import glob
import os
import random
from string import ascii_lowercase, digits
import cv2
"""
Flip image and bounding box for computer vision task
https://paperswithcode.com/method/randomhorizontalflip
"""
LABEL_DIR = ""
IMAGE_DIR = ""
OUTPUT_DIR = ""
FLIP_TYPE = 1
def main() -> None:
"""
Get images list and annotations list from input dir.
Update new images and annotations.
Save images and annotations in output dir.
"""
img_paths, annos = get_dataset(LABEL_DIR, IMAGE_DIR)
print("Processing...")
new_images, new_annos, paths = update_image_and_anno(img_paths, annos, FLIP_TYPE)
for index, image in enumerate(new_images):
letter_code = random_chars(32)
file_name = paths[index].split(os.sep)[-1].rsplit(".", 1)[0]
file_root = f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
cv2.imwrite(f"{file_root}.jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 85])
print(f"Success {index+1}/{len(new_images)} with {file_name}")
annos_list = []
for anno in new_annos[index]:
obj = f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
annos_list.append(obj)
with open(f"{file_root}.txt", "w") as outfile:
outfile.write("\n".join(line for line in annos_list))
def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
"""
- label_dir <type: str>: Path to label include annotation of images
- img_dir <type: str>: Path to folder contain images
Return <type: list>: List of images path and labels
"""
img_paths = []
labels = []
for label_file in glob.glob(os.path.join(label_dir, "*.txt")):
label_name = label_file.split(os.sep)[-1].rsplit(".", 1)[0]
with open(label_file) as in_file:
obj_lists = in_file.readlines()
img_path = os.path.join(img_dir, f"{label_name}.jpg")
boxes = []
for obj_list in obj_lists:
obj = obj_list.rstrip("\n").split(" ")
boxes.append(
[
int(obj[0]),
float(obj[1]),
float(obj[2]),
float(obj[3]),
float(obj[4]),
]
)
if not boxes:
continue
img_paths.append(img_path)
labels.append(boxes)
return img_paths, labels
def update_image_and_anno(
img_list: list, anno_list: list, flip_type: int = 1
) -> tuple[list, list, list]:
"""
- img_list <type: list>: list of all images
- anno_list <type: list>: list of all annotations of specific image
- flip_type <type: int>: 0 is vertical, 1 is horizontal
Return:
- new_imgs_list <type: narray>: image after resize
- new_annos_lists <type: list>: list of new annotation after scale
- path_list <type: list>: list the name of image file
"""
new_annos_lists = []
path_list = []
new_imgs_list = []
for idx in range(len(img_list)):
new_annos = []
path = img_list[idx]
path_list.append(path)
img_annos = anno_list[idx]
img = cv2.imread(path)
if flip_type == 1:
new_img = cv2.flip(img, flip_type)
for bbox in img_annos:
x_center_new = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]])
elif flip_type == 0:
new_img = cv2.flip(img, flip_type)
for bbox in img_annos:
y_center_new = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]])
new_annos_lists.append(new_annos)
new_imgs_list.append(new_img)
return new_imgs_list, new_annos_lists, path_list
def random_chars(number_char: int = 32) -> str:
"""
Automatic generate random 32 characters.
Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
>>> len(random_chars(32))
32
"""
assert number_char > 1, "The number of character should greater than 1"
letter_code = ascii_lowercase + digits
return "".join(random.choice(letter_code) for _ in range(number_char))
if __name__ == "__main__":
main()
print("DONE ✅")