Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-02-05 00:15:51 +01:00
Code Issues Projects Releases Wiki Activity

Face recognition logic improvements (#15679)

Browse Source 
* Always initialize face model on startup

* Add ability to save face images for debugging

* Implement better face recognition reasonability
This commit is contained in:
Nicolas Mowen 2024-12-25 20:52:35 -06:00
parent 5a2113a62d
commit 5c6f169975
3 changed files with 25 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
frigate/config/semantic_search.py
View File

@ -32,6 +32,9 @@ class FaceRecognitionConfig(FrigateBaseModel):
min_area: int = Field(
default=500, title="Min area of face box to consider running face recognition."
)
debug_save_images: bool = Field(
default=False, title="Save images of face detections for debugging."
)
class LicensePlateRecognitionConfig(FrigateBaseModel):

21
frigate/embeddings/maintainer.py
View File

@ -505,13 +505,26 @@ class EmbeddingMaintainer(threading.Thread):
sub_label, score = res
# calculate the overall face score as the probability * area of face
# this will help to reduce false positives from small side-angle faces
# if a large front-on face image may have scored slightly lower but
# is more likely to be accurate due to the larger face area
face_score = round(score * face_frame.shape[0] * face_frame.shape[1], 2)
logger.debug(
f"Detected best face for person as: {sub_label} with score {score}"
f"Detected best face for person as: {sub_label} with probability {score} and overall face score {face_score}"
)
if id in self.detected_faces and score <= self.detected_faces[id]:
if self.config.face_recognition.debug_save_images:
# write face to library
folder = os.path.join(FACE_DIR, "debug")
file = os.path.join(folder, f"{id}-{sub_label}-{score}-{face_score}.webp")
os.makedirs(folder, exist_ok=True)
cv2.imwrite(file, face_frame)
if id in self.detected_faces and face_score <= self.detected_faces[id]:
logger.debug(
f"Recognized face distance {score} is less than previous face distance ({self.detected_faces.get(id)})."
f"Recognized face distance {score} and overall score {face_score} is less than previous overall face score ({self.detected_faces.get(id)})."
)
return
@ -525,7 +538,7 @@ class EmbeddingMaintainer(threading.Thread):
)
if resp.status_code == 200:
self.detected_faces[id] = score
self.detected_faces[id] = face_score
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]."""

5
frigate/util/model.py
View File

@ -170,6 +170,7 @@ class FaceClassificationModel:
)
)
self.label_map: dict[int, str] = {}
self.__build_classifier()
def __build_classifier(self) -> None:
labels = []
@ -177,6 +178,9 @@ class FaceClassificationModel:
dir = "/media/frigate/clips/faces"
for idx, name in enumerate(os.listdir(dir)):
if name == "debug":
continue
self.label_map[idx] = name
face_folder = os.path.join(dir, name)
for image in os.listdir(face_folder):
@ -248,6 +252,7 @@ class FaceClassificationModel:
def clear_classifier(self) -> None:
self.classifier = None
self.labeler = None
self.label_map = {}
def classify_face(self, face_image: np.ndarray) -> Optional[tuple[str, float]]:
if not self.label_map: