feat: Common result dataclasses and refactoring several methods. (#50)

* chore: Rename scripts to tools folder and unify argument parser

* refactor: Centralize dataclasses in types.py and add __call__ to all models

- Move Face and result dataclasses to uniface/types.py
- Add GazeResult, SpoofingResult, EmotionResult (frozen=True)
- Add __call__ to BaseDetector, BaseRecognizer, BaseLandmarker
- Add __repr__ to all dataclasses
- Replace print() with Logger in onnx_utils.py
- Update tools and docs to use new dataclass return types
- Add test_types.py with comprehensive dataclass testschore: Rename files under tools folder and unitify argument parser for them

scripts/run_detection.py

@@ -1,119 +0,0 @@
-# Copyright 2025 Yakhyokhuja Valikhujaev
-# Author: Yakhyokhuja Valikhujaev
-# GitHub: https://github.com/yakhyo
-
-"""Face detection on image or webcam.
-
-Usage:
-    python run_detection.py --image path/to/image.jpg
-    python run_detection.py --webcam
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-
-import cv2
-
-from uniface.detection import SCRFD, RetinaFace, YOLOv5Face
-from uniface.visualization import draw_detections
-
-
-def process_image(detector, image_path: str, threshold: float = 0.6, save_dir: str = 'outputs'):
-    image = cv2.imread(image_path)
-    if image is None:
-        print(f"Error: Failed to load image from '{image_path}'")
-        return
-
-    faces = detector.detect(image)
-
-    if faces:
-        bboxes = [face.bbox for face in faces]
-        scores = [face.confidence for face in faces]
-        landmarks = [face.landmarks for face in faces]
-        draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)
-
-    os.makedirs(save_dir, exist_ok=True)
-    output_path = os.path.join(save_dir, f'{os.path.splitext(os.path.basename(image_path))[0]}_out.jpg')
-    cv2.imwrite(output_path, image)
-    print(f'Output saved: {output_path}')
-
-
-def run_webcam(detector, threshold: float = 0.6):
-    cap = cv2.VideoCapture(0)  # 0 = default webcam
-    if not cap.isOpened():
-        print('Cannot open webcam')
-        return
-
-    print("Press 'q' to quit")
-
-    while True:
-        ret, frame = cap.read()
-        frame = cv2.flip(frame, 1)  # mirror for natural interaction
-        if not ret:
-            break
-
-        faces = detector.detect(frame)
-
-        # unpack face data for visualization
-        bboxes = [f.bbox for f in faces]
-        scores = [f.confidence for f in faces]
-        landmarks = [f.landmarks for f in faces]
-        draw_detections(
-            image=frame,
-            bboxes=bboxes,
-            scores=scores,
-            landmarks=landmarks,
-            vis_threshold=threshold,
-            draw_score=True,
-            fancy_bbox=True,
-        )
-
-        cv2.putText(
-            frame,
-            f'Faces: {len(faces)}',
-            (10, 30),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            1,
-            (0, 255, 0),
-            2,
-        )
-        cv2.imshow('Face Detection', frame)
-
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-
-    cap.release()
-    cv2.destroyAllWindows()
-
-
-def main():
-    parser = argparse.ArgumentParser(description='Run face detection')
-    parser.add_argument('--image', type=str, help='Path to input image')
-    parser.add_argument('--webcam', action='store_true', help='Use webcam')
-    parser.add_argument('--method', type=str, default='retinaface', choices=['retinaface', 'scrfd', 'yolov5face'])
-    parser.add_argument('--threshold', type=float, default=0.25, help='Visualization threshold')
-    parser.add_argument('--save_dir', type=str, default='outputs')
-    args = parser.parse_args()
-
-    if not args.image and not args.webcam:
-        parser.error('Either --image or --webcam must be specified')
-
-    if args.method == 'retinaface':
-        detector = RetinaFace()
-    elif args.method == 'scrfd':
-        detector = SCRFD()
-    else:
-        from uniface.constants import YOLOv5FaceWeights
-
-        detector = YOLOv5Face(model_name=YOLOv5FaceWeights.YOLOV5M)
-
-    if args.webcam:
-        run_webcam(detector, args.threshold)
-    else:
-        process_image(detector, args.image, args.threshold, args.save_dir)
-
-
-if __name__ == '__main__':
-    main()