add apple silicon support and update documentation

- add dynamic onnx provider selection for m1/m2/m3/m4 macs
- replace mkdocs with simple markdown files
- fix model download and scrfd detection issues
- update ci/cd workflows

tests/test_retinaface.py

@@ -1,7 +1,8 @@
-import pytest
 import numpy as np
-from uniface import RetinaFace
+import pytest
+
 from uniface.constants import RetinaFaceWeights
+from uniface.detection import RetinaFace
 
 
 @pytest.fixture
@@ -32,20 +33,27 @@ def test_inference_on_640x640_image(retinaface_model):
     # Generate a mock 640x640 BGR image
     mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
 
-    # Run inference
-    detections, landmarks = retinaface_model.detect(mock_image)
+    # Run inference - returns list of dictionaries
+    faces = retinaface_model.detect(mock_image)
 
-    # Check output types
-    assert isinstance(detections, np.ndarray), "Detections should be a numpy array."
-    assert isinstance(landmarks, np.ndarray), "Landmarks should be a numpy array."
+    # Check output type
+    assert isinstance(faces, list), "Detections should be a list."
 
-    # Check that detections have the expected shape
-    if detections.size > 0:  # If faces are detected
-        assert detections.shape[1] == 5, "Each detection should have 5 values (x1, y1, x2, y2, score)."
+    # Check that each face has the expected structure
+    for face in faces:
+        assert isinstance(face, dict), "Each detection should be a dictionary."
+        assert "bbox" in face, "Each detection should have a 'bbox' key."
+        assert "confidence" in face, "Each detection should have a 'confidence' key."
+        assert "landmarks" in face, "Each detection should have a 'landmarks' key."
 
-    # Check landmarks shape
-    if landmarks.size > 0:
-        assert landmarks.shape[1:] == (5, 2), "Landmarks should have shape (N, 5, 2)."
+        # Check bbox format
+        bbox = face["bbox"]
+        assert len(bbox) == 4, "BBox should have 4 values (x1, y1, x2, y2)."
+
+        # Check landmarks format
+        landmarks = face["landmarks"]
+        assert len(landmarks) == 5, "Should have 5 landmark points."
+        assert all(len(pt) == 2 for pt in landmarks), "Each landmark should be (x, y)."
 
 
 def test_confidence_threshold(retinaface_model):
@@ -56,12 +64,12 @@ def test_confidence_threshold(retinaface_model):
     mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
 
     # Run inference
-    detections, _ = retinaface_model.detect(mock_image)
+    faces = retinaface_model.detect(mock_image)
 
     # Ensure all detections have confidence scores above the threshold
-    if detections.size > 0:  # If faces are detected
-        confidence_scores = detections[:, 4]
-        assert (confidence_scores >= 0.5).all(), "Some detections have confidence below the threshold."
+    for face in faces:
+        confidence = face["confidence"]
+        assert confidence >= 0.5, f"Detection has confidence {confidence} below threshold 0.5"
 
 
 def test_no_faces_detected(retinaface_model):
@@ -72,8 +80,7 @@ def test_no_faces_detected(retinaface_model):
     empty_image = np.zeros((640, 640, 3), dtype=np.uint8)
 
     # Run inference
-    detections, landmarks = retinaface_model.detect(empty_image)
+    faces = retinaface_model.detect(empty_image)
 
-    # Ensure no detections or landmarks are found
-    assert detections.size == 0, "Detections should be empty for a blank image."
-    assert landmarks.size == 0, "Landmarks should be empty for a blank image."
+    # Ensure no detections are found
+    assert len(faces) == 0, "Should detect no faces in a blank image."