Update app.py

app.py

@@ -2,60 +2,39 @@ import gradio as gr
 import cv2
 import numpy as np
 import tempfile
-from PIL import Image
 import mediapipe as mp
-from sklearn.cluster import KMeans
+from PIL import Image
 
-# MediaPipe setup
-mp_face_mesh = mp.solutions.face_mesh
-face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True)
+# Mediapipe face detection setup
+mp_face_detection = mp.solutions.face_detection
 mp_drawing = mp.solutions.drawing_utils
 
-# Step 1 & 2 combined: Capture, analyze, and return image with annotations
-def analyze_face(image):
-    if image is None:
-        return "No image provided"
-
-    # Save image temporarily
-    temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    image_pil = Image.fromarray(image)
-    image_pil.save(temp_file.name)
-
-    # Convert for MediaPipe
-    img_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    result = face_mesh.process(img_rgb)
+def capture_and_analyze(image):
+    # Convert to BGR for OpenCV compatibility
+    image_np = np.array(image)
+    image_np = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
 
-    if not result.multi_face_landmarks:
-        return "No face detected"
+    # Run face detection
+    with mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5) as face_detection:
+        results = face_detection.process(cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
 
-    # Draw landmarks
-    for face_landmarks in result.multi_face_landmarks:
-        mp_drawing.draw_landmarks(
-            image=image,
-            landmark_list=face_landmarks,
-            connections=mp_face_mesh.FACEMESH_TESSELATION,
-            landmark_drawing_spec=None,
-            connection_drawing_spec=mp_drawing.DrawingSpec(color=(0,255,0), thickness=1, circle_radius=1),
-        )
+        if results.detections:
+            for detection in results.detections:
+                mp_drawing.draw_detection(image_np, detection)
+        else:
+            print("No face detected")
 
-    # Skin tone detection with KMeans
-    pixels = img_rgb.reshape((-1, 3))
-    kmeans = KMeans(n_clusters=1, random_state=42).fit(pixels)
-    dominant_color = kmeans.cluster_centers_[0].astype(int)
+    # Save image temporarily
+    temp_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
+    cv2.imwrite(temp_file.name, image_np)
 
-    return image, f"Dominant skin tone RGB: {tuple(dominant_color)}"
+    return Image.fromarray(cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
 
 # Gradio Interface
-demo = gr.Interface(
-    fn=analyze_face,
-    inputs=gr.Image(type="numpy", image_mode="BGR", label="Capture or Upload Your Face"),
-    outputs=[
-        gr.Image(type="numpy", label="Face Analysis Output"),
-        gr.Textbox(label="Detected Skin Tone (RGB)")
-    ],
-    title="Face Scanner for Mask Recommendation",
-    description="Upload or capture a photo to analyze face landmarks and detect skin tone."
+iface = gr.Interface(
+    fn=capture_and_analyze,
+    inputs=gr.Image(label="Upload or Capture a Face Image"),
+    outputs=gr.Image(label="Processed Image with Face Detection")
 )
 
-if __name__ == "__main__":
-    demo.launch()
+iface.launch()