blur_images.py

import os
import glob
import json
import cv2
import pybboxes as pbx
import yaml
import argparse
from ultralytics import YOLO


parser = argparse.ArgumentParser()
parser.add_argument("--config", help = "path of the training configuartion file", required = True)
args = parser.parse_args()

if (os.path.exists("annot_txt")):
    os.rmdir("annot_txt")

#Reading the configuration file
with open(args.config, 'r') as f:
    try:
        config = yaml.safe_load(f)
    except yaml.YAMLError as exc:
        print(exc)

model = YOLO(config["model_path"])

if(config["gpu_avail"]):
    _ = model(source=config['images_path'],
            save=False,
            save_txt=True,
            conf=config['detection_conf_thresh'],
            device='cuda:0',
            project='runs/detect/',
            name="yolo_images_pred")
else:
    _ = model(source=config['images_path'],
            save=False,
            save_txt=True,
            conf=config['detection_conf_thresh'],
            device='cpu',
            project="runs/detect/",
            name="yolo_images_pred")


#images = [int(item.split("/")[1].replace(config['img_format'], "")) for item in images]
images = sorted(glob.glob(config['images_path']+"/*"+config["img_format"]))

os.mkdir("annot_txt")

annot_dir = f'runs/detect/yolo_images_pred/labels/'

try:
    for file in os.listdir(annot_dir):
        if (file.endswith('.txt')):
            #frame_num = int(file.replace(".txt","").split("_")[1])
            with open(annot_dir+file, 'r') as fin:
                for line in fin.readlines():
                    line = [float(item) for item in line.split()[1:]]
                    line = pbx.convert_bbox(line, from_type="yolo", to_type="voc", image_size=(config["img_width"], config["img_height"]))
                    data_string = " ".join(str(num) for num in line)
                    with open(f"annot_txt/{os.path.basename(file)}", "a") as f:
                        f.write(data_string+"\n")
except:
    print(f'{os.path.basename(file)} has no detected objects.')


def blur_regions(image, regions):
    """
    Blurs the image, given the x1,y1,x2,y2 cordinates using Gaussian Blur.
    """
    for region in regions:
        x1,y1,x2,y2 = region
        x1, y1, x2, y2 = round(x1), round(y1), round(x2), round(y2)
        roi = image[y1:y2, x1:x2]
        blurred_roi = cv2.GaussianBlur(roi, (config['blur_radius'], config['blur_radius']), 0)
        image[y1:y2, x1:x2] = blurred_roi
    return image

txt_folder = 'annot_txt/'
image_folder = config['images_path']
output_folder = config['output_folder']

# Create the output folder if it doesn't exist
if not os.path.exists(output_folder):
    os.makedirs(output_folder)

# List all text files in the 'dir' folder
txt_files = [f for f in os.listdir(txt_folder) if f.endswith('.txt')]

for txt_file in txt_files:
    # Read the text file containing bounding box information
    with open(os.path.join(txt_folder, txt_file), 'r') as f:
        lines = f.readlines()

    # Extract bounding box coordinates from the txt file
    bboxes = []
    for line in lines:
        values = line.strip().split()
        x_min, y_min, x_max, y_max = map(int, values)  # Assuming VOC format with x_min, y_min, x_max, y_max
        bboxes.append([x_min, y_min, x_max, y_max])

    # Read the corresponding image
    image_file = txt_file.replace('.txt', config["img_format"])  # Assuming image files have .jpg extension
    image_path = os.path.join(image_folder, image_file)
    image = cv2.imread(image_path)

    # Apply Gaussian blur to each bounding box region
    for bbox in bboxes:
        image = blur_regions(image, bboxes)

    # Save the blurred image to the output folder
    output_file = txt_file.replace('.txt', '_blurred.jpg')
    output_path = os.path.join(output_folder, output_file)
    cv2.imwrite(output_path, image)

print(f"@@ The bluured images are saved in Directory -------> {config['output_folder']}")