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129 lines
4.1 KiB
Python
129 lines
4.1 KiB
Python
# Age and gender prediction on detected faces
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# Usage: python run_age_gender.py --image path/to/image.jpg
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# python run_age_gender.py --webcam
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import argparse
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import os
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from pathlib import Path
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import cv2
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from uniface import SCRFD, AgeGender, RetinaFace
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from uniface.visualization import draw_detections
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def draw_age_gender_label(image, bbox, gender_id: int, age: int):
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"""Draw age/gender label above the bounding box."""
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x1, y1 = int(bbox[0]), int(bbox[1])
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gender_str = 'Female' if gender_id == 0 else 'Male'
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text = f'{gender_str}, {age}y'
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(tw, th), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
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cv2.rectangle(image, (x1, y1 - th - 10), (x1 + tw + 10, y1), (0, 255, 0), -1)
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cv2.putText(image, text, (x1 + 5, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)
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def process_image(
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detector,
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age_gender,
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image_path: str,
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save_dir: str = 'outputs',
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threshold: float = 0.6,
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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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print(f'Detected {len(faces)} face(s)')
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if not faces:
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return
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bboxes = [f['bbox'] for f in faces]
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scores = [f['confidence'] for f in faces]
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landmarks = [f['landmarks'] for f in faces]
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draw_detections(
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image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True
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)
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for i, face in enumerate(faces):
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gender_id, age = age_gender.predict(image, face['bbox'])
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gender_str = 'Female' if gender_id == 0 else 'Male'
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print(f' Face {i + 1}: {gender_str}, {age} years old')
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draw_age_gender_label(image, face['bbox'], gender_id, age)
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os.makedirs(save_dir, exist_ok=True)
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output_path = os.path.join(save_dir, f'{Path(image_path).stem}_age_gender.jpg')
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cv2.imwrite(output_path, image)
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print(f'Output saved: {output_path}')
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def run_webcam(detector, age_gender, threshold: float = 0.6):
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cap = cv2.VideoCapture(0) # 0 = default webcam
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if not cap.isOpened():
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print('Cannot open webcam')
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return
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print("Press 'q' to quit")
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while True:
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ret, frame = cap.read()
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frame = cv2.flip(frame, 1) # mirror for natural interaction
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if not ret:
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break
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faces = detector.detect(frame)
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# unpack face data for visualization
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bboxes = [f['bbox'] for f in faces]
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scores = [f['confidence'] for f in faces]
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landmarks = [f['landmarks'] for f in faces]
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draw_detections(frame, bboxes, scores, landmarks, vis_threshold=threshold)
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for face in faces:
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gender_id, age = age_gender.predict(frame, face['bbox']) # predict per face
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draw_age_gender_label(frame, face['bbox'], gender_id, age)
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cv2.putText(
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frame,
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f'Faces: {len(faces)}',
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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1,
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(0, 255, 0),
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2,
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)
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cv2.imshow('Age & Gender Detection', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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def main():
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parser = argparse.ArgumentParser(description='Run age and gender detection')
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parser.add_argument('--image', type=str, help='Path to input image')
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parser.add_argument('--webcam', action='store_true', help='Use webcam')
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parser.add_argument('--detector', type=str, default='retinaface', choices=['retinaface', 'scrfd'])
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parser.add_argument('--threshold', type=float, default=0.6, help='Visualization threshold')
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parser.add_argument('--save_dir', type=str, default='outputs')
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args = parser.parse_args()
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if not args.image and not args.webcam:
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parser.error('Either --image or --webcam must be specified')
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detector = RetinaFace() if args.detector == 'retinaface' else SCRFD()
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age_gender = AgeGender()
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if args.webcam:
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run_webcam(detector, age_gender, args.threshold)
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else:
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process_image(detector, age_gender, args.image, args.save_dir, args.threshold)
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if __name__ == '__main__':
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main()
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