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ADD the algorithms of image augmentation (#5792)
* ADD the algorithms of image augmentation * ADD the algorithms of image augmentation * ADD the algorithms of image augmentation * ADD the algorithms of image augmentation * ADD the algorithms of image augmentation * ADD the algorithms of image augmentation * UPDATE format code * UPDATE format and recode structure * UPDATE format import library * UPDATE code structure * Fix all checks have failded * FIX variable format * FIX variable format * FIX variable format * FIX code structure * FIX code structure * FIX code structure * FIX code structure
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Khoi Vo
GitHub
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a98465230f
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131
computer_vision/flip_augmentation.py
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131
computer_vision/flip_augmentation.py
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import glob
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import os
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import random
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from string import ascii_lowercase, digits
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import cv2
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"""
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Flip image and bounding box for computer vision task
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https://paperswithcode.com/method/randomhorizontalflip
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"""
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# Params
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LABEL_DIR = ""
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IMAGE_DIR = ""
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OUTPUT_DIR = ""
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FLIP_TYPE = 1 # (0 is vertical, 1 is horizontal)
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def main() -> None:
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"""
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Get images list and annotations list from input dir.
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Update new images and annotations.
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Save images and annotations in output dir.
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>>> pass # A doctest is not possible for this function.
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"""
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img_paths, annos = get_dataset(LABEL_DIR, IMAGE_DIR)
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print("Processing...")
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new_images, new_annos, paths = update_image_and_anno(img_paths, annos, FLIP_TYPE)
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for index, image in enumerate(new_images):
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# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
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letter_code = random_chars(32)
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file_name = paths[index].split(os.sep)[-1].rsplit(".", 1)[0]
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file_root = f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
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cv2.imwrite(f"/{file_root}.jpg", image, [cv2.IMWRITE_JPEG_QUALITY, 85])
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print(f"Success {index+1}/{len(new_images)} with {file_name}")
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annos_list = []
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for anno in new_annos[index]:
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obj = f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
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annos_list.append(obj)
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with open(f"/{file_root}.txt", "w") as outfile:
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outfile.write("\n".join(line for line in annos_list))
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def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
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"""
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- label_dir <type: str>: Path to label include annotation of images
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- img_dir <type: str>: Path to folder contain images
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Return <type: list>: List of images path and labels
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>>> pass # A doctest is not possible for this function.
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"""
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img_paths = []
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labels = []
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for label_file in glob.glob(os.path.join(label_dir, "*.txt")):
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label_name = label_file.split(os.sep)[-1].rsplit(".", 1)[0]
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with open(label_file) as in_file:
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obj_lists = in_file.readlines()
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img_path = os.path.join(img_dir, f"{label_name}.jpg")
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boxes = []
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for obj_list in obj_lists:
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obj = obj_list.rstrip("\n").split(" ")
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boxes.append(
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[
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int(obj[0]),
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float(obj[1]),
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float(obj[2]),
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float(obj[3]),
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float(obj[4]),
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]
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)
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if not boxes:
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continue
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img_paths.append(img_path)
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labels.append(boxes)
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return img_paths, labels
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def update_image_and_anno(
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img_list: list, anno_list: list, flip_type: int = 1
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) -> tuple[list, list, list]:
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"""
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- img_list <type: list>: list of all images
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- anno_list <type: list>: list of all annotations of specific image
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- flip_type <type: int>: 0 is vertical, 1 is horizontal
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Return:
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- new_imgs_list <type: narray>: image after resize
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- new_annos_lists <type: list>: list of new annotation after scale
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- path_list <type: list>: list the name of image file
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>>> pass # A doctest is not possible for this function.
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"""
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new_annos_lists = []
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path_list = []
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new_imgs_list = []
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for idx in range(len(img_list)):
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new_annos = []
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path = img_list[idx]
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path_list.append(path)
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img_annos = anno_list[idx]
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img = cv2.imread(path)
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if flip_type == 1:
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new_img = cv2.flip(img, flip_type)
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for bbox in img_annos:
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x_center_new = 1 - bbox[1]
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new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]])
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elif flip_type == 0:
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new_img = cv2.flip(img, flip_type)
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for bbox in img_annos:
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y_center_new = 1 - bbox[2]
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new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]])
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new_annos_lists.append(new_annos)
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new_imgs_list.append(new_img)
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return new_imgs_list, new_annos_lists, path_list
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def random_chars(number_char: int = 32) -> str:
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"""
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Automatic generate random 32 characters.
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Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
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>>> len(random_chars(32))
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32
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"""
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assert number_char > 1, "The number of character should greater than 1"
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letter_code = ascii_lowercase + digits
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return "".join(random.choice(letter_code) for _ in range(number_char))
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if __name__ == "__main__":
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main()
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print("DONE ✅")
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189
computer_vision/mosaic_augmentation.py
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computer_vision/mosaic_augmentation.py
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"""Source: https://github.com/jason9075/opencv-mosaic-data-aug"""
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import glob
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import os
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import random
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from string import ascii_lowercase, digits
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import cv2
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import numpy as np
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# Parrameters
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OUTPUT_SIZE = (720, 1280) # Height, Width
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SCALE_RANGE = (0.4, 0.6) # if height or width lower than this scale, drop it.
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FILTER_TINY_SCALE = 1 / 100
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LABEL_DIR = ""
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IMG_DIR = ""
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OUTPUT_DIR = ""
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NUMBER_IMAGES = 250
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def main() -> None:
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"""
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Get images list and annotations list from input dir.
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Update new images and annotations.
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Save images and annotations in output dir.
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>>> pass # A doctest is not possible for this function.
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"""
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img_paths, annos = get_dataset(LABEL_DIR, IMG_DIR)
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for index in range(NUMBER_IMAGES):
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idxs = random.sample(range(len(annos)), 4)
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new_image, new_annos, path = update_image_and_anno(
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img_paths,
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annos,
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idxs,
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OUTPUT_SIZE,
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SCALE_RANGE,
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filter_scale=FILTER_TINY_SCALE,
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)
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# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
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letter_code = random_chars(32)
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file_name = path.split(os.sep)[-1].rsplit(".", 1)[0]
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file_root = f"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
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cv2.imwrite(f"{file_root}.jpg", new_image, [cv2.IMWRITE_JPEG_QUALITY, 85])
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print(f"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}")
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annos_list = []
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for anno in new_annos:
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width = anno[3] - anno[1]
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height = anno[4] - anno[2]
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x_center = anno[1] + width / 2
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y_center = anno[2] + height / 2
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obj = f"{anno[0]} {x_center} {y_center} {width} {height}"
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annos_list.append(obj)
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with open(f"{file_root}.txt", "w") as outfile:
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outfile.write("\n".join(line for line in annos_list))
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def get_dataset(label_dir: str, img_dir: str) -> tuple[list, list]:
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"""
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- label_dir <type: str>: Path to label include annotation of images
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- img_dir <type: str>: Path to folder contain images
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Return <type: list>: List of images path and labels
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>>> pass # A doctest is not possible for this function.
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"""
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img_paths = []
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labels = []
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for label_file in glob.glob(os.path.join(label_dir, "*.txt")):
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label_name = label_file.split(os.sep)[-1].rsplit(".", 1)[0]
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with open(label_file) as in_file:
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obj_lists = in_file.readlines()
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img_path = os.path.join(img_dir, f"{label_name}.jpg")
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boxes = []
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for obj_list in obj_lists:
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obj = obj_list.rstrip("\n").split(" ")
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xmin = float(obj[1]) - float(obj[3]) / 2
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ymin = float(obj[2]) - float(obj[4]) / 2
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xmax = float(obj[1]) + float(obj[3]) / 2
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ymax = float(obj[2]) + float(obj[4]) / 2
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boxes.append([int(obj[0]), xmin, ymin, xmax, ymax])
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if not boxes:
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continue
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img_paths.append(img_path)
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labels.append(boxes)
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return img_paths, labels
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def update_image_and_anno(
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all_img_list: list,
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all_annos: list,
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idxs: list[int],
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output_size: tuple[int, int],
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scale_range: tuple[float, float],
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filter_scale: float = 0.0,
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) -> tuple[list, list, str]:
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"""
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- all_img_list <type: list>: list of all images
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- all_annos <type: list>: list of all annotations of specific image
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- idxs <type: list>: index of image in list
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- output_size <type: tuple>: size of output image (Height, Width)
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- scale_range <type: tuple>: range of scale image
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- filter_scale <type: float>: the condition of downscale image and bounding box
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Return:
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- output_img <type: narray>: image after resize
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- new_anno <type: list>: list of new annotation after scale
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- path[0] <type: string>: get the name of image file
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>>> pass # A doctest is not possible for this function.
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"""
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output_img = np.zeros([output_size[0], output_size[1], 3], dtype=np.uint8)
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scale_x = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
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scale_y = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
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divid_point_x = int(scale_x * output_size[1])
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divid_point_y = int(scale_y * output_size[0])
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new_anno = []
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path_list = []
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for i, index in enumerate(idxs):
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path = all_img_list[index]
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path_list.append(path)
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img_annos = all_annos[index]
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img = cv2.imread(path)
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if i == 0: # top-left
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img = cv2.resize(img, (divid_point_x, divid_point_y))
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output_img[:divid_point_y, :divid_point_x, :] = img
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for bbox in img_annos:
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xmin = bbox[1] * scale_x
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ymin = bbox[2] * scale_y
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xmax = bbox[3] * scale_x
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ymax = bbox[4] * scale_y
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new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
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elif i == 1: # top-right
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img = cv2.resize(img, (output_size[1] - divid_point_x, divid_point_y))
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output_img[:divid_point_y, divid_point_x : output_size[1], :] = img
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for bbox in img_annos:
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xmin = scale_x + bbox[1] * (1 - scale_x)
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ymin = bbox[2] * scale_y
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xmax = scale_x + bbox[3] * (1 - scale_x)
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ymax = bbox[4] * scale_y
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new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
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elif i == 2: # bottom-left
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img = cv2.resize(img, (divid_point_x, output_size[0] - divid_point_y))
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output_img[divid_point_y : output_size[0], :divid_point_x, :] = img
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for bbox in img_annos:
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xmin = bbox[1] * scale_x
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ymin = scale_y + bbox[2] * (1 - scale_y)
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xmax = bbox[3] * scale_x
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ymax = scale_y + bbox[4] * (1 - scale_y)
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new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
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else: # bottom-right
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img = cv2.resize(
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img, (output_size[1] - divid_point_x, output_size[0] - divid_point_y)
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)
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output_img[
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divid_point_y : output_size[0], divid_point_x : output_size[1], :
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] = img
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for bbox in img_annos:
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xmin = scale_x + bbox[1] * (1 - scale_x)
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ymin = scale_y + bbox[2] * (1 - scale_y)
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xmax = scale_x + bbox[3] * (1 - scale_x)
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ymax = scale_y + bbox[4] * (1 - scale_y)
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new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
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# Remove bounding box small than scale of filter
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if 0 < filter_scale:
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new_anno = [
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anno
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for anno in new_anno
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if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
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]
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return output_img, new_anno, path_list[0]
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def random_chars(number_char: int) -> str:
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"""
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Automatic generate random 32 characters.
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Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
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>>> len(random_chars(32))
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32
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"""
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assert number_char > 1, "The number of character should greater than 1"
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letter_code = ascii_lowercase + digits
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return "".join(random.choice(letter_code) for _ in range(number_char))
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if __name__ == "__main__":
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main()
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print("DONE ✅")
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