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use yolov9 for users without frigate+ and update retention algorithm

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Josh Hawkins 2025-02-13 10:55:11 -06:00
parent 0dcfcb2cf7
commit 793d0fb129
1 changed files with 201 additions and 40 deletions
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241
frigate/data_processing/real_time/license_plate_processor.py
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@ -24,6 +24,7 @@ from .api import RealTimeProcessorApi
logger = logging.getLogger(__name__)
MIN_PLATE_LENGTH = 3
WRITE_DEBUG_IMAGES = False
class LicensePlateProcessor(RealTimeProcessorApi):
@ -86,12 +87,24 @@ class LicensePlateProcessor(RealTimeProcessorApi):
requestor=self.requestor,
device="CPU",
)
self.yolov9_detection_model = GenericONNXEmbedding(
model_name="yolov9_license_plate",
model_file="yolov9-256-license-plates.onnx",
download_urls={
"yolov9-256-license-plates.onnx": "https://github.com/hawkeye217/yolov9-license-plates/raw/refs/heads/master/models/yolov9-256-license-plates.onnx"
},
model_size="large",
model_type=ModelTypeEnum.yolov9_lpr_detect,
requestor=self.requestor,
device="CPU",
)
if self.lpr_config.enabled:
# all models need to be loaded to run LPR
self.detection_model._load_model_and_utils()
self.classification_model._load_model_and_utils()
self.recognition_model._load_model_and_utils()
self.yolov9_detection_model._load_model_and_utils()
def _detect(self, image: np.ndarray) -> List[np.ndarray]:
"""
@ -112,6 +125,13 @@ class LicensePlateProcessor(RealTimeProcessorApi):
resized_image = self._resize_image(image)
normalized_image = self._normalize_image(resized_image)
if WRITE_DEBUG_IMAGES:
current_time = int(datetime.datetime.now().timestamp())
cv2.imwrite(
f"debug/frames/license_plate_resized_{current_time}.jpg",
resized_image,
)
outputs = self.detection_model([normalized_image])[0]
outputs = outputs[0, :, :]
@ -207,12 +227,27 @@ class LicensePlateProcessor(RealTimeProcessorApi):
plate_points = self._detect(image)
if len(plate_points) == 0:
logger.debug("No points found by OCR detector model")
return [], [], []
plate_points = self._sort_polygon(list(plate_points))
plate_images = [self._crop_license_plate(image, x) for x in plate_points]
rotated_images, _ = self._classify(plate_images)
# debug rotated and classification result
if WRITE_DEBUG_IMAGES:
current_time = int(datetime.datetime.now().timestamp())
for i, img in enumerate(plate_images):
cv2.imwrite(
f"debug/frames/license_plate_rotated_{current_time}_{i + 1}.jpg",
img,
)
for i, img in enumerate(rotated_images):
cv2.imwrite(
f"debug/frames/license_plate_classified_{current_time}_{i + 1}.jpg",
img,
)
# keep track of the index of each image for correct area calc later
sorted_indices = np.argsort([x.shape[1] / x.shape[0] for x in rotated_images])
reverse_mapping = {
@ -331,6 +366,7 @@ class LicensePlateProcessor(RealTimeProcessorApi):
# get minimum bounding box (rotated rectangle) around the contour and the smallest side length.
points, min_side = self._get_min_boxes(contour)
logger.debug(f"min side {index}, {min_side}")
if min_side < self.min_size:
continue
@ -338,6 +374,7 @@ class LicensePlateProcessor(RealTimeProcessorApi):
points = np.array(points)
score = self._box_score(output, contour)
logger.debug(f"box score {index}, {score}")
if self.box_thresh > score:
continue
@ -492,7 +529,7 @@ class LicensePlateProcessor(RealTimeProcessorApi):
def _sort_polygon(points):
"""
Sort polygons based on their position in the image. If polygons are close in vertical
position (within 10 pixels), sort them by horizontal position.
position (within 5 pixels), sort them by horizontal position.
Args:
points: List of polygons to sort.
@ -503,7 +540,7 @@ class LicensePlateProcessor(RealTimeProcessorApi):
points.sort(key=lambda x: (x[0][1], x[0][0]))
for i in range(len(points) - 1):
for j in range(i, -1, -1):
if abs(points[j + 1][0][1] - points[j][0][1]) < 10 and (
if abs(points[j + 1][0][1] - points[j][0][1]) < 5 and (
points[j + 1][0][0] < points[j][0][0]
):
temp = points[j]
@ -602,7 +639,8 @@ class LicensePlateProcessor(RealTimeProcessorApi):
for j in range(len(outputs)):
label, score = outputs[j]
results[indices[i + j]] = [label, score]
if "180" in label and score >= self.lpr_config.threshold:
# make sure we have high confidence if we need to flip a box, this will be rare in lpr
if "180" in label and score >= 0.9:
images[indices[i + j]] = cv2.rotate(images[indices[i + j]], 1)
return images, results
@ -701,10 +739,122 @@ class LicensePlateProcessor(RealTimeProcessorApi):
self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10
def _detect_license_plate(self, input: np.ndarray) -> tuple[int, int, int, int]:
"""Return the dimensions of the input image as [x, y, width, height]."""
# TODO: use a small model here to detect plates
height, width = input.shape[:2]
return (0, 0, width, height)
"""
Use a lightweight YOLOv9 model to detect license plates for users without Frigate+
Return the dimensions of the detected plate as [x1, y1, x2, y2].
"""
predictions = self.yolov9_detection_model(input)
confidence_threshold = self.lpr_config.threshold
top_score = -1
top_box = None
# Loop over predictions
for prediction in predictions:
score = prediction[6]
if score >= confidence_threshold:
bbox = prediction[1:5]
# Scale boxes back to original image size
scale_x = input.shape[1] / 256
scale_y = input.shape[0] / 256
bbox[0] *= scale_x
bbox[1] *= scale_y
bbox[2] *= scale_x
bbox[3] *= scale_y
if score > top_score:
top_score = score
top_box = bbox
# Return the top scoring bounding box if found
if top_box is not None:
logger.debug("Found license plate: {}".format(top_box.astype(int)))
return tuple(top_box.astype(int))
else:
return None # No detection above the threshold
def _should_keep_previous_plate(
self, id, top_plate, top_char_confidences, top_area, avg_confidence
):
if id not in self.detected_license_plates:
return False
prev_data = self.detected_license_plates[id]
prev_plate = prev_data["plate"]
prev_char_confidences = prev_data["char_confidences"]
prev_area = prev_data["area"]
prev_avg_confidence = (
sum(prev_char_confidences) / len(prev_char_confidences)
if prev_char_confidences
else 0
)
# 1. Normalize metrics
# Length score - use relative comparison
# If lengths are equal, score is 0.5 for both
# If one is longer, it gets a higher score up to 1.0
max_length_diff = 4 # Maximum expected difference in plate lengths
length_diff = len(top_plate) - len(prev_plate)
curr_length_score = 0.5 + (
length_diff / (2 * max_length_diff)
) # Normalize to 0-1
curr_length_score = max(0, min(1, curr_length_score)) # Clamp to 0-1
prev_length_score = 1 - curr_length_score # Inverse relationship
# Area score (normalize based on max of current and previous)
max_area = max(top_area, prev_area)
curr_area_score = top_area / max_area
prev_area_score = prev_area / max_area
# Average confidence score (already normalized 0-1)
curr_conf_score = avg_confidence
prev_conf_score = prev_avg_confidence
# Character confidence comparison score
min_length = min(len(top_plate), len(prev_plate))
if min_length > 0:
curr_char_conf = sum(top_char_confidences[:min_length]) / min_length
prev_char_conf = sum(prev_char_confidences[:min_length]) / min_length
else:
curr_char_conf = 0
prev_char_conf = 0
# 2. Define weights
weights = {
"length": 0.4,
"area": 0.3,
"avg_confidence": 0.2,
"char_confidence": 0.1,
}
# 3. Calculate weighted scores
curr_score = (
curr_length_score * weights["length"]
+ curr_area_score * weights["area"]
+ curr_conf_score * weights["avg_confidence"]
+ curr_char_conf * weights["char_confidence"]
)
prev_score = (
prev_length_score * weights["length"]
+ prev_area_score * weights["area"]
+ prev_conf_score * weights["avg_confidence"]
+ prev_char_conf * weights["char_confidence"]
)
# 4. Log the comparison for debugging
logger.debug(
f"Plate comparison - Current plate: {top_plate} (score: {curr_score:.3f}) vs "
f"Previous plate: {prev_plate} (score: {prev_score:.3f})\n"
f"Metrics - Length: {len(top_plate)} vs {len(prev_plate)} (scores: {curr_length_score:.2f} vs {prev_length_score:.2f}), "
f"Area: {top_area} vs {prev_area}, "
f"Avg Conf: {avg_confidence:.2f} vs {prev_avg_confidence:.2f}"
)
# 5. Return True if we should keep the previous plate (i.e., if it scores higher)
return prev_score > curr_score
def process_frame(self, obj_data: dict[str, any], frame: np.ndarray):
"""Look for license plates in image."""
@ -739,19 +889,41 @@ class LicensePlateProcessor(RealTimeProcessorApi):
if not car_box:
return
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
left, top, right, bottom = car_box
car = rgb[top:bottom, left:right]
# double the size of the car for better box detection
car = cv2.resize(car, (int(2 * car.shape[1]), int(2 * car.shape[0])))
if WRITE_DEBUG_IMAGES:
current_time = int(datetime.datetime.now().timestamp())
cv2.imwrite(
f"debug/frames/car_frame_{current_time}.jpg",
car,
)
license_plate = self._detect_license_plate(car)
if not license_plate:
logger.debug("Detected no license plates for car object.")
return
# double the size of the license plate for better OCR
license_plate_area = max(
0,
(license_plate[2] - license_plate[0])
* (license_plate[3] - license_plate[1]),
)
# check that license plate is valid
if license_plate_area < self.config.lpr.min_area:
logger.debug("License plate is less than min_area")
return
license_plate_frame = car[
license_plate[1] : license_plate[3], license_plate[0] : license_plate[2]
]
license_plate_frame = cv2.cvtColor(license_plate_frame, cv2.COLOR_RGB2BGR)
else:
# don't run for object without attributes
if not obj_data.get("current_attributes"):
@ -788,6 +960,22 @@ class LicensePlateProcessor(RealTimeProcessorApi):
license_plate_box[0] : license_plate_box[2],
]
# double the size of the license plate frame for better OCR
license_plate_frame = cv2.resize(
license_plate_frame,
(
int(2 * license_plate_frame.shape[1]),
int(2 * license_plate_frame.shape[0]),
),
)
if WRITE_DEBUG_IMAGES:
current_time = int(datetime.datetime.now().timestamp())
cv2.imwrite(
f"debug/frames/license_plate_frame_{current_time}.jpg",
license_plate_frame,
)
# run detection, returns results sorted by confidence, best first
license_plates, confidences, areas = self._process_license_plate(
license_plate_frame
@ -824,38 +1012,11 @@ class LicensePlateProcessor(RealTimeProcessorApi):
# Check if we have a previously detected plate for this ID
if id in self.detected_license_plates:
prev_plate = self.detected_license_plates[id]["plate"]
prev_char_confidences = self.detected_license_plates[id]["char_confidences"]
prev_area = self.detected_license_plates[id]["area"]
prev_avg_confidence = (
(sum(prev_char_confidences) / len(prev_char_confidences))
if prev_char_confidences
else 0
)
# Define conditions for keeping the previous plate
shorter_than_previous = len(top_plate) < len(prev_plate)
lower_avg_confidence = avg_confidence <= prev_avg_confidence
smaller_area = top_area < prev_area
# Compare character-by-character confidence where possible
min_length = min(len(top_plate), len(prev_plate))
char_confidence_comparison = sum(
1
for i in range(min_length)
if top_char_confidences[i] <= prev_char_confidences[i]
)
worse_char_confidences = char_confidence_comparison >= min_length / 2
if (shorter_than_previous or smaller_area) and (
lower_avg_confidence and worse_char_confidences
if self._should_keep_previous_plate(
id, top_plate, top_char_confidences, top_area, avg_confidence
):
logger.debug(
f"Keeping previous plate. New plate stats: "
f"length={len(top_plate)}, avg_conf={avg_confidence:.2f}, area={top_area} "
f"vs Previous: length={len(prev_plate)}, avg_conf={prev_avg_confidence:.2f}, area={prev_area}"
)
return True
logger.debug("Keeping previous plate")
return
# Check against minimum confidence threshold
if avg_confidence < self.lpr_config.threshold: