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Code Issues Projects Releases Wiki Activity

Fixes (#18077)

Browse Source 
* fix onvif reinitialization

* api docs: clarify usage of clip.mp4 endpoint

* Always show train tab

* Add description to API

* catch lpr model inference exceptions

* always apply motion mask when using yolov9 plate detection

* lpr faq

* fix incorrect focus when reopening search detail dialog on video tab

* only use keyboard listener in face library when train tab is active

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
This commit is contained in:
Josh Hawkins
2025-05-07 17:31:24 -05:00
committed by GitHub
parent da1fb935b4
commit ac8e647b92
7 changed files with 135 additions and 105 deletions
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28
frigate/data_processing/common/license_plate/mixin.py
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@@ -79,7 +79,12 @@ class LicensePlateProcessingMixin:
resized_image,
)
outputs = self.model_runner.detection_model([normalized_image])[0]
try:
outputs = self.model_runner.detection_model([normalized_image])[0]
except Exception as e:
logger.warning(f"Error running LPR box detection model: {e}")
return []
outputs = outputs[0, :, :]
if False:
@@ -115,7 +120,11 @@ class LicensePlateProcessingMixin:
norm_img = norm_img[np.newaxis, :]
norm_images.append(norm_img)
outputs = self.model_runner.classification_model(norm_images)
try:
outputs = self.model_runner.classification_model(norm_images)
except Exception as e:
logger.warning(f"Error running LPR classification model: {e}")
return
return self._process_classification_output(images, outputs)
@@ -152,7 +161,10 @@ class LicensePlateProcessingMixin:
norm_image = norm_image[np.newaxis, :]
norm_images.append(norm_image)
outputs = self.model_runner.recognition_model(norm_images)
try:
outputs = self.model_runner.recognition_model(norm_images)
except Exception as e:
logger.warning(f"Error running LPR recognition model: {e}")
return self.ctc_decoder(outputs)
def _process_license_plate(
@@ -968,7 +980,11 @@ class LicensePlateProcessingMixin:
Return the dimensions of the detected plate as [x1, y1, x2, y2].
"""
predictions = self.model_runner.yolov9_detection_model(input)
try:
predictions = self.model_runner.yolov9_detection_model(input)
except Exception as e:
logger.warning(f"Error running YOLOv9 license plate detection model: {e}")
return None
confidence_threshold = self.lpr_config.detection_threshold
@@ -1281,6 +1297,10 @@ class LicensePlateProcessingMixin:
return
rgb = cv2.cvtColor(frame, cv2.COLOR_YUV2BGR_I420)
# apply motion mask
rgb[self.config.cameras[camera].motion.mask == 0] = [0, 0, 0]
left, top, right, bottom = car_box
car = rgb[top:bottom, left:right]