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| """ | |
| Modified from https://github.com/mlomnitz/DiffJPEG | |
| For images not divisible by 8 | |
| https://dsp.stackexchange.com/questions/35339/jpeg-dct-padding/35343#35343 | |
| """ | |
| import itertools | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| # ------------------------ utils ------------------------# | |
| y_table = np.array( | |
| [[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], | |
| [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], | |
| [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], | |
| dtype=np.float32).T | |
| y_table = nn.Parameter(torch.from_numpy(y_table)) | |
| c_table = np.empty((8, 8), dtype=np.float32) | |
| c_table.fill(99) | |
| c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], [24, 26, 56, 99], [47, 66, 99, 99]]).T | |
| c_table = nn.Parameter(torch.from_numpy(c_table)) | |
| def diff_round(x): | |
| """ Differentiable rounding function | |
| """ | |
| return torch.round(x) + (x - torch.round(x))**3 | |
| def quality_to_factor(quality): | |
| """ Calculate factor corresponding to quality | |
| Args: | |
| quality(float): Quality for jpeg compression. | |
| Returns: | |
| float: Compression factor. | |
| """ | |
| if quality < 50: | |
| quality = 5000. / quality | |
| else: | |
| quality = 200. - quality * 2 | |
| return quality / 100. | |
| # ------------------------ compression ------------------------# | |
| class RGB2YCbCrJpeg(nn.Module): | |
| """ Converts RGB image to YCbCr | |
| """ | |
| def __init__(self): | |
| super(RGB2YCbCrJpeg, self).__init__() | |
| matrix = np.array([[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], [0.5, -0.418688, -0.081312]], | |
| dtype=np.float32).T | |
| self.shift = nn.Parameter(torch.tensor([0., 128., 128.])) | |
| self.matrix = nn.Parameter(torch.from_numpy(matrix)) | |
| def forward(self, image): | |
| """ | |
| Args: | |
| image(Tensor): batch x 3 x height x width | |
| Returns: | |
| Tensor: batch x height x width x 3 | |
| """ | |
| image = image.permute(0, 2, 3, 1) | |
| result = torch.tensordot(image, self.matrix, dims=1) + self.shift | |
| return result.view(image.shape) | |
| class ChromaSubsampling(nn.Module): | |
| """ Chroma subsampling on CbCr channels | |
| """ | |
| def __init__(self): | |
| super(ChromaSubsampling, self).__init__() | |
| def forward(self, image): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width x 3 | |
| Returns: | |
| y(tensor): batch x height x width | |
| cb(tensor): batch x height/2 x width/2 | |
| cr(tensor): batch x height/2 x width/2 | |
| """ | |
| image_2 = image.permute(0, 3, 1, 2).clone() | |
| cb = F.avg_pool2d(image_2[:, 1, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) | |
| cr = F.avg_pool2d(image_2[:, 2, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) | |
| cb = cb.permute(0, 2, 3, 1) | |
| cr = cr.permute(0, 2, 3, 1) | |
| return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) | |
| class BlockSplitting(nn.Module): | |
| """ Splitting image into patches | |
| """ | |
| def __init__(self): | |
| super(BlockSplitting, self).__init__() | |
| self.k = 8 | |
| def forward(self, image): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width | |
| Returns: | |
| Tensor: batch x h*w/64 x h x w | |
| """ | |
| height, _ = image.shape[1:3] | |
| batch_size = image.shape[0] | |
| image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) | |
| image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) | |
| return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) | |
| class DCT8x8(nn.Module): | |
| """ Discrete Cosine Transformation | |
| """ | |
| def __init__(self): | |
| super(DCT8x8, self).__init__() | |
| tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) | |
| for x, y, u, v in itertools.product(range(8), repeat=4): | |
| tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos((2 * y + 1) * v * np.pi / 16) | |
| alpha = np.array([1. / np.sqrt(2)] + [1] * 7) | |
| self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) | |
| self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float()) | |
| def forward(self, image): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width | |
| Returns: | |
| Tensor: batch x height x width | |
| """ | |
| image = image - 128 | |
| result = self.scale * torch.tensordot(image, self.tensor, dims=2) | |
| result.view(image.shape) | |
| return result | |
| class YQuantize(nn.Module): | |
| """ JPEG Quantization for Y channel | |
| Args: | |
| rounding(function): rounding function to use | |
| """ | |
| def __init__(self, rounding): | |
| super(YQuantize, self).__init__() | |
| self.rounding = rounding | |
| self.y_table = y_table | |
| def forward(self, image, factor=1): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width | |
| Returns: | |
| Tensor: batch x height x width | |
| """ | |
| if isinstance(factor, (int, float)): | |
| image = image.float() / (self.y_table * factor) | |
| else: | |
| b = factor.size(0) | |
| table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) | |
| image = image.float() / table | |
| image = self.rounding(image) | |
| return image | |
| class CQuantize(nn.Module): | |
| """ JPEG Quantization for CbCr channels | |
| Args: | |
| rounding(function): rounding function to use | |
| """ | |
| def __init__(self, rounding): | |
| super(CQuantize, self).__init__() | |
| self.rounding = rounding | |
| self.c_table = c_table | |
| def forward(self, image, factor=1): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width | |
| Returns: | |
| Tensor: batch x height x width | |
| """ | |
| if isinstance(factor, (int, float)): | |
| image = image.float() / (self.c_table * factor) | |
| else: | |
| b = factor.size(0) | |
| table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) | |
| image = image.float() / table | |
| image = self.rounding(image) | |
| return image | |
| class CompressJpeg(nn.Module): | |
| """Full JPEG compression algorithm | |
| Args: | |
| rounding(function): rounding function to use | |
| """ | |
| def __init__(self, rounding=torch.round): | |
| super(CompressJpeg, self).__init__() | |
| self.l1 = nn.Sequential(RGB2YCbCrJpeg(), ChromaSubsampling()) | |
| self.l2 = nn.Sequential(BlockSplitting(), DCT8x8()) | |
| self.c_quantize = CQuantize(rounding=rounding) | |
| self.y_quantize = YQuantize(rounding=rounding) | |
| def forward(self, image, factor=1): | |
| """ | |
| Args: | |
| image(tensor): batch x 3 x height x width | |
| Returns: | |
| dict(tensor): Compressed tensor with batch x h*w/64 x 8 x 8. | |
| """ | |
| y, cb, cr = self.l1(image * 255) | |
| components = {'y': y, 'cb': cb, 'cr': cr} | |
| for k in components.keys(): | |
| comp = self.l2(components[k]) | |
| if k in ('cb', 'cr'): | |
| comp = self.c_quantize(comp, factor=factor) | |
| else: | |
| comp = self.y_quantize(comp, factor=factor) | |
| components[k] = comp | |
| return components['y'], components['cb'], components['cr'] | |
| # ------------------------ decompression ------------------------# | |
| class YDequantize(nn.Module): | |
| """Dequantize Y channel | |
| """ | |
| def __init__(self): | |
| super(YDequantize, self).__init__() | |
| self.y_table = y_table | |
| def forward(self, image, factor=1): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width | |
| Returns: | |
| Tensor: batch x height x width | |
| """ | |
| if isinstance(factor, (int, float)): | |
| out = image * (self.y_table * factor) | |
| else: | |
| b = factor.size(0) | |
| table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) | |
| out = image * table | |
| return out | |
| class CDequantize(nn.Module): | |
| """Dequantize CbCr channel | |
| """ | |
| def __init__(self): | |
| super(CDequantize, self).__init__() | |
| self.c_table = c_table | |
| def forward(self, image, factor=1): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width | |
| Returns: | |
| Tensor: batch x height x width | |
| """ | |
| if isinstance(factor, (int, float)): | |
| out = image * (self.c_table * factor) | |
| else: | |
| b = factor.size(0) | |
| table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) | |
| out = image * table | |
| return out | |
| class iDCT8x8(nn.Module): | |
| """Inverse discrete Cosine Transformation | |
| """ | |
| def __init__(self): | |
| super(iDCT8x8, self).__init__() | |
| alpha = np.array([1. / np.sqrt(2)] + [1] * 7) | |
| self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) | |
| tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) | |
| for x, y, u, v in itertools.product(range(8), repeat=4): | |
| tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos((2 * v + 1) * y * np.pi / 16) | |
| self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) | |
| def forward(self, image): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width | |
| Returns: | |
| Tensor: batch x height x width | |
| """ | |
| image = image * self.alpha | |
| result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 | |
| result.view(image.shape) | |
| return result | |
| class BlockMerging(nn.Module): | |
| """Merge patches into image | |
| """ | |
| def __init__(self): | |
| super(BlockMerging, self).__init__() | |
| def forward(self, patches, height, width): | |
| """ | |
| Args: | |
| patches(tensor) batch x height*width/64, height x width | |
| height(int) | |
| width(int) | |
| Returns: | |
| Tensor: batch x height x width | |
| """ | |
| k = 8 | |
| batch_size = patches.shape[0] | |
| image_reshaped = patches.view(batch_size, height // k, width // k, k, k) | |
| image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) | |
| return image_transposed.contiguous().view(batch_size, height, width) | |
| class ChromaUpsampling(nn.Module): | |
| """Upsample chroma layers | |
| """ | |
| def __init__(self): | |
| super(ChromaUpsampling, self).__init__() | |
| def forward(self, y, cb, cr): | |
| """ | |
| Args: | |
| y(tensor): y channel image | |
| cb(tensor): cb channel | |
| cr(tensor): cr channel | |
| Returns: | |
| Tensor: batch x height x width x 3 | |
| """ | |
| def repeat(x, k=2): | |
| height, width = x.shape[1:3] | |
| x = x.unsqueeze(-1) | |
| x = x.repeat(1, 1, k, k) | |
| x = x.view(-1, height * k, width * k) | |
| return x | |
| cb = repeat(cb) | |
| cr = repeat(cr) | |
| return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) | |
| class YCbCr2RGBJpeg(nn.Module): | |
| """Converts YCbCr image to RGB JPEG | |
| """ | |
| def __init__(self): | |
| super(YCbCr2RGBJpeg, self).__init__() | |
| matrix = np.array([[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], dtype=np.float32).T | |
| self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) | |
| self.matrix = nn.Parameter(torch.from_numpy(matrix)) | |
| def forward(self, image): | |
| """ | |
| Args: | |
| image(tensor): batch x height x width x 3 | |
| Returns: | |
| Tensor: batch x 3 x height x width | |
| """ | |
| result = torch.tensordot(image + self.shift, self.matrix, dims=1) | |
| return result.view(image.shape).permute(0, 3, 1, 2) | |
| class DeCompressJpeg(nn.Module): | |
| """Full JPEG decompression algorithm | |
| Args: | |
| rounding(function): rounding function to use | |
| """ | |
| def __init__(self, rounding=torch.round): | |
| super(DeCompressJpeg, self).__init__() | |
| self.c_dequantize = CDequantize() | |
| self.y_dequantize = YDequantize() | |
| self.idct = iDCT8x8() | |
| self.merging = BlockMerging() | |
| self.chroma = ChromaUpsampling() | |
| self.colors = YCbCr2RGBJpeg() | |
| def forward(self, y, cb, cr, imgh, imgw, factor=1): | |
| """ | |
| Args: | |
| compressed(dict(tensor)): batch x h*w/64 x 8 x 8 | |
| imgh(int) | |
| imgw(int) | |
| factor(float) | |
| Returns: | |
| Tensor: batch x 3 x height x width | |
| """ | |
| components = {'y': y, 'cb': cb, 'cr': cr} | |
| for k in components.keys(): | |
| if k in ('cb', 'cr'): | |
| comp = self.c_dequantize(components[k], factor=factor) | |
| height, width = int(imgh / 2), int(imgw / 2) | |
| else: | |
| comp = self.y_dequantize(components[k], factor=factor) | |
| height, width = imgh, imgw | |
| comp = self.idct(comp) | |
| components[k] = self.merging(comp, height, width) | |
| # | |
| image = self.chroma(components['y'], components['cb'], components['cr']) | |
| image = self.colors(image) | |
| image = torch.min(255 * torch.ones_like(image), torch.max(torch.zeros_like(image), image)) | |
| return image / 255 | |
| # ------------------------ main DiffJPEG ------------------------ # | |
| class DiffJPEG(nn.Module): | |
| """This JPEG algorithm result is slightly different from cv2. | |
| DiffJPEG supports batch processing. | |
| Args: | |
| differentiable(bool): If True, uses custom differentiable rounding function, if False, uses standard torch.round | |
| """ | |
| def __init__(self, differentiable=True): | |
| super(DiffJPEG, self).__init__() | |
| if differentiable: | |
| rounding = diff_round | |
| else: | |
| rounding = torch.round | |
| self.compress = CompressJpeg(rounding=rounding) | |
| self.decompress = DeCompressJpeg(rounding=rounding) | |
| def forward(self, x, quality): | |
| """ | |
| Args: | |
| x (Tensor): Input image, bchw, rgb, [0, 1] | |
| quality(float): Quality factor for jpeg compression scheme. | |
| """ | |
| factor = quality | |
| if isinstance(factor, (int, float)): | |
| factor = quality_to_factor(factor) | |
| else: | |
| for i in range(factor.size(0)): | |
| factor[i] = quality_to_factor(factor[i]) | |
| h, w = x.size()[-2:] | |
| h_pad, w_pad = 0, 0 | |
| # why should use 16 | |
| if h % 16 != 0: | |
| h_pad = 16 - h % 16 | |
| if w % 16 != 0: | |
| w_pad = 16 - w % 16 | |
| x = F.pad(x, (0, w_pad, 0, h_pad), mode='constant', value=0) | |
| y, cb, cr = self.compress(x, factor=factor) | |
| recovered = self.decompress(y, cb, cr, (h + h_pad), (w + w_pad), factor=factor) | |
| recovered = recovered[:, :, 0:h, 0:w] | |
| return recovered | |
| if __name__ == '__main__': | |
| import cv2 | |
| from basicsr.utils import img2tensor, tensor2img | |
| img_gt = cv2.imread('test.png') / 255. | |
| # -------------- cv2 -------------- # | |
| encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 20] | |
| _, encimg = cv2.imencode('.jpg', img_gt * 255., encode_param) | |
| img_lq = np.float32(cv2.imdecode(encimg, 1)) | |
| cv2.imwrite('cv2_JPEG_20.png', img_lq) | |
| # -------------- DiffJPEG -------------- # | |
| jpeger = DiffJPEG(differentiable=False).cuda() | |
| img_gt = img2tensor(img_gt) | |
| img_gt = torch.stack([img_gt, img_gt]).cuda() | |
| quality = img_gt.new_tensor([20, 40]) | |
| out = jpeger(img_gt, quality=quality) | |
| cv2.imwrite('pt_JPEG_20.png', tensor2img(out[0])) | |
| cv2.imwrite('pt_JPEG_40.png', tensor2img(out[1])) | |