https://github.com/aayush-bhargav/vr_assignment1_aayushbhargav_imt2022089
https://github.com/aayush-bhargav/vr_assignment1_aayushbhargav_imt2022089
Last synced: 4 days ago
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- Host: GitHub
- URL: https://github.com/aayush-bhargav/vr_assignment1_aayushbhargav_imt2022089
- Owner: Aayush-Bhargav
- Created: 2025-03-01T13:32:26.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-01T13:47:23.000Z (4 months ago)
- Last Synced: 2025-03-01T14:30:59.447Z (4 months ago)
- Language: Python
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Computer Vision Project: Coin Detection and Image Stitching
This repository contains Python scripts for performing two computer vision tasks:
1. Coin detection, segmentation, and counting
2. Image stitching to create panoramas
## Project Structure
```
IMT2022089_VR_ASSIGNMENT1/
├── EdgeDetection_Results_Image1/ # Output from coin detection for Image1
├── EdgeDetection_Results_Image2/ # Output from coin detection for Image2
├── EdgeDetection_Results_Image3/ # Output from coin detection for Image3
├── Images/ # Contains input images for coin detection
│ ├── Image1.jpeg
│ ├── Image2.jpeg
│ └── Image3.jpeg
├── PanoramaResults_Stitching1/ # Stitching results for folder Stitching1
│ ├── composite_0.jpg # Initial composite (first image)
│ ├── composite_1.jpg # Result after blending second image
│ ├── image_1_with_features.jpg # First image with detected features
│ ├── image_2_with_features.jpg # Second image with detected features
│ ├── matches_1to2.jpg # Visualization of matches between images
│ └── stitched_panorama_Stitching1.jpg # Final panorama result
├── PanoramaResults_Stitching2/ # Stitching results for folder Stitching2
│ ├── composite_0.jpg # Initial composite (first image)
│ ├── composite_1.jpg # Result after blending second image
│ ├── composite_2.jpg # Result after blending third image
│ ├── image_1_with_features.jpg # First image with detected features
│ ├── image_2_with_features.jpg # Second image with detected features
│ ├── image_3_with_features.jpg # Third image with detected features
│ ├── matches_1to2.jpg # Matches between first and second images
│ ├── matches_2to3.jpg # Matches between second and third images
│ └── stitched_panorama_Stitching2.jpg # Final panorama result
├── Processed_Coins_Image1/ # Contains extracted coins from Image1
│ ├── ExtractedCoins/
│ └── detected_coins_overlay.png
├── Processed_Coins_Image2/ # Contains extracted coins from Image2
│ ├── ExtractedCoins/
│ └── detected_coins_overlay.png
├── Processed_Coins_Image3/ # Contains extracted coins from Image3
│ ├── ExtractedCoins/
│ └── detected_coins_overlay.png
├── Stitching1/ # Contains 2 split images for panorama stitching
│ ├── *.jpg
├── Stitching2/ # Contains 3 split images for panorama stitching
│ ├── *.jpg
├── coins.py # Script for coin detection and analysis
└── Stitching.py # Script for panorama creation
```
## Coin Detection and Analysis (coins.py)
This script processes images containing coins and performs three main tasks:
### a. Coin Detection
- Uses multiple edge detection techniques (Canny, Sobel, Laplacian)
- Visualizes detected coins by outlining them in the original image
- Outputs edge maps to the EdgeDetection_Results folders
### b. Coin Segmentation
- Extracts each individual coin from the image
- Uses adaptive thresholding and contour detection
- Outputs individual coin images to the Processed_Coins folders
- Creates a visualization overlay (detected_coins_overlay.png) in the Processed_Coins folder showing all detected coins
### c. Coin Counting
- Counts the total number of coins in each image
- Filters out non-coin objects using size and circularity criteria
- Prints the count to the console
### Methods Implemented
- Image enhancement with Gaussian blur
- Adaptive thresholding for binary mask creation
- Morphological operations for mask refinement
- Contour detection to identify coin boundaries
- Circularity calculation to filter out non-coin objects
## Image Stitching (Stitching.py)
This script creates panoramic images by stitching together multiple image segments.
## Features
- Processes two sets of images (Stitching1 with 2 images, Stitching2 with 3 images)
- SIFT feature detection for robust key point extraction
- Homography-based image alignment
- Distance-weighted blending for seamless transitions
- Automatic dark edge trimming for clean results
- Visualization of detected features and matches
- Support for processing multiple folders of images
- Results will be saved in folders named "PanoramaResults_Stitching1" and "PanoramaResults_Stitching2"
## Output Files
For each processed folder, the following files are generated:
- `image_X_with_features.jpg`: Shows detected features for each input image
- `matches_XtoY.jpg`: Visualizes the matching features between pairs of images
- `composite_X.jpg`: Intermediate results after blending X+1 images
- `stitched_panorama_[foldername].jpg`: The final panorama image
## Requirements
- Python 3.x
- OpenCV (cv2)
- NumPy
- Standard libraries: os, glob, shutil
## Installation
```bash
pip install opencv-python numpy glob2 shutil
```
## How to Run
### Clone the repository
```
git clone https://github.com/Aayush-Bhargav/VR_Assignment1_AayushBhargav_IMT2022089.git
```
### Navigate to the right directory
```
cd VR_Assignment1_AayushBhargav_IMT2022089
cd IMT2022089_VR_Assignment1
```
### Coin Detection
```bash
python3 coins.py
```
This will process all three images in the Images folder and output the results to the respective folders.
### Image Stitching
```bash
python3 Stitching.py
```
This will process the image sets in Stitching1 and Stitching2 folders and create panoramic images.
## Output
### Coin Detection
- Edge maps using different detection methods
- Visualization of detected coin boundaries
- Individual extracted coin images
- detected_coins_overlay.png showing all identified coins
- Console output with the count of coins detected
### Image Stitching
- Visualizations of detected features in each input image
- Visualizations of matched features between consecutive images
- Final stitched panorama images
- Step-by-step processing visualizations
## Notes
- The coin detection algorithm is designed to handle various lighting conditions and coin arrangements
- All input images are standardized to a consistent width before processing