, np.log(dist_squared))
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points_X_batch = np.expand_dims(points[:,:,:,0], axis=3)
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points_Y_batch = np.expand_dims(points[:,:,:,1], axis=3)
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if points_b == 1:
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points_X_batch = self._expand_torch(points_X_batch, (batch_size, ) + points_X_batch.shape[1:])
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points_Y_batch = self._expand_torch(points_Y_batch, (batch_size, ) + points_Y_batch.shape[1:])
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points_X_prime = A_X[:,:,:,:,0]+ \
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np.multiply(A_X[:,:,:,:,1], points_X_batch) + \
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np.multiply(A_X[:,:,:,:,2], points_Y_batch) + \
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np.sum(np.multiply(W_X, self._expand_torch(U, W_X.shape)), 4)
|
|
|
|
points_Y_prime = A_Y[:,:,:,:,0]+ \
|
|
np.multiply(A_Y[:,:,:,:,1], points_X_batch) + \
|
|
np.multiply(A_Y[:,:,:,:,2], points_Y_batch) + \
|
|
np.sum(np.multiply(W_Y, self._expand_torch(U, W_Y.shape)), 4)
|
|
|
|
return np.concatenate((points_X_prime, points_Y_prime), 3)
|
|
|
|
def _generate_grid(self, theta):
|
|
grid_X, grid_Y, N, P_X, P_Y = self._prepare_to_transform()
|
|
warped_grid = self._apply_transformation(theta, np.concatenate((grid_X, grid_Y), axis=3), N, P_X, P_Y)
|
|
return warped_grid
|
|
|
|
def _bilinear_sampler(self, img, grid):
|
|
x, y = grid[:,:,:,0], grid[:,:,:,1]
|
|
|
|
H = img.shape[2]
|
|
W = img.shape[3]
|
|
max_y = H - 1
|
|
max_x = W - 1
|
|
|
|
# rescale x and y to [0, W-1/H-1]
|
|
x = 0.5 * (x + 1.0) * (max_x - 1)
|
|
y = 0.5 * (y + 1.0) * (max_y - 1)
|
|
|
|
# grab 4 nearest corner points for each (x_i, y_i)
|
|
x0 = np.floor(x).astype(int)
|
|
x1 = x0 + 1
|
|
y0 = np.floor(y).astype(int)
|
|
y1 = y0 + 1
|
|
|
|
# calculate deltas
|
|
wa = (x1 - x) * (y1 - y)
|
|
wb = (x1 - x) * (y - y0)
|
|
wc = (x - x0) * (y1 - y)
|
|
wd = (x - x0) * (y - y0)
|
|
|
|
# clip to range [0, H-1/W-1] to not violate img boundaries
|
|
x0 = np.clip(x0, 0, max_x)
|
|
x1 = np.clip(x1, 0, max_x)
|
|
y0 = np.clip(y0, 0, max_y)
|
|
y1 = np.clip(y1, 0, max_y)
|
|
|
|
# get pixel value at corner coords
|
|
img = img.reshape(-1, H, W)
|
|
Ia = img[:, y0, x0].swapaxes(0, 1)
|
|
Ib = img[:, y1, x0].swapaxes(0, 1)
|
|
Ic = img[:, y0, x1].swapaxes(0, 1)
|
|
Id = img[:, y1, x1].swapaxes(0, 1)
|
|
|
|
wa = np.expand_dims(wa, axis=0)
|
|
wb = np.expand_dims(wb, axis=0)
|
|
wc = np.expand_dims(wc, axis=0)
|
|
wd = np.expand_dims(wd, axis=0)
|
|
|
|
# compute output
|
|
out = wa*Ia + wb*Ib + wc*Ic + wd*Id
|
|
return out
|
|
|
|
|
|
class CorrelationLayer(object):
|
|
def __init__(self, params, blobs):
|
|
super(CorrelationLayer, self).__init__()
|
|
|
|
def getMemoryShapes(self, inputs):
|
|
fetureAShape = inputs[0]
|
|
b, _, h, w = fetureAShape
|
|
return [[b, h * w, h, w]]
|
|
|
|
def forward(self, inputs):
|
|
feature_A, feature_B = inputs
|
|
b, c, h, w = feature_A.shape
|
|
feature_A = feature_A.transpose(0, 1, 3, 2)
|
|
feature_A = np.reshape(feature_A, (b, c, h * w))
|
|
feature_B = np.reshape(feature_B, (b, c, h * w))
|
|
feature_B = feature_B.transpose(0, 2, 1)
|
|
feature_mul = feature_B @ feature_A
|
|
feature_mul= np.reshape(feature_mul, (b, h, w, h * w))
|
|
feature_mul = feature_mul.transpose(0, 1, 3, 2)
|
|
correlation_tensor = feature_mul.transpose(0, 2, 1, 3)
|
|
correlation_tensor = np.ascontiguousarray(correlation_tensor)
|
|
return [correlation_tensor]
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if not os.path.isfile(args.gmm_model):
|
|
raise OSError("GMM model not exist")
|
|
if not os.path.isfile(args.tom_model):
|
|
raise OSError("TOM model not exist")
|
|
if not os.path.isfile(args.segmentation_model):
|
|
raise OSError("Segmentation model not exist")
|
|
if not os.path.isfile(findFile(args.openpose_proto)):
|
|
raise OSError("OpenPose proto not exist")
|
|
if not os.path.isfile(findFile(args.openpose_model)):
|
|
raise OSError("OpenPose model not exist")
|
|
|
|
person_img = cv.imread(args.input_image)
|
|
ratio = 256 / 192
|
|
inp_h, inp_w, _ = person_img.shape
|
|
current_ratio = inp_h / inp_w
|
|
if current_ratio > ratio:
|
|
center_h = inp_h // 2
|
|
out_h = inp_w * ratio
|
|
start = int(center_h - out_h // 2)
|
|
end = int(center_h + out_h // 2)
|
|
person_img = person_img[start:end, ...]
|
|
else:
|
|
center_w = inp_w // 2
|
|
out_w = inp_h / ratio
|
|
start = int(center_w - out_w // 2)
|
|
end = int(center_w + out_w // 2)
|
|
person_img = person_img[:, start:end, :]
|
|
|
|
cloth_img = cv.imread(args.input_cloth)
|
|
pose = get_pose_map(person_img, findFile(args.openpose_proto),
|
|
findFile(args.openpose_model), args.backend, args.target)
|
|
segm_image = parse_human(person_img, args.segmentation_model)
|
|
segm_image = cv.resize(segm_image, (192, 256), cv.INTER_LINEAR)
|
|
|
|
cv.dnn_registerLayer('Correlation', CorrelationLayer)
|
|
|
|
model = CpVton(args.gmm_model, args.tom_model, args.backend, args.target)
|
|
agnostic = model.prepare_agnostic(segm_image, person_img, pose)
|
|
warped_cloth = model.get_warped_cloth(cloth_img, agnostic)
|
|
output = model.get_tryon(agnostic, warped_cloth)
|
|
|
|
cv.dnn_unregisterLayer('Correlation')
|
|
|
|
winName = 'Virtual Try-On'
|
|
cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
|
|
cv.imshow(winName, output)
|
|
cv.waitKey()
|