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import tensorflow.compat.v1 as tf | |
from adjust_brightness import adjust_brightness_from_src_to_dst, read_img | |
import cv2 | |
import numpy as np | |
from PIL import Image | |
# def load_input_image(image_path, size=[256,256]): | |
# img = cv2.imread(image_path).astype(np.float32) | |
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
# img = preprocessing(img,size) | |
# img = np.expand_dims(img, axis=0) | |
# return img | |
def load_input_image(image_file_buffer, size=[256, 256]): | |
img = Image.open(image_file_buffer).convert('RGB') | |
img = np.array(img).astype(np.float32) | |
img = preprocessing(img, size) | |
img = np.expand_dims(img, axis=0) | |
return img | |
def preprocessing(img, size): | |
h, w = img.shape[:2] | |
if h <= size[0]: | |
h = size[0] | |
else: | |
x = h % 32 | |
h = h - x | |
if w < size[1]: | |
w = size[1] | |
else: | |
y = w % 32 | |
w = w - y | |
# the cv2 resize func : dsize format is (W ,H) | |
img = cv2.resize(img, (w, h)) | |
return img/127.5 - 1.0 | |
def inverse_transform(images): | |
images = (images + 1.) / 2 * 255 | |
# The calculation of floating-point numbers is inaccurate, | |
# and the range of pixel values must be limited to the boundary, | |
# otherwise, image distortion or artifacts will appear during display. | |
images = np.clip(images, 0, 255) | |
return images.astype(np.uint8) | |
# def imsave(images, path): | |
# return cv2.imwrite(path, cv2.cvtColor(images, cv2.COLOR_BGR2RGB)) |