OpenCV (Open Source Computer Vision Library), accessed in Python via cv2, is a powerful library for computer vision tasks. With it, you can read/write images, manipulate them through filtering, blurring, or resizing, and even perform advanced operations like facial recognition, object detection, and feature matching. It supports video processing, making it ideal for real-time applications. From hobby projects to professional-grade software, OpenCV serves a vast array of computer vision needs.
Introduction:
The provided code leverages OpenCV (cv2), a popular library for computer vision tasks. This code has two primary functions: one for blurring a specific region of a single image and the other for processing multiple images within a directory. By specifying coordinates and dimensions, users can obscure sensitive information or divert focus in photos. This can be especially useful for privacy-preserving operations, e.g., hiding license plates in car images, anonymizing faces, or preparing datasets without revealing confidential segments. The code's modular nature means it can easily integrate into larger photo-processing pipelines.
1. Import statements:
import cv2
import os- `cv2`: This is OpenCV, which will handle the image processing tasks.
- `os`: This will help in interacting with the operating system, like listing files in a directory.
2. The `blur_image_portion` function:
def blur_image_portion(img_path, x_offset, y_offset, blur_width, blur_height, kernel_size=51):
image = cv2.imread(img_path)
blur_area = image[y_offset:y_offset+blur_height, x_offset:x_offset+blur_width]
blurred_region = cv2.GaussianBlur(blur_area, (kernel_size, kernel_size), 0)
image[y_offset:y_offset+blur_height, x_offset:x_offset+blur_width] = blurred_region
return image- This function is meant to blur a specific area in a single image.
- The parameters define the image path, the coordinates for the top-left corner of the region to be blurred, the dimensions of the region, and the Gaussian blur kernel size.
- The function reads the image, extracts the region to be blurred, applies the Gaussian blur to this region, and then replaces the original region in the image with the blurred one.
3. The `blur_images_in_folder` function:
def blur_images_in_folder(folder_path, x_offset, y_offset, blur_width, blur_height, kernel_size=51):
for filename in os.listdir(folder_path):
img_path = os.path.join(folder_path, filename)
blurred_image = blur_image_portion(img_path, x_offset, y_offset, blur_width, blur_height, kernel_size)
cv2.imwrite(img_path, blurred_image)- This function processes all images in a given directory.
- It iterates over each image in the specified directory, calls the previously defined `blur_image_portion` function to blur a specific portion of the image, and then overwrites the original image with the blurred version.
4. Example usage:
blurred_img = blur_image_portion("path_to_image.jpg", 30, 27, 60, 45)
cv2.imwrite("blurred_image.jpg", blurred_img)
blur_images_in_folder("path_to_folder", 30, 27, 60, 45)- This segment demonstrates how to use the functions. The first set of lines shows how to blur a region in a single image, while the second line shows how to apply this to all images in a directory.
Full Code:
import cv2
import os
def blur_image_portion(img_path, x_offset, y_offset, blur_width, blur_height, kernel_size=51):
"""
Blurs a specific region of an image.
Parameters:
- img_path (str): The path to the image.
- x_offset, y_offset (int): Coordinates for the top-left corner of the region to be blurred.
- blur_width, blur_height (int): Width and height of the region to be blurred.
- kernel_size (int, optional): The size of the Gaussian blur kernel. Must be odd. Default is 51.
Returns:
- cv2 image: The modified image with the blurred region.
"""
image = cv2.imread(img_path)
blur_area = image[y_offset:y_offset+blur_height, x_offset:x_offset+blur_width]
blurred_region = cv2.GaussianBlur(blur_area, (kernel_size, kernel_size), 0)
image[y_offset:y_offset+blur_height, x_offset:x_offset+blur_width] = blurred_region
return image
def blur_images_in_folder(folder_path, x_offset, y_offset, blur_width, blur_height, kernel_size=51):
"""
Blurs a specific region of all images in the provided folder.
Parameters:
- folder_path (str): The path to the folder containing the images.
- x_offset, y_offset (int): Coordinates for the top-left corner of the region to be blurred.
- blur_width, blur_height (int): Width and height of the region to be blurred.
- kernel_size (int, optional): The size of the Gaussian blur kernel. Must be odd. Default is 51.
"""
for filename in os.listdir(folder_path):
img_path = os.path.join(folder_path, filename)
blurred_image = blur_image_portion(img_path, x_offset, y_offset, blur_width, blur_height, kernel_size)
cv2.imwrite(img_path, blurred_image)
# Example usage
# For a single image
blurred_img = blur_image_portion("path_to_image.jpg", 30, 27, 60, 45)
cv2.imwrite("blurred_image.jpg", blurred_img)
# For a folder containing multiple images
blur_images_in_folder("path_to_folder", 30, 27, 60, 45)
