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Implement better face recognition reasonability

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This commit is contained in:
Nicolas Mowen 2024-12-25 06:56:13 -07:00
parent 9aaa0af72e
commit 445a4068db
2 changed files with 14 additions and 5 deletions
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16
frigate/embeddings/maintainer.py
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@ -476,20 +476,26 @@ class EmbeddingMaintainer(threading.Thread):
sub_label, score = res 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( 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 self.config.face_recognition.debug_save_images: if self.config.face_recognition.debug_save_images:
# write face to library # write face to library
folder = os.path.join(FACE_DIR, "debug") folder = os.path.join(FACE_DIR, "debug")
file = os.path.join(folder, f"{id}-{sub_label}-{score}.webp") file = os.path.join(folder, f"{id}-{sub_label}-{score}-{face_score}.webp")
os.makedirs(folder, exist_ok=True) os.makedirs(folder, exist_ok=True)
cv2.imwrite(file, face_frame) cv2.imwrite(file, face_frame)
if id in self.detected_faces and score <= self.detected_faces[id]: if id in self.detected_faces and face_score <= self.detected_faces[id]:
logger.debug( 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 return
@ -503,7 +509,7 @@ class EmbeddingMaintainer(threading.Thread):
) )
if resp.status_code == 200: 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]: 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].""" """Return the dimensions of the input image as [x, y, width, height]."""

3
frigate/util/model.py
View File

@ -178,6 +178,9 @@ class FaceClassificationModel:
dir = "/media/frigate/clips/faces" dir = "/media/frigate/clips/faces"
for idx, name in enumerate(os.listdir(dir)): for idx, name in enumerate(os.listdir(dir)):
if name == "debug":
continue
self.label_map[idx] = name self.label_map[idx] = name
face_folder = os.path.join(dir, name) face_folder = os.path.join(dir, name)
for image in os.listdir(face_folder): for image in os.listdir(face_folder):