1.5 KiB
1.5 KiB
Parallel Image Processor
Demonstrates how AsyncParallelBatchFlow processes multiple images with multiple filters >8x faster than sequential processing.
Features
graph TD
subgraph AsyncParallelBatchFlow[Image Processing Flow]
subgraph AsyncFlow[Per Image-Filter Flow]
A[Load Image] --> B[Apply Filter]
B --> C[Save Image]
end
end
- Processes images with multiple filters in parallel
- Applies three different filters (grayscale, blur, sepia)
- Shows significant speed improvement over sequential processing
- Manages system resources with semaphores
Run It
pip install -r requirements.txt
python main.py
Output
Parallel Image Processor
------------------------------
Found 3 images:
- images/bird.jpg
- images/cat.jpg
- images/dog.jpg
Running sequential batch flow...
Processing 3 images with 3 filters...
Total combinations: 9
Loading image: images/bird.jpg
Applying grayscale filter...
Saved: output/bird_grayscale.jpg
...etc
Timing Results:
Sequential batch processing: 13.76 seconds
Parallel batch processing: 1.71 seconds
Speedup: 8.04x
Processing complete! Check the output/ directory for results.
Key Points
-
Sequential: Total time = sum of all item times
- Good for: Rate-limited APIs, maintaining order
-
Parallel: Total time ≈ longest single item time
- Good for: I/O-bound tasks, independent operations