administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2025-12-06 13:34:13 +03:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Don't save redundant state changes

Browse Source
This commit is contained in:
Nicolas Mowen 2025-11-28 14:31:16 -07:00
parent 6c172ed095
commit 4dfea29f28
1 changed files with 45 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

62
frigate/data_processing/real_time/custom_classification.py
View File

@ -99,6 +99,31 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
if self.inference_speed: if self.inference_speed:
self.inference_speed.update(duration) self.inference_speed.update(duration)
def _should_save_image(self, camera: str, detected_state: str) -> bool:
"""
Determine if we should save the image for training.
Only save when the state is changing or being verified, not when it's stable.
"""
if camera not in self.state_history:
# First detection for this camera, save it
return True
verification = self.state_history[camera]
current_state = verification.get("current_state")
pending_state = verification.get("pending_state")
# Save if there's a pending state change being verified
if pending_state is not None:
return True
# Save if the detected state differs from the current verified state
# (state is changing)
if current_state is not None and detected_state != current_state:
return True
# Don't save if state is stable (detected_state == current_state)
return False
def verify_state_change(self, camera: str, detected_state: str) -> str | None: def verify_state_change(self, camera: str, detected_state: str) -> str | None:
""" """
Verify state change requires 3 consecutive identical states before publishing. Verify state change requires 3 consecutive identical states before publishing.
@ -212,14 +237,15 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
return return
if self.interpreter is None: if self.interpreter is None:
write_classification_attempt( if self._should_save_image(camera, "unknown"):
self.train_dir, write_classification_attempt(
cv2.cvtColor(frame, cv2.COLOR_RGB2BGR), self.train_dir,
"none-none", cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
now, "none-none",
"unknown", now,
0.0, "unknown",
) 0.0,
)
return return
input = np.expand_dims(resized_frame, axis=0) input = np.expand_dims(resized_frame, axis=0)
@ -236,14 +262,17 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
score = round(probs[best_id], 2) score = round(probs[best_id], 2)
self.__update_metrics(datetime.datetime.now().timestamp() - now) self.__update_metrics(datetime.datetime.now().timestamp() - now)
write_classification_attempt( detected_state = self.labelmap[best_id]
self.train_dir,
cv2.cvtColor(frame, cv2.COLOR_RGB2BGR), if self._should_save_image(camera, detected_state):
"none-none", write_classification_attempt(
now, self.train_dir,
self.labelmap[best_id], cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
score, "none-none",
) now,
detected_state,
score,
)
if score < self.model_config.threshold: if score < self.model_config.threshold:
logger.debug( logger.debug(
@ -251,7 +280,6 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
) )
return return
detected_state = self.labelmap[best_id]
verified_state = self.verify_state_change(camera, detected_state) verified_state = self.verify_state_change(camera, detected_state)
if verified_state is not None: if verified_state is not None: