Multithreaded Image‑Processing

Objectives

1. Load an image – Read a single copy of the image into memory in the main thread; worker threads treat it as a shared resource.
2. Divide the image – Split the image into smaller, manageable sub‑matrices (chunks) and assign a specific range of the main matrix to each worker thread.
3. Apply filters – Implement various filters; each thread writes its results into its own matrix for its pixel range.
4. Multithreading – Process chunks in parallel using at least nine threads, chosen dynamically based on image size.
5. Synchronization – Without cloning the whole matrix, threads modify the shared image safely, ensuring neighbouring chunks don’t overwrite each other’s border pixels.
6. Reassemble and save – Merge all processed chunks back into a single image and write it to disk.

Features

• Sobel edge‑detection – 3 × 3 Sobel kernels highlight horizontal and vertical edges (apply_sobel).
• Grayscale conversion – luminance transform via rgb_to_grayscale for downstream filters.
• Mean thresholding – converts to high‑contrast black‑and‑white using the global mean (threshold_image).
• Oil‑painting effect – radius / intensity‑based stylisation that mimics brush strokes (apply_oil_painting).
• Warm tone filter – boosts reds and greens for a cosy film vibe (apply_warm_filter).
• Negative filter – inverts colours to produce an x‑ray look (apply_negative).
• Automatic chunking & thread pooling – divide_image splits the frame; number_of_threads scales from 9 → 25 threads depending on resolution.
• Thread‑safe in‑place writes – a shared threading.Lock ensures each worker writes its stripe without data races or extra memory.
• Pluggable architecture – add new effects by writing a filter_func(chunk, source_image, lock, **kwargs) and passing it to multi_threading.