Update app.py

app.py

@@ -414,7 +414,7 @@ mp_pose = mp.solutions.pose
 pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5)
 
 def detect_humanoid(image_path):
-    image = imread(image_path)
+    image = cv2.imread(image_path)
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     results = pose.process(image_rgb)
     if results.pose_landmarks:
@@ -429,7 +429,9 @@ def detect_humanoid(image_path):
     return []
 
 def apply_touch_points(image_path, keypoints):
-    image = imread(image_path)
+    image = cv2.imread(image_path)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = Image.fromarray(image)
     draw = ImageDraw.Draw(image)
     for point in keypoints:
         draw.ellipse([point[0]-5, point[1]-5, point[0]+5, point[1]+5], fill='red')
@@ -462,10 +464,6 @@ def create_sensation_map(width, height, keypoints):
 
     return sensation_map
 
-def create_heatmap(sensation_map, sensation_type):
-    plt.figure(figsize=(10, 15))
-    sns.heatmap(sensation_map[:, :, sensation_type], cmap='viridis')
-    
 def create_heatmap(sensation_map, sensation_type):
     plt.figure(figsize=(10, 15))
     sns.heatmap(sensation_map[:, :, sensation_type], cmap='viridis')
@@ -494,34 +492,27 @@ def generate_ai_response(keypoints, sensation_map):
     
     return response
 
-# Create and display avatar with heatmap
-st.subheader("Avatar with Sensation Heatmap")
-
-# You need to define sensation_map and sensation_type before this
-sensation_map = np.random.rand(AVATAR_HEIGHT, 600, AVATAR_WIDTH, 300)  # Example random sensation map
-sensation_type = 0  # Example sensation type (0 for Pain)
-
-avatar_with_heatmap = create_avatar_with_heatmap(sensation_map, sensation_type)
-st.image(avatar_with_heatmap, use_column_width=True)
-
 uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
     # Read the image
-    image = Image.open(uploaded_file)
-    image_np = np.array(image)
+    image_path = 'temp.jpg'
+    with open(image_path, 'wb') as f:
+        f.write(uploaded_file.getvalue())
     
     # Detect humanoid keypoints
-    keypoints = detect_humanoid(image_np)
+    keypoints = detect_humanoid(image_path)
     
     # Apply touch points to the image
-    processed_image = apply_touch_points(image.copy(), keypoints)
+    processed_image = apply_touch_points(image_path, keypoints)
     
     # Display the processed image
     st.image(processed_image, caption='Processed Image with Touch Points', use_column_width=True)
     
     # Create sensation map
-    sensation_map = create_sensation_map(image.width, image.height, keypoints)
+    image = cv2.imread(image_path)
+    image_height, image_width, _ = image.shape
+    sensation_map = create_sensation_map(image_width, image_height, keypoints)
     
     # Display heatmaps for different sensations
     sensation_types = ["Pain", "Pleasure", "Pressure", "Temperature", "Texture", "EM Field", 
@@ -535,199 +526,8 @@ if uploaded_file is not None:
     if st.button("Generate AI Response"):
         response = generate_ai_response(keypoints, sensation_map)
         st.write("AI Response:", response)
-
-
-   
-
-
-# Create futuristic human-like avatar
-def create_avatar():
-   img = Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0, 0, 0, 0))
-   draw = ImageDraw.Draw(img)
-  
-   # Body
-   draw.polygon([(300, 100), (200, 250), (250, 600), (300, 750), (350, 600), (400, 250)], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
-  
-   # Head
-   draw.ellipse([250, 50, 350, 150], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
-  
-   # Eyes
-   draw.ellipse([275, 80, 295, 100], fill=(255, 255, 255, 200), outline=(0, 255, 255, 255))
-   draw.ellipse([305, 80, 325, 100], fill=(255, 255, 255, 200), outline=(0, 255, 255, 255))
-  
-   # Nose
-   draw.polygon([(300, 90), (290, 110), (310, 110)], fill=(0, 255, 255, 150))
-  
-   # Mouth
-   draw.arc([280, 110, 320, 130], 0, 180, fill=(0, 255, 255, 200), width=2)
-  
-  # Arms
-   draw.line([(200, 250), (150, 400)], fill=(0, 255, 255, 200), width=5)
-   draw.line([(400, 250), (450, 400)], fill=(0, 255, 255, 200), width=5)
-  
-   # Hands
-   draw.ellipse([140, 390, 160, 410], fill=(0, 255, 255, 150))
-   draw.ellipse([440, 390, 460, 410], fill=(0, 255, 255, 150))
-  
-   # Fingers
-   for i in range(5):
-       draw.line([(150 + i*5, 400), (145 + i*5, 420)], fill=(0, 255, 255, 200), width=2)
-       draw.line([(450 - i*5, 400), (455 - i*5, 420)], fill=(0, 255, 255, 200), width=2)
-  
-   # Legs
-   draw.line([(250, 600), (230, 780)], fill=(0, 255, 255, 200), width=5)
-   draw.line([(350, 600), (370, 780)], fill=(0, 255, 255, 200), width=5)
-  
-   # Feet
-   draw.ellipse([220, 770, 240, 790], fill=(0, 255, 255, 150))
-   draw.ellipse([360, 770, 380, 790], fill=(0, 255, 255, 150))
-  
-   # Toes
-   for i in range(5):
-       draw.line([(225 + i*3, 790), (223 + i*3, 800)], fill=(0, 255, 255, 200), width=2)
-       draw.line([(365 + i*3, 790), (363 + i*3, 800)], fill=(0, 255, 255, 200), width=2)
-  
-def generate_neural_network_lines(img, draw):
-    # Neural network lines
-    for _ in range(100):
-        start = (np.random.randint(0, AVATAR_WIDTH), np.random.randint(0, AVATAR_HEIGHT))
-        end = (np.random.randint(0, AVATAR_WIDTH), np.random.randint(0, AVATAR_HEIGHT))
-        draw.line([start, end], fill=(0, 255, 255, 50), width=1)
-    return img
-    
-
-# Create and display avatar with heatmap
-st.subheader("Avatar with Sensation Heatmap")
-avatar_with_heatmap = create_avatar_with_heatmap()
-st.image(avatar_with_heatmap, use_column_width=True)
-# Create avatar function
-def create_avatar():
-    img = Image.new('RGBA', (AVATAR_WIDTH, AVATAR_HEIGHT), color=(0, 0, 0, 0))
-    draw = ImageDraw.Draw(img)
-    
-    # Body
-    draw.polygon([(300, 100), (200, 250), (250, 600), (300, 750), (350, 600), (400, 250)], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
-    
-    # Head
-    draw.ellipse([250, 50, 350, 150], fill=(0, 255, 255, 100), outline=(0, 255, 255, 255))
-    
-    # Eyes
-    draw.ellipse([275, 80, 295, 100], fill=(255, 255, 255, 200), outline=(0, 255, 255, 255))
-    draw.ellipse([305, 80, 325, 100], fill=(255, 255, 255, 200), outline=(0, 255, 255, 255))
-    
-    # Nose
-    draw.polygon([(300, 90), (290, 110), (310, 110)], fill=(0, 255, 255, 150))
-    
-    # Mouth
-    draw.arc([280, 110, 320, 130], 0, 180, fill=(0, 255, 255, 200), width=2)
-    
-    # Arms
-    draw.line([(200, 250), (150, 400)], fill=(0, 255, 255, 200), width=5)
-    draw.line([(400, 250), (450, 400)], fill=(0, 255, 255, 200), width=5)
-    
-    # Hands
-    draw.ellipse([140, 390, 160, 410], fill=(0, 255, 255, 150))
-    draw.ellipse([440, 390, 460, 410], fill=(0, 255, 255, 150))
-    
-    # Fingers
-    for i in range(5):
-        draw.line([(150 + i*5, 400), (145 + i*5, 420)], fill=(0, 255, 255, 200), width=2)
-        draw.line([(450 - i*5, 400), (455 - i*5, 420)], fill=(0, 255, 255, 200), width=2)
-    
-    # Legs
-    draw.line([(250, 600), (230, 780)], fill=(0, 255, 255, 200), width=5)
-    draw.line([(350, 600), (370, 780)], fill=(0, 255, 255, 200), width=5)
-    
-    # Feet
-    draw.ellipse([220, 770, 240, 790], fill=(0, 255, 255, 150))
-    draw.ellipse([360, 770, 380, 790], fill=(0, 255, 255, 150))
-    
-    # Toes
-    for i in range(5):
-        draw.line([(225 + i*3, 790), (223 + i*3, 800)], fill=(0, 255, 255, 200), width=2)
-        draw.line([(365 + i*3, 790), (363 + i*3, 800)], fill=(0, 255, 255, 200), width=2)
-    
-    # Neural network lines
-    for _ in range(100):
-        start = (np.random.randint(0, AVATAR_WIDTH), np.random.randint(0, AVATAR_HEIGHT))
-        end = (np.random.randint(0, AVATAR_WIDTH), np.random.randint(0, AVATAR_HEIGHT))
-        draw.line([start, end], fill=(0, 255, 255, 50), width=1)
-    
-    return img
-def create_avatar_with_heatmap(image_path, show_heatmap=True):
-    # Load the image
-    avatar_img = Image.open(image_path)
-    
-    if not show_heatmap:
-        return avatar_img
-    
-    # Create a heatmap
-    heatmap_img = create_heatmap(sensation_map)
-    
-    # Resize heatmap to match avatar size
-    heatmap_img = heatmap_img.resize((image.width, image.height))
-    
-    # Adjust alpha channel of heatmap
-    data = np.array(heatmap_img)
-    if data.shape[2] == 3:  # If RGB, add an alpha channel
-        data = np.concatenate([data, np.full((data.shape[0], data.shape[1], 1), 255, dtype=np.uint8)], axis=2)
-    data[:, :, 3] = data[:, :, 3] * 0.5  # Reduce opacity to 50%
-    heatmap_img = Image.fromarray(data)
-    
-    # Combine avatar and heatmap
-    combined_img = Image.alpha_composite(avatar_img.convert('RGBA'), heatmap_img.convert('RGBA'))
-    return combined_img
-
-# Create and display avatar with optional heatmap
-st.subheader("Avatar with Optional Sensation Heatmap")
-avatar_with_heatmap = create_avatar_with_heatmap(show_heatmap)
-st.image(avatar_with_heatmap, use_column_width=True)
-
-# Load the chosen humanoid image
-image_path = 'chosen_avatar.jpg'
-keypoints = detect_humanoid(image_path)
-image_with_touch_points = apply_touch_points(image_path, keypoints)
-heatmap_avatar = create_avatar_with_heatmap(image_path)
-
-# Display the images
-plt.figure(figsize=(15, 5))
-plt.subplot(1, 3, 1)
-plt.imshow(image_with_touch_points)
-plt.title('Image with Touch Points')
-
-plt.subplot(1, 3, 2)
-plt.imshow(heatmap_avatar)
-plt.title('Avatar with Heatmap')
-
-plt.show()
-
-# Create three columns
-col1, col2, col3 = st.columns(3)
-
-
-
-# Avatar display with touch interface
-with col1:
-   st.subheader("Humanoid Avatar Interface")
-  
- # Use st_canvas for touch input
-canvas_result = st_canvas(
-        fill_color="rgba(0, 255, 255, 0.3)",
-        stroke_width=2,
-        stroke_color="#00FFFF",
-        background_image=avatar_with_heatmap,
-        height=AVATAR_HEIGHT,
-        width=AVATAR_WIDTH,
-        drawing_mode="point",
-        key="canvas",
-    )
-
-
-with col3:
-    st.subheader("Sensation Heatmap")
-    heatmap = create_heatmap(avatar_sensation_map)
-    st.image(heatmap, use_column_width=True)
-        # Touch controls and output
+       
+    # Touch controls and output
 with col2:
     st.subheader("Neural Interface Controls")