mirror of
https://github.com/yakhyo/uniface.git
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666438909d
- silent by default (only warnings/errors) - add --verbose flag to all scripts - add enable_logging() function for library users - cleaner output for end users
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import cv2
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import argparse
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import numpy as np
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# Use the new high-level factory functions for consistency
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from uniface.detection import create_detector
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from uniface.recognition import create_recognizer
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def run_inference(detector, recognizer, image_path: str):
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"""
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Detect faces and extract embeddings from a single image.
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Args:
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detector: Initialized face detector.
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recognizer: Initialized face recognition model.
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image_path (str): Path to the input image.
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"""
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image = cv2.imread(image_path)
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if image is None:
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print(f"Error: Failed to load image from '{image_path}'")
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return
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faces = detector.detect(image)
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if not faces:
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print("No faces detected.")
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return
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print(f"Detected {len(faces)} face(s). Extracting embeddings for the first face...")
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# Process the first detected face
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first_face = faces[0]
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landmarks = np.array(first_face['landmarks']) # Convert landmarks to numpy array
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# Extract embedding using the landmarks from the face dictionary
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embedding = recognizer.get_embedding(image, landmarks)
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norm_embedding = recognizer.get_normalized_embedding(image, landmarks)
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# Print some info about the embeddings
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print(f" - Embedding shape: {embedding.shape}")
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print(f" - L2 norm of unnormalized embedding: {np.linalg.norm(embedding):.4f}")
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print(f" - L2 norm of normalized embedding: {np.linalg.norm(norm_embedding):.4f}")
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def main():
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parser = argparse.ArgumentParser(description="Extract face embeddings from a single image.")
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parser.add_argument("--image", type=str, required=True, help="Path to the input image.")
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parser.add_argument(
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"--detector",
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type=str,
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default="retinaface",
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choices=['retinaface', 'scrfd'],
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help="Face detection method to use."
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)
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parser.add_argument(
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"--recognizer",
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type=str,
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default="arcface",
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choices=['arcface', 'mobileface', 'sphereface'],
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help="Face recognition method to use."
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)
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parser.add_argument("--verbose", action="store_true", help="Enable verbose logging")
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args = parser.parse_args()
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if args.verbose:
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from uniface import enable_logging
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enable_logging()
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print(f"Initializing detector: {args.detector}")
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detector = create_detector(method=args.detector)
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print(f"Initializing recognizer: {args.recognizer}")
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recognizer = create_recognizer(method=args.recognizer)
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run_inference(detector, recognizer, args.image)
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if __name__ == "__main__":
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main()
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