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utils.py
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utils.py
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import PIL
import cv2
import numpy as np
import h5py
import math
import glob
import os
def PIL_resize(image, ratio, mode):
PIL_image = PIL.Image.fromarray(image.astype(dtype=np.uint8))
PIL_image_resize = PIL_image.resize((int(PIL_image.size[0] * ratio), int(PIL_image.size[1] * ratio)), mode)
image_resize = (np.array(PIL_image_resize)).astype(dtype=np.uint8)
return image_resize
def rgb2ycbcr(img):
y = 16 + (65.481 * img[:, :, 0]) + (128.553 * img[:, :, 1]) + (24.966 * img[:, :, 2])
return y / 255
def PSNR(target, ref, scale):
target_data = np.array(target, dtype=np.float32)
ref_data = np.array(ref, dtype=np.float32)
target_y = rgb2ycbcr(target_data)
ref_y = rgb2ycbcr(ref_data)
diff = ref_y - target_y
shave = scale
diff = diff[shave:-shave, shave:-shave]
mse = np.mean((diff / 255) ** 2)
if mse == 0:
return 100
return -10 * math.log10(mse)
def imread(path):
img = cv2.imread(path)
return img
def imsave(image, path):
cv2.imwrite(os.path.join(os.getcwd(),path),image)
def checkimage(image):
cv2.imshow("test",image)
cv2.waitKey(0)
def modcrop(img, scale =3):
if len(img.shape) ==3:
h, w, _ = img.shape
h = int((h / scale)) * scale
w = int((w / scale)) * scale
img = img[0:h, 0:w, :]
else:
h, w = img.shape
h = int((h / scale)) * scale
w = int((w / scale)) * scale
img = img[0:h, 0:w]
return img
def preprocess(path, scale = 3, eng = None, mdouble = None):
img = imread(path)
label_ = modcrop(img, scale)
if eng is None:
# input_ = cv2.resize(label_, None, fx=1.0/scale, fy=1.0/scale, interpolation=cv2.INTER_CUBIC)
input_ = PIL_resize(label_, 1.0/scale, PIL.Image.BICUBIC)
else:
input_ = np.asarray(eng.imresize(mdouble(label_.tolist()), 1.0/scale, 'bicubic'))
input_ = input_[:, :, ::-1]
label_ = label_[:, :, ::-1]
return input_, label_
def make_data_hf(input_, label_, config, times):
if not os.path.isdir(os.path.join(os.getcwd(),config.checkpoint_dir)):
os.makedirs(os.path.join(os.getcwd(),config.checkpoint_dir))
if config.is_train:
savepath = os.path.join(os.path.join(os.getcwd(), config.checkpoint_dir), 'train_x%d.h5' % config.scale)
else:
savepath = os.path.join(os.path.join(os.getcwd(), config.checkpoint_dir), 'test.h5')
if times == 0:
if os.path.exists(savepath):
print("\n%s have existed!\n" % (savepath))
return False
else:
hf = h5py.File(savepath, 'w')
if config.is_train:
input_h5 = hf.create_dataset("input", (1, config.image_size, config.image_size, config.c_dim),
maxshape=(None, config.image_size, config.image_size, config.c_dim),
chunks=(1, config.image_size, config.image_size, config.c_dim), dtype='float32')
label_h5 = hf.create_dataset("label", (1, config.image_size*config.scale, config.image_size*config.scale, config.c_dim),
maxshape=(None, config.image_size*config.scale, config.image_size*config.scale, config.c_dim),
chunks=(1, config.image_size*config.scale, config.image_size*config.scale, config.c_dim),dtype='float32')
else:
input_h5 = hf.create_dataset("input", (1, input_.shape[0], input_.shape[1], input_.shape[2]),
maxshape=(None, input_.shape[0], input_.shape[1], input_.shape[2]),
chunks=(1, input_.shape[0], input_.shape[1], input_.shape[2]), dtype='float32')
label_h5 = hf.create_dataset("label", (1, label_.shape[0], label_.shape[1], label_.shape[2]),
maxshape=(None, label_.shape[0], label_.shape[1], label_.shape[2]),
chunks=(1, label_.shape[0], label_.shape[1], label_.shape[2]),dtype='float32')
else:
hf = h5py.File(savepath, 'a')
input_h5 = hf["input"]
label_h5 = hf["label"]
if config.is_train:
input_h5.resize([times + 1, config.image_size, config.image_size, config.c_dim])
input_h5[times : times+1] = input_
label_h5.resize([times + 1, config.image_size*config.scale, config.image_size*config.scale, config.c_dim])
label_h5[times : times+1] = label_
else:
input_h5.resize([times + 1, input_.shape[0], input_.shape[1], input_.shape[2]])
input_h5[times : times+1] = input_
label_h5.resize([times + 1, label_.shape[0], label_.shape[1], label_.shape[2]])
label_h5[times : times+1] = label_
hf.close()
return True
def make_sub_data(data, config):
if config.matlab_bicubic:
import matlab.engine
eng = matlab.engine.start_matlab()
mdouble = matlab.double
else:
eng = None
mdouble = None
times = 0
for i in range(len(data)):
input_, label_, = preprocess(data[i], config.scale, eng, mdouble)
if len(input_.shape) == 3:
h, w, c = input_.shape
else:
h, w = input_.shape
for x in range(0, h * config.scale - config.image_size * config.scale + 1, config.stride * config.scale):
for y in range(0, w * config.scale - config.image_size * config.scale + 1, config.stride * config.scale):
sub_label = label_[x: x + config.image_size * config.scale, y: y + config.image_size * config.scale]
sub_label = sub_label.reshape([config.image_size * config.scale , config.image_size * config.scale, config.c_dim])
t = cv2.cvtColor(sub_label, cv2.COLOR_BGR2YCR_CB)
t = t[:, :, 0]
gx = t[1:, 0:-1] - t[0:-1, 0:-1]
gy = t[0:-1, 1:] - t[0:-1, 0:-1]
Gxy = (gx**2 + gy**2)**0.5
r_gxy = float((Gxy > 10).sum()) / ((config.image_size*config.scale)**2) * 100
if r_gxy < 10:
continue
sub_label = sub_label / 255.0
x_i = int(x / config.scale)
y_i = int(y / config.scale)
sub_input = input_[x_i: x_i + config.image_size, y_i: y_i + config.image_size]
sub_input = sub_input.reshape([config.image_size, config.image_size, config.c_dim])
sub_input = sub_input / 255.0
# checkimage(sub_input)
# checkimage(sub_label)
save_flag = make_data_hf(sub_input, sub_label, config, times)
if not save_flag:
return
times += 1
print("image: [%2d], total: [%2d]"%(i, len(data)))
if config.matlab_bicubic:
eng.quit()
def prepare_data(config):
if config.is_train:
data_dir = os.path.join(os.path.join(os.getcwd(), "Train"), config.train_set)
data = glob.glob(os.path.join(data_dir, "*.png"))
else:
if config.test_img != "":
data = [os.path.join(os.getcwd(), config.test_img)]
else:
data_dir = os.path.join(os.path.join(os.getcwd(), "Test"), config.test_set)
data = glob.glob(os.path.join(data_dir, "*.png"))
return data
def input_setup(config):
"""
Read image files and make their sub-images and saved them as a h5 file format
"""
data = prepare_data(config)
make_sub_data(data, config)
return data
def augmentation(batch, random):
if random[0] < 0.3:
batch_flip = np.flip(batch, 1)
elif random[0] > 0.7:
batch_flip = np.flip(batch, 2)
else:
batch_flip = batch
if random[1] < 0.5:
batch_rot = np.rot90(batch_flip, 1, [1, 2])
else:
batch_rot = batch_flip
return batch_rot
def get_data_dir(checkpoint_dir, is_train, scale):
if is_train:
return os.path.join(os.path.join(os.getcwd(), checkpoint_dir), 'train_x%d.h5' % scale)
else:
return os.path.join(os.path.join(os.getcwd(), checkpoint_dir), 'test.h5')
def get_data_num(path):
with h5py.File(path, 'r') as hf:
input_ = hf['input']
return input_.shape[0]
def get_batch(path, data_num, batch_size):
with h5py.File(path, 'r') as hf:
input_ = hf['input']
label_ = hf['label']
random_batch = np.random.rand(batch_size) * (data_num - 1)
batch_images = np.zeros([batch_size, input_[0].shape[0], input_[0].shape[1], input_[0].shape[2]])
batch_labels = np.zeros([batch_size, label_[0].shape[0], label_[0].shape[1], label_[0].shape[2]])
for i in range(batch_size):
batch_images[i, :, :, :] = np.asarray(input_[int(random_batch[i])])
batch_labels[i, :, :, :] = np.asarray(label_[int(random_batch[i])])
random_aug = np.random.rand(2)
batch_images = augmentation(batch_images, random_aug)
batch_labels = augmentation(batch_labels, random_aug)
return batch_images, batch_labels
def get_image(path, scale, matlab_bicubic):
if matlab_bicubic:
import matlab.engine
eng = matlab.engine.start_matlab()
mdouble = matlab.double
else:
eng = None
mdouble = None
image, label = preprocess(path, scale, eng, mdouble)
image = image[np.newaxis, :]
label = label[np.newaxis, :]
if matlab_bicubic:
eng.quit()
return image, label