Merge pull request #16472 from l-bat:cp_vton

Add CP-VTON sample

* Support resize from PyTorch

* Add CP-VTON sample

* Fix downsampling

* Fix test

* Add model links

* Add default args

* Speed up resize

* Fix TOM link

* Add default args

* Fix comments

* Set aspect ratio for input

* Update links

* Check files exist

modules/dnn/src/layers/resize_layer.cpp

@@ -35,7 +35,7 @@ public:
             CV_Assert(params.has("zoom_factor_x") && params.has("zoom_factor_y"));
         }
         interpolation = params.get<String>("interpolation");
-        CV_Assert(interpolation == "nearest" || interpolation == "bilinear");
+        CV_Assert(interpolation == "nearest" || interpolation == "opencv_linear" || interpolation == "bilinear");
 
         alignCorners = params.get<bool>("align_corners", false);
     }
@@ -106,14 +106,15 @@ public:
 
         Mat& inp = inputs[0];
         Mat& out = outputs[0];
-        if (interpolation == "nearest")
+        if (interpolation == "nearest" || interpolation == "opencv_linear")
         {
+            InterpolationFlags mode = interpolation == "nearest" ? INTER_NEAREST : INTER_LINEAR;
             for (size_t n = 0; n < inputs[0].size[0]; ++n)
             {
                 for (size_t ch = 0; ch < inputs[0].size[1]; ++ch)
                 {
                     resize(getPlane(inp, n, ch), getPlane(out, n, ch),
-                           Size(outWidth, outHeight), 0, 0, INTER_NEAREST);
+                           Size(outWidth, outHeight), 0, 0, mode);
                 }
             }
         }

modules/dnn/src/onnx/onnx_importer.cpp

@@ -980,6 +980,15 @@ void ONNXImporter::populateNet(Net dstNet)
                 replaceLayerParam(layerParams, "width_scale", "zoom_factor_x");
             }
             replaceLayerParam(layerParams, "mode", "interpolation");
+
+            if (layerParams.get<String>("interpolation") == "linear" && framework_name == "pytorch") {
+                layerParams.type = "Resize";
+                Mat scales = getBlob(node_proto, constBlobs, 1);
+                CV_Assert(scales.total() == 4);
+                layerParams.set("interpolation", "opencv_linear");
+                layerParams.set("zoom_factor_y", scales.at<float>(2));
+                layerParams.set("zoom_factor_x", scales.at<float>(3));
+            }
         }
         else if (layer_type == "LogSoftmax")
         {

modules/dnn/test/test_onnx_importer.cpp

@@ -311,6 +311,9 @@ TEST_P(Test_ONNX_layers, Padding)
 TEST_P(Test_ONNX_layers, Resize)
 {
     testONNXModels("resize_nearest");
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
+    testONNXModels("resize_bilinear");
 }
 
 TEST_P(Test_ONNX_layers, MultyInputs)

samples/dnn/human_parsing.py

@@ -40,6 +40,7 @@ Follow these steps if you want to convert the original model yourself:
 '''
 
 import argparse
+import os.path
 import numpy as np
 import cv2 as cv
 
@@ -48,12 +49,11 @@ backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.
 targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD)
 
 
-def preprocess(image_path):
+def preprocess(image):
     """
     Create 4-dimensional blob from image and flip image
-    :param image_path: path to input image
+    :param image: 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
@@ -137,15 +137,15 @@ def decode_labels(gray_image):
     return segm
 
 
-def parse_human(image_path, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
+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_path: path to input image
+    :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_path)
+    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))
@@ -157,7 +157,7 @@ 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', required=True, help='Path to pb model.')
+    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), "
@@ -171,7 +171,11 @@ if __name__ == '__main__':
                              '%d: VPU' % targets)
     args, _ = parser.parse_known_args()
 
-    output = parse_human(args.input, args.model, args.backend, args.target)
+    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)

samples/dnn/virtual_try_on.py

+#!/usr/bin/env python3
+'''
+You can download the Geometric Matching Module model from https://www.dropbox.com/s/tyhc73xa051grjp/cp_vton_gmm.onnx?dl=0
+You can download the Try-On Module model from https://www.dropbox.com/s/q2x97ve2h53j66k/cp_vton_tom.onnx?dl=0
+You can download the cloth segmentation model from https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
+You can find the OpenPose proto in opencv_extra/testdata/dnn/openpose_pose_coco.prototxt
+and get .caffemodel using opencv_extra/testdata/dnn/download_models.py
+'''
+
+import argparse
+import os.path
+import numpy as np
+import cv2 as cv
+
+from numpy import linalg
+from common import findFile
+from human_parsing import parse_human
+
+backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_HALIDE, 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 virtial try-on using CP-VTON',
+                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--input_image', '-i', required=True, help='Path to image with person.')
+parser.add_argument('--input_cloth', '-c', required=True, help='Path to target cloth image')
+parser.add_argument('--gmm_model', '-gmm', default='cp_vton_gmm.onnx', help='Path to Geometric Matching Module .onnx model.')
+parser.add_argument('--tom_model', '-tom', default='cp_vton_tom.onnx', help='Path to Try-On Module .onnx model.')
+parser.add_argument('--segmentation_model', default='lip_jppnet_384.pb', help='Path to cloth segmentation .pb model.')
+parser.add_argument('--openpose_proto', default='openpose_pose_coco.prototxt', help='Path to OpenPose .prototxt model was trained on COCO dataset.')
+parser.add_argument('--openpose_model', default='openpose_pose_coco.caffemodel', help='Path to OpenPose .caffemodel model was trained on COCO dataset.')
+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: Halide language (http://halide-lang.org/), "
+                            "%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()
+
+
+def get_pose_map(image, proto_path, model_path, backend, target, height=256, width=192):
+    radius = 5
+    inp = cv.dnn.blobFromImage(image, 1.0 / 255, (width, height))
+
+    net = cv.dnn.readNet(proto_path, model_path)
+    net.setPreferableBackend(backend)
+    net.setPreferableTarget(target)
+    net.setInput(inp)
+    out = net.forward()
+
+    threshold = 0.1
+    _, out_c, out_h, out_w = out.shape
+    pose_map = np.zeros((height, width, out_c - 1))
+    # last label: Background
+    for i in range(0, out.shape[1] - 1):
+        heatMap = out[0, i, :, :]
+        keypoint = np.full((height, width), -1)
+        _, conf, _, point = cv.minMaxLoc(heatMap)
+        x = width * point[0] // out_w
+        y = height * point[1] // out_h
+        if conf > threshold and x > 0 and y > 0:
+            keypoint[y - radius:y + radius, x - radius:x + radius] = 1
+        pose_map[:, :, i] = keypoint
+
+    pose_map = pose_map.transpose(2, 0, 1)
+    return pose_map
+
+
+class BilinearFilter(object):
+    """
+    PIL bilinear resize implementation
+    image = image.resize((image_width // 16, image_height // 16), Image.BILINEAR)
+    """
+    def _precompute_coeffs(self, inSize, outSize):
+        filterscale = max(1.0, inSize / outSize)
+        ksize = int(np.ceil(filterscale)) * 2 + 1
+
+        kk = np.zeros(shape=(outSize * ksize, ), dtype=np.float32)
+        bounds = np.empty(shape=(outSize * 2, ), dtype=np.int32)
+
+        centers = (np.arange(outSize) + 0.5) * filterscale + 0.5
+        bounds[::2] = np.where(centers - filterscale < 0, 0, centers - filterscale)
+        bounds[1::2] = np.where(centers + filterscale > inSize, inSize, centers + filterscale) - bounds[::2]
+        xmins = bounds[::2] - centers + 1
+
+        points = np.array([np.arange(row) + xmins[i] for i, row in enumerate(bounds[1::2])]) / filterscale
+        for xx in range(0, outSize):
+            point = points[xx]
+            bilinear = np.where(point < 1.0, 1.0 - abs(point), 0.0)
+            ww = np.sum(bilinear)
+            kk[xx * ksize : xx * ksize + bilinear.size] = np.where(ww == 0.0, bilinear, bilinear / ww)
+        return bounds, kk, ksize
+
+    def _resample_horizontal(self, out, img, ksize, bounds, kk):
+        for yy in range(0, out.shape[0]):
+            for xx in range(0, out.shape[1]):
+                xmin = bounds[xx * 2 + 0]
+                xmax = bounds[xx * 2 + 1]
+                k = kk[xx * ksize : xx * ksize + xmax]
+                out[yy, xx] = np.round(np.sum(img[yy, xmin : xmin + xmax] * k))
+
+    def _resample_vertical(self, out, img, ksize, bounds, kk):
+        for yy in range(0, out.shape[0]):
+            ymin = bounds[yy * 2 + 0]
+            ymax = bounds[yy * 2 + 1]
+            k = kk[yy * ksize: yy * ksize + ymax]
+            out[yy] = np.round(np.sum(img[ymin : ymin + ymax, 0:out.shape[1]] * k[:, np.newaxis], axis=0))
+
+    def imaging_resample(self, img, xsize, ysize):
+        height, width, *args = img.shape
+        bounds_horiz, kk_horiz, ksize_horiz = self._precompute_coeffs(width, xsize)
+        bounds_vert, kk_vert, ksize_vert    = self._precompute_coeffs(height, ysize)
+
+        out_hor = np.empty((img.shape[0], xsize), dtype=np.uint8)
+        self._resample_horizontal(out_hor, img, ksize_horiz, bounds_horiz, kk_horiz)
+        out = np.empty((ysize, xsize), dtype=np.uint8)
+        self._resample_vertical(out, out_hor, ksize_vert, bounds_vert, kk_vert)
+        return out
+
+
+class CpVton(object):
+    def __init__(self, gmm_model, tom_model, backend, target):
+        super(CpVton, self).__init__()
+        self.gmm_net = cv.dnn.readNet(gmm_model)
+        self.tom_net = cv.dnn.readNet(tom_model)
+        self.gmm_net.setPreferableBackend(backend)
+        self.gmm_net.setPreferableTarget(target)
+        self.tom_net.setPreferableBackend(backend)
+        self.tom_net.setPreferableTarget(target)
+
+    def prepare_agnostic(self, segm_image, input_image, pose_map, height=256, width=192):
+        palette = {
+            'Background'   : (0, 0, 0),
+            'Hat'          : (128, 0, 0),
+            'Hair'         : (255, 0, 0),
+            'Glove'        : (0, 85, 0),
+            'Sunglasses'   : (170, 0, 51),
+            'UpperClothes' : (255, 85, 0),
+            'Dress'        : (0, 0, 85),
+            'Coat'         : (0, 119, 221),
+            'Socks'        : (85, 85, 0),
+            'Pants'        : (0, 85, 85),
+            'Jumpsuits'    : (85, 51, 0),
+            'Scarf'        : (52, 86, 128),
+            'Skirt'        : (0, 128, 0),
+            'Face'         : (0, 0, 255),
+            'Left-arm'     : (51, 170, 221),
+            'Right-arm'    : (0, 255, 255),
+            'Left-leg'     : (85, 255, 170),
+            'Right-leg'    : (170, 255, 85),
+            'Left-shoe'    : (255, 255, 0),
+            'Right-shoe'   : (255, 170, 0)
+        }
+        color2label = {val: key for key, val in palette.items()}
+        head_labels = ['Hat', 'Hair', 'Sunglasses', 'Face', 'Pants', 'Skirt']
+
+        segm_image = cv.cvtColor(segm_image, cv.COLOR_BGR2RGB)
+        phead = np.zeros((1, height, width), dtype=np.float32)
+        pose_shape = np.zeros((height, width), dtype=np.uint8)
+        for r in range(height):
+            for c in range(width):
+                pixel = tuple(segm_image[r, c])
+                if tuple(pixel) in color2label:
+                    if color2label[pixel] in head_labels:
+                        phead[0, r, c] = 1
+                    if color2label[pixel] != 'Background':
+                        pose_shape[r, c] = 255
+
+        input_image = cv.dnn.blobFromImage(input_image, 1.0 / 127.5, (width, height), mean=(127.5, 127.5, 127.5), swapRB=True)
+        input_image = input_image.squeeze(0)
+
+        img_head = input_image * phead - (1 - phead)
+
+        downsample = BilinearFilter()
+        down = downsample.imaging_resample(pose_shape, width // 16, height // 16)
+        res_shape = cv.resize(down, (width, height), cv.INTER_LINEAR)
+
+        res_shape = cv.dnn.blobFromImage(res_shape, 1.0 / 127.5, mean=(127.5, 127.5, 127.5), swapRB=True)
+        res_shape = res_shape.squeeze(0)
+
+        agnostic = np.concatenate((res_shape, img_head, pose_map), axis=0)
+        agnostic = np.expand_dims(agnostic, axis=0)
+        return agnostic
+
+    def get_warped_cloth(self, cloth_img, agnostic, height=256, width=192):
+        cloth = cv.dnn.blobFromImage(cloth_img, 1.0 / 127.5, (width, height), mean=(127.5, 127.5, 127.5), swapRB=True)
+
+        self.gmm_net.setInput(agnostic, "input.1")
+        self.gmm_net.setInput(cloth, "input.18")
+        theta = self.gmm_net.forward()
+
+        grid = self._generate_grid(theta)
+        warped_cloth = self._bilinear_sampler(cloth, grid).astype(np.float32)
+        return warped_cloth
+
+    def get_tryon(self, agnostic, warp_cloth):
+        inp = np.concatenate([agnostic, warp_cloth], axis=1)
+        self.tom_net.setInput(inp)
+        out = self.tom_net.forward()
+
+        p_rendered, m_composite = np.split(out, [3], axis=1)
+        p_rendered = np.tanh(p_rendered)
+        m_composite = 1 / (1 + np.exp(-m_composite))
+
+        p_tryon = warp_cloth * m_composite + p_rendered * (1 - m_composite)
+        rgb_p_tryon = cv.cvtColor(p_tryon.squeeze(0).transpose(1, 2, 0), cv.COLOR_BGR2RGB)
+        rgb_p_tryon = (rgb_p_tryon + 1) / 2
+        return rgb_p_tryon
+
+    def _compute_L_inverse(self, X, Y):
+        N = X.shape[0]
+
+        Xmat = np.tile(X, (1, N))
+        Ymat = np.tile(Y, (1, N))
+        P_dist_squared = np.power(Xmat - Xmat.transpose(1, 0), 2) + np.power(Ymat - Ymat.transpose(1, 0), 2)
+
+        P_dist_squared[P_dist_squared == 0] = 1
+        K = np.multiply(P_dist_squared, np.log(P_dist_squared))
+
+        O = np.ones([N, 1], dtype=np.float32)
+        Z = np.zeros([3, 3], dtype=np.float32)
+        P = np.concatenate([O, X, Y], axis=1)
+        first = np.concatenate((K, P), axis=1)
+        second = np.concatenate((P.transpose(1, 0), Z), axis=1)
+        L = np.concatenate((first, second), axis=0)
+        Li = linalg.inv(L)
+        return Li
+
+    def _prepare_to_transform(self, out_h=256, out_w=192, grid_size=5):
+        grid = np.zeros([out_h, out_w, 3], dtype=np.float32)
+        grid_X, grid_Y = np.meshgrid(np.linspace(-1, 1, out_w), np.linspace(-1, 1, out_h))
+        grid_X = np.expand_dims(np.expand_dims(grid_X, axis=0), axis=3)
+        grid_Y = np.expand_dims(np.expand_dims(grid_Y, axis=0), axis=3)
+
+        axis_coords = np.linspace(-1, 1, grid_size)
+        N = grid_size ** 2
+        P_Y, P_X = np.meshgrid(axis_coords, axis_coords)
+
+        P_X = np.reshape(P_X,(-1, 1))
+        P_Y = np.reshape(P_Y,(-1, 1))
+
+        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)
+        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)
+        return grid_X, grid_Y, N, P_X, P_Y
+
+    def _expand_torch(self, X, shape):
+        if len(X.shape) != len(shape):
+            return X.flatten().reshape(shape)
+        else:
+            axis = [1 if src == dst else dst for src, dst in zip(X.shape, shape)]
+            return np.tile(X, axis)
+
+    def _apply_transformation(self, theta, points, N, P_X, P_Y):
+        if len(theta.shape) == 2:
+            theta = np.expand_dims(np.expand_dims(theta, axis=2), axis=3)
+
+        batch_size = theta.shape[0]
+
+        P_X_base = np.copy(P_X)
+        P_Y_base = np.copy(P_Y)
+
+        Li = self._compute_L_inverse(np.reshape(P_X, (N, -1)), np.reshape(P_Y, (N, -1)))
+        Li = np.expand_dims(Li, axis=0)
+
+        # split theta into point coordinates
+        Q_X = np.squeeze(theta[:, :N, :, :], axis=3)
+        Q_Y = np.squeeze(theta[:, N:, :, :], axis=3)
+
+        Q_X += self._expand_torch(P_X_base, Q_X.shape)
+        Q_Y += self._expand_torch(P_Y_base, Q_Y.shape)
+
+        points_b = points.shape[0]
+        points_h = points.shape[1]
+        points_w = points.shape[2]
+
+        P_X = self._expand_torch(P_X, (1, points_h, points_w, 1, N))
+        P_Y = self._expand_torch(P_Y, (1, points_h, points_w, 1, N))
+
+        W_X = self._expand_torch(Li[:,:N,:N], (batch_size, N, N)) @ Q_X
+        W_Y = self._expand_torch(Li[:,:N,:N], (batch_size, N, N)) @ Q_Y
+
+        W_X = np.expand_dims(np.expand_dims(W_X, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
+        W_X = np.repeat(W_X, points_h, axis=1)
+        W_X = np.repeat(W_X, points_w, axis=2)
+
+        W_Y = np.expand_dims(np.expand_dims(W_Y, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
+        W_Y = np.repeat(W_Y, points_h, axis=1)
+        W_Y = np.repeat(W_Y, points_w, axis=2)
+
+        A_X = self._expand_torch(Li[:, N:, :N], (batch_size, 3, N)) @ Q_X
+        A_Y = self._expand_torch(Li[:, N:, :N], (batch_size, 3, N)) @ Q_Y
+
+        A_X = np.expand_dims(np.expand_dims(A_X, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
+        A_X = np.repeat(A_X, points_h, axis=1)
+        A_X = np.repeat(A_X, points_w, axis=2)
+
+        A_Y = np.expand_dims(np.expand_dims(A_Y, axis=3), axis=4).transpose(0, 4, 2, 3, 1)
+        A_Y = np.repeat(A_Y, points_h, axis=1)
+        A_Y = np.repeat(A_Y, points_w, axis=2)
+
+        points_X_for_summation = np.expand_dims(np.expand_dims(points[:, :, :, 0], axis=3), axis=4)
+        points_X_for_summation = self._expand_torch(points_X_for_summation, points[:, :, :, 0].shape + (1, N))
+
+        points_Y_for_summation = np.expand_dims(np.expand_dims(points[:, :, :, 1], axis=3), axis=4)
+        points_Y_for_summation = self._expand_torch(points_Y_for_summation, points[:, :, :, 0].shape + (1, N))
+
+        if points_b == 1:
+            delta_X = points_X_for_summation - P_X
+            delta_Y = points_Y_for_summation - P_Y
+        else:
+            delta_X = points_X_for_summation - self._expand_torch(P_X, points_X_for_summation.shape)
+            delta_Y = points_Y_for_summation - self._expand_torch(P_Y, points_Y_for_summation.shape)
+
+        dist_squared = np.power(delta_X, 2) + np.power(delta_Y, 2)
+        dist_squared[dist_squared == 0] = 1
+        U = np.multiply(dist_squared, np.log(dist_squared))
+
+        points_X_batch = np.expand_dims(points[:,:,:,0], axis=3)
+        points_Y_batch = np.expand_dims(points[:,:,:,1], axis=3)
+
+        if points_b == 1:
+            points_X_batch = self._expand_torch(points_X_batch, (batch_size, ) + points_X_batch.shape[1:])
+            points_Y_batch = self._expand_torch(points_Y_batch, (batch_size, ) + points_Y_batch.shape[1:])
+
+        points_X_prime = A_X[:,:,:,:,0]+ \
+                        np.multiply(A_X[:,:,:,:,1], points_X_batch) + \
+                        np.multiply(A_X[:,:,:,:,2], points_Y_batch) + \
+                        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, c, 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()