Peppa Puzzle Reconstructor

This project automatically reconstructs a shuffled image of a Peppa Pig puzzle using computer vision techniques. The algorithm identifies and extracts puzzle pieces, matches them with their correct positions in a reference image using feature matching and RANSAC, and overlays them to restore the original image.

Preprocessing

Before reconstruction can take place, the shuffled puzzle image undergoes several preprocessing steps to isolate individual puzzle pieces:

1. Grayscale Conversion
   The input image is converted to grayscale to simplify edge detection and reduce computational load.

2. Edge Detection
   Canny edge detection is applied to highlight the contours of each puzzle piece.

3. Morphological Operations
   Dilation and closing operations are used to strengthen the edges and close small gaps, making contours more distinct.

4. Contour Detection
   External contours are identified to extract bounding boxes around each puzzle piece.

5. Filtering by Area
   Small contours (noise or irrelevant fragments) are filtered out using a minimum area threshold to retain only meaningful puzzle segments.

The result of this preprocessing step is a set of cropped images — each representing a potential puzzle piece — which are then passed on for feature matching and reconstruction.

Visual Output of Preprocessing

How It Works

1. Piece Extraction
   Detects individual puzzle pieces from a shuffled image using edge detection and contour analysis via OpenCV.

3. Feature Matching with RANSAC
   Each puzzle piece is matched to a reference image using SIFT keypoints and descriptors. RANSAC is applied to estimate a homography matrix for accurate alignment.

4. Transparent Background
   White areas in the puzzle pieces are made transparent to blend seamlessly during reconstruction.

5. Puzzle Reconstruction
   Each aligned piece is overlayed onto a transparent canvas, rebuilding the complete puzzle.

Results

The reconstruction works even when the pieces are rotated or slightly misaligned, thanks to the robustness of the SIFT + RANSAC approach. While it performs well on clean images with distinct features, accuracy may decrease with blurry or low-contrast images.

Limitations

While the reconstruction pipeline is generally accurate, there are cases where two connected puzzle pieces are mistakenly treated as a single object during contour detection. This typically happens when:

• The gap between pieces is too small or visually subtle
• Morphological operations (dilation, closing) merge adjacent edges
• The white background isn't fully separated from piece boundaries

As a result, a few puzzle segments may be incorrectly aligned or misplaced in the final reconstruction.

Some extracted regions contained multiple puzzle pieces, as it's shown during the detection phase:

Future improvements could include:

• Adaptive contour separation techniques
• Color-based segmentation to better split adjacent regions
• Manual validation for ambiguous cases

Files Used

• peppa/peppa.png: The original complete puzzle image (reference).
• peppa/pz.png: The image containing randomly placed/shuffled puzzle pieces.

Requirements

• Python 3
• OpenCV (with cv2.SIFT_create() support, e.g., via opencv-contrib-python)
• NumPy

Install dependencies:

pip install opencv-contrib-python numpy