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https://github.com/nuhmanpk/cv2filters
CV2Filters a powerful Python package designed as a wrapper around OpenCV,cv2Filters simplifies image processing tasks by providing a higher-level abstraction of the underlying OpenCV functionality
https://github.com/nuhmanpk/cv2filters
computer-vision computer-vision-opencv dataset hacktoberfest hacktoberfest-accepted hacktoberfest2022-accepted image-analysis image-manipulation image-processing object-detection ocr-processing opencv opencv-python pip preprocessing pypi python wrapper
Last synced: 25 days ago
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CV2Filters a powerful Python package designed as a wrapper around OpenCV,cv2Filters simplifies image processing tasks by providing a higher-level abstraction of the underlying OpenCV functionality
- Host: GitHub
- URL: https://github.com/nuhmanpk/cv2filters
- Owner: nuhmanpk
- License: mit
- Created: 2023-07-01T14:31:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-08T09:51:13.000Z (about 1 year ago)
- Last Synced: 2024-10-09T14:29:21.142Z (about 1 month ago)
- Topics: computer-vision, computer-vision-opencv, dataset, hacktoberfest, hacktoberfest-accepted, hacktoberfest2022-accepted, image-analysis, image-manipulation, image-processing, object-detection, ocr-processing, opencv, opencv-python, pip, preprocessing, pypi, python, wrapper
- Language: Python
- Homepage: https://pypi.org/project/cv2filters/
- Size: 68.7 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# CV2Filters
[](https://pepy.tech/project/cv2filters)
[](https://github.com/nuhmanpk/cv2filters/blob/main/LICENSE)
[](https://github.com/nuhmanpk/cv2filters/actions/workflows/publish.yml)
[](https://pypi.org/project/cv2filters)
[](https://pepy.tech/project/cv2filters)
CV2Filters a powerful Python package designed as a wrapper around OpenCV, the popular open-source computer vision library. cv2Filters simplifies image processing tasks by providing a higher-level abstraction of the underlying OpenCV functionality. This package aims to make image processing more accessible to both beginner and advanced users, enabling them to efficiently perform a wide range of image manipulation and analysis tasks
CV2Filters empowers users to harness the power of OpenCV in a simplified and intuitive manner. By abstracting away the complexities, the package enables a broader audience to explore image processing, drive innovation, and unlock new possibilities in the field of computer vision.
[](https://github.com/nuhmanpk/cv2filters)
## Installation
[Refer Example](https://github.com/nuhmanpk/cv2filters/wiki)
To install the library, you can use pip:
```shell
pip install cv2filters
```
## Usage
First, import the `Filters` class from the library:
```python
from cv2filters import Filters
# Create an instance of the Filters class
filters = Filters()
```
Now you can use the various filtering functions available in the library. For example, let's show how to use the increase_brightness and apply_blur functions:
Create an instance of the Filters class and then call the desired methods on the instance. For example:
```python
from cv2filters import Filters
# Create an instance of the Filters class
filters = Filters()
import cv2
# Read an image
cv2.imread('./path-to-img.jpg')
# Increase brightness of the image
brightened_image = filters.increase_brightness(image,value=10)
# Blur the image
blurred_image = filters.blur(image,kernal_size=(5,5))
# Rotate the image
rotated_image = filters.rotate_image(image, angle=45)
# Flip the image horizontally
flipped_image = filters.flip_image(image, flip_code=1)
# Crop a region from the image
cropped_image = filters.crop_image(image, x=100, y=100, width=200, height=200)
# Resize the image
resized_image = filters.resize_image(image, width=500, height=500)
# Convert the image to grayscale
grayscale_image = filters.convert_to_grayscale(image)
# Detect edges in the image
edges_image = filters.detect_edges(image,threshold1=100, threshold2=200)
# Apply the Sobel filter to detect edges
sobel_image = filters.sobel_filter(image,dx=1, dy= 1, ksize=3)
# Apply bilateral filtering to reduce noise
filtered_image = filters.bilateral_filter(image,d=9, sigma_color=75, sigma_space=75)
# Perform erosion on the image
eroded_image = filters.erosion(image,kernel=None, iterations=1)
# Perform dilation on the image
dilated_image = filters.dilation(image,kernel=None, iterations=1)
# Apply perspective transformation to the image
transformed_image = filters.perspective_transform(image, src_points, dst_points)
# Perform morphological opening on the image
opened_image = filters.morphological_opening(image,kernel=None, iterations=2)
# Perform morphological closing on the image
closed_image = filters.closing(image,kernel=None, iterations=2)
# Highlight a box region in the image
highlighted_image = filters.highlight_box(image, (x, y, width, height))
```
* Make sure to replace 'path_to_image.jpg' with the actual path to the image file you want to process, and adjust the method arguments as needed.
## Methods
1. **read_image**(file_path: str) -> np.ndarray:
This method reads an image from the specified file path and returns it as a NumPy array.
2. **increase_brightness**(image: np.ndarray, value: int = 10) -> np.ndarray:
This method increases the brightness of an image by the specified value.
3. **blur**(image: np.ndarray, kernel_size: tuple = (5, 5)) -> np.ndarray:
This method applies a Gaussian blur to the image using the specified kernel size.
4. **rotate_image**(image: np.ndarray, angle: float) -> np.ndarray:
This method rotates an image by the specified angle.
5. **flip_image**(image: np.ndarray, flip_code: int) -> np.ndarray:
This method flips an image horizontally or vertically based on the flip code.
6. **crop_image**(image: np.ndarray, x: int, y: int, width: int, height: int) -> np.ndarray:
This method crops a rectangular region from an image based on the specified coordinates and dimensions.
7. **resize_image**(image: np.ndarray, width: int = 250, height: int = 250) -> np.ndarray:
This method resizes an image to the specified width and height.
8. **convert_to_grayscale**(image: np.ndarray) -> np.ndarray:
This method converts an image to grayscale.
9. **detect_edges**(image: np.ndarray, threshold1: float = 100, threshold2: float = 200) -> np.ndarray:
This method detects edges in an image using the Canny edge detection algorithm.
10. **sobel_filter**(image: np.ndarray, dx: int = 1, dy: int = 1, ksize: int = 3) -> np.ndarray:
This method applies the Sobel filter to an image for edge detection.
11. **bilateral_filter**(image: np.ndarray, d: int = 9, sigma_color: float = 75, sigma_space: float = 75) -> np.ndarray:
This method applies a bilateral filter to an image for noise reduction.
12. **erosion**(image: np.ndarray, kernel: np.ndarray = None, iterations: int = 1) -> np.ndarray:
This method applies erosion to an image using the specified kernel and number of iterations.
13. **dilation**(image: np.ndarray, kernel: np.ndarray = None, iterations: int = 1) -> np.ndarray:
This method applies dilation to an image using the specified kernel and number of iterations.
14. **perspective_transform**(image: np.ndarray, src_points: np.ndarray, dst_points: np.ndarray) -> np.ndarray:
This method applies a perspective transformation to an image using the specified source and destination points.
15. **morphological_opening**(image: np.ndarray, kernel: np.ndarray = None, iterations: int = 2) -> np.ndarray:
This method applies morphological opening to an image to remove noise and small objects.
16. **closing**(image: np.ndarray, kernel: np.ndarray = None, iterations: int = 2) -> np.ndarray:
This method applies morphological closing to an image to close small holes.
17. **highlight_box**(image: np.ndarray, box: Tuple[int, int, int, int]) -> np.ndarray:
This method highlights a box region in an image by turning the rest of the image to black.
## For testing
```bash
python -m unittest tests.test_image_filters
```
## Contributing
If you'd like to contribute to this library, please follow these steps:
* Fork the repository.
* Create a new branch.
* Make your changes and test them.
* Submit a pull request.