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| import numpy as np | |
| import torch | |
| from PIL import Image | |
| import os | |
| import io | |
| import imageio | |
| def pad_reflect(image, pad_size): | |
| imsize = image.shape | |
| height, width = imsize[:2] | |
| new_img = np.zeros([height+pad_size*2, width+pad_size*2, imsize[2]]).astype(np.uint8) | |
| new_img[pad_size:-pad_size, pad_size:-pad_size, :] = image | |
| new_img[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0) #top | |
| new_img[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0) #bottom | |
| new_img[:, 0:pad_size, :] = np.flip(new_img[:, pad_size:pad_size*2, :], axis=1) #left | |
| new_img[:, -pad_size:, :] = np.flip(new_img[:, -pad_size*2:-pad_size, :], axis=1) #right | |
| return new_img | |
| def unpad_image(image, pad_size): | |
| return image[pad_size:-pad_size, pad_size:-pad_size, :] | |
| def jpegBlur(im,q): | |
| buf = io.BytesIO() | |
| imageio.imwrite(buf,im,format='jpg',quality=q) | |
| s = buf.getbuffer() | |
| return imageio.imread(s,format='jpg') | |
| def process_array(image_array, expand=True): | |
| """ Process a 3-dimensional array into a scaled, 4 dimensional batch of size 1. """ | |
| image_batch = image_array / 255.0 | |
| if expand: | |
| image_batch = np.expand_dims(image_batch, axis=0) | |
| return image_batch | |
| def process_output(output_tensor): | |
| """ Transforms the 4-dimensional output tensor into a suitable image format. """ | |
| sr_img = output_tensor.clip(0, 1) * 255 | |
| sr_img = np.uint8(sr_img) | |
| return sr_img | |
| def pad_patch(image_patch, padding_size, channel_last=True): | |
| """ Pads image_patch with with padding_size edge values. """ | |
| if channel_last: | |
| return np.pad( | |
| image_patch, | |
| ((padding_size, padding_size), (padding_size, padding_size), (0, 0)), | |
| 'edge', | |
| ) | |
| else: | |
| return np.pad( | |
| image_patch, | |
| ((0, 0), (padding_size, padding_size), (padding_size, padding_size)), | |
| 'edge', | |
| ) | |
| def unpad_patches(image_patches, padding_size): | |
| return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :] | |
| def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2): | |
| """ Splits the image into partially overlapping patches. | |
| The patches overlap by padding_size pixels. | |
| Pads the image twice: | |
| - first to have a size multiple of the patch size, | |
| - then to have equal padding at the borders. | |
| Args: | |
| image_array: numpy array of the input image. | |
| patch_size: size of the patches from the original image (without padding). | |
| padding_size: size of the overlapping area. | |
| """ | |
| xmax, ymax, _ = image_array.shape | |
| x_remainder = xmax % patch_size | |
| y_remainder = ymax % patch_size | |
| # modulo here is to avoid extending of patch_size instead of 0 | |
| x_extend = (patch_size - x_remainder) % patch_size | |
| y_extend = (patch_size - y_remainder) % patch_size | |
| # make sure the image is divisible into regular patches | |
| extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), 'edge') | |
| # add padding around the image to simplify computations | |
| padded_image = pad_patch(extended_image, padding_size, channel_last=True) | |
| xmax, ymax, _ = padded_image.shape | |
| patches = [] | |
| x_lefts = range(padding_size, xmax - padding_size, patch_size) | |
| y_tops = range(padding_size, ymax - padding_size, patch_size) | |
| for x in x_lefts: | |
| for y in y_tops: | |
| x_left = x - padding_size | |
| y_top = y - padding_size | |
| x_right = x + patch_size + padding_size | |
| y_bottom = y + patch_size + padding_size | |
| patch = padded_image[x_left:x_right, y_top:y_bottom, :] | |
| patches.append(patch) | |
| return np.array(patches), padded_image.shape | |
| def stich_together(patches, padded_image_shape, target_shape, padding_size=4): | |
| """ Reconstruct the image from overlapping patches. | |
| After scaling, shapes and padding should be scaled too. | |
| Args: | |
| patches: patches obtained with split_image_into_overlapping_patches | |
| padded_image_shape: shape of the padded image contructed in split_image_into_overlapping_patches | |
| target_shape: shape of the final image | |
| padding_size: size of the overlapping area. | |
| """ | |
| xmax, ymax, _ = padded_image_shape | |
| patches = unpad_patches(patches, padding_size) | |
| patch_size = patches.shape[1] | |
| n_patches_per_row = ymax // patch_size | |
| complete_image = np.zeros((xmax, ymax, 3)) | |
| row = -1 | |
| col = 0 | |
| for i in range(len(patches)): | |
| if i % n_patches_per_row == 0: | |
| row += 1 | |
| col = 0 | |
| complete_image[ | |
| row * patch_size: (row + 1) * patch_size, col * patch_size: (col + 1) * patch_size,: | |
| ] = patches[i] | |
| col += 1 | |
| return complete_image[0: target_shape[0], 0: target_shape[1], :] |