data_creation.py
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import os
import cv2
import numpy as np
from PIL import Image
from tqdm.contrib import tzip
import threading
src_root = '/home/mly/data/datasets/text_recognition/CDLA/CDLA_DATASET'
seg_root = '/home/mly/data/datasets/text_recognition/CDLA/CDLA_DATASET_SEG_ANNOTATIONS_FULL'
train_seg_root = os.path.join(seg_root, 'train')
val_seg_root = os.path.join(seg_root, 'val')
bg_root = '/home/mly/data/datasets/humanMatting/bg'
train_root = os.path.join(src_root, 'train')
val_root = os.path.join(src_root, 'val')
# gen_root = '/home/mly/data/datasets/text_recognition/CDLA/CDLA_DATASET_SYN/'
gen_root = '/home/mly/data/datasets/text_recognition/CDLA/new_syn'
if not os.path.exists(gen_root):
os.mkdir(gen_root)
if not os.path.exists(os.path.join(gen_root, 'img')):
os.mkdir(os.path.join(gen_root, 'img'))
if not os.path.exists(os.path.join(gen_root, 'mask')):
os.mkdir(os.path.join(gen_root, 'mask'))
if not os.path.exists(os.path.join(gen_root, 'edge')):
os.mkdir(os.path.join(gen_root, 'edge'))
gen_img_root = os.path.join(gen_root, 'img')
gen_mask_root = os.path.join(gen_root, 'mask')
gen_edge_root = os.path.join(gen_root, 'edge')
def get_img_mask_list(root):
file_list = os.listdir(root)
img_list = list()
for file in file_list:
if file[-1] == 'g':
img_list.append(file)
return img_list
def get_img_mask_full_path_list(img_list, img_root, mask_root):
img_full_path_list = list()
mask_full_path_list = list()
for img_name in img_list:
img_full_path_list.append(os.path.join(img_root, img_name))
mask_full_path_list.append(os.path.join(mask_root, img_name))
return img_full_path_list, mask_full_path_list
def read_bg_list(root):
img_list = os.listdir(root)
needed = list()
for img_name in img_list:
if img_name[-1] == 'g':
needed.append(os.path.join(root, img_name))
return needed
def paste_img_and_mask(img, mask, bg):
img = cv2.imread(img)
# mask = cv2.imread(mask)
# img = cv2.resize(img, (512, 512))
# mask = cv2.resize(mask, (512, 512), cv2.INTER_NEAREST)
resize_h = int(np.random.randint(448, 2048, 1))
resize_w = int(np.random.randint(448, 2048, 1))
img = cv2.resize(img, (resize_h, resize_w))
mask = np.ones(img.shape) * 255
y_max = 2048 - img.shape[1]
x_max = 2048 - img.shape[0]
x = int(np.random.randint(0, x_max, 1))
y = int(np.random.randint(0, y_max, 1))
point = (x, y)
try:
bg = cv2.imread(bg)
bg = cv2.resize(bg, (2048, 2048))
except BaseException:
print('error and replace by 4257')
bg = cv2.imread('/home/mly/data/datasets/humanMatting/bg/办公桌_4257.jpg')
bg = cv2.resize(bg, (2048, 2048))
bg = cv2.resize(bg, (2048, 2048))
bg_mask = np.zeros_like(bg)
# img[img == 0] = 100
bg[point[0]: point[0] + img.shape[0], point[1]: point[1] + img.shape[1], :] = img
bg_mask[point[0]: point[0] + img.shape[0], point[1]: point[1] + img.shape[1], :] = mask
edge = np.asarray(bg_mask.copy())
edge = cv2.Canny(edge, 50, 255)
return bg, bg_mask, edge
def generate(img_list, mask_list, gen_iter=5):
# img_list = img_list[:10]
bg_list = read_bg_list(bg_root)
len_bg_list = len(bg_list)
len_img_list = len(img_list)
for it in range(gen_iter):
for img, mask in tzip(img_list, mask_list):
idx = int(np.random.randint(0, len_bg_list, 1))
while os.path.getsize(bg_list[idx]) < 100:
idx = int(np.random.randint(0, len_bg_list, 1))
bn = img.split('/')[-2]
name = img.split('/')[-1].split('.')[0]
img, mask, edge = paste_img_and_mask(img, mask, bg_list[idx])
if not os.path.exists(os.path.join(gen_img_root, bn)):
os.mkdir(os.path.join(gen_img_root, bn))
cv2.imwrite(os.path.join(gen_img_root, bn, name + '_' + str(it) + '.jpg'), img)
if not os.path.exists(os.path.join(gen_mask_root, bn)):
os.mkdir(os.path.join(gen_mask_root, bn))
cv2.imwrite(os.path.join(gen_mask_root, bn, name + '_' + str(it) + '.jpg'), mask)
if not os.path.exists(os.path.join(gen_edge_root, bn)):
os.mkdir(os.path.join(gen_edge_root, bn))
cv2.imwrite(os.path.join(gen_edge_root, bn, name + '_' + str(it) + '.jpg'), edge)
def main():
train_img_mask_list = get_img_mask_list(root=train_root)
val_img_mask_list = get_img_mask_list(root=val_root)
train_img_full_path, train_mask_full_path = get_img_mask_full_path_list(train_img_mask_list, train_root,
train_seg_root)
val_img_full_path, val_mask_full_path = get_img_mask_full_path_list(val_img_mask_list, val_root, val_seg_root)
print('processing train!')
generate(train_img_full_path, train_mask_full_path, gen_iter=6)
print('processing val!')
generate(val_img_full_path, val_mask_full_path, gen_iter=4)
if __name__ == '__main__':
main()
# t1 = threading.Thread(target=main)
# t2 = threading.Thread(target=main)
# t3 = threading.Thread(target=main)
# t4 = threading.Thread(target=main)
# t1.start()
# t2.start()
# t3.start()
# t4.start()
# t1.join()
# t2.join()
# t3.join()
# t4.join()