Upload utils_sr.py

utils_sr.py

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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], :]