red lines for FMesh

app.py

@@ -7,7 +7,6 @@ from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights
 from PIL import Image
 import mediapipe as mp
 from fer import FER  # Facial emotion recognition
-# from transformers import AutoFeatureExtractor, AutoModel  # (Unused now for facial recognition)
 
 # -----------------------------
 # Configuration
@@ -144,34 +143,58 @@ def compute_faces_overlay(image):
     return boxes, text
 
 # -----------------------------
-# New Facemesh Functions (for Facial Recognition)
+# New Facemesh Functions (with connected red lines and mask output)
 # -----------------------------
 def compute_facemesh_overlay(image):
     """
     Uses MediaPipe Face Mesh to detect and draw facial landmarks.
+    Draws green dots for landmarks and connects them with thin red lines.
+    Returns two images:
+      - annotated: the original image overlaid with the facemesh
+      - mask: a black background image with only the facemesh drawn
     """
     frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
     h, w, _ = frame_bgr.shape
+    # Create a copy for annotated output and a black mask
+    annotated = frame_bgr.copy()
+    mask = np.zeros_like(frame_bgr)
+    
     # Initialize Face Mesh in static mode
     face_mesh = mp.solutions.face_mesh.FaceMesh(
         static_image_mode=True, max_num_faces=1, refine_landmarks=True, min_detection_confidence=0.5
     )
     results = face_mesh.process(cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB))
+    
     if results.multi_face_landmarks:
         for face_landmarks in results.multi_face_landmarks:
-            for landmark in face_landmarks.landmark:
-                x = int(landmark.x * w)
-                y = int(landmark.y * h)
-                cv2.circle(frame_bgr, (x, y), 1, (0, 255, 0), -1)
+            # Convert landmarks to pixel coordinates
+            landmark_points = []
+            for lm in face_landmarks.landmark:
+                x = int(lm.x * w)
+                y = int(lm.y * h)
+                landmark_points.append((x, y))
+            # Draw thin red lines between connected landmarks using the FACEMESH_TESSELATION
+            for connection in mp.solutions.face_mesh.FACEMESH_TESSELATION:
+                start_idx, end_idx = connection
+                if start_idx < len(landmark_points) and end_idx < len(landmark_points):
+                    pt1 = landmark_points[start_idx]
+                    pt2 = landmark_points[end_idx]
+                    cv2.line(annotated, pt1, pt2, (0, 0, 255), 1)
+                    cv2.line(mask, pt1, pt2, (0, 0, 255), 1)
+            # Draw green dots for each landmark
+            for pt in landmark_points:
+                cv2.circle(annotated, pt, 2, (0, 255, 0), -1)
+                cv2.circle(mask, pt, 2, (0, 255, 0), -1)
         text = "Facemesh detected"
     else:
         text = "No facemesh detected"
     face_mesh.close()
-    return frame_bgr, text
+    return annotated, mask, text
 
 def analyze_facemesh(image):
-    annotated_image, text = compute_facemesh_overlay(image)
-    return annotated_image, f"<div style='color: lime !important;'>Facemesh Analysis: {text}</div>"
+    annotated_image, mask_image, text = compute_facemesh_overlay(image)
+    return (annotated_image, mask_image, 
+            f"<div style='color: #ff6347 !important;'>Facemesh Analysis: {text}</div>")
 
 # -----------------------------
 # Main Analysis Functions for Single Image
@@ -187,7 +210,7 @@ def analyze_posture_current(image):
     output = current_frame.copy()
     if posture_cache["landmarks"]:
         output = draw_posture_overlay(output, posture_cache["landmarks"])
-    return output, f"<div style='color: lime !important;'>Posture Analysis: {posture_cache['text']}</div>"
+    return output, f"<div style='color: #ff6347 !important;'>Posture Analysis: {posture_cache['text']}</div>"
 
 def analyze_emotion_current(image):
     global emotion_cache
@@ -196,7 +219,7 @@ def analyze_emotion_current(image):
     if emotion_cache["counter"] % SKIP_RATE == 0 or emotion_cache["text"] is None:
         text = compute_emotion_overlay(image)
         emotion_cache["text"] = text
-    return current_frame, f"<div style='color: lime !important;'>Emotion Analysis: {emotion_cache['text']}</div>"
+    return current_frame, f"<div style='color: #ff6347 !important;'>Emotion Analysis: {emotion_cache['text']}</div>"
 
 def analyze_objects_current(image):
     global objects_cache
@@ -211,7 +234,7 @@ def analyze_objects_current(image):
     if objects_cache["boxes"]:
         output = draw_boxes_overlay(output, objects_cache["boxes"], (255, 255, 0))
     combined_text = f"Object Detection: {objects_cache['text']}<br>Details: {objects_cache['object_list_text']}"
-    return output, f"<div style='color: lime !important;'>{combined_text}</div>"
+    return output, f"<div style='color: #ff6347 !important;'>{combined_text}</div>"
 
 def analyze_faces_current(image):
     global faces_cache
@@ -224,7 +247,7 @@ def analyze_faces_current(image):
     output = current_frame.copy()
     if faces_cache["boxes"]:
         output = draw_boxes_overlay(output, faces_cache["boxes"], (0, 0, 255))
-    return output, f"<div style='color: lime !important;'>Face Detection: {faces_cache['text']}</div>"
+    return output, f"<div style='color: #ff6347 !important;'>Face Detection: {faces_cache['text']}</div>"
 
 def analyze_all(image):
     current_frame = np.array(image).copy()
@@ -249,37 +272,38 @@ def analyze_all(image):
         description_text = f"Image Description: The scene features {object_list_text}."
     else:
         description_text = "Image Description: No prominent objects detected."
-    combined_text += f"<br><br><div style='border:1px solid lime; padding:10px; box-shadow: 0 0 10px lime;'><b>{description_text}</b></div>"
-    combined_text_html = f"<div style='color: lime !important;'>{combined_text}</div>"
+    combined_text += f"<br><br><div style='border:1px solid #ff6347; padding:10px; box-shadow: 0 0 10px #ff6347;'><b>{description_text}</b></div>"
+    combined_text_html = f"<div style='color: #ff6347 !important;'>{combined_text}</div>"
     return current_frame, combined_text_html
 
 # -----------------------------
-# Custom CSS (High-Tech Neon Theme)
+# Custom CSS (Revamped High-Contrast Neon Theme)
 # -----------------------------
 custom_css = """
 @import url('https://fonts.googleapis.com/css2?family=Orbitron:wght@400;700&display=swap');
 body {
-    background-color: #0e0e0e;
+    background-color: #121212;
     font-family: 'Orbitron', sans-serif;
-    color: #32CD32;
+    color: #ffffff;
 }
 .gradio-container {
-    background: linear-gradient(135deg, #1a1a1a, #333333);
-    border: 2px solid #32CD32;
-    box-shadow: 0 0 15px #32CD32;
+    background: linear-gradient(135deg, #2d2d2d, #1a1a1a);
+    border: 2px solid #ff6347;
+    box-shadow: 0 0 15px #ff6347;
     border-radius: 10px;
     padding: 20px;
     max-width: 1200px;
     margin: auto;
 }
 .gradio-title, .gradio-description, .tab-item, .tab-item * {
-    color: #32CD32 !important;
-    text-shadow: 0 0 10px #32CD32;
+    color: #ff6347 !important;
+    text-shadow: 0 0 10px #ff6347;
 }
 input, button, .output {
-    border: 1px solid #32CD32;
-    box-shadow: 0 0 8px #32CD32;
-    color: #32CD32;
+    border: 1px solid #ff6347;
+    box-shadow: 0 0 8px #ff6347;
+    color: #ffffff;
+    background-color: #1a1a1a;
 }
 """
 
@@ -323,14 +347,18 @@ faces_interface = gr.Interface(
 )
 
 # -----------------------------
-# New Facemesh Interface (Used for facial recognition via facemesh)
+# New Facemesh Interface (Outputs annotated image and mask)
 # -----------------------------
 facemesh_interface = gr.Interface(
     fn=analyze_facemesh,
     inputs=gr.Image(label="Upload an Image for Facemesh"),
-    outputs=[gr.Image(type="numpy", label="Annotated Output"), gr.HTML(label="Facemesh Analysis")],
+    outputs=[
+        gr.Image(type="numpy", label="Annotated Output"),
+        gr.Image(type="numpy", label="Mask Output"),
+        gr.HTML(label="Facemesh Analysis")
+    ],
     title="Facemesh",
-    description="Detects facial landmarks using MediaPipe Face Mesh.",
+    description="Detects facial landmarks using MediaPipe Face Mesh and outputs both an annotated image and a mask on a black background.",
     live=False
 )
 
@@ -367,8 +395,8 @@ tabbed_interface = gr.TabbedInterface(
 # -----------------------------
 demo = gr.Blocks(css=custom_css)
 with demo:
-    gr.Markdown("<h1 class='gradio-title' style='color: #32CD32;'>Multi-Analysis Image App</h1>")
-    gr.Markdown("<p class='gradio-description' style='color: #32CD32;'>Upload an image to run high-tech analysis for posture, emotions, objects, faces, and facemesh landmarks.</p>")
+    gr.Markdown("<h1 class='gradio-title'>Multi-Analysis Image App</h1>")
+    gr.Markdown("<p class='gradio-description'>Upload an image to run high-tech analysis for posture, emotions, objects, faces, and facemesh landmarks.</p>")
     tabbed_interface.render()
 
 if __name__ == "__main__":