def resize_img(image, crop_size=(128, 128), algo=None):
w = crop_size[0]
h = crop_size[1]
newdim = (w, h)
if algo is None:
scale_algos = [
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC,
cv2.INTER_AREA, cv2.INTER_LANCZOS4, cv2.INTER_LINEAR_EXACT
] #scaling interpolation options
interpol = random.choice(scale_algos)
else:
if isinstance(algo, list):
interpol = random.choice(algo)
elif isinstance(algo, int):
interpol = algo
if interpol == 777: #'matlab_bicubic'
resized = util.imresize_np(image, 1 / scale, True)
# force to 3 channels
# if resized.ndim == 2:
# resized = np.expand_dims(resized, axis=2)
else:
# use the provided OpenCV2 algorithms
resized = cv2.resize(image, newdim, interpolation=interpol)
return resized, interpol
def scale_img(image, scale, algo=None):
h, w, c = image.shape
newdim = (int(w / scale), int(h / scale))
# randomly use OpenCV2 algorithms if none are provided
if algo is None:
scale_algos = [
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC,
cv2.INTER_AREA, cv2.INTER_LANCZOS4, cv2.INTER_LINEAR_EXACT
] #scaling interpolation options
interpol = random.choice(scale_algos)
resized = cv2.resize(image, newdim, interpolation=interpol)
# using matlab imresize
else:
if isinstance(algo, list):
interpol = random.choice(algo)
elif isinstance(algo, int):
interpol = algo
if interpol == 777: #'matlab_bicubic'
resized = util.imresize_np(image, 1 / scale, True)
# force to 3 channels
# if resized.ndim == 2:
# resized = np.expand_dims(resized, axis=2)
else:
# use the provided OpenCV2 algorithms
resized = cv2.resize(image, newdim, interpolation=interpol)
#resized = np.clip(resized, 0, 1)
return resized, interpol
def generate_LR(img_HR, scale=4):
"""generate LR image,
size of img_HR should be multiple of scale"""
assert scale > 1, "scale should be greater than 1!"
H_s, W_s, _ = img_HR.shape
# maybe the assert is not necessary
temp = (H_s % scale == 0) and (W_s % scale == 0)
assert temp, "img size should be multiple of {}".format(scale)
# using matlab imresize
img_LR = imresize_np(img_HR, 1 / scale, True)
return img_LR
def resize_img(image, out_size=(128, 128), algo=None):
interpol = select_algo(algo)
if interpol == 777:
# using matlab imresize
resized = util.imresize_np(image, out_size[1] / image.shape[0], True)
# force to 3 channels
# if resized.ndim == 2:
# resized = np.expand_dims(resized, axis=2)
else:
# use the provided OpenCV2 algorithms
resized = cv2.resize(image, out_size, interpolation=interpol)
return resized, interpol
def scale_img(image, scale, algo=None):
h, w, c = image.shape
newdim = (int(w / scale), int(h / scale))
interpol = select_algo(algo)
if interpol == 777:
# using matlab imresize
resized = util.imresize_np(image, 1 / scale, True)
# force to 3 channels
# if resized.ndim == 2:
# resized = np.expand_dims(resized, axis=2)
else:
# use the provided OpenCV2 algorithms
resized = cv2.resize(image, newdim, interpolation=interpol)
#resized = np.clip(resized, 0, 1)
return resized, interpol
def generate_mod_LR_bic():
# set parameters
up_scale = 4
mod_scale = 4
# set data dir
sourcedir = '/content/own_dataset-20190716T130232Z-001/training_tiles/'
savedir = '/content/own_dataset-20190716T130232Z-001/training_tiles_LQ/'
saveHRpath = os.path.join(savedir, 'HR', 'x' + str(mod_scale))
saveLRpath = os.path.join(savedir, 'LR', 'x' + str(up_scale))
saveBicpath = os.path.join(savedir, 'Bic', 'x' + str(up_scale))
if not os.path.isdir(sourcedir):
print('Error: No source data found')
exit(0)
if not os.path.isdir(savedir):
os.mkdir(savedir)
if not os.path.isdir(os.path.join(savedir, 'HR')):
os.mkdir(os.path.join(savedir, 'HR'))
if not os.path.isdir(os.path.join(savedir, 'LR')):
os.mkdir(os.path.join(savedir, 'LR'))
if not os.path.isdir(os.path.join(savedir, 'Bic')):
os.mkdir(os.path.join(savedir, 'Bic'))
if not os.path.isdir(saveHRpath):
os.mkdir(saveHRpath)
else:
print('It will cover ' + str(saveHRpath))
if not os.path.isdir(saveLRpath):
os.mkdir(saveLRpath)
else:
print('It will cover ' + str(saveLRpath))
if not os.path.isdir(saveBicpath):
os.mkdir(saveBicpath)
else:
print('It will cover ' + str(saveBicpath))
filepaths = [f for f in os.listdir(sourcedir) if f.endswith('.png')]
num_files = len(filepaths)
# prepare data with augementation
for i in range(num_files):
filename = filepaths[i]
print('No.{} -- Processing {}'.format(i, filename))
# read image
image = cv2.imread(os.path.join(sourcedir, filename))
width = int(np.floor(image.shape[1] / mod_scale))
height = int(np.floor(image.shape[0] / mod_scale))
# modcrop
if len(image.shape) == 3:
image_HR = image[0:mod_scale * height, 0:mod_scale * width, :]
else:
image_HR = image[0:mod_scale * height, 0:mod_scale * width]
# LR
image_LR = imresize_np(image_HR, 1 / up_scale, True)
# bic
image_Bic = imresize_np(image_LR, up_scale, True)
cv2.imwrite(os.path.join(saveHRpath, filename), image_HR)
cv2.imwrite(os.path.join(saveLRpath, filename), image_LR)
cv2.imwrite(os.path.join(saveBicpath, filename), image_Bic)