Add InsightFace face detection and analysis example

computer_vision/insightface_face_detection.py

@@ -0,0 +1,114 @@
+"""
+This code demonstrates the use of SOTA face embeddings model ARCFACE
+through wrapper called insightface
+
+It uses Retinaface Face detector to detect faces.
+
+References : https://www.insightface.ai/
+"""
+
+import cv2
+import numpy as np
+from insightface.app import FaceAnalysis
+
+
+def prepare_model(model_name: str = "buffalo_l", ctx_id: int = -1) -> FaceAnalysis:
+    """
+    Initialize and Prepare face analysis model
+
+    Args:
+        - model_name = default 'buffalo_l'
+        - ctx_id = gpu number 1,2... , for CPU -1
+
+    Return:
+        Configured FaceAnalysis instance ready for inference
+
+    """
+    app = FaceAnalysis(model_name)
+    app.prepare(ctx_id=ctx_id)
+
+    return app
+
+
+def get_facial_data(face_analysis_model: FaceAnalysis, frame: np.ndarray) -> list[dict]:
+    """
+    Extract facial data from a frame using the face analysis model
+
+    Args:
+        - face analysis model : prepared FaceanAlysis instance
+        - frame : BGR image as numpy array (opencv format)
+    Returns:
+        - List of dict, each containing facial data for one detected face:
+            - bounding_box: Face bounding box coordinates
+            - keypoints: 5-point facial landmark
+            - landmark_3d_68: 68-point 3d landmarks
+            - landmakr_2d_106: 106-points 2d landmarks
+            - pose: Head pose (pitch yaw roll)
+            - gender: Predicted gender (0-female,1:male)
+            - age: Predicted age
+            - embeddings: 512-dimensional face vector
+
+    Example:
+            >>> # model = prepare_model(ctx_id = -1)
+            >>> # frame = cv2.imread("test_face.jpg")
+            >>> # faces = get_facial_data(model,frame)
+            >>> # len(faces)>=0
+
+    """
+
+    results = face_analysis_model.get(frame)
+    faces = []
+    for result in results:
+        face_data = {
+            "bounding_box": result["bbox"],
+            "keypoints": result["kps"],
+            "landmark_3d_68": result["landmark_3d_68"],
+            "pose": result["pose"],
+            "landmark_2d_106": result["landmark_2d_106"],
+            "gender": result["gender"],
+            "age": result["age"],
+            "embedding": result["embedding"],
+        }
+        faces.append(face_data)
+
+    return faces
+
+
+def run_webcam_demo(ctx_id: int = -1, source: str | int = 0) -> None:
+    """
+    Run live face analysis on wecam feed
+
+    Args:
+        - ctx_id: GPU context id -1 for cpu and >1 for gpu
+        - source: camera int for webcam or path for video file
+    """
+    face_model = prepare_model(ctx_id=ctx_id)  # doctest: +SKIP
+    capture = cv2.VideoCapture(source)
+
+    while True:
+        ret, frame = capture.read()
+        if not ret:
+            break
+
+        faces = get_facial_data(face_model, frame)
+
+        for face in faces:
+            bbox = [int(coord) for coord in face["bounding_box"]]
+            cv2.rectangle(
+                frame,
+                (bbox[0], bbox[1]),
+                (bbox[2], bbox[3]),
+                color=(0, 255, 0),
+                thickness=2,
+            )
+
+        cv2.imshow("Face Analysis", frame)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+    capture.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    run_webcam_demo(ctx_id=1)

pyproject.toml

@@ -14,6 +14,7 @@ dependencies = [
   "fake-useragent>=1.5.1",
   "httpx>=0.28.1",
   "imageio>=2.36.1",
+  "insightface>=0.7.3",
   "keras>=3.7",
   "lxml>=6",
   "matplotlib>=3.9.3",