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Code Issues Projects Releases Wiki Activity

Face recognition improvements (#16034)

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This commit is contained in:
Nicolas Mowen
2025-01-18 10:52:01 -07:00
parent 5c4aa6120d
commit 25d332753c
7 changed files with 64 additions and 39 deletions
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5
frigate/data_processing/real_time/api.py
View File

@@ -32,9 +32,12 @@ class RealTimeProcessorApi(ABC):
pass
@abstractmethod
def handle_request(self, request_data: dict[str, any]) -> dict[str, any] | None:
def handle_request(
self, topic: str, request_data: dict[str, any]
) -> dict[str, any] | None:
"""Handle metadata requests.
Args:
topic (str): topic that dictates what work is requested.
request_data (dict): containing data about requested change to process.
Returns:

2
frigate/data_processing/real_time/bird_processor.py
View File

@@ -146,7 +146,7 @@ class BirdProcessor(RealTimeProcessorApi):
if resp.status_code == 200:
self.detected_birds[obj_data["id"]] = score
def handle_request(self, request_data):
def handle_request(self, topic, request_data):
return None
def expire_object(self, object_id):

74
frigate/data_processing/real_time/face_processor.py
View File

@@ -12,6 +12,7 @@ import cv2
import numpy as np
import requests
from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
from frigate.config import FrigateConfig
from frigate.const import FACE_DIR, FRIGATE_LOCALHOST, MODEL_CACHE_DIR
from frigate.util.image import area
@@ -353,45 +354,52 @@ class FaceProcessor(RealTimeProcessorApi):
self.__update_metrics(datetime.datetime.now().timestamp() - start)
def handle_request(self, request_data) -> dict[str, any] | None:
rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
label = request_data["face_name"]
id = f"{label}-{rand_id}"
if request_data.get("cropped"):
thumbnail = request_data["image"]
else:
img = cv2.imdecode(
np.frombuffer(base64.b64decode(request_data["image"]), dtype=np.uint8),
cv2.IMREAD_COLOR,
def handle_request(self, topic, request_data) -> dict[str, any] | None:
if topic == EmbeddingsRequestEnum.clear_face_classifier.value:
self.__clear_classifier()
elif topic == EmbeddingsRequestEnum.register_face.value:
rand_id = "".join(
random.choices(string.ascii_lowercase + string.digits, k=6)
)
face_box = self.__detect_face(img)
label = request_data["face_name"]
id = f"{label}-{rand_id}"
if not face_box:
return {
"message": "No face was detected.",
"success": False,
}
if request_data.get("cropped"):
thumbnail = request_data["image"]
else:
img = cv2.imdecode(
np.frombuffer(
base64.b64decode(request_data["image"]), dtype=np.uint8
),
cv2.IMREAD_COLOR,
)
face_box = self.__detect_face(img)
face = img[face_box[1] : face_box[3], face_box[0] : face_box[2]]
ret, thumbnail = cv2.imencode(
".webp", face, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
)
if not face_box:
return {
"message": "No face was detected.",
"success": False,
}
# write face to library
folder = os.path.join(FACE_DIR, label)
file = os.path.join(folder, f"{id}.webp")
os.makedirs(folder, exist_ok=True)
face = img[face_box[1] : face_box[3], face_box[0] : face_box[2]]
_, thumbnail = cv2.imencode(
".webp", face, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
)
# save face image
with open(file, "wb") as output:
output.write(thumbnail.tobytes())
# write face to library
folder = os.path.join(FACE_DIR, label)
file = os.path.join(folder, f"{id}.webp")
os.makedirs(folder, exist_ok=True)
self.__clear_classifier()
return {
"message": "Successfully registered face.",
"success": True,
}
# save face image
with open(file, "wb") as output:
output.write(thumbnail.tobytes())
self.__clear_classifier()
return {
"message": "Successfully registered face.",
"success": True,
}
def expire_object(self, object_id: str):
if object_id in self.detected_faces: