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_recognition.py

@@ -1,103 +0,0 @@
-# Face recognition: extract embeddings or compare two faces
-# Usage: python run_recognition.py --image path/to/image.jpg
-#        python run_recognition.py --image1 face1.jpg --image2 face2.jpg
-
-import argparse
-
-import cv2
-import numpy as np
-
-from uniface.detection import SCRFD, RetinaFace
-from uniface.face_utils import compute_similarity
-from uniface.recognition import ArcFace, MobileFace, SphereFace
-
-
-def get_recognizer(name: str):
-    if name == 'arcface':
-        return ArcFace()
-    elif name == 'mobileface':
-        return MobileFace()
-    else:
-        return SphereFace()
-
-
-def run_inference(detector, recognizer, image_path: str):
-    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 not faces:
-        print('No faces detected.')
-        return
-
-    print(f'Detected {len(faces)} face(s). Extracting embedding for the first face...')
-
-    landmarks = faces[0]['landmarks']  # 5-point landmarks for alignment (already np.ndarray)
-    embedding = recognizer.get_embedding(image, landmarks)
-    norm_embedding = recognizer.get_normalized_embedding(image, landmarks)  # L2 normalized
-
-    print(f'  Embedding shape: {embedding.shape}')
-    print(f'  L2 norm (raw): {np.linalg.norm(embedding):.4f}')
-    print(f'  L2 norm (normalized): {np.linalg.norm(norm_embedding):.4f}')
-
-
-def compare_faces(detector, recognizer, image1_path: str, image2_path: str, threshold: float = 0.35):
-    img1 = cv2.imread(image1_path)
-    img2 = cv2.imread(image2_path)
-
-    if img1 is None or img2 is None:
-        print('Error: Failed to load one or both images')
-        return
-
-    faces1 = detector.detect(img1)
-    faces2 = detector.detect(img2)
-
-    if not faces1 or not faces2:
-        print('Error: No faces detected in one or both images')
-        return
-
-    landmarks1 = faces1[0]['landmarks']
-    landmarks2 = faces2[0]['landmarks']
-
-    embedding1 = recognizer.get_normalized_embedding(img1, landmarks1)
-    embedding2 = recognizer.get_normalized_embedding(img2, landmarks2)
-
-    # cosine similarity for normalized embeddings
-    similarity = compute_similarity(embedding1, embedding2, normalized=True)
-    is_match = similarity > threshold
-
-    print(f'Similarity: {similarity:.4f}')
-    print(f'Result: {"Same person" if is_match else "Different person"} (threshold: {threshold})')
-
-
-def main():
-    parser = argparse.ArgumentParser(description='Face recognition and comparison')
-    parser.add_argument('--image', type=str, help='Single image for embedding extraction')
-    parser.add_argument('--image1', type=str, help='First image for comparison')
-    parser.add_argument('--image2', type=str, help='Second image for comparison')
-    parser.add_argument('--threshold', type=float, default=0.35, help='Similarity threshold')
-    parser.add_argument('--detector', type=str, default='retinaface', choices=['retinaface', 'scrfd'])
-    parser.add_argument(
-        '--recognizer',
-        type=str,
-        default='arcface',
-        choices=['arcface', 'mobileface', 'sphereface'],
-    )
-    args = parser.parse_args()
-
-    detector = RetinaFace() if args.detector == 'retinaface' else SCRFD()
-    recognizer = get_recognizer(args.recognizer)
-
-    if args.image1 and args.image2:
-        compare_faces(detector, recognizer, args.image1, args.image2, args.threshold)
-    elif args.image:
-        run_inference(detector, recognizer, args.image)
-    else:
-        print('Error: Provide --image or both --image1 and --image2')
-        parser.print_help()
-
-
-if __name__ == '__main__':
-    main()