Simplified compare algo

src/label_episodes.py

@@ -7,41 +7,9 @@ def normalize_image(image: np.ndarray) -> np.ndarray:
     return cv2.resize(image, (160*2,90*2))
 
 def compare_images(image: np.ndarray, target_image: np.ndarray) -> float:
-    """
+    err = np.mean((image.astype(float) - target_image.astype(float)) ** 2)
-    Compare two images pixel-wise, threshold differences, and return a similarity score (0-1).
+    max_val = 255.0 if image.dtype == np.uint8 else 1.0
-    
+    return 1 - (err / (max_val**2))
-    Parameters:
-        image, target_image : np.ndarray
-            Input images (must be same shape, HxWxC).
-    
-    Returns:
-        float : similarity score (1 = identical, 0 = completely different)
-    """
-    THRESHOLD = 0.2 * 255
-    
-    # Ensure images are uint8
-    if image.dtype != np.uint8:
-        image = (255 * image).astype(np.uint8) if image.max() <= 1.0 else image.astype(np.uint8)
-    if target_image.dtype != np.uint8:
-        target_image = (255 * target_image).astype(np.uint8) if target_image.max() <= 1.0 else target_image.astype(np.uint8)
-    
-    # Ensure same shape
-    if image.shape != target_image.shape:
-        raise ValueError(f"Images must have the same shape: {image.shape} vs {target_image.shape}")
-    
-    # Compute absolute difference per pixel
-    delta = cv2.absdiff(image, target_image)  # shape HxWxC
-    
-    # If multi-channel, take max across channels
-    if len(delta.shape) == 3:
-        delta = delta.max(axis=2)  # HxW
-    
-    # Threshold to binary
-    binary_diff = (delta > THRESHOLD).astype(np.uint8)
-    
-    # Score = 1 - fraction of different pixels
-    score = 1.0 - binary_diff.sum() / binary_diff.size
-    return score
 
 def compare_episode_to_image(path_to_episode: str, image: np.ndarray) -> float:
     video = cv2.VideoCapture(path_to_episode)
@@ -120,20 +88,9 @@ def compare_episode_to_images(path_to_episode: str, images: list[np.ndarray]) ->
 def match_episodes_to_references(episodes: list[str], references: dict[str, list[np.ndarray]]) -> dict[str, dict[str, float]]:
     results = {}
 
-    # Normalize references
-    print("Normalizing reference images ...")
-    for ref in references.keys():
-        for i in range(len(references[ref])):
-            references[ref][i] = normalize_image(references[ref][i])
-            # cv2.imwrite(f"{i}.png", references[ref][i])
-
     # Compare to episodes
     for episode in episodes:
-        print(f"Processing: {episode}")
+        results[episode] = efficient_episode_matching(episode, references)
-        results[episode] = {}
-        for ref, images in references.items():
-            print(f" -- Reference {ref}")
-            results[episode][ref] = compare_episode_to_images(episode, images)
 
         print(results[episode])
 
@@ -274,33 +231,33 @@ if __name__ == "__main__":
     references = {
         "1" : [
             cv2.imread("./1/9hYDr40VIWbAHGjmlpSkyqlDaVQ.webp"),
-            # cv2.imread("./1/cmYUHroQ9zRhOYiTf0ozMWe1Bzs.webp"),
+            cv2.imread("./1/cmYUHroQ9zRhOYiTf0ozMWe1Bzs.webp"),
-            # cv2.imread("./1/yLwaP7Y7O3Wyrht9lPmTDk7LSuG.webp"),
+            cv2.imread("./1/yLwaP7Y7O3Wyrht9lPmTDk7LSuG.webp"),
+        ],
+        "2" : [
+            cv2.imread("./2/g2pyyFRFBS18jYByRdiunWr7nap.webp"),
+            cv2.imread("./2/wKCm3I7efXfsWGNrG28d3cIsL9B.webp"),
+            cv2.imread("./2/yhL1JBnTDK30BtPbr8gZ5ZXCASL.webp"),
+        ],
+        "3" : [
+            cv2.imread("./3/d9F2x01XNhi65GTXwQjrdDJKgqm.webp"),
+            cv2.imread("./3/kV7VAvLgjjhPTI56OnNvcNaHjDM.webp"),
+            cv2.imread("./3/mLxkumagHSYg4KNLcEIgHpiJrx.webp"),
+        ],
+        "4" : [
+            cv2.imread("./4/9q62zw89SJiRD3kjNuhUIswWHTa.webp"),
+            cv2.imread("./4/lyi4XUUssCacaEQs2FCGSDCKDUK.webp"),
+        ],
+        "5" : [
+            cv2.imread("./5/uIztge10KNlRU4KUFNXWPGCiG8e.webp"),
+            cv2.imread("./5/vuhdflodFfPkvekVWbS4JZMYHa7.webp"),
+            cv2.imread("./5/xmvi4bBk5TQyuL6tpiYvNoIPft.webp"),
+        ],
+        "6" : [
+            cv2.imread("./6/2I7WgvRwnYDNjbiztvbOAldYGde.webp"),
+            cv2.imread("./6/dIaBagmUbP1AO6xFQzfQud5lk4I.webp"),
+            cv2.imread("./6/ekDZrFRDJDyx78uttMyVT9mPhjL.webp"),
         ],
-        # "2" : [
-        #     cv2.imread("./2/g2pyyFRFBS18jYByRdiunWr7nap.webp"),
-        #     cv2.imread("./2/wKCm3I7efXfsWGNrG28d3cIsL9B.webp"),
-        #     cv2.imread("./2/yhL1JBnTDK30BtPbr8gZ5ZXCASL.webp"),
-        # ],
-        # "3" : [
-        #     cv2.imread("./3/d9F2x01XNhi65GTXwQjrdDJKgqm.webp"),
-        #     cv2.imread("./3/kV7VAvLgjjhPTI56OnNvcNaHjDM.webp"),
-        #     cv2.imread("./3/mLxkumagHSYg4KNLcEIgHpiJrx.webp"),
-        # ],
-        # "4" : [
-        #     cv2.imread("./4/9q62zw89SJiRD3kjNuhUIswWHTa.webp"),
-        #     cv2.imread("./4/lyi4XUUssCacaEQs2FCGSDCKDUK.webp"),
-        # ],
-        # "5" : [
-        #     cv2.imread("./5/uIztge10KNlRU4KUFNXWPGCiG8e.webp"),
-        #     cv2.imread("./5/vuhdflodFfPkvekVWbS4JZMYHa7.webp"),
-        #     cv2.imread("./5/xmvi4bBk5TQyuL6tpiYvNoIPft.webp"),
-        # ],
-        # "6" : [
-        #     cv2.imread("./6/2I7WgvRwnYDNjbiztvbOAldYGde.webp"),
-        #     cv2.imread("./6/dIaBagmUbP1AO6xFQzfQud5lk4I.webp"),
-        #     cv2.imread("./6/ekDZrFRDJDyx78uttMyVT9mPhjL.webp"),
-        # ],
     }
 
     results = match_episodes_to_references(episodes, references)