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create camera type, add manual event and save snapshot

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This commit is contained in:
Josh Hawkins
2025-03-21 07:38:02 -05:00
parent ed1cffd4ae
commit 61efdadd63
8 changed files with 107 additions and 78 deletions
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92
frigate/data_processing/common/license_plate/mixin.py
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@@ -1,5 +1,6 @@
"""Handle processing images for face detection and recognition."""
import base64
import datetime
import logging
import math
@@ -18,6 +19,7 @@ from frigate.comms.event_metadata_updater import (
EventMetadataPublisher,
EventMetadataTypeEnum,
)
from frigate.config.camera.camera import CameraTypeEnum
from frigate.embeddings.onnx.lpr_embedding import LPR_EMBEDDING_SIZE
from frigate.util.image import area
@@ -46,6 +48,9 @@ class LicensePlateProcessingMixin:
self.box_thresh = 0.6
self.mask_thresh = 0.6
# matching
self.similarity_threshold = 0.8
def _detect(self, image: np.ndarray) -> List[np.ndarray]:
"""
Detect possible license plates in the input image by first resizing and normalizing it,
@@ -870,14 +875,14 @@ class LicensePlateProcessingMixin:
"""
self.metrics.alpr_pps.value = (self.metrics.alpr_pps.value * 9 + duration) / 10
def _generate_plate_event(self, camera: str, plate_score: float) -> str:
def _generate_plate_event(self, camera: str, plate: str, plate_score: float) -> str:
"""Generate a unique ID for a plate event based on camera and text."""
now = datetime.datetime.now().timestamp()
rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
event_id = f"{now}-{rand_id}"
self.event_metadata_publisher.publish(
EventMetadataTypeEnum.manual_event_create,
EventMetadataTypeEnum.lpr_event_create,
(
now,
camera,
@@ -886,9 +891,7 @@ class LicensePlateProcessingMixin:
True,
plate_score,
None,
None,
"api",
{},
plate,
),
)
return event_id
@@ -903,6 +906,9 @@ class LicensePlateProcessingMixin:
if not self.config.cameras[camera].lpr.enabled:
return
if not dedicated_lpr and self.config.cameras[camera].type == CameraTypeEnum.lpr:
return
if dedicated_lpr:
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
@@ -941,27 +947,6 @@ class LicensePlateProcessingMixin:
license_plate[0] : license_plate[2],
]
if WRITE_DEBUG_IMAGES:
if license_plate:
frame_with_rect = rgb.copy()
cv2.rectangle(
frame_with_rect,
(
int(license_plate[0]),
int(license_plate[1]),
),
(
int(license_plate[2]),
int(license_plate[3]),
),
(0, 255, 0),
2,
)
cv2.imwrite(
f"debug/frames/dedicated_lpr_with_rect_{current_time}.jpg",
frame_with_rect,
)
# Double the size for better OCR
license_plate_frame = cv2.resize(
license_plate_frame,
@@ -971,12 +956,6 @@ class LicensePlateProcessingMixin:
),
)
if WRITE_DEBUG_IMAGES:
cv2.imwrite(
f"debug/frames/dedicated_lpr_doubled_{current_time}.jpg",
license_plate_frame,
)
else:
id = obj_data["id"]
@@ -1036,26 +1015,6 @@ class LicensePlateProcessingMixin:
logger.debug("Detected no license plates for car object.")
return
if WRITE_DEBUG_IMAGES and license_plate:
frame_with_rect = car.copy()
cv2.rectangle(
frame_with_rect,
(
int(license_plate[0]),
int(license_plate[1]),
),
(
int(license_plate[2]),
int(license_plate[3]),
),
(0, 255, 0),
2,
)
cv2.imwrite(
f"debug/frames/car_frame_with_rect_{current_time}.jpg",
frame_with_rect,
)
license_plate_area = max(
0,
(license_plate[2] - license_plate[0])
@@ -1145,19 +1104,13 @@ class LicensePlateProcessingMixin:
license_plate_frame,
)
start = datetime.datetime.now().timestamp()
# run detection, returns results sorted by confidence, best first
start = datetime.datetime.now().timestamp()
license_plates, confidences, areas = self._process_license_plate(
license_plate_frame
)
self.__update_lpr_metrics(datetime.datetime.now().timestamp() - start)
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 = (
@@ -1168,7 +1121,6 @@ class LicensePlateProcessingMixin:
f"Detected text: {plate} (average confidence: {avg_confidence:.2f}, area: {text_area} pixels)"
)
else:
# no plates found
logger.debug("No text detected")
return
@@ -1193,8 +1145,6 @@ class LicensePlateProcessingMixin:
# For LPR cameras, match or assign plate ID using Jaro-Winkler distance
if dedicated_lpr:
plate_id = None
# Similarity threshold for matching plates
jaro_winkler_threshold = 0.8
for existing_id, data in self.detected_license_plates.items():
if (
@@ -1204,15 +1154,16 @@ class LicensePlateProcessingMixin:
<= self.config.cameras[camera].lpr.expire_time
):
similarity = jaro_winkler(data["plate"], top_plate)
if similarity >= jaro_winkler_threshold:
if similarity >= self.similarity_threshold:
plate_id = existing_id
logger.debug(
f"Matched plate {top_plate} to {data['plate']} (similarity: {similarity:.3f})"
)
break
if plate_id is None:
# start new manual lpr event
plate_id = self._generate_plate_event(obj_data, avg_confidence)
plate_id = self._generate_plate_event(
obj_data, top_plate, avg_confidence
)
logger.debug(
f"New plate event for dedicated LPR camera {plate_id}: {top_plate}"
)
@@ -1253,12 +1204,21 @@ class LicensePlateProcessingMixin:
EventMetadataTypeEnum.sub_label, (id, sub_label, avg_confidence)
)
logger.debug("publishing plate for", id, top_plate)
self.sub_label_publisher.publish(
EventMetadataTypeEnum.recognized_license_plate,
(id, top_plate, avg_confidence),
)
if dedicated_lpr:
# save the best snapshot
logger.debug(f"Writing snapshot for {id}, {top_plate}, {current_time}")
frame_bgr = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
_, buffer = cv2.imencode(".jpg", frame_bgr)
self.sub_label_publisher.publish(
EventMetadataTypeEnum.save_lpr_snapshot,
(base64.b64encode(buffer).decode("ASCII"), id, camera),
)
self.detected_license_plates[id] = {
"plate": top_plate,
"char_confidences": top_char_confidences,