pic2triangle

Welcome to img2triangle! img2triangle is a specialized tool designed for image triangulation. It processes an input image to identify boundaries and subsequently generate triangle mesh through boundaries. The triangulated data is saved in a structured JSON format, suitable for further rendering or analysis.

Features

1. Image Boundary Detection: Identifies the primary contours and structures within the image by recognizing its edges.
2. Triangulation: Segments the image into triangular regions.
3. Visualization: Provides a visual representation of the triangulated image.
4. JSON Export: Outputs the triangulated data in a structured JSON format.

How to use it

Environment Setup

Before using the tool, ensure the following Python libraries are installed:

• OpenCV (cv2)
• NumPy (numpy)
• Triangle (triangle)
• Matplotlib (matplotlib)

You can install all these libraries using pip:

pip install opencv-python numpy triangle matplotlib

Execution Steps

To utilize the tool, execute the following command:

python img2triangle.py

Systematic Breakdown

Input Image

Start by designing your own graphic image. This could be an image you've created using a digital drawing tool on your PC, an artwork on your iPad, or even a hand-drawn sketch that you've scanned.

Boundary Detection Phase

The tool utilizes OpenCV's robust functions to detect image boundaries, thereby outlining major image segments.

• Edge Detection: The image is first converted to grayscale, and then the Canny function from the OpenCV library is used to detect edges within this grayscale image.

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 220, 240)

• Contour Identification: The detected edges are accentuated using dilation. After dilation, the contours (or boundaries) are extracted from these edges.

kernel = np.ones((5,5),np.uint8)
dilated = cv2.dilate(edges, kernel, iterations=1)
contours, hierarchy = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

• Polygonal Simplification: Each contour is simplified into polygonal shapes using the approxPolyDP function. This allows intricate designs to be represented as basic polygons, which allows to futher generate triangle mesh.

epsilon = 0.001 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)

Triangulation Process

The identified boundaries are further processed to produce a triangular partitioning of the image.

• Triangulation Operation: The Triangle library is used to partition the identified regions into triangles.

vertex_array = np.vstack(points).astype(np.float32)
vertex_dict = {'vertices': vertex_array, 'segments': segments}
tri_data = triangle.triangulate(vertex_dict, 'pa800')

Visualization via JSON

The derived triangular data is transcribed into a JSON file. This JSON file can then be processed by various renderers to visualize the triangulated design.

• JSON Compilation: All the triangular data is documented in a structured JSON format, which enhances its usability across different applications.

json_data = triangles_to_json(triangle_points, image)
save_to_json(json_data, 'triangles.json')

Contributing

Contributions to enhance the tool's functionality are welcome. Please fork the repository, make modifications, and submit pull requests.

License

This project is under the GNU GPLv3 License. Refer to the LICENSE file for more information.

Thank you for using img2triangle.