class input:
path = DataManager.get_path()
if os.path.exists(path + 'test_images/'):
path += 'test_images/'
else:
path += 'train_images/'
print("Path to images: ", path)
if glob.glob(path+'*.jpeg'):
tiff_format = False
else:
tiff_format = True
print("Image tiff-format: ", tiff_format)
tiff_level = 1 # only if tiff_format is Ture
resize_ratio = 1 # 1 (N x N) or 2 (-> N//2 x N//2)
input_shape = (1280, 1280, 3)
patch_size = 256
sample_size = 25
preprocess_mode = 'float'
objective = 'bce'
label_smoothing = 0.0 # only if objective is 'cce'
img_masked = remove_penmarks(img_path)
img_masked = Image.fromarray(img_masked)
#img_masked.save(img_path, subsampling=0, quality=100)
def main(path, marked_images):
paths = path + marked_images + '.jpeg'
with multiprocessing.Pool() as pool:
for c in tqdm.tqdm(pool.imap(save_masked_image, paths),
total=len(paths)):
pass
if __name__ == '__main__':
marked_images = np.load('input_/marked_images.npy', allow_pickle=True)
path = DataManager.get_path() + 'train_images/'
print(
f"[Old] images with marks in {path} ({len(marked_images)} of them) "
"will be overwritten by the newly generated images (with marks removed)\n"
"Are you sure you want to continue? (y/n)")
c = input()
if c.lower() == 'y' or c.lower() == 'yes':
if Config.input.tiff_format:
print("Requires images to be .jpeg format")
print("Script cancelled")
else:
main(path, marked_images)
else:
print("Script cancelled")