mirror of
https://github.com/yakhyo/uniface.git
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2c78f39e5d
- Add new test files for age_gender, factory, landmark, recognition, scrfd, and utils - Add new scripts for age_gender, landmarks, and video detection - Update documentation in README.md, MODELS.md, QUICKSTART.md - Improve model constants and face utilities - Update detection models (retinaface, scrfd) with enhanced functionality - Update project configuration in pyproject.toml
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
import os
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import cv2
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import time
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import argparse
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import numpy as np
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from uniface.detection import RetinaFace, SCRFD
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from uniface.visualization import draw_detections
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def run_inference(detector, image_path: str, vis_threshold: float = 0.6, save_dir: str = "outputs"):
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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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# 1. Get the list of face dictionaries from the detector
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faces = detector.detect(image)
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if faces:
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# 2. Unpack the data into separate lists
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bboxes = [face['bbox'] for face in faces]
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scores = [face['confidence'] for face in faces]
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landmarks = [face['landmarks'] for face in faces]
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# 3. Pass the unpacked lists to the drawing function
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draw_detections(image, bboxes, scores, landmarks, vis_threshold=0.6)
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os.makedirs(save_dir, exist_ok=True)
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output_path = os.path.join(save_dir, f"{os.path.splitext(os.path.basename(image_path))[0]}_out.jpg")
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cv2.imwrite(output_path, image)
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print(f"Output saved at: {output_path}")
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def main():
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parser = argparse.ArgumentParser(description="Run face detection on an 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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"--method",
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type=str,
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default="retinaface",
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choices=['retinaface', 'scrfd'],
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help="Detection method to use."
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)
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parser.add_argument("--threshold", type=float, default=0.6, help="Visualization confidence threshold")
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parser.add_argument("--iterations", type=int, default=1, help="Number of inference runs for benchmarking")
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parser.add_argument("--save_dir", type=str, default="outputs", help="Directory to save output images")
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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.method}")
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if args.method == 'retinaface':
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detector = RetinaFace()
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else:
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detector = SCRFD()
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avg_time = 0
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for i in range(args.iterations):
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start = time.time()
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run_inference(detector, args.image, args.threshold, args.save_dir)
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elapsed = time.time() - start
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print(f"[{i + 1}/{args.iterations}] Inference time: {elapsed:.4f} seconds")
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if i >= 0: # Avoid counting the first run if it includes model loading time
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avg_time += elapsed
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if args.iterations > 1:
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# Adjust average calculation to exclude potential first-run overhead
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effective_iterations = max(1, args.iterations)
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print(
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f"\nAverage inference time over {effective_iterations} runs: {avg_time / effective_iterations:.4f} seconds")
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if __name__ == "__main__":
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main()
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