Update app.py

app.py

@@ -21,19 +21,22 @@ st.set_page_config(
 class DogBehaviorAnalyzer:
     def __init__(self):
         self.behaviors = {
-            'tail_wagging': {'threshold': 0.6, 'description': 'Your dog is displaying happiness and excitement!'},
-            'movement': {'threshold': 0.5, 'description': 'Your dog is active and moving around.'},
-            'stationary': {'threshold': 0.7, 'description': 'Your dog is calm and still.'},
-            'high_activity': {'threshold': 0.65, 'description': 'Your dog is very energetic!'}
+            'tail_wagging': {'threshold': 0.15, 'description': 'Your dog is displaying happiness and excitement!'},
+            'movement': {'threshold': 0.02, 'description': 'Your dog is active and moving around.'},
+            'stationary': {'threshold': 0.01, 'description': 'Your dog is calm and still.'},
+            'high_activity': {'threshold': 0.05, 'description': 'Your dog is very energetic!'}
         }
         
         # Motion detection parameters
         self.history = []
         self.max_history = 10
         self.prev_frame = None
+        self.motion_history = deque(maxlen=5)  # Store recent motion scores
         
     def detect_motion(self, frame):
-        """Detect motion in frame"""
+        """Detect motion in frame with improved sensitivity"""
+        # Resize frame for consistent motion detection
+        frame = cv2.resize(frame, (300, 300))
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
         gray = cv2.GaussianBlur(gray, (21, 21), 0)
         
@@ -42,57 +45,74 @@ class DogBehaviorAnalyzer:
             return 0.0
             
         frame_delta = cv2.absdiff(self.prev_frame, gray)
-        thresh = cv2.threshold(frame_delta, 25, 255, cv2.THRESH_BINARY)[1]
+        thresh = cv2.threshold(frame_delta, 20, 255, cv2.THRESH_BINARY)[1]  # Lower threshold for better sensitivity
         thresh = cv2.dilate(thresh, None, iterations=2)
         
+        # Calculate motion score
         motion_score = np.sum(thresh > 0) / thresh.size
         self.prev_frame = gray
         
-        return motion_score
+        # Add to motion history
+        self.motion_history.append(motion_score)
         
+        # Return average of recent motion scores for stability
+        return np.mean(self.motion_history) if len(self.motion_history) > 0 else motion_score
+
     def detect_color_changes(self, frame):
-        """Detect significant color changes that might indicate tail wagging"""
+        """Detect significant color changes with improved sensitivity"""
+        frame = cv2.resize(frame, (300, 300))
         hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-        lower = np.array([0, 0, 0])
-        upper = np.array([180, 255, 255])
-        mask = cv2.inRange(hsv, lower, upper)
-        
-        if len(self.history) > 0:
-            prev_mask = self.history[-1]
-            diff = cv2.absdiff(mask, prev_mask)
-            change_score = np.sum(diff > 0) / diff.size
-        else:
-            change_score = 0.0
+        
+        # Define range for common dog colors
+        color_ranges = [
+            (np.array([0, 30, 30]), np.array([30, 255, 255])),    # Brown/Red
+            (np.array([0, 0, 0]), np.array([180, 50, 255])),      # White/Gray/Black
+        ]
+        
+        total_change_score = 0
+        for lower, upper in color_ranges:
+            mask = cv2.inRange(hsv, lower, upper)
+            
+            if len(self.history) > 0:
+                prev_mask = self.history[-1]
+                diff = cv2.absdiff(mask, prev_mask)
+                change_score = np.sum(diff > 0) / diff.size
+                total_change_score = max(total_change_score, change_score)
             
         self.history.append(mask)
         if len(self.history) > self.max_history:
             self.history.pop(0)
             
-        return change_score
+        return total_change_score
 
     def analyze_frame(self, frame):
-        """Analyze frame using motion and color change detection"""
+        """Analyze frame with improved behavior detection logic"""
         motion_score = self.detect_motion(frame)
         color_change_score = self.detect_color_changes(frame)
         
         detected_behaviors = []
         
-        # Detect tail wagging based on localized color changes
-        if color_change_score > self.behaviors['tail_wagging']['threshold']:
-            detected_behaviors.append(('tail_wagging', color_change_score))
-            
-        # Detect movement
-        if motion_score > self.behaviors['movement']['threshold']:
-            detected_behaviors.append(('movement', motion_score))
-            
-        # Detect stationary behavior
-        if motion_score < self.behaviors['stationary']['threshold']:
-            detected_behaviors.append(('stationary', 1.0 - motion_score))
-            
-        # Detect high activity
+        # High activity detection (running, jumping)
         if motion_score > self.behaviors['high_activity']['threshold']:
             detected_behaviors.append(('high_activity', motion_score))
-            
+        
+        # Regular movement detection
+        elif motion_score > self.behaviors['movement']['threshold']:
+            detected_behaviors.append(('movement', motion_score))
+        
+        # Stationary detection - only if very little motion
+        elif motion_score < self.behaviors['stationary']['threshold']:
+            detected_behaviors.append(('stationary', 1.0 - motion_score))
+        
+        # Tail wagging detection - based on localized color changes
+        if color_change_score > self.behaviors['tail_wagging']['threshold']:
+            detected_behaviors.append(('tail_wagging', color_change_score))
+        
+        # Debug information
+        if not detected_behaviors:
+            st.sidebar.write(f"Debug - Motion Score: {motion_score:.4f}")
+            st.sidebar.write(f"Debug - Color Change Score: {color_change_score:.4f}")
+        
         return detected_behaviors
 
 def main():