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import numpy as np |
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import torch |
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def rgb2ycbcr(img, y_only=False): |
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"""Convert a RGB image to YCbCr image. |
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This function produces the same results as Matlab's `rgb2ycbcr` function. |
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It implements the ITU-R BT.601 conversion for standard-definition |
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television. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. |
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It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`. |
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In OpenCV, it implements a JPEG conversion. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. |
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Args: |
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img (ndarray): The input image. It accepts: |
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1. np.uint8 type with range [0, 255]; |
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2. np.float32 type with range [0, 1]. |
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y_only (bool): Whether to only return Y channel. Default: False. |
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Returns: |
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ndarray: The converted YCbCr image. The output image has the same type |
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and range as input image. |
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""" |
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img_type = img.dtype |
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img = _convert_input_type_range(img) |
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if y_only: |
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out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0 |
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else: |
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out_img = np.matmul( |
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img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128] |
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out_img = _convert_output_type_range(out_img, img_type) |
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return out_img |
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def bgr2ycbcr(img, y_only=False): |
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"""Convert a BGR image to YCbCr image. |
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The bgr version of rgb2ycbcr. |
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It implements the ITU-R BT.601 conversion for standard-definition |
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television. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. |
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It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. |
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In OpenCV, it implements a JPEG conversion. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. |
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Args: |
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img (ndarray): The input image. It accepts: |
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1. np.uint8 type with range [0, 255]; |
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2. np.float32 type with range [0, 1]. |
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y_only (bool): Whether to only return Y channel. Default: False. |
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Returns: |
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ndarray: The converted YCbCr image. The output image has the same type |
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and range as input image. |
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""" |
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img_type = img.dtype |
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img = _convert_input_type_range(img) |
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if y_only: |
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out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0 |
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else: |
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out_img = np.matmul( |
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img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128] |
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out_img = _convert_output_type_range(out_img, img_type) |
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return out_img |
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def ycbcr2rgb(img): |
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"""Convert a YCbCr image to RGB image. |
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This function produces the same results as Matlab's ycbcr2rgb function. |
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It implements the ITU-R BT.601 conversion for standard-definition |
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television. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. |
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It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`. |
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In OpenCV, it implements a JPEG conversion. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. |
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Args: |
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img (ndarray): The input image. It accepts: |
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1. np.uint8 type with range [0, 255]; |
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2. np.float32 type with range [0, 1]. |
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Returns: |
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ndarray: The converted RGB image. The output image has the same type |
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and range as input image. |
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""" |
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img_type = img.dtype |
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img = _convert_input_type_range(img) * 255 |
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out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], |
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[0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] |
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out_img = _convert_output_type_range(out_img, img_type) |
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return out_img |
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def ycbcr2bgr(img): |
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"""Convert a YCbCr image to BGR image. |
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The bgr version of ycbcr2rgb. |
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It implements the ITU-R BT.601 conversion for standard-definition |
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television. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. |
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It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. |
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In OpenCV, it implements a JPEG conversion. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. |
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Args: |
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img (ndarray): The input image. It accepts: |
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1. np.uint8 type with range [0, 255]; |
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2. np.float32 type with range [0, 1]. |
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Returns: |
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ndarray: The converted BGR image. The output image has the same type |
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and range as input image. |
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""" |
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img_type = img.dtype |
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img = _convert_input_type_range(img) * 255 |
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out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0], |
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[0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921] |
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out_img = _convert_output_type_range(out_img, img_type) |
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return out_img |
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def _convert_input_type_range(img): |
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"""Convert the type and range of the input image. |
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It converts the input image to np.float32 type and range of [0, 1]. |
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It is mainly used for pre-processing the input image in colorspace |
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conversion functions such as rgb2ycbcr and ycbcr2rgb. |
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Args: |
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img (ndarray): The input image. It accepts: |
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1. np.uint8 type with range [0, 255]; |
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2. np.float32 type with range [0, 1]. |
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Returns: |
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(ndarray): The converted image with type of np.float32 and range of |
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[0, 1]. |
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""" |
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img_type = img.dtype |
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img = img.astype(np.float32) |
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if img_type == np.float32: |
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pass |
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elif img_type == np.uint8: |
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img /= 255. |
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else: |
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raise TypeError(f'The img type should be np.float32 or np.uint8, but got {img_type}') |
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return img |
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def _convert_output_type_range(img, dst_type): |
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"""Convert the type and range of the image according to dst_type. |
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It converts the image to desired type and range. If `dst_type` is np.uint8, |
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images will be converted to np.uint8 type with range [0, 255]. If |
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`dst_type` is np.float32, it converts the image to np.float32 type with |
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range [0, 1]. |
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It is mainly used for post-processing images in colorspace conversion |
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functions such as rgb2ycbcr and ycbcr2rgb. |
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Args: |
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img (ndarray): The image to be converted with np.float32 type and |
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range [0, 255]. |
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dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it |
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converts the image to np.uint8 type with range [0, 255]. If |
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dst_type is np.float32, it converts the image to np.float32 type |
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with range [0, 1]. |
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Returns: |
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(ndarray): The converted image with desired type and range. |
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""" |
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if dst_type not in (np.uint8, np.float32): |
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raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}') |
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if dst_type == np.uint8: |
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img = img.round() |
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else: |
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img /= 255. |
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return img.astype(dst_type) |
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def rgb2ycbcr_pt(img, y_only=False): |
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"""Convert RGB images to YCbCr images (PyTorch version). |
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It implements the ITU-R BT.601 conversion for standard-definition television. See more details in |
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https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. |
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Args: |
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img (Tensor): Images with shape (n, 3, h, w), the range [0, 1], float, RGB format. |
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y_only (bool): Whether to only return Y channel. Default: False. |
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Returns: |
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(Tensor): converted images with the shape (n, 3/1, h, w), the range [0, 1], float. |
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""" |
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if y_only: |
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weight = torch.tensor([[65.481], [128.553], [24.966]]).to(img) |
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out_img = torch.matmul(img.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + 16.0 |
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else: |
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weight = torch.tensor([[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]).to(img) |
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bias = torch.tensor([16, 128, 128]).view(1, 3, 1, 1).to(img) |
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out_img = torch.matmul(img.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + bias |
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out_img = out_img / 255. |
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return out_img |
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