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mass_patch_split.py
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import glob
import os
import numpy as np
import cv2
import multiprocessing.pool as mpp
import multiprocessing as mp
import time
import argparse
import torch
import albumentations as albu
import random
SEED = 42
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
Building = np.array([255, 255, 255]) # label 0
Clutter = np.array([0, 0, 0]) # label 1
num_classes = 2
# split huge RS image to small patches
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input-img-dir", default="data/mass_build/png/train")
parser.add_argument("--input-mask-dir", default="data/mass_build/png/train_labels")
parser.add_argument("--output-img-dir", default="data/mass_build/png/train_images")
parser.add_argument("--output-mask-dir", default="data/mass_build/png/train_masks")
parser.add_argument("--mode", type=str, default='train')
return parser.parse_args()
def label2rgb(mask):
h, w = mask.shape[0], mask.shape[1]
mask_rgb = np.zeros(shape=(h, w, 3), dtype=np.uint8)
mask_convert = mask[np.newaxis, :, :]
mask_rgb[np.all(mask_convert == 0, axis=0)] = Building
mask_rgb[np.all(mask_convert == 1, axis=0)] = Clutter
return mask_rgb
def rgb2label(label):
label_seg = np.zeros(label.shape[:2], dtype=np.uint8)
label_seg[np.all(label == Building, axis=-1)] = 0
label_seg[np.all(label == Clutter, axis=-1)] = 1
return label_seg
def image_augment(image, mask, mode='train'):
image_list = []
mask_list = []
image_width, image_height = image.shape[1], image.shape[0]
mask_width, mask_height = mask.shape[1], mask.shape[0]
assert image_height == mask_height and image_width == mask_width
if mode == 'train':
hflip = albu.HorizontalFlip(p=1)(image=image.copy(), mask=mask.copy())
img_h, mask_h = hflip['image'], hflip['mask']
vflip = albu.VerticalFlip(p=1)(image=image.copy(), mask=mask.copy())
img_v, mask_v = vflip['image'], vflip['mask']
image_list_train = [image, img_h, img_v]
mask_list_train = [mask, mask_h, mask_v]
for i in range(len(image_list_train)):
mask_tmp = rgb2label(mask_list_train[i])
image_list.append(image_list_train[i])
mask_list.append(mask_tmp)
else:
mask = rgb2label(mask.copy())
image_list.append(image)
mask_list.append(mask)
return image_list, mask_list
def patch_format(inp):
(img_path, mask_path, imgs_output_dir, masks_output_dir, mode) = inp
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
mask = cv2.imread(mask_path, cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)
id = os.path.splitext(os.path.basename(img_path))[0]
assert img.shape == mask.shape
if mode == 'train':
mask_tmp = np.zeros(mask.shape[:2], dtype=np.uint8)
mask_tmp[np.all(img == [255, 255, 255], axis=-1)] = 1
mask_c = mask_tmp[np.newaxis, :, :]
mask[np.all(mask_c == 1, axis=0)] = [0, 0, 0]
img[np.all(img == [255, 255, 255], axis=-1)] = [0, 0, 0]
image_list, mask_list = image_augment(image=img.copy(), mask=mask.copy(), mode=mode)
assert len(image_list) == len(mask_list)
for m in range(len(image_list)):
img = image_list[m]
mask = mask_list[m]
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
out_img_path = os.path.join(imgs_output_dir, "{}_{}.png".format(id, m))
cv2.imwrite(out_img_path, img)
out_mask_path = os.path.join(masks_output_dir, "{}_{}.png".format(id, m))
cv2.imwrite(out_mask_path, mask)
if __name__ == "__main__":
seed_everything(SEED)
args = parse_args()
input_img_dir = args.input_img_dir
input_mask_dir = args.input_mask_dir
img_paths = glob.glob(os.path.join(input_img_dir, "*.png"))
mask_paths = glob.glob(os.path.join(input_mask_dir, "*.png"))
img_paths.sort()
mask_paths.sort()
imgs_output_dir = args.output_img_dir
masks_output_dir = args.output_mask_dir
mode = args.mode
if not os.path.exists(imgs_output_dir):
os.makedirs(imgs_output_dir)
if not os.path.exists(masks_output_dir):
os.makedirs(masks_output_dir)
inp = [(img_path, mask_path, imgs_output_dir, masks_output_dir, mode)
for img_path, mask_path in zip(img_paths, mask_paths)]
t0 = time.time()
mpp.Pool(processes=mp.cpu_count()).map(patch_format, inp)
t1 = time.time()
split_time = t1 - t0
print('images spliting spends: {} s'.format(split_time))