File size: 3,097 Bytes
82a4cce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import numpy as np
import math
def crop_seamless(img):
img_height, img_width = img.shape[:2]
y, x = 16, 16
h, w = img_height - 32, img_width - 32
img = img[y:y+h, x:x+w]
return img
# from https://github.com/ata4/esrgan-launcher/blob/master/upscale.py
def esrgan_launcher_split_merge(input_image, upscale_function, models, scale_factor=4, tile_size=512, tile_padding=0.125):
width, height, depth = input_image.shape
output_width = width * scale_factor
output_height = height * scale_factor
output_shape = (output_width, output_height, depth)
# start with black image
output_images = [np.zeros(output_shape, np.uint8) for i in range(len(models))]
tile_padding = math.ceil(tile_size * tile_padding)
tile_size = math.ceil(tile_size / scale_factor)
tiles_x = math.ceil(width / tile_size)
tiles_y = math.ceil(height / tile_size)
for y in range(tiles_y):
for x in range(tiles_x):
# extract tile from input image
ofs_x = x * tile_size
ofs_y = y * tile_size
# input tile area on total image
input_start_x = ofs_x
input_end_x = min(ofs_x + tile_size, width)
input_start_y = ofs_y
input_end_y = min(ofs_y + tile_size, height)
# input tile area on total image with padding
input_start_x_pad = max(input_start_x - tile_padding, 0)
input_end_x_pad = min(input_end_x + tile_padding, width)
input_start_y_pad = max(input_start_y - tile_padding, 0)
input_end_y_pad = min(input_end_y + tile_padding, height)
# input tile dimensions
input_tile_width = input_end_x - input_start_x
input_tile_height = input_end_y - input_start_y
input_tile = input_image[input_start_x_pad:input_end_x_pad, input_start_y_pad:input_end_y_pad]
for idx, model in enumerate(models):
# upscale tile
output_tile = upscale_function(input_tile, model)
# output tile area on total image
output_start_x = input_start_x * scale_factor
output_end_x = input_end_x * scale_factor
output_start_y = input_start_y * scale_factor
output_end_y = input_end_y * scale_factor
# output tile area without padding
output_start_x_tile = (input_start_x - input_start_x_pad) * scale_factor
output_end_x_tile = output_start_x_tile + input_tile_width * scale_factor
output_start_y_tile = (input_start_y - input_start_y_pad) * scale_factor
output_end_y_tile = output_start_y_tile + input_tile_height * scale_factor
# put tile into output image
output_images[idx][output_start_x:output_end_x, output_start_y:output_end_y] = \
output_tile[output_start_x_tile:output_end_x_tile, output_start_y_tile:output_end_y_tile]
return output_images |