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Code Issues Projects Releases Wiki Activity

LPR improvements (#16559)

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* use a small yolov9 model for detection

* use yolov9 for users without frigate+ and update retention algorithm

* new lpr config fields

* levenshtein distance package

* tweaks

* docs
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Josh Hawkins 2025-02-13 17:08:56 -06:00 committed by GitHub
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5 changed files with 297 additions and 49 deletions
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1
docker/main/requirements-wheels.txt
View File

@ -53,6 +53,7 @@ pywebpush == 2.0.*
# alpr
pyclipper == 1.3.*
shapely == 2.0.*
Levenshtein==0.26.*
prometheus-client == 0.21.*
# HailoRT Wheels
appdirs==1.4.*

10
docs/docs/configuration/license_plate_recognition.md
View File

@ -24,14 +24,20 @@ lpr:
## Advanced Configuration
Several options are available to fine-tune the LPR feature. For example, you can adjust the `min_area` setting, which defines the minimum size in pixels a license plate must be before LPR runs. The default is 500 pixels.
Several options are available to fine-tune the LPR feature. For example, you can adjust the `min_area` setting, which defines the minimum size in pixels a license plate must be before LPR runs. The default is 1000 pixels.
The `min_plate_length` field specifies the minimum number of characters a license plate must have to be added to the object as a sub label.
If you want to allow a number of number of missing/incorrect characters to still cause a detected plate to match a known plate, set the `match_distance` field. For example, setting `match_distance` to 1 would cause a detected plate of ABCDE to match ABCBE or ABCD.
Additionally, you can define `known_plates` as strings or regular expressions, allowing Frigate to label tracked vehicles with custom sub_labels when a recognized plate is detected. This information is then accessible in the UI, filters, and notifications.
```yaml
lpr:
enabled: true
min_area: 500
min_area: 1500
min_plate_length: 4
match_distance: 1
known_plates:
Wife's Car:
- "ABC-1234"

12
frigate/config/classification.py
View File

@ -66,8 +66,16 @@ class LicensePlateRecognitionConfig(FrigateBaseModel):
title="License plate confidence score required to be added to the object as a sub label.",
)
min_area: int = Field(
default=500,
title="Min area of license plate to consider running license plate recognition.",
default=1000,
title="Minimum area of license plate to consider running license plate recognition.",
)
min_plate_length: int = Field(
default=4,
title="Minimum number of characters a license plate must have to be added to the object as a sub label.",
)
match_distance: int = Field(
default=1,
title="Allow this number of missing/incorrect characters to still cause a detected plate to match a known plate.",
)
known_plates: Optional[Dict[str, List[str]]] = Field(
default={}, title="Known plates to track."

279
frigate/data_processing/real_time/license_plate_processor.py
View File

@ -9,6 +9,7 @@ from typing import List, Optional, Tuple
import cv2
import numpy as np
import requests
from Levenshtein import distance
from pyclipper import ET_CLOSEDPOLYGON, JT_ROUND, PyclipperOffset
from shapely.geometry import Polygon
@ -23,7 +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 = {
@ -240,7 +275,7 @@ class LicensePlateProcessor(RealTimeProcessorApi):
save_image = cv2.cvtColor(
rotated_images[original_idx], cv2.COLOR_RGB2BGR
)
filename = f"/config/plate_{original_idx}_{plate}_{area}.jpg"
filename = f"debug/frames/plate_{original_idx}_{plate}_{area}.jpg"
cv2.imwrite(filename, save_image)
license_plates[original_idx] = plate
@ -255,7 +290,7 @@ class LicensePlateProcessor(RealTimeProcessorApi):
for plate, conf, area in zip(
license_plates, average_confidences, areas
)
if len(plate) >= MIN_PLATE_LENGTH
if len(plate) >= self.lpr_config.min_plate_length
],
key=lambda x: (x[2], len(x[0]), x[1]),
reverse=True,
@ -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
@ -646,7 +684,11 @@ class LicensePlateProcessor(RealTimeProcessorApi):
resized_image = resized_image.transpose((2, 0, 1))
resized_image = (resized_image.astype("float32") / 255.0 - 0.5) / 0.5
padded_image = np.zeros((input_shape[0], input_h, input_w), dtype=np.float32)
# Compute mean pixel value of the resized image (per channel)
mean_pixel = np.mean(resized_image, axis=(1, 2), keepdims=True)
padded_image = np.full(
(input_shape[0], input_h, input_w), mean_pixel, dtype=np.float32
)
padded_image[:, :, :resized_w] = resized_image
return padded_image
@ -698,13 +740,141 @@ class LicensePlateProcessor(RealTimeProcessorApi):
return image
def __update_metrics(self, duration: float) -> None:
"""
Update inference metrics.
"""
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:
# expand box by 15% to help with OCR
expansion = (top_box[2:] - top_box[:2]) * 0.1
# Expand box
expanded_box = np.array(
[
top_box[0] - expansion[0], # x1
top_box[1] - expansion[1], # y1
top_box[2] + expansion[0], # x2
top_box[3] + expansion[1], # y2
]
).clip(0, [input.shape[1], input.shape[0]] * 2)
logger.debug(f"Found license plate: {expanded_box.astype(int)}")
return tuple(expanded_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 +909,45 @@ 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,
)
yolov9_start = datetime.datetime.now().timestamp()
license_plate = self._detect_license_plate(car)
logger.debug(
f"YOLOv9 LPD inference time: {(datetime.datetime.now().timestamp() - yolov9_start) * 1000:.2f} ms"
)
if not license_plate:
logger.debug("Detected no license plates for car object.")
return
license_plate_area = max(
0,
(license_plate[2] - license_plate[0])
* (license_plate[3] - license_plate[1]),
)
# check that license plate is valid
# double the value because we've doubled the size of the car
if license_plate_area < self.config.lpr.min_area * 2:
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 +984,22 @@ 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 +1036,11 @@ 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:
@ -870,7 +1055,11 @@ class LicensePlateProcessor(RealTimeProcessorApi):
(
label
for label, plates in self.lpr_config.known_plates.items()
if any(re.match(f"^{plate}$", top_plate) for plate in plates)
if any(
re.match(f"^{plate}$", top_plate)
or distance(plate, top_plate) <= self.lpr_config.match_distance
for plate in plates
)
),
top_plate,
)

44
frigate/embeddings/functions/onnx.py
View File

@ -5,6 +5,7 @@ from enum import Enum
from io import BytesIO
from typing import Dict, List, Optional, Union
import cv2
import numpy as np
import requests
from PIL import Image
@ -32,6 +33,7 @@ disable_progress_bar()
logger = logging.getLogger(__name__)
FACE_EMBEDDING_SIZE = 160
LPR_EMBEDDING_SIZE = 256
class ModelTypeEnum(str, Enum):
@ -41,6 +43,7 @@ class ModelTypeEnum(str, Enum):
lpr_detect = "lpr_detect"
lpr_classify = "lpr_classify"
lpr_recognize = "lpr_recognize"
yolov9_lpr_detect = "yolov9_lpr_detect"
class GenericONNXEmbedding:
@ -148,6 +151,8 @@ class GenericONNXEmbedding:
self.feature_extractor = []
elif self.model_type == ModelTypeEnum.lpr_recognize:
self.feature_extractor = []
elif self.model_type == ModelTypeEnum.yolov9_lpr_detect:
self.feature_extractor = []
self.runner = ONNXModelRunner(
os.path.join(self.download_path, self.model_file),
@ -237,6 +242,45 @@ class GenericONNXEmbedding:
for img in raw_inputs:
processed.append({"x": img})
return processed
elif self.model_type == ModelTypeEnum.yolov9_lpr_detect:
if isinstance(raw_inputs, list):
raise ValueError(
"License plate embedding does not support batch inputs."
)
# Get image as numpy array
img = self._process_image(raw_inputs)
height, width, channels = img.shape
# Resize maintaining aspect ratio
if width > height:
new_height = int(((height / width) * LPR_EMBEDDING_SIZE) // 4 * 4)
img = cv2.resize(img, (LPR_EMBEDDING_SIZE, new_height))
else:
new_width = int(((width / height) * LPR_EMBEDDING_SIZE) // 4 * 4)
img = cv2.resize(img, (new_width, LPR_EMBEDDING_SIZE))
# Get new dimensions after resize
og_h, og_w, channels = img.shape
# Create black square frame
frame = np.full(
(LPR_EMBEDDING_SIZE, LPR_EMBEDDING_SIZE, channels),
(0, 0, 0),
dtype=np.float32,
)
# Center the resized image in the square frame
x_center = (LPR_EMBEDDING_SIZE - og_w) // 2
y_center = (LPR_EMBEDDING_SIZE - og_h) // 2
frame[y_center : y_center + og_h, x_center : x_center + og_w] = img
# Normalize to 0-1
frame = frame / 255.0
# Convert from HWC to CHW format and add batch dimension
frame = np.transpose(frame, (2, 0, 1))
frame = np.expand_dims(frame, axis=0)
return [{"images": frame}]
else:
raise ValueError(f"Unable to preprocess inputs for {self.model_type}")