[Feature Updated] > Updated anomaly_gif_maker a lot, to be able to precisely extract frames from the video, where the model and pipeline think that part is AI.

backend/app/ai/video/anomaly_gif_maker.py

@@ -20,30 +20,21 @@ def generate_anomaly_gif(frames: List[np.ndarray], center_idx: int) -> str:
         end_idx = min(len(frames), center_idx + 10)
 
         gif_frames = []
+        # Create a list to track exactly how many seconds EVERY individual frame stays on screen
+        frame_durations = []
 
         for i in range(start_idx, end_idx):
             frame = frames[i]
             
-            # 1. Take the grayscale X-Ray of the current frame
             gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-            # 2. Extract sharp structural edges
             laplacian = cv2.Laplacian(gray, cv2.CV_64F)
             abs_laplacian = np.absolute(laplacian)
-            
-            # 3. Normalize to force the highest anomaly to glow at maximum brightness (255)
             laplacian_8u = cv2.normalize(abs_laplacian, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)
-            # 4. Blur it to turn sharp noise into soft glowing clouds
             heatmap_blurred = cv2.GaussianBlur(laplacian_8u, (15, 15), 0)
-            
-            # 5. Filter out natural background camera noise (anything under 70 brightness)
             _, heatmap_mask = cv2.threshold(heatmap_blurred, 70, 255, cv2.THRESH_TOZERO)
-            # 6. Apply Jet Thermal Colors (Blue = natural, Red = AI Glitch)
             colored_heatmap = cv2.applyColorMap(heatmap_mask, cv2.COLORMAP_JET)
-
-            # 7. Apply the thermal glow onto a copy of the video frame
             heatmap_overlay = cv2.addWeighted(frame.copy(), 0.5, colored_heatmap, 0.7, 0)
             
-            # 8. Find the contours (puddles) of the highest glowing areas and draw Red Boxes
             _, thresh = cv2.threshold(laplacian_8u, 100, 255, cv2.THRESH_BINARY)
             contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
             contours = sorted(contours, key=cv2.contourArea, reverse=True)[:3]
@@ -53,35 +44,28 @@ def generate_anomaly_gif(frames: List[np.ndarray], center_idx: int) -> str:
                     x, y, w, h = cv2.boundingRect(contour)
                     cv2.rectangle(heatmap_overlay, (x, y), (x+w, y+h), (0, 0, 255), 2)
             
-            # --- CREATE THE SIDE-BY-SIDE SPLIT SCREEN ---
-            
-            # 9. Resize both halves by 50% so the final GIF file size stays small
             h, w = frame.shape[:2]
             small_original = cv2.resize(frame, (w//2, h//2))
             small_heatmap = cv2.resize(heatmap_overlay, (w//2, h//2))
             
-            # 10. Add clear text labels to the top of both screens
             cv2.putText(small_original, "ORIGINAL", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
             cv2.putText(small_heatmap, "AI RADIANCE MAP", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)
             
-            # 11. Add an extra flashing text warning when it reaches the absolute worst frame
+            # --- THE FIX ---
+            # If this is the peak glitch, assign it a massive 3-Second duration!
             if i == center_idx:
                 cv2.putText(small_heatmap, "PEAK GLITCH", (10, 55), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
+                frame_durations.append(3.0) # Pause for 3.0 real seconds!
+            else:
+                # Normal frames zip by at fast normal speed (0.15 seconds per frame)
+                frame_durations.append(0.15) 
 
-            # 12. Mathematically glue the two images together side-by-side using axis=1 (horizontal)
             combined_frame = np.concatenate((small_original, small_heatmap), axis=1)
-            
-            # 13. Convert from BGR back to RGB before saving so the GIF colors aren't inverted
             rgb_frame = cv2.cvtColor(combined_frame, cv2.COLOR_BGR2RGB)
             gif_frames.append(rgb_frame)
 
-            # 13b. If this is the absolute peak glitch, append it 8 extra times to create a 1.2-second "Freeze-Frame" pause!
-            if i == center_idx:
-                for _ in range(8):
-                    gif_frames.append(rgb_frame)
-
-        # 14. Synthesize the final GIF animation (Slowed down to ~6.5fps for better readability, and loop=0 for infinite looping)
-        imageio.mimsave(save_path, gif_frames, format='GIF', duration=5, loop=0)
+        # We pass our explicit list of durations to tell the GIF EXACTLY how long to hold each frame
+        imageio.mimsave(save_path, gif_frames, format='GIF', duration=frame_durations, loop=0)
 
         return save_path