https://github.com/shamiul5201/selfie_app_features_application
OpenCV project to build skills in image processing: features include Cartoonify and Pencil Sketch effects in a Jupyter Notebook, blemish removal in a Python script, and chroma keying for videos. Perfect for exploring creative and practical computer vision techniques.
https://github.com/shamiul5201/selfie_app_features_application
computer-vision numpy opencv python
Last synced: about 2 months ago
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OpenCV project to build skills in image processing: features include Cartoonify and Pencil Sketch effects in a Jupyter Notebook, blemish removal in a Python script, and chroma keying for videos. Perfect for exploring creative and practical computer vision techniques.
- Host: GitHub
- URL: https://github.com/shamiul5201/selfie_app_features_application
- Owner: shamiul5201
- Created: 2024-11-09T19:16:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-27T20:03:05.000Z (over 1 year ago)
- Last Synced: 2025-09-14T07:39:58.429Z (9 months ago)
- Topics: computer-vision, numpy, opencv, python
- Language: Jupyter Notebook
- Homepage:
- Size: 19.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Selfie App Features Application
Welcome to this repository! This project showcases a variety of image processing features implemented using Python. Below, you'll find an overview of the three key features included in this repository:
3. Blemish Removal output
https://github.com/user-attachments/assets/bfd299eb-ed15-4f58-ae57-2fc2414e2e41
4. Chroma Keying Output
https://github.com/user-attachments/assets/e6b96933-6904-46da-a102-698107a35f2b
## Blemish Removal
### Overview
The blemish removal tool is an interactive image-editing utility designed to remove unwanted spots or blemishes from an image. It allows users to click on a blemish in the image, automatically identifies the best replacement patch, and blends the patch seamlessly into the selected area. This is achieved through gradient-based patch selection and seamless cloning using OpenCV.
### Key Functions and Their Purposes
1. **`sobel_filter(crop_img)`**
- Calculates gradients in the x and y directions for a given image patch using the Sobel operator.
- These gradients are used to identify texture changes, which help find smooth patches for replacement.
2. **`append_dictionary(x, y, r, source)`**
- Extracts a patch from the image and calculates its gradients using `sobel_filter`.
- Returns the gradient information, which helps assess the patch's smoothness.
3. **`identify_best_patch(x, y, r, source)`**
- Searches for candidate patches around the blemish location.
- Compares patches and selects the one with the smoothest gradients (lowest combined x and y gradients) for replacement.
4. **`selected_blemish(x, y, r, source)`**
- Wrapper function that calls `identify_best_patch` and returns the optimal patch location for a blemish.
5. **`blemish_removal(action, x, y, flags, userdata)`**
- Handles mouse events, allowing the user to select blemishes interactively:
- **Left Mouse Click:** Selects a blemish and replaces it with the best-matching patch using OpenCV's `seamlessClone`.
- **Mouse Release:** Updates and displays the modified image.
6. **Main Loop**
- Sets up the OpenCV window and listens for user actions:
- **Key 'C':** Resets the image to its original state.
- **Esc Key:** Exits the application.
- Displays the interactive blemish removal tool.
### Usage and Prerequisites
#### Prerequisites
1. Install OpenCV and NumPy:
```python
pip install opencv-python numpy
```
2. Execute the script:
```python
python 02_blemish_removal.py
```
#### Example Use Case
- **Removing small imperfections in portrait images for photo editing.**
---
## Chroma Keying
### Overview
The chroma keying tool replaces a specific color (e.g., green screen) in a video or image with a new background. This technique is widely used in video editing, film production, and real-time streaming to create visually dynamic content.
### Key Functions and Their Purposes
1. **`chroma_key(foreground_frame, background_frame, lower_color, upper_color, softness=0)`**
- Implements the chroma key (green screen) effect by performing the following steps:
- Converts the foreground image to HSV color space.
- Creates a binary mask to isolate the specified color range (e.g., green screen).
- Optionally applies Gaussian blur to soften the edges of the mask.
- Extracts the subject from the foreground using the inverted mask.
- Resizes the background to match the size of the foreground frame.
- Replaces the masked area with the resized background.
2. **Main Loop**
- Reads frames from the foreground (green screen) and background videos.
- Applies the `chroma_key` function frame-by-frame.
- If the background video runs out of frames, loops it to ensure continuous playback.
- Displays the composited video output in real-time.
- Exits the application when the **'Q' key** is pressed.