wip

app.py

@@ -44,8 +44,9 @@ with gr.Blocks() as demo:
         # Process Button
         face_process_btn = gr.Button("Process Image")
 
-        # Output Component
-        face_image_output = gr.Image(label="Detected Faces")
+        # Output Components
+        face_image_output = gr.Image(label="Detected Faces Image")
+        face_bbox_output = gr.JSON(label="Raw Bounding Box Data")
 
         # Link radio button change to visibility update function
         face_input_type.change(
@@ -57,7 +58,7 @@ with gr.Blocks() as demo:
         )
 
         # Link process button to the face detection function
-        # The face_detection function will need to be updated to handle these inputs
+        # The face_detection function will now return a tuple
         face_process_btn.click(
             fn=face_detection,
             inputs=[
@@ -67,7 +68,7 @@ with gr.Blocks() as demo:
                 face_base64_input,
                 face_detection_method,
             ],
-            outputs=face_image_output,
+            outputs=[face_image_output, face_bbox_output],
         )
     # Create a tab for age estimation
     with gr.Tab("Age Estimation"):

detection/face_detection.py

@@ -25,14 +25,21 @@ def face_detection(input_type, uploaded_image, image_url, base64_string, face_de
         face_detection_method (str): The selected face detection method ("OpenCV" or "dlib").
 
     Returns:
-        numpy.ndarray: The image with detected faces, or None if an error occurred.
+        tuple: A tuple containing:
+            - numpy.ndarray: The image with detected faces, or None if an error occurred.
+            - list: A list of dictionaries, where each dictionary represents a bounding box
+                    with keys 'x', 'y', 'w', 'h', or an empty list if no faces were detected
+                    or an error occurred.
     """
     # Use the centralized function to get the image
     image = get_image_from_input(input_type, uploaded_image, image_url, base64_string)
 
     if image is None:
         print("Image is None after loading/selection.")
-        return None # No valid input or loading failed
+        return None, [] # Return None for image and empty list for bboxes
+
+    processed_image = None
+    bounding_boxes = []
 
     try:
         # Preprocess the image (convert PIL to numpy, ensure RGB)
@@ -53,13 +60,14 @@ def face_detection(input_type, uploaded_image, image_url, base64_string, face_de
                 if not os.path.exists(cascade_path):
                      error_message = f"Error: Haar cascade file not found at {cascade_path}. Please ensure OpenCV is installed correctly and the file exists."
                      print(error_message)
-                     return None
+                     return None, [] # Return None for image and empty list for bboxes
 
                 face_cascade = cv2.CascadeClassifier(cascade_path)
 
                 faces = face_cascade.detectMultiScale(gray, 1.1, 4)
                 for x, y, w, h in faces:
                     cv2.rectangle(processed_image, (x, y), (x + w, y + h), (255, 0, 0), 2)
+                    bounding_boxes.append({'x': int(x), 'y': int(y), 'w': int(w), 'h': int(h)})
 
             elif face_detection_method == "dlib":
                 print("Using dlib for face detection.")
@@ -71,10 +79,11 @@ def face_detection(input_type, uploaded_image, image_url, base64_string, face_de
                 for face in faces:
                     x, y, w, h = face.left(), face.top(), face.width(), face.height()
                     cv2.rectangle(processed_image, (x, y), (x + w, y + h), (255, 0, 0), 2)
+                    bounding_boxes.append({'x': int(x), 'y': int(y), 'w': int(w), 'h': int(h)})
 
-            return processed_image
+            return processed_image, bounding_boxes
         else:
-            return None
+            return None, [] # Return None for image and empty list for bboxes
     except Exception as e:
         print(f"Error in face detection processing: {e}")
-        return None
+        return None, [] # Return None for image and empty list for bboxes