🥔 ⁉️ ⛄️ OpenCV中的虚拟试衣间 🦊 👋🏽 👉

您是否有喜欢在线商店中的东西，但又不想在不尝试的情况下购买它？当然，在某些商店中，有机会在付款后订购后试穿衣服。但是，据统计，服装和鞋类在线商店中的在线订单份额每年都在增长，但退货份额也在增长，达到50％至70％-这些巨大的物流成本可以通过使用在线试衣间而大大降低。想象一下，您上传了照片，选择了衣服并将其传输到图像中。虚拟的鞋子试衣间已经存在，它们工作非常成功。前一段时间，我们对这个话题很感兴趣，衣服呢？这样的作品也存在，但成功的程度要低得多；在许多作品中，除了本文之外，什么都找不到，一个人只能梦想一个可行的例子。我们决定修复此问题并支持OpenCV库中的网络之一。可以看出来virtual_try_on.py示例。

结果并不完美，但是在这一方面被认为是相当不错的。

如果您想了解虚拟试衣间的工作原理以及将模型集成到OpenCV中时遇到的困难-欢迎来到猫！

2019 CP-VTON . CP-VTON , , (, ). 3D , 3D-. . github. CP-VTON , , .

CP-VTON : GMM (Geometric Matching Module) — TOM (Try-On Module) — .

GMM $(I, c, c_t )$ , TOM — $(I, \hat{c}, I_t)$ , $I$ — , $c$ — , $\hat{c}$ — GMM, $I_t$ — ground truth ( $c$ ), $c_t$ — ground truth ( , $I_t$ ). , $I_t$ $c$ . VITON, $I$ $p$ . $p$ , , , , (, , ). . , GMM $(p, c)$ $\hat{c}$ , $c_t$ . TOM $(p, \hat{c})$ , $I_o$ $I_t$ .

$p$ . , . OpenPose. . LIP_JPPNet. OpenCV () sample human_parsing.py.

, — , : , , . , .

预处理

GMM . , , $L_2-$ . . . , . $\hat{c}$ $c_t$ $L_1-loss$ .

gmm

TOM . Unet. , . Unet , . , Unet . , . Upsample . (encoder) VGG-19. . Unet $I_r$ $M$ . $c$ $I_r$ , $(1 - M)$ $I_o$ .

I_{o} = M ⊙ c + (1 - M) ⊙ I_{r}

$I_o=M\odot c + (1 - M)\odot I_r$

$\odot$ — .

Try-On — $I_o$ $I_t$ . . , , . $L_1- loss$ perceptual loss. VGG , . VGG — , .

OpenCV

. json , OpenPose Caffe, LIP.

, , .

python3 virtual_try_on.py -i person_img.jpg -c cloth.jpg

OpenCV PIL. . LIP , . CP-VTON . , :

shape = (segm > 0).astype(np.float32)
head  = (segm == 1).astype(np.float32) + \
        (segm == 2).astype(np.float32) + \
        (segm == 4).astype(np.float32) + \
        (segm == 13).astype(np.float32)
cloth = (segm == 5).astype(np.float32) + \
        (segm == 6).astype(np.float32) + \
        (segm == 7).astype(np.float32)

, PIL , . human_colormap.mat. ? Matlab . , scipy . () .

– . 16 , .

mask = mask.resize((width // 16, height // 16), Image.BILINEAR)
mask = mask.resize((width, height), Image.BILINEAR)

. . , OpenCV. PIL resize cv.resize .

, PIL resize, — cv.resize.

, ? .

mask_pil mask_ocv

, PIL resize, — cv.resize.

, . ? , . , – . , bilinear resize bilinear, area. scale factor, 33 = 16 * 2 + 1, OpenCV – 3. , , . . . , . . :

PIL

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

4 , , OpenCV. . , . , . 256 192, . sample . , - , .

P.S. 2020 OpenCV 20-. . !

OpenCV中的虚拟试衣间

OpenCV

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