Simplified compare algo
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1 changed files with 30 additions and 73 deletions
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@ -7,41 +7,9 @@ def normalize_image(image: np.ndarray) -> np.ndarray:
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return cv2.resize(image, (160*2,90*2))
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def compare_images(image: np.ndarray, target_image: np.ndarray) -> float:
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"""
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Compare two images pixel-wise, threshold differences, and return a similarity score (0-1).
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Parameters:
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image, target_image : np.ndarray
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Input images (must be same shape, HxWxC).
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Returns:
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float : similarity score (1 = identical, 0 = completely different)
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"""
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THRESHOLD = 0.2 * 255
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# Ensure images are uint8
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if image.dtype != np.uint8:
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image = (255 * image).astype(np.uint8) if image.max() <= 1.0 else image.astype(np.uint8)
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if target_image.dtype != np.uint8:
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target_image = (255 * target_image).astype(np.uint8) if target_image.max() <= 1.0 else target_image.astype(np.uint8)
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# Ensure same shape
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if image.shape != target_image.shape:
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raise ValueError(f"Images must have the same shape: {image.shape} vs {target_image.shape}")
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# Compute absolute difference per pixel
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delta = cv2.absdiff(image, target_image) # shape HxWxC
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# If multi-channel, take max across channels
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if len(delta.shape) == 3:
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delta = delta.max(axis=2) # HxW
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# Threshold to binary
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binary_diff = (delta > THRESHOLD).astype(np.uint8)
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# Score = 1 - fraction of different pixels
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score = 1.0 - binary_diff.sum() / binary_diff.size
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return score
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err = np.mean((image.astype(float) - target_image.astype(float)) ** 2)
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max_val = 255.0 if image.dtype == np.uint8 else 1.0
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return 1 - (err / (max_val**2))
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def compare_episode_to_image(path_to_episode: str, image: np.ndarray) -> float:
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video = cv2.VideoCapture(path_to_episode)
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@ -120,20 +88,9 @@ def compare_episode_to_images(path_to_episode: str, images: list[np.ndarray]) ->
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def match_episodes_to_references(episodes: list[str], references: dict[str, list[np.ndarray]]) -> dict[str, dict[str, float]]:
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results = {}
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# Normalize references
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print("Normalizing reference images ...")
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for ref in references.keys():
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for i in range(len(references[ref])):
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references[ref][i] = normalize_image(references[ref][i])
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# cv2.imwrite(f"{i}.png", references[ref][i])
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# Compare to episodes
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for episode in episodes:
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print(f"Processing: {episode}")
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results[episode] = {}
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for ref, images in references.items():
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print(f" -- Reference {ref}")
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results[episode][ref] = compare_episode_to_images(episode, images)
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results[episode] = efficient_episode_matching(episode, references)
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print(results[episode])
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@ -274,33 +231,33 @@ if __name__ == "__main__":
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references = {
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"1" : [
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cv2.imread("./1/9hYDr40VIWbAHGjmlpSkyqlDaVQ.webp"),
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# cv2.imread("./1/cmYUHroQ9zRhOYiTf0ozMWe1Bzs.webp"),
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# cv2.imread("./1/yLwaP7Y7O3Wyrht9lPmTDk7LSuG.webp"),
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cv2.imread("./1/cmYUHroQ9zRhOYiTf0ozMWe1Bzs.webp"),
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cv2.imread("./1/yLwaP7Y7O3Wyrht9lPmTDk7LSuG.webp"),
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],
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"2" : [
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cv2.imread("./2/g2pyyFRFBS18jYByRdiunWr7nap.webp"),
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cv2.imread("./2/wKCm3I7efXfsWGNrG28d3cIsL9B.webp"),
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cv2.imread("./2/yhL1JBnTDK30BtPbr8gZ5ZXCASL.webp"),
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],
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"3" : [
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cv2.imread("./3/d9F2x01XNhi65GTXwQjrdDJKgqm.webp"),
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cv2.imread("./3/kV7VAvLgjjhPTI56OnNvcNaHjDM.webp"),
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cv2.imread("./3/mLxkumagHSYg4KNLcEIgHpiJrx.webp"),
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],
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"4" : [
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cv2.imread("./4/9q62zw89SJiRD3kjNuhUIswWHTa.webp"),
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cv2.imread("./4/lyi4XUUssCacaEQs2FCGSDCKDUK.webp"),
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],
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"5" : [
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cv2.imread("./5/uIztge10KNlRU4KUFNXWPGCiG8e.webp"),
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cv2.imread("./5/vuhdflodFfPkvekVWbS4JZMYHa7.webp"),
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cv2.imread("./5/xmvi4bBk5TQyuL6tpiYvNoIPft.webp"),
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],
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"6" : [
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cv2.imread("./6/2I7WgvRwnYDNjbiztvbOAldYGde.webp"),
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cv2.imread("./6/dIaBagmUbP1AO6xFQzfQud5lk4I.webp"),
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cv2.imread("./6/ekDZrFRDJDyx78uttMyVT9mPhjL.webp"),
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],
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# "2" : [
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# cv2.imread("./2/g2pyyFRFBS18jYByRdiunWr7nap.webp"),
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# cv2.imread("./2/wKCm3I7efXfsWGNrG28d3cIsL9B.webp"),
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# cv2.imread("./2/yhL1JBnTDK30BtPbr8gZ5ZXCASL.webp"),
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# ],
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# "3" : [
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# cv2.imread("./3/d9F2x01XNhi65GTXwQjrdDJKgqm.webp"),
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# cv2.imread("./3/kV7VAvLgjjhPTI56OnNvcNaHjDM.webp"),
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# cv2.imread("./3/mLxkumagHSYg4KNLcEIgHpiJrx.webp"),
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# ],
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# "4" : [
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# cv2.imread("./4/9q62zw89SJiRD3kjNuhUIswWHTa.webp"),
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# cv2.imread("./4/lyi4XUUssCacaEQs2FCGSDCKDUK.webp"),
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# ],
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# "5" : [
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# cv2.imread("./5/uIztge10KNlRU4KUFNXWPGCiG8e.webp"),
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# cv2.imread("./5/vuhdflodFfPkvekVWbS4JZMYHa7.webp"),
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# cv2.imread("./5/xmvi4bBk5TQyuL6tpiYvNoIPft.webp"),
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# ],
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# "6" : [
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# cv2.imread("./6/2I7WgvRwnYDNjbiztvbOAldYGde.webp"),
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# cv2.imread("./6/dIaBagmUbP1AO6xFQzfQud5lk4I.webp"),
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# cv2.imread("./6/ekDZrFRDJDyx78uttMyVT9mPhjL.webp"),
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# ],
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}
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results = match_episodes_to_references(episodes, references)
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