Update

cpp-package/inspireface/python/sample_face_detection.py

@@ -5,85 +5,91 @@ import click
 import numpy as np
 
 race_tags = ["Black", "Asian", "Latino/Hispanic", "Middle Eastern", "White"]
-gender_tags = ["Female", "Male", ]
-age_bracket_tags = ["0-2 years old", "3-9 years old", "10-19 years old", "20-29 years old", "30-39 years old",
-                    "40-49 years old", "50-59 years old", "60-69 years old", "more than 70 years old"]
+gender_tags = ["Female", "Male"]
+age_bracket_tags = [
+    "0-2 years old", "3-9 years old", "10-19 years old", "20-29 years old", "30-39 years old",
+    "40-49 years old", "50-59 years old", "60-69 years old", "more than 70 years old"
+]
 
 @click.command()
 @click.argument('image_path')
-def case_face_detection_image(image_path):
+@click.option('--show', is_flag=True, help='Display the image with detected faces.')
+def case_face_detection_image(image_path, show):
     """
     This is a sample application for face detection and tracking using an image.
     It also includes pipeline extensions such as RGB liveness, mask detection, and face quality evaluation.
     """
-    # Optional features, loaded during session creation based on the modules specified.
-    opt = isf.HF_ENABLE_FACE_RECOGNITION | isf.HF_ENABLE_QUALITY | isf.HF_ENABLE_MASK_DETECT | isf.HF_ENABLE_LIVENESS | isf.HF_ENABLE_INTERACTION | isf.HF_ENABLE_FACE_ATTRIBUTE
+    opt = isf.HF_ENABLE_FACE_RECOGNITION | isf.HF_ENABLE_QUALITY | isf.HF_ENABLE_MASK_DETECT | \
+          isf.HF_ENABLE_LIVENESS | isf.HF_ENABLE_INTERACTION | isf.HF_ENABLE_FACE_ATTRIBUTE
     session = isf.InspireFaceSession(opt, isf.HF_DETECT_MODE_ALWAYS_DETECT)
-
-    # Set detection confidence threshold
     session.set_detection_confidence_threshold(0.5)
 
-    # Load the image using OpenCV.
+    # Load image
     image = cv2.imread(image_path)
     assert image is not None, "Please check that the image path is correct."
 
-    # Perform face detection on the image.
+    # Dynamic drawing parameters (adjusted to image size)
+    h, w = image.shape[:2]
+    scale = max(w, h) / 1000.0
+    line_thickness = max(1, int(2 * scale))
+    circle_radius = max(1, int(1.5 * scale))
+    font_scale = 0.5 * scale
+
+    # Detect faces
     faces = session.face_detection(image)
     print(f"face detection: {len(faces)} found")
-    
-    # Copy the image for drawing the bounding boxes.
+
     draw = image.copy()
     for idx, face in enumerate(faces):
         print(f"{'==' * 20}")
         print(f"idx: {idx}")
         print(f"detection confidence: {face.detection_confidence}")
-        # Print Euler angles of the face.
         print(f"roll: {face.roll}, yaw: {face.yaw}, pitch: {face.pitch}")
 
-        # Get face bounding box
         x1, y1, x2, y2 = face.location
-
-        # Calculate center, size, and angle
         center = ((x1 + x2) / 2, (y1 + y2) / 2)
         size = (x2 - x1, y2 - y1)
         angle = face.roll
 
-        # Apply rotation to the bounding box corners
         rect = ((center[0], center[1]), (size[0], size[1]), angle)
-        box = cv2.boxPoints(rect)
-        box = box.astype(int)
+        box = cv2.boxPoints(rect).astype(int)
+        cv2.drawContours(draw, [box], 0, (100, 180, 29), line_thickness)
 
-        # Draw the rotated bounding box
-        cv2.drawContours(draw, [box], 0, (100, 180, 29), 2)
-
-        # Draw landmarks
+        # Draw landmark
         lmk = session.get_face_dense_landmark(face)
         for x, y in lmk.astype(int):
-            cv2.circle(draw, (x, y), 0, (220, 100, 0), 2)
+            cv2.circle(draw, (x, y), circle_radius, (220, 100, 0), -1)
 
-    # Features must be enabled during session creation to use them here.
-    select_exec_func = isf.HF_ENABLE_QUALITY | isf.HF_ENABLE_MASK_DETECT | isf.HF_ENABLE_LIVENESS | isf.HF_ENABLE_INTERACTION | isf.HF_ENABLE_FACE_ATTRIBUTE
-    # Execute the pipeline to obtain richer face information.
+        # Optional: Add detection confidence (text) on the face box
+        # label = f"{face.detection_confidence:.2f}"
+        # cv2.putText(draw, label, (x1, max(y1 - 10, 0)), cv2.FONT_HERSHEY_SIMPLEX,
+        #             font_scale, (255, 255, 255), line_thickness)
+
+    # Execute extended functions (optional modules)
+    select_exec_func = isf.HF_ENABLE_QUALITY | isf.HF_ENABLE_MASK_DETECT | \
+                       isf.HF_ENABLE_LIVENESS | isf.HF_ENABLE_INTERACTION | isf.HF_ENABLE_FACE_ATTRIBUTE
     extends = session.face_pipeline(image, faces, select_exec_func)
+
     for idx, ext in enumerate(extends):
         print(f"{'==' * 20}")
         print(f"idx: {idx}")
-        # For these pipeline results, you can set thresholds based on the specific scenario to make judgments.
         print(f"quality: {ext.quality_confidence}")
         print(f"rgb liveness: {ext.rgb_liveness_confidence}")
         print(f"face mask: {ext.mask_confidence}")
-        print(
-            f"face eyes status: left eye: {ext.left_eye_status_confidence} right eye: {ext.right_eye_status_confidence}")
+        print(f"face eyes status: left eye: {ext.left_eye_status_confidence} right eye: {ext.right_eye_status_confidence}")
         print(f"gender: {gender_tags[ext.gender]}")
         print(f"race: {race_tags[ext.race]}")
         print(f"age: {age_bracket_tags[ext.age_bracket]}")
 
-    # Save the annotated image to the 'tmp/' directory.
-    save_path = os.path.join("tmp/", "det.jpg")
+    # Save the annotated image
+    save_path = os.path.join("tmp", "det.jpg")
+    os.makedirs("tmp", exist_ok=True)
     cv2.imwrite(save_path, draw)
+    if show:
+        cv2.imshow("Face Detection", draw)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
     print(f"\nSave annotated image to {save_path}")
 
-
 if __name__ == '__main__':
-    os.makedirs("tmp", exist_ok=True)
     case_face_detection_image()