| |
| """ |
| 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 |
|
|
| |
| 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. |
|
|
|
|
| |
| 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'] |
|
|
|
|
| |
|
|
|
|
| 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 |
|
|
|
|
| |
|
|
|
|
| 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 |
| |
| 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 |
|
|
