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"""Source: https://github.com/jason9075/opencv-mosaic-data-aug"""

import glob

import os

import random

from string import ascii_lowercase, digits

import cv2

import numpy as np

# Parrameters

OUTPUT_SIZE = (720, 1280) # Height, Width

SCALE_RANGE = (0.4, 0.6) # if height or width lower than this scale, drop it.

FILTER_TINY_SCALE = 1 / 100

LABEL_DIR = ""

IMG_DIR = ""

OUTPUT_DIR = ""

NUMBER_IMAGES = 250

def main() -> None:

"""

Get images list and annotations list from input dir.

Update new images and annotations.

Save images and annotations in output dir.

>>> pass # A doctest is not possible for this function.

"""

img_paths, annos = get_dataset(LABEL_DIR, IMG_DIR)

for index in range(NUMBER_IMAGES):

idxs = random.sample(range(len(annos)), 4)

new_image, new_annos, path = update_image_and_anno(

img_paths,

annos,

idxs,

OUTPUT_SIZE,

SCALE_RANGE,

filter_scale=FILTER_TINY_SCALE,

)

# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'

letter_code = random_chars(32)

file_name = path.split(os.sep)[-1].rsplit(".", 1)[0]

file_root = f"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"

cv2.imwrite(f"{file_root}.jpg", new_image, [cv2.IMWRITE_JPEG_QUALITY, 85])

print(f"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}")

annos_list = []

for anno in new_annos:

width = anno[3] - anno[1]

height = anno[4] - anno[2]

x_center = anno[1] + width / 2

y_center = anno[2] + height / 2

obj = f"{anno[0]} {x_center} {y_center} {width} {height}"

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

>>> pass # A doctest is not possible for this function.

"""

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(" ")

xmin = float(obj[1]) - float(obj[3]) / 2

ymin = float(obj[2]) - float(obj[4]) / 2

xmax = float(obj[1]) + float(obj[3]) / 2

ymax = float(obj[2]) + float(obj[4]) / 2

boxes.append([int(obj[0]), xmin, ymin, xmax, ymax])

if not boxes:

continue

img_paths.append(img_path)

labels.append(boxes)

return img_paths, labels

def update_image_and_anno(

all_img_list: list,

all_annos: list,

idxs: list[int],

output_size: tuple[int, int],

scale_range: tuple[float, float],

filter_scale: float = 0.0,

) -> tuple[list, list, str]:

"""

- all_img_list <type: list>: list of all images

- all_annos <type: list>: list of all annotations of specific image

- idxs <type: list>: index of image in list

- output_size <type: tuple>: size of output image (Height, Width)

- scale_range <type: tuple>: range of scale image

- filter_scale <type: float>: the condition of downscale image and bounding box

Return:

- output_img <type: narray>: image after resize

- new_anno <type: list>: list of new annotation after scale

- path[0] <type: string>: get the name of image file

>>> pass # A doctest is not possible for this function.

"""

output_img = np.zeros([output_size[0], output_size[1], 3], dtype=np.uint8)

scale_x = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])

scale_y = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])

divid_point_x = int(scale_x * output_size[1])

divid_point_y = int(scale_y * output_size[0])

new_anno = []

path_list = []

for i, index in enumerate(idxs):

path = all_img_list[index]

path_list.append(path)

img_annos = all_annos[index]

img = cv2.imread(path)

if i == 0: # top-left

img = cv2.resize(img, (divid_point_x, divid_point_y))

output_img[:divid_point_y, :divid_point_x, :] = img

for bbox in img_annos:

xmin = bbox[1] * scale_x

ymin = bbox[2] * scale_y

xmax = bbox[3] * scale_x

ymax = bbox[4] * scale_y

new_anno.append([bbox[0], xmin, ymin, xmax, ymax])

elif i == 1: # top-right

img = cv2.resize(img, (output_size[1] - divid_point_x, divid_point_y))

output_img[:divid_point_y, divid_point_x : output_size[1], :] = img

for bbox in img_annos:

xmin = scale_x + bbox[1] * (1 - scale_x)

ymin = bbox[2] * scale_y

xmax = scale_x + bbox[3] * (1 - scale_x)

ymax = bbox[4] * scale_y

new_anno.append([bbox[0], xmin, ymin, xmax, ymax])

elif i == 2: # bottom-left

img = cv2.resize(img, (divid_point_x, output_size[0] - divid_point_y))

output_img[divid_point_y : output_size[0], :divid_point_x, :] = img

for bbox in img_annos:

xmin = bbox[1] * scale_x

ymin = scale_y + bbox[2] * (1 - scale_y)

xmax = bbox[3] * scale_x

ymax = scale_y + bbox[4] * (1 - scale_y)

new_anno.append([bbox[0], xmin, ymin, xmax, ymax])

else: # bottom-right

img = cv2.resize(

img, (output_size[1] - divid_point_x, output_size[0] - divid_point_y)

)

output_img[

divid_point_y : output_size[0], divid_point_x : output_size[1], :

] = img

for bbox in img_annos:

xmin = scale_x + bbox[1] * (1 - scale_x)

ymin = scale_y + bbox[2] * (1 - scale_y)

xmax = scale_x + bbox[3] * (1 - scale_x)

ymax = scale_y + bbox[4] * (1 - scale_y)

new_anno.append([bbox[0], xmin, ymin, xmax, ymax])

# Remove bounding box small than scale of filter

if 0 < filter_scale:

new_anno = [

anno

for anno in new_anno

if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])

]

return output_img, new_anno, path_list[0]

def random_chars(number_char: int) -> str:

"""

Automatic generate random 32 characters.

Get random string code: '7b7ad245cdff75241935e4dd860f3bad'

>>> len(random_chars(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 ✅")

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