mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
Refactor lpr into real time data processor (#16497)
This commit is contained in:
Josh Hawkins
GitHub
parent
f3e2cf0a58
commit
2458f667c4
@ -1,34 +1,41 @@
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"""Handle processing images for face detection and recognition."""
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import datetime
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import logging
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import math
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from typing import List, Tuple
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import re
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from typing import List, Optional, Tuple
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import cv2
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import numpy as np
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import requests
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from pyclipper import ET_CLOSEDPOLYGON, JT_ROUND, PyclipperOffset
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from shapely.geometry import Polygon
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from frigate.comms.inter_process import InterProcessRequestor
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from frigate.config.classification import LicensePlateRecognitionConfig
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from frigate.embeddings.embeddings import Embeddings
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from frigate.config import FrigateConfig
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from frigate.const import FRIGATE_LOCALHOST
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from frigate.embeddings.functions.onnx import GenericONNXEmbedding, ModelTypeEnum
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from frigate.util.image import area
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from ..types import DataProcessorMetrics
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from .api import RealTimeProcessorApi
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logger = logging.getLogger(__name__)
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MIN_PLATE_LENGTH = 3
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class LicensePlateRecognition:
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def __init__(
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self,
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config: LicensePlateRecognitionConfig,
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requestor: InterProcessRequestor,
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embeddings: Embeddings,
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):
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self.lpr_config = config
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self.requestor = requestor
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self.embeddings = embeddings
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self.detection_model = self.embeddings.lpr_detection_model
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self.classification_model = self.embeddings.lpr_classification_model
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self.recognition_model = self.embeddings.lpr_recognition_model
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class LicensePlateProcessor(RealTimeProcessorApi):
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def __init__(self, config: FrigateConfig, metrics: DataProcessorMetrics):
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super().__init__(config, metrics)
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self.requestor = InterProcessRequestor()
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self.lpr_config = config.lpr
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self.requires_license_plate_detection = (
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"license_plate" not in self.config.objects.all_objects
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)
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self.detected_license_plates: dict[str, dict[str, any]] = {}
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self.ctc_decoder = CTCDecoder()
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self.batch_size = 6
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@ -39,13 +46,54 @@ class LicensePlateRecognition:
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self.box_thresh = 0.8
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self.mask_thresh = 0.8
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self.lpr_detection_model = None
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self.lpr_classification_model = None
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self.lpr_recognition_model = None
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if self.config.lpr.enabled:
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self.detection_model = GenericONNXEmbedding(
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model_name="paddleocr-onnx",
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model_file="detection.onnx",
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download_urls={
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"detection.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/detection.onnx"
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},
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model_size="large",
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model_type=ModelTypeEnum.lpr_detect,
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requestor=self.requestor,
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device="CPU",
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)
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self.classification_model = GenericONNXEmbedding(
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model_name="paddleocr-onnx",
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model_file="classification.onnx",
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download_urls={
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"classification.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx"
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},
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model_size="large",
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model_type=ModelTypeEnum.lpr_classify,
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requestor=self.requestor,
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device="CPU",
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)
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self.recognition_model = GenericONNXEmbedding(
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model_name="paddleocr-onnx",
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model_file="recognition.onnx",
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download_urls={
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"recognition.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx"
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},
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model_size="large",
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model_type=ModelTypeEnum.lpr_recognize,
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requestor=self.requestor,
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device="CPU",
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)
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if self.lpr_config.enabled:
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# all models need to be loaded to run LPR
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self.detection_model._load_model_and_utils()
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self.classification_model._load_model_and_utils()
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self.recognition_model._load_model_and_utils()
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def detect(self, image: np.ndarray) -> List[np.ndarray]:
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def _detect(self, image: np.ndarray) -> List[np.ndarray]:
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"""
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Detect possible license plates in the input image by first resizing and normalizing it,
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running a detection model, and filtering out low-probability regions.
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@ -59,18 +107,18 @@ class LicensePlateRecognition:
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h, w = image.shape[:2]
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if sum([h, w]) < 64:
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image = self.zero_pad(image)
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image = self._zero_pad(image)
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resized_image = self.resize_image(image)
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normalized_image = self.normalize_image(resized_image)
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resized_image = self._resize_image(image)
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normalized_image = self._normalize_image(resized_image)
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outputs = self.detection_model([normalized_image])[0]
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outputs = outputs[0, :, :]
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boxes, _ = self.boxes_from_bitmap(outputs, outputs > self.mask_thresh, w, h)
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return self.filter_polygon(boxes, (h, w))
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boxes, _ = self._boxes_from_bitmap(outputs, outputs > self.mask_thresh, w, h)
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return self._filter_polygon(boxes, (h, w))
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def classify(
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def _classify(
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self, images: List[np.ndarray]
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) -> Tuple[List[np.ndarray], List[Tuple[str, float]]]:
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"""
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@ -97,7 +145,7 @@ class LicensePlateRecognition:
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return self._process_classification_output(images, outputs)
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def recognize(
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def _recognize(
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self, images: List[np.ndarray]
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) -> Tuple[List[str], List[List[float]]]:
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"""
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@ -136,7 +184,7 @@ class LicensePlateRecognition:
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outputs = self.recognition_model(norm_images)
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return self.ctc_decoder(outputs)
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def process_license_plate(
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def _process_license_plate(
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self, image: np.ndarray
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) -> Tuple[List[str], List[float], List[int]]:
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"""
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@ -157,13 +205,13 @@ class LicensePlateRecognition:
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logger.debug("Model runners not loaded")
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return [], [], []
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plate_points = self.detect(image)
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plate_points = self._detect(image)
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if len(plate_points) == 0:
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return [], [], []
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plate_points = self.sort_polygon(list(plate_points))
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plate_points = self._sort_polygon(list(plate_points))
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plate_images = [self._crop_license_plate(image, x) for x in plate_points]
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rotated_images, _ = self.classify(plate_images)
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rotated_images, _ = self._classify(plate_images)
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# keep track of the index of each image for correct area calc later
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sorted_indices = np.argsort([x.shape[1] / x.shape[0] for x in rotated_images])
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@ -171,7 +219,7 @@ class LicensePlateRecognition:
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idx: original_idx for original_idx, idx in enumerate(sorted_indices)
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}
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results, confidences = self.recognize(rotated_images)
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results, confidences = self._recognize(rotated_images)
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if results:
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license_plates = [""] * len(rotated_images)
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@ -218,7 +266,7 @@ class LicensePlateRecognition:
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return [], [], []
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def resize_image(self, image: np.ndarray) -> np.ndarray:
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def _resize_image(self, image: np.ndarray) -> np.ndarray:
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"""
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Resize the input image while maintaining the aspect ratio, ensuring dimensions are multiples of 32.
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@ -234,7 +282,7 @@ class LicensePlateRecognition:
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resize_w = max(int(round(int(w * ratio) / 32) * 32), 32)
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return cv2.resize(image, (resize_w, resize_h))
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def normalize_image(self, image: np.ndarray) -> np.ndarray:
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def _normalize_image(self, image: np.ndarray) -> np.ndarray:
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"""
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Normalize the input image by subtracting the mean and multiplying by the standard deviation.
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@ -252,7 +300,7 @@ class LicensePlateRecognition:
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cv2.multiply(image, std, image)
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return image.transpose((2, 0, 1))[np.newaxis, ...]
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def boxes_from_bitmap(
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def _boxes_from_bitmap(
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self, output: np.ndarray, mask: np.ndarray, dest_width: int, dest_height: int
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) -> Tuple[np.ndarray, List[float]]:
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"""
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@ -282,14 +330,14 @@ class LicensePlateRecognition:
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contour = contours[index]
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# get minimum bounding box (rotated rectangle) around the contour and the smallest side length.
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points, min_side = self.get_min_boxes(contour)
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points, min_side = self._get_min_boxes(contour)
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if min_side < self.min_size:
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continue
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points = np.array(points)
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score = self.box_score(output, contour)
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score = self._box_score(output, contour)
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if self.box_thresh > score:
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continue
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@ -302,7 +350,7 @@ class LicensePlateRecognition:
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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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box, min_side = self._get_min_boxes(points)
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if min_side < self.min_size + 2:
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continue
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@ -321,7 +369,7 @@ class LicensePlateRecognition:
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return np.array(boxes, dtype="int32"), scores
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@staticmethod
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def get_min_boxes(contour: np.ndarray) -> Tuple[List[Tuple[float, float]], float]:
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def _get_min_boxes(contour: np.ndarray) -> Tuple[List[Tuple[float, float]], float]:
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"""
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Calculate the minimum bounding box (rotated rectangle) for a given contour.
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@ -340,7 +388,7 @@ class LicensePlateRecognition:
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return box, min(bounding_box[1])
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@staticmethod
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def box_score(bitmap: np.ndarray, contour: np.ndarray) -> float:
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def _box_score(bitmap: np.ndarray, contour: np.ndarray) -> float:
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"""
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Calculate the average score within the bounding box of a contour.
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@ -360,7 +408,7 @@ class LicensePlateRecognition:
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return cv2.mean(bitmap[y1 : y2 + 1, x1 : x2 + 1], mask)[0]
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@staticmethod
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def expand_box(points: List[Tuple[float, float]]) -> np.ndarray:
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def _expand_box(points: List[Tuple[float, float]]) -> np.ndarray:
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"""
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Expand a polygonal shape slightly by a factor determined by the area-to-perimeter ratio.
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@ -377,7 +425,7 @@ class LicensePlateRecognition:
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expanded = np.array(offset.Execute(distance * 1.5)).reshape((-1, 2))
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return expanded
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def filter_polygon(
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def _filter_polygon(
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self, points: List[np.ndarray], shape: Tuple[int, int]
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) -> np.ndarray:
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"""
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@ -394,14 +442,14 @@ class LicensePlateRecognition:
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height, width = shape
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return np.array(
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[
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self.clockwise_order(point)
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self._clockwise_order(point)
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for point in points
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if self.is_valid_polygon(point, width, height)
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if self._is_valid_polygon(point, width, height)
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]
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)
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@staticmethod
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def is_valid_polygon(point: np.ndarray, width: int, height: int) -> bool:
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def _is_valid_polygon(point: np.ndarray, width: int, height: int) -> bool:
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"""
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Check if a polygon is valid, meaning it fits within the image bounds
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and has sides of a minimum length.
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@ -424,7 +472,7 @@ class LicensePlateRecognition:
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)
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@staticmethod
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def clockwise_order(point: np.ndarray) -> np.ndarray:
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def _clockwise_order(point: np.ndarray) -> np.ndarray:
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"""
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Arrange the points of a polygon in clockwise order based on their angular positions
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around the polygon's center.
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@ -441,7 +489,7 @@ class LicensePlateRecognition:
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]
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@staticmethod
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def sort_polygon(points):
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def _sort_polygon(points):
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"""
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Sort polygons based on their position in the image. If polygons are close in vertical
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position (within 10 pixels), sort them by horizontal position.
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@ -466,7 +514,7 @@ class LicensePlateRecognition:
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return points
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@staticmethod
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def zero_pad(image: np.ndarray) -> np.ndarray:
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def _zero_pad(image: np.ndarray) -> np.ndarray:
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"""
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Apply zero-padding to an image, ensuring its dimensions are at least 32x32.
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The padding is added only if needed.
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@ -649,6 +697,210 @@ class LicensePlateRecognition:
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image = np.rot90(image, k=3)
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return image
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def __update_metrics(self, duration: float) -> None:
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self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10
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def _detect_license_plate(self, input: np.ndarray) -> tuple[int, int, int, int]:
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"""Return the dimensions of the input image as [x, y, width, height]."""
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# TODO: use a small model here to detect plates
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height, width = input.shape[:2]
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return (0, 0, width, height)
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def process_frame(self, obj_data: dict[str, any], frame: np.ndarray):
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"""Look for license plates in image."""
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start = datetime.datetime.now().timestamp()
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id = obj_data["id"]
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# don't run for non car objects
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if obj_data.get("label") != "car":
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logger.debug("Not a processing license plate for non car object.")
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return
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# don't run for stationary car objects
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if obj_data.get("stationary") == True:
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logger.debug("Not a processing license plate for a stationary car object.")
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return
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# don't overwrite sub label for objects that have a sub label
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# that is not a license plate
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if obj_data.get("sub_label") and id not in self.detected_license_plates:
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logger.debug(
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f"Not processing license plate due to existing sub label: {obj_data.get('sub_label')}."
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)
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return
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license_plate: Optional[dict[str, any]] = None
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if self.requires_license_plate_detection:
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logger.debug("Running manual license_plate detection.")
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car_box = obj_data.get("box")
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if not car_box:
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return
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rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
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left, top, right, bottom = car_box
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car = rgb[top:bottom, left:right]
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license_plate = self._detect_license_plate(car)
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if not license_plate:
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logger.debug("Detected no license plates for car object.")
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return
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license_plate_frame = car[
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license_plate[1] : license_plate[3], license_plate[0] : license_plate[2]
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]
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license_plate_frame = cv2.cvtColor(license_plate_frame, cv2.COLOR_RGB2BGR)
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else:
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# don't run for object without attributes
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if not obj_data.get("current_attributes"):
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logger.debug("No attributes to parse.")
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return
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attributes: list[dict[str, any]] = obj_data.get("current_attributes", [])
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for attr in attributes:
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if attr.get("label") != "license_plate":
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continue
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if license_plate is None or attr.get("score", 0.0) > license_plate.get(
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"score", 0.0
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):
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license_plate = attr
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# no license plates detected in this frame
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if not license_plate:
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return
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license_plate_box = license_plate.get("box")
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# check that license plate is valid
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if (
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not license_plate_box
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or area(license_plate_box) < self.config.lpr.min_area
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):
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logger.debug(f"Invalid license plate box {license_plate}")
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return
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license_plate_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
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license_plate_frame = license_plate_frame[
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license_plate_box[1] : license_plate_box[3],
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license_plate_box[0] : license_plate_box[2],
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]
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# run detection, returns results sorted by confidence, best first
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license_plates, confidences, areas = self._process_license_plate(
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license_plate_frame
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)
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logger.debug(f"Text boxes: {license_plates}")
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logger.debug(f"Confidences: {confidences}")
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logger.debug(f"Areas: {areas}")
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if license_plates:
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for plate, confidence, text_area in zip(license_plates, confidences, areas):
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avg_confidence = (
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(sum(confidence) / len(confidence)) if confidence else 0
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)
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logger.debug(
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f"Detected text: {plate} (average confidence: {avg_confidence:.2f}, area: {text_area} pixels)"
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)
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else:
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# no plates found
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logger.debug("No text detected")
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return
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top_plate, top_char_confidences, top_area = (
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license_plates[0],
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confidences[0],
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areas[0],
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)
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avg_confidence = (
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(sum(top_char_confidences) / len(top_char_confidences))
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if top_char_confidences
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else 0
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)
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# Check if we have a previously detected plate for this ID
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if id in self.detected_license_plates:
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prev_plate = self.detected_license_plates[id]["plate"]
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prev_char_confidences = self.detected_license_plates[id]["char_confidences"]
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||||
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
|
||||
):
|
||||
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
|
||||
|
||||
# Check against minimum confidence threshold
|
||||
if avg_confidence < self.lpr_config.threshold:
|
||||
logger.debug(
|
||||
f"Average confidence {avg_confidence} is less than threshold ({self.lpr_config.threshold})"
|
||||
)
|
||||
return
|
||||
|
||||
# Determine subLabel based on known plates, use regex matching
|
||||
# Default to the detected plate, use label name if there's a match
|
||||
sub_label = next(
|
||||
(
|
||||
label
|
||||
for label, plates in self.lpr_config.known_plates.items()
|
||||
if any(re.match(f"^{plate}$", top_plate) for plate in plates)
|
||||
),
|
||||
top_plate,
|
||||
)
|
||||
|
||||
# Send the result to the API
|
||||
resp = requests.post(
|
||||
f"{FRIGATE_LOCALHOST}/api/events/{id}/sub_label",
|
||||
json={
|
||||
"camera": obj_data.get("camera"),
|
||||
"subLabel": sub_label,
|
||||
"subLabelScore": avg_confidence,
|
||||
},
|
||||
)
|
||||
|
||||
if resp.status_code == 200:
|
||||
self.detected_license_plates[id] = {
|
||||
"plate": top_plate,
|
||||
"char_confidences": top_char_confidences,
|
||||
"area": top_area,
|
||||
}
|
||||
|
||||
self.__update_metrics(datetime.datetime.now().timestamp() - start)
|
||||
|
||||
def handle_request(self, topic, request_data) -> dict[str, any] | None:
|
||||
return
|
||||
|
||||
def expire_object(self, object_id: str):
|
||||
if object_id in self.detected_license_plates:
|
||||
self.detected_license_plates.pop(object_id)
|
||||
|
||||
|
||||
class CTCDecoder:
|
||||
"""
|
||||
@ -131,47 +131,6 @@ class Embeddings:
|
||||
device="GPU" if config.semantic_search.model_size == "large" else "CPU",
|
||||
)
|
||||
|
||||
self.lpr_detection_model = None
|
||||
self.lpr_classification_model = None
|
||||
self.lpr_recognition_model = None
|
||||
|
||||
if self.config.lpr.enabled:
|
||||
self.lpr_detection_model = GenericONNXEmbedding(
|
||||
model_name="paddleocr-onnx",
|
||||
model_file="detection.onnx",
|
||||
download_urls={
|
||||
"detection.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/detection.onnx"
|
||||
},
|
||||
model_size="large",
|
||||
model_type=ModelTypeEnum.lpr_detect,
|
||||
requestor=self.requestor,
|
||||
device="CPU",
|
||||
)
|
||||
|
||||
self.lpr_classification_model = GenericONNXEmbedding(
|
||||
model_name="paddleocr-onnx",
|
||||
model_file="classification.onnx",
|
||||
download_urls={
|
||||
"classification.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/classification.onnx"
|
||||
},
|
||||
model_size="large",
|
||||
model_type=ModelTypeEnum.lpr_classify,
|
||||
requestor=self.requestor,
|
||||
device="CPU",
|
||||
)
|
||||
|
||||
self.lpr_recognition_model = GenericONNXEmbedding(
|
||||
model_name="paddleocr-onnx",
|
||||
model_file="recognition.onnx",
|
||||
download_urls={
|
||||
"recognition.onnx": "https://github.com/hawkeye217/paddleocr-onnx/raw/refs/heads/master/models/recognition.onnx"
|
||||
},
|
||||
model_size="large",
|
||||
model_type=ModelTypeEnum.lpr_recognize,
|
||||
requestor=self.requestor,
|
||||
device="CPU",
|
||||
)
|
||||
|
||||
def embed_thumbnail(
|
||||
self, event_id: str, thumbnail: bytes, upsert: bool = True
|
||||
) -> ndarray:
|
||||
|
||||
@ -1,10 +1,8 @@
|
||||
"""Maintain embeddings in SQLite-vec."""
|
||||
|
||||
import base64
|
||||
import datetime
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import threading
|
||||
from multiprocessing.synchronize import Event as MpEvent
|
||||
from pathlib import Path
|
||||
@ -12,7 +10,6 @@ from typing import Optional
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import requests
|
||||
from peewee import DoesNotExist
|
||||
from playhouse.sqliteq import SqliteQueueDatabase
|
||||
|
||||
@ -26,20 +23,21 @@ from frigate.comms.inter_process import InterProcessRequestor
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.const import (
|
||||
CLIPS_DIR,
|
||||
FRIGATE_LOCALHOST,
|
||||
UPDATE_EVENT_DESCRIPTION,
|
||||
)
|
||||
from frigate.data_processing.real_time.api import RealTimeProcessorApi
|
||||
from frigate.data_processing.real_time.bird_processor import BirdProcessor
|
||||
from frigate.data_processing.real_time.face_processor import FaceProcessor
|
||||
from frigate.data_processing.real_time.license_plate_processor import (
|
||||
LicensePlateProcessor,
|
||||
)
|
||||
from frigate.data_processing.types import DataProcessorMetrics
|
||||
from frigate.embeddings.lpr.lpr import LicensePlateRecognition
|
||||
from frigate.events.types import EventTypeEnum
|
||||
from frigate.genai import get_genai_client
|
||||
from frigate.models import Event
|
||||
from frigate.types import TrackedObjectUpdateTypesEnum
|
||||
from frigate.util.builtin import serialize
|
||||
from frigate.util.image import SharedMemoryFrameManager, area, calculate_region
|
||||
from frigate.util.image import SharedMemoryFrameManager, calculate_region
|
||||
|
||||
from .embeddings import Embeddings
|
||||
|
||||
@ -82,24 +80,15 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
if self.config.classification.bird.enabled:
|
||||
self.processors.append(BirdProcessor(self.config, metrics))
|
||||
|
||||
if self.config.lpr.enabled:
|
||||
self.processors.append(LicensePlateProcessor(self.config, metrics))
|
||||
|
||||
# create communication for updating event descriptions
|
||||
self.requestor = InterProcessRequestor()
|
||||
self.stop_event = stop_event
|
||||
self.tracked_events: dict[str, list[any]] = {}
|
||||
self.genai_client = get_genai_client(config)
|
||||
|
||||
# set license plate recognition conditions
|
||||
self.lpr_config = self.config.lpr
|
||||
self.requires_license_plate_detection = (
|
||||
"license_plate" not in self.config.objects.all_objects
|
||||
)
|
||||
self.detected_license_plates: dict[str, dict[str, any]] = {}
|
||||
|
||||
if self.lpr_config.enabled:
|
||||
self.license_plate_recognition = LicensePlateRecognition(
|
||||
self.lpr_config, self.requestor, self.embeddings
|
||||
)
|
||||
|
||||
def run(self) -> None:
|
||||
"""Maintain a SQLite-vec database for semantic search."""
|
||||
while not self.stop_event.is_set():
|
||||
@ -164,11 +153,7 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
camera_config = self.config.cameras[camera]
|
||||
|
||||
# no need to process updated objects if face recognition, lpr, genai are disabled
|
||||
if (
|
||||
not camera_config.genai.enabled
|
||||
and not self.lpr_config.enabled
|
||||
and len(self.processors) == 0
|
||||
):
|
||||
if not camera_config.genai.enabled and len(self.processors) == 0:
|
||||
return
|
||||
|
||||
# Create our own thumbnail based on the bounding box and the frame time
|
||||
@ -188,16 +173,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
for processor in self.processors:
|
||||
processor.process_frame(data, yuv_frame)
|
||||
|
||||
if self.lpr_config.enabled:
|
||||
start = datetime.datetime.now().timestamp()
|
||||
processed = self._process_license_plate(data, yuv_frame)
|
||||
|
||||
if processed:
|
||||
duration = datetime.datetime.now().timestamp() - start
|
||||
self.metrics.alpr_pps.value = (
|
||||
self.metrics.alpr_pps.value * 9 + duration
|
||||
) / 10
|
||||
|
||||
# no need to save our own thumbnails if genai is not enabled
|
||||
# or if the object has become stationary
|
||||
if self.genai_client is not None and not data["stationary"]:
|
||||
@ -229,9 +204,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
for processor in self.processors:
|
||||
processor.expire_object(event_id)
|
||||
|
||||
if event_id in self.detected_license_plates:
|
||||
self.detected_license_plates.pop(event_id)
|
||||
|
||||
if updated_db:
|
||||
try:
|
||||
event: Event = Event.get(Event.id == event_id)
|
||||
@ -354,199 +326,6 @@ class EmbeddingMaintainer(threading.Thread):
|
||||
if event_id:
|
||||
self.handle_regenerate_description(event_id, source)
|
||||
|
||||
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]."""
|
||||
height, width = input.shape[:2]
|
||||
return (0, 0, width, height)
|
||||
|
||||
def _process_license_plate(
|
||||
self, obj_data: dict[str, any], frame: np.ndarray
|
||||
) -> bool:
|
||||
"""Look for license plates in image."""
|
||||
id = obj_data["id"]
|
||||
|
||||
# don't run for non car objects
|
||||
if obj_data.get("label") != "car":
|
||||
logger.debug("Not a processing license plate for non car object.")
|
||||
return False
|
||||
|
||||
# don't run for stationary car objects
|
||||
if obj_data.get("stationary") == True:
|
||||
logger.debug("Not a processing license plate for a stationary car object.")
|
||||
return False
|
||||
|
||||
# don't overwrite sub label for objects that have a sub label
|
||||
# that is not a license plate
|
||||
if obj_data.get("sub_label") and id not in self.detected_license_plates:
|
||||
logger.debug(
|
||||
f"Not processing license plate due to existing sub label: {obj_data.get('sub_label')}."
|
||||
)
|
||||
return False
|
||||
|
||||
license_plate: Optional[dict[str, any]] = None
|
||||
|
||||
if self.requires_license_plate_detection:
|
||||
logger.debug("Running manual license_plate detection.")
|
||||
car_box = obj_data.get("box")
|
||||
|
||||
if not car_box:
|
||||
return False
|
||||
|
||||
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2RGB_I420)
|
||||
left, top, right, bottom = car_box
|
||||
car = rgb[top:bottom, left:right]
|
||||
license_plate = self._detect_license_plate(car)
|
||||
|
||||
if not license_plate:
|
||||
logger.debug("Detected no license plates for car object.")
|
||||
return False
|
||||
|
||||
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"):
|
||||
logger.debug("No attributes to parse.")
|
||||
return False
|
||||
|
||||
attributes: list[dict[str, any]] = obj_data.get("current_attributes", [])
|
||||
for attr in attributes:
|
||||
if attr.get("label") != "license_plate":
|
||||
continue
|
||||
|
||||
if license_plate is None or attr.get("score", 0.0) > license_plate.get(
|
||||
"score", 0.0
|
||||
):
|
||||
license_plate = attr
|
||||
|
||||
# no license plates detected in this frame
|
||||
if not license_plate:
|
||||
return False
|
||||
|
||||
license_plate_box = license_plate.get("box")
|
||||
|
||||
# check that license plate is valid
|
||||
if (
|
||||
not license_plate_box
|
||||
or area(license_plate_box) < self.config.lpr.min_area
|
||||
):
|
||||
logger.debug(f"Invalid license plate box {license_plate}")
|
||||
return False
|
||||
|
||||
license_plate_frame = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
|
||||
license_plate_frame = license_plate_frame[
|
||||
license_plate_box[1] : license_plate_box[3],
|
||||
license_plate_box[0] : license_plate_box[2],
|
||||
]
|
||||
|
||||
# run detection, returns results sorted by confidence, best first
|
||||
license_plates, confidences, areas = (
|
||||
self.license_plate_recognition.process_license_plate(license_plate_frame)
|
||||
)
|
||||
|
||||
logger.debug(f"Text boxes: {license_plates}")
|
||||
logger.debug(f"Confidences: {confidences}")
|
||||
logger.debug(f"Areas: {areas}")
|
||||
|
||||
if license_plates:
|
||||
for plate, confidence, text_area in zip(license_plates, confidences, areas):
|
||||
avg_confidence = (
|
||||
(sum(confidence) / len(confidence)) if confidence else 0
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
f"Detected text: {plate} (average confidence: {avg_confidence:.2f}, area: {text_area} pixels)"
|
||||
)
|
||||
else:
|
||||
# no plates found
|
||||
logger.debug("No text detected")
|
||||
return True
|
||||
|
||||
top_plate, top_char_confidences, top_area = (
|
||||
license_plates[0],
|
||||
confidences[0],
|
||||
areas[0],
|
||||
)
|
||||
avg_confidence = (
|
||||
(sum(top_char_confidences) / len(top_char_confidences))
|
||||
if top_char_confidences
|
||||
else 0
|
||||
)
|
||||
|
||||
# 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
|
||||
):
|
||||
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
|
||||
|
||||
# Check against minimum confidence threshold
|
||||
if avg_confidence < self.lpr_config.threshold:
|
||||
logger.debug(
|
||||
f"Average confidence {avg_confidence} is less than threshold ({self.lpr_config.threshold})"
|
||||
)
|
||||
return True
|
||||
|
||||
# Determine subLabel based on known plates, use regex matching
|
||||
# Default to the detected plate, use label name if there's a match
|
||||
sub_label = next(
|
||||
(
|
||||
label
|
||||
for label, plates in self.lpr_config.known_plates.items()
|
||||
if any(re.match(f"^{plate}$", top_plate) for plate in plates)
|
||||
),
|
||||
top_plate,
|
||||
)
|
||||
|
||||
# Send the result to the API
|
||||
resp = requests.post(
|
||||
f"{FRIGATE_LOCALHOST}/api/events/{id}/sub_label",
|
||||
json={
|
||||
"camera": obj_data.get("camera"),
|
||||
"subLabel": sub_label,
|
||||
"subLabelScore": avg_confidence,
|
||||
},
|
||||
)
|
||||
|
||||
if resp.status_code == 200:
|
||||
self.detected_license_plates[id] = {
|
||||
"plate": top_plate,
|
||||
"char_confidences": top_char_confidences,
|
||||
"area": top_area,
|
||||
}
|
||||
|
||||
return True
|
||||
|
||||
def _create_thumbnail(self, yuv_frame, box, height=500) -> Optional[bytes]:
|
||||
"""Return jpg thumbnail of a region of the frame."""
|
||||
frame = cv2.cvtColor(yuv_frame, cv2.COLOR_YUV2BGR_I420)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user