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

blemish_removal_output.mp4

4. Chroma Keying Output

chroma_keying_output.mp4

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:

   pip install opencv-python numpy

2. Execute the script:

   python 02_blemish_removal.py

Example Use Case

• Removing small imperfections in portrait images for photo editing.

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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.