Update app.py

app.py

@@ -413,10 +413,10 @@ class NeuralNetworkSimulator:
 mp_pose = mp.solutions.pose
 pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5)
 
-def detect_humanoid(image):
+def detect_humanoid(image_path):
+    image = imread(image_path)
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     results = pose.process(image_rgb)
-    
     if results.pose_landmarks:
         landmarks = results.pose_landmarks.landmark
         image_height, image_width, _ = image.shape
@@ -428,7 +428,8 @@ def detect_humanoid(image):
         return keypoints
     return []
 
-def apply_touch_points(image, keypoints):
+def apply_touch_points(image_path, keypoints):
+    image = imread(image_path)
     draw = ImageDraw.Draw(image)
     for point in keypoints:
         draw.ellipse([point[0]-5, point[1]-5, point[0]+5, point[1]+5], fill='red')
@@ -653,18 +654,18 @@ def create_avatar():
         draw.line([start, end], fill=(0, 255, 255, 50), width=1)
     
     return img
-def create_avatar_with_heatmap(show_heatmap=True):
-    # Load avatar image
-    avatar_img = Image.open("avatar.png").resize((AVATAR_WIDTH, 600, AVATAR_HEIGHT,800))
+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  # Return the avatar image without heatmap
+        return avatar_img
     
     # Create a heatmap
-    heatmap_img = create_heatmap(sensation_map, sensation_type)
+    heatmap_img = create_heatmap(sensation_map)
     
     # Resize heatmap to match avatar size
-    heatmap_img = heatmap_img.resize((AVATAR_WIDTH, AVATAR_HEIGHT))
+    heatmap_img = heatmap_img.resize((image.width, image.height))
     
     # Adjust alpha channel of heatmap
     data = np.array(heatmap_img)
@@ -682,7 +683,23 @@ 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)
@@ -843,11 +860,6 @@ st.write(response)
 
 
 
-import streamlit as st
-import matplotlib.pyplot as plt
-import numpy as np
-from PIL import Image
-import io
 
 # Constants
 AVATAR_WIDTH = 50  # Reduced size