Mirrors/blakeblackshear.frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2024-11-21 19:07:46 +01:00
Code Issues Projects Releases Wiki Activity

add mask as object filter

Browse Source
This commit is contained in:
Blake Blackshear 2021-01-15 07:52:28 -06:00
parent 96ac2c29d6
commit b2c7fc8f5b
4 changed files with 26 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
frigate/config.py
View File

@ -500,7 +500,7 @@ class FilterConfig():
self._threshold = config['threshold']
self._min_score = config.get('min_score')
self._raw_mask = config.get('mask')
self._mask = create_mask(frame_shape, self._raw_mask) if frame_shape else None
self._mask = create_mask(frame_shape, self._raw_mask) if self._raw_mask else None
@property
def min_area(self):
@ -686,7 +686,12 @@ class CameraRtmpConfig():
class MotionConfig():
def __init__(self, global_config, config, frame_shape):
self._raw_mask = config.get('mask')
self._mask = create_mask(frame_shape, self._raw_mask) if self._raw_mask else None
if self._raw_mask:
self._mask = create_mask(frame_shape, self._raw_mask)
else:
default_mask = np.zeros(frame_shape, np.uint8)
default_mask[:] = 255
self._mask = default_mask
self._threshold = config.get('threshold', global_config.get('threshold', 25))
self._contour_area = config.get('contour_area', global_config.get('contour_area', 100))
self._delta_alpha = config.get('delta_alpha', global_config.get('delta_alpha', 0.2))

4
frigate/motion.py
View File

@ -5,7 +5,7 @@ from frigate.config import MotionConfig
class MotionDetector():
def __init__(self, frame_shape, mask, config: MotionConfig):
def __init__(self, frame_shape, config: MotionConfig):
self.config = config
self.frame_shape = frame_shape
self.resize_factor = frame_shape[0]/config.frame_height
@ -14,7 +14,7 @@ class MotionDetector():
self.avg_delta = np.zeros(self.motion_frame_size, np.float)
self.motion_frame_count = 0
self.frame_counter = 0
resized_mask = cv2.resize(mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
resized_mask = cv2.resize(config.mask, dsize=(self.motion_frame_size[1], self.motion_frame_size[0]), interpolation=cv2.INTER_LINEAR)
self.mask = np.where(resized_mask==[0])
def detect(self, frame):

2
frigate/object_processing.py
View File

@ -291,7 +291,7 @@ class CameraState():
cv2.drawContours(frame_copy, [zone.contour], -1, zone.color, thickness)
if draw_options.get('mask'):
mask_overlay = np.where(self.camera_config.mask==[0])
mask_overlay = np.where(self.camera_config.motion.mask==[0])
frame_copy[mask_overlay] = [0,0,0]
if draw_options.get('motion_boxes'):

32
frigate/video.py
View File

@ -31,7 +31,7 @@ from frigate.util import (EventsPerSecond, FrameManager,
logger = logging.getLogger(__name__)
def filtered(obj, objects_to_track, object_filters, mask=None):
def filtered(obj, objects_to_track, object_filters):
object_name = obj[0]
if not object_name in objects_to_track:
@ -54,14 +54,15 @@ def filtered(obj, objects_to_track, object_filters, mask=None):
if obj_settings.min_score > obj[1]:
return True
# compute the coordinates of the object and make sure
# the location isnt outside the bounds of the image (can happen from rounding)
y_location = min(int(obj[2][3]), len(mask)-1)
x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(mask[0])-1)
if not obj_settings.mask is None:
# compute the coordinates of the object and make sure
# the location isnt outside the bounds of the image (can happen from rounding)
y_location = min(int(obj[2][3]), len(obj_settings.mask)-1)
x_location = min(int((obj[2][2]-obj[2][0])/2.0)+obj[2][0], len(obj_settings.mask[0])-1)
# if the object is in a masked location, don't add it to detected objects
if (not mask is None) and (mask[y_location][x_location] == 0):
return True
# if the object is in a masked location, don't add it to detected objects
if obj_settings.mask[y_location][x_location] == 0:
return True
return False
@ -258,9 +259,8 @@ def track_camera(name, config: CameraConfig, model_shape, detection_queue, resul
frame_shape = config.frame_shape
objects_to_track = config.objects.track
object_filters = config.objects.filters
mask = config.mask
motion_detector = MotionDetector(frame_shape, mask, config.motion)
motion_detector = MotionDetector(frame_shape, config.motion)
object_detector = RemoteObjectDetector(name, '/labelmap.txt', detection_queue, result_connection, model_shape)
object_tracker = ObjectTracker(config.detect)
@ -268,7 +268,7 @@ def track_camera(name, config: CameraConfig, model_shape, detection_queue, resul
frame_manager = SharedMemoryFrameManager()
process_frames(name, frame_queue, frame_shape, model_shape, frame_manager, motion_detector, object_detector,
object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, mask, stop_event)
object_tracker, detected_objects_queue, process_info, objects_to_track, object_filters, stop_event)
logger.info(f"{name}: exiting subprocess")
@ -278,7 +278,7 @@ def reduce_boxes(boxes):
reduced_boxes = cv2.groupRectangles([list(b) for b in itertools.chain(boxes, boxes)], 1, 0.2)[0]
return [tuple(b) for b in reduced_boxes]
def detect(object_detector, frame, model_shape, region, objects_to_track, object_filters, mask):
def detect(object_detector, frame, model_shape, region, objects_to_track, object_filters):
tensor_input = create_tensor_input(frame, model_shape, region)
detections = []
@ -296,7 +296,7 @@ def detect(object_detector, frame, model_shape, region, objects_to_track, object
(x_max-x_min)*(y_max-y_min),
region)
# apply object filters
if filtered(det, objects_to_track, object_filters, mask):
if filtered(det, objects_to_track, object_filters):
continue
detections.append(det)
return detections
@ -305,7 +305,7 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
frame_manager: FrameManager, motion_detector: MotionDetector,
object_detector: RemoteObjectDetector, object_tracker: ObjectTracker,
detected_objects_queue: mp.Queue, process_info: Dict,
objects_to_track: List[str], object_filters, mask, stop_event,
objects_to_track: List[str], object_filters, stop_event,
exit_on_empty: bool = False):
fps = process_info['process_fps']
@ -358,7 +358,7 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
# resize regions and detect
detections = []
for region in regions:
detections.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters, mask))
detections.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
#########
# merge objects, check for clipped objects and look again up to 4 times
@ -393,7 +393,7 @@ def process_frames(camera_name: str, frame_queue: mp.Queue, frame_shape, model_s
regions.append(region)
selected_objects.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters, mask))
selected_objects.extend(detect(object_detector, frame, model_shape, region, objects_to_track, object_filters))
refining = True
else: