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add preprocessing and penalization for low confidence chars
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Josh Hawkins
parent
8798efb18d
commit
8c6ba5ce27
@ -210,11 +210,8 @@ class LicensePlateProcessingMixin:
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# set to True to write each cropped image for debugging
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if False:
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save_image = cv2.cvtColor(
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plate_images[original_idx], cv2.COLOR_RGB2BGR
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)
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filename = f"debug/frames/plate_{original_idx}_{plate}_{area}.jpg"
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cv2.imwrite(filename, save_image)
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cv2.imwrite(filename, plate_images[original_idx])
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license_plates[original_idx] = plate
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average_confidences[original_idx] = average_confidence
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@ -333,7 +330,7 @@ class LicensePlateProcessingMixin:
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# Use pyclipper to shrink the polygon slightly based on the computed distance.
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offset = PyclipperOffset()
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offset.AddPath(points, JT_ROUND, ET_CLOSEDPOLYGON)
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points = np.array(offset.Execute(distance * 1.75)).reshape((-1, 1, 2))
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points = np.array(offset.Execute(distance * 1.5)).reshape((-1, 1, 2))
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# get the minimum bounding box around the shrunken polygon.
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box, min_side = self._get_min_boxes(points)
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@ -637,6 +634,47 @@ class LicensePlateProcessingMixin:
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assert image.shape[2] == input_shape[0], "Unexpected number of image channels."
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# convert to grayscale
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if image.shape[2] == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
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else:
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gray = image
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# detect noise with Laplacian variance
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laplacian = cv2.Laplacian(gray, cv2.CV_64F)
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noise_variance = np.var(laplacian)
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brightness = cv2.mean(gray)[0]
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noise_threshold = 70
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brightness_threshold = 150
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is_noisy = (
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noise_variance > noise_threshold and brightness < brightness_threshold
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)
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# apply bilateral filter and sharpening only if noisy
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if is_noisy:
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logger.debug(
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f"Noise detected (variance: {noise_variance:.1f}, brightness: {brightness:.1f}) - denoising"
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)
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smoothed = cv2.bilateralFilter(gray, d=15, sigmaColor=100, sigmaSpace=100)
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sharpening_kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
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processed = cv2.filter2D(smoothed, -1, sharpening_kernel)
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else:
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logger.debug(
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f"No noise detected (variance: {noise_variance:.1f}, brightness: {brightness:.1f}) - skipping denoising and sharpening"
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)
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processed = gray
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# apply CLAHE for contrast enhancement
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grid_size = (
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max(4, input_w // 40),
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max(4, input_h // 40),
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)
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clahe = cv2.createCLAHE(clipLimit=1.5, tileGridSize=grid_size)
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enhanced = clahe.apply(processed)
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# Convert back to 3-channel for model compatibility
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image = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2RGB)
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# dynamically adjust input width based on max_wh_ratio
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input_w = int(input_h * max_wh_ratio)
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@ -662,6 +700,13 @@ class LicensePlateProcessingMixin:
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)
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padded_image[:, :, :resized_w] = resized_image
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if False:
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current_time = int(datetime.datetime.now().timestamp() * 1000)
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cv2.imwrite(
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f"debug/frames/preprocessed_recognition_{current_time}.jpg",
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image,
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)
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return padded_image
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@staticmethod
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@ -783,6 +828,7 @@ class LicensePlateProcessingMixin:
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def _should_keep_previous_plate(
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self, id, top_plate, top_char_confidences, top_area, avg_confidence
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):
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"""Determine if the previous plate should be kept over the current one."""
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if id not in self.detected_license_plates:
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return False
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@ -797,68 +843,88 @@ class LicensePlateProcessingMixin:
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)
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# 1. Normalize metrics
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# Length score - use relative comparison
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# If lengths are equal, score is 0.5 for both
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# If one is longer, it gets a higher score up to 1.0
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max_length_diff = 4 # Maximum expected difference in plate lengths
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# Length score: Equal lengths = 0.5, penalize extra characters if low confidence
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length_diff = len(top_plate) - len(prev_plate)
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curr_length_score = 0.5 + (
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length_diff / (2 * max_length_diff)
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) # Normalize to 0-1
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curr_length_score = max(0, min(1, curr_length_score)) # Clamp to 0-1
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prev_length_score = 1 - curr_length_score # Inverse relationship
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max_length_diff = 3
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curr_length_score = 0.5 + (length_diff / (2 * max_length_diff))
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curr_length_score = max(0, min(1, curr_length_score))
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prev_length_score = 0.5 - (length_diff / (2 * max_length_diff))
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prev_length_score = max(0, min(1, prev_length_score))
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# Area score (normalize based on max of current and previous)
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# Adjust length score based on confidence of extra characters
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conf_threshold = 0.75 # Minimum confidence for a character to be "trusted"
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if len(top_plate) > len(prev_plate):
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extra_conf = min(
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top_char_confidences[len(prev_plate) :]
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) # Lowest extra char confidence
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if extra_conf < conf_threshold:
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curr_length_score *= extra_conf / conf_threshold # Penalize if weak
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elif len(prev_plate) > len(top_plate):
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extra_conf = min(prev_char_confidences[len(top_plate) :])
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if extra_conf < conf_threshold:
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prev_length_score *= extra_conf / conf_threshold
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# Area score: Normalize by max area
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max_area = max(top_area, prev_area)
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curr_area_score = top_area / max_area
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prev_area_score = prev_area / max_area
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curr_area_score = top_area / max_area if max_area > 0 else 0
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prev_area_score = prev_area / max_area if max_area > 0 else 0
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# Average confidence score (already normalized 0-1)
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# Confidence scores
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curr_conf_score = avg_confidence
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prev_conf_score = prev_avg_confidence
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# Character confidence comparison score
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# Character confidence comparison (average over shared length)
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min_length = min(len(top_plate), len(prev_plate))
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if min_length > 0:
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curr_char_conf = sum(top_char_confidences[:min_length]) / min_length
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prev_char_conf = sum(prev_char_confidences[:min_length]) / min_length
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else:
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curr_char_conf = 0
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prev_char_conf = 0
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curr_char_conf = prev_char_conf = 0
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# 2. Define weights
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# Penalize any character below threshold
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curr_min_conf = min(top_char_confidences) if top_char_confidences else 0
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prev_min_conf = min(prev_char_confidences) if prev_char_confidences else 0
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curr_conf_penalty = (
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1.0 if curr_min_conf >= conf_threshold else (curr_min_conf / conf_threshold)
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)
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prev_conf_penalty = (
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1.0 if prev_min_conf >= conf_threshold else (prev_min_conf / conf_threshold)
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)
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# 2. Define weights (boost confidence importance)
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weights = {
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"length": 0.4,
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"area": 0.3,
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"avg_confidence": 0.2,
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"char_confidence": 0.1,
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"length": 0.2,
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"area": 0.2,
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"avg_confidence": 0.35,
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"char_confidence": 0.25,
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}
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# 3. Calculate weighted scores
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# 3. Calculate weighted scores with penalty
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curr_score = (
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curr_length_score * weights["length"]
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+ curr_area_score * weights["area"]
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+ curr_conf_score * weights["avg_confidence"]
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+ curr_char_conf * weights["char_confidence"]
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)
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) * curr_conf_penalty
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prev_score = (
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prev_length_score * weights["length"]
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+ prev_area_score * weights["area"]
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+ prev_conf_score * weights["avg_confidence"]
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+ prev_char_conf * weights["char_confidence"]
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)
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) * prev_conf_penalty
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# 4. Log the comparison for debugging
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# 4. Log the comparison
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logger.debug(
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f"Plate comparison - Current plate: {top_plate} (score: {curr_score:.3f}) vs "
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f"Previous plate: {prev_plate} (score: {prev_score:.3f})\n"
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f"Plate comparison - Current: {top_plate} (score: {curr_score:.3f}, min_conf: {curr_min_conf:.2f}) vs "
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f"Previous: {prev_plate} (score: {prev_score:.3f}, min_conf: {prev_min_conf:.2f})\n"
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f"Metrics - Length: {len(top_plate)} vs {len(prev_plate)} (scores: {curr_length_score:.2f} vs {prev_length_score:.2f}), "
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f"Area: {top_area} vs {prev_area}, "
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f"Avg Conf: {avg_confidence:.2f} vs {prev_avg_confidence:.2f}"
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f"Avg Conf: {avg_confidence:.2f} vs {prev_avg_confidence:.2f}, "
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f"Char Conf: {curr_char_conf:.2f} vs {prev_char_conf:.2f}"
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)
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# 5. Return True if we should keep the previous plate (i.e., if it scores higher)
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# 5. Return True if previous plate scores higher
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return prev_score > curr_score
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def __update_yolov9_metrics(self, duration: float) -> None:
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