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* feat: Add BYTETrack for face/person tracking * docs: Update documentation * ref: Update tools folder file naming and imports * docs: Update jupyter notebook examples * ref: Rename the file and remove duplicate codes * docs: Update README.md * chore: Update description in mkdocs, add keywords for face tracking * docs: Add announcement section * feat: Remove expand bbox for tracking and update docs
85 lines
1.8 KiB
Markdown
85 lines
1.8 KiB
Markdown
# Batch Processing
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Process multiple images efficiently.
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!!! note "Work in Progress"
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This page contains example code patterns. Test thoroughly before using in production.
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---
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## Basic Batch Processing
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```python
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import cv2
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from pathlib import Path
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from uniface.detection import RetinaFace
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detector = RetinaFace()
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def process_directory(input_dir, output_dir):
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"""Process all images in a directory."""
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input_path = Path(input_dir)
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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for image_path in input_path.glob("*.jpg"):
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print(f"Processing {image_path.name}...")
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image = cv2.imread(str(image_path))
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faces = detector.detect(image)
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print(f" Found {len(faces)} face(s)")
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# Process and save results
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# ... your code here ...
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# Usage
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process_directory("input_images/", "output_images/")
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```
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---
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## With Progress Bar
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```python
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from tqdm import tqdm
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for image_path in tqdm(image_files, desc="Processing"):
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# ... process image ...
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pass
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```
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---
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## Extract Embeddings
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```python
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from uniface.detection import RetinaFace
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from uniface.recognition import ArcFace
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import numpy as np
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detector = RetinaFace()
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recognizer = ArcFace()
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embeddings = {}
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for image_path in Path("faces/").glob("*.jpg"):
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image = cv2.imread(str(image_path))
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faces = detector.detect(image)
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if faces:
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embedding = recognizer.get_normalized_embedding(image, faces[0].landmarks)
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embeddings[image_path.stem] = embedding
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# Save embeddings
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np.savez("embeddings.npz", **embeddings)
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```
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---
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## See Also
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- [Video & Webcam](video-webcam.md) - Real-time processing
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- [Face Search](face-search.md) - Search through embeddings
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- [Image Pipeline](image-pipeline.md) - Full analysis pipeline
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- [Detection Module](../modules/detection.md) - Detection options
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