Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-07-26 13:47:03 +02:00
Code Issues Projects Releases Wiki Activity

Face blur factor (#17099)

Browse Source 
* Add option to apply factor to face blurring

* Adjust blur factors

* Add debug log
This commit is contained in:
Nicolas Mowen 2025-03-11 13:18:43 -06:00 committed by GitHub
parent c9382f8969
commit 300f85720c
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 27 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
frigate/config/classification.py
View File

@ -67,6 +67,9 @@ class FaceRecognitionConfig(FrigateBaseModel):
save_attempts: bool = Field(
default=True, title="Save images of face detections for training."
)
blur_confidence_filter: bool = Field(
default=True, title="Apply blur quality filter to face confidence."
)
class LicensePlateRecognitionConfig(FrigateBaseModel):

25
frigate/data_processing/real_time/face.py
View File

@ -192,6 +192,22 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
image, M, (output_width, output_height), flags=cv2.INTER_CUBIC
)
def __get_blur_factor(self, input: np.ndarray) -> float:
"""Calculates the factor for the confidence based on the blur of the image."""
if not self.face_config.blur_confidence_filter:
return 1.0
variance = cv2.Laplacian(input, cv2.CV_64F).var()
if variance < 60: # image is very blurry
return 0.96
elif variance < 70: # image moderately blurry
return 0.98
elif variance < 80: # image is slightly blurry
return 0.99
else:
return 1.0
def __clear_classifier(self) -> None:
self.face_recognizer = None
self.label_map = {}
@ -232,14 +248,21 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
if not self.recognizer:
return None
# face recognition is best run on grayscale images
img = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
# get blur factor before aligning face
blur_factor = self.__get_blur_factor(img)
logger.debug(f"face detected with bluriness {blur_factor}")
# align face and run recognition
img = self.__align_face(img, img.shape[1], img.shape[0])
index, distance = self.recognizer.predict(img)
if index == -1:
return None
score = 1.0 - (distance / 1000)
score = (1.0 - (distance / 1000)) * blur_factor
return self.label_map[index], round(score, 2)
def __update_metrics(self, duration: float) -> None: