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 min person area per region

Browse Source
This commit is contained in:
blakeblackshear 2019-02-25 06:48:01 -06:00
parent bf6d6c3796
commit 039782ad2e
1 changed files with 8 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
detect_objects.py
View File

@ -200,13 +200,14 @@ def main():
regions = [] regions = []
for region_string in REGIONS.split(':'): for region_string in REGIONS.split(':'):
region_parts = region_string.split(',') region_parts = region_string.split(',')
region_mask_image = cv2.imread("/config/{}".format(region_parts[4]), cv2.IMREAD_GRAYSCALE) region_mask_image = cv2.imread("/config/{}".format(region_parts[5]), cv2.IMREAD_GRAYSCALE)
region_mask = np.where(region_mask_image==[0]) region_mask = np.where(region_mask_image==[0])
regions.append({ regions.append({
'size': int(region_parts[0]), 'size': int(region_parts[0]),
'x_offset': int(region_parts[1]), 'x_offset': int(region_parts[1]),
'y_offset': int(region_parts[2]), 'y_offset': int(region_parts[2]),
'min_object_size': int(region_parts[3]), 'min_person_area': int(region_parts[3]),
'min_object_size': int(region_parts[4]),
'mask': region_mask, 'mask': region_mask,
# Event for motion detection signaling # Event for motion detection signaling
'motion_detected': mp.Event(), 'motion_detected': mp.Event(),
@ -260,6 +261,7 @@ def main():
region['motion_detected'], region['motion_detected'],
frame_shape, frame_shape,
region['size'], region['x_offset'], region['y_offset'], region['size'], region['x_offset'], region['y_offset'],
region['min_person_area'],
DEBUG)) DEBUG))
detection_process.daemon = True detection_process.daemon = True
detection_processes.append(detection_process) detection_processes.append(detection_process)
@ -405,7 +407,7 @@ def fetch_frames(shared_arr, shared_frame_time, frame_lock, frame_ready, frame_s
# do the actual object detection # do the actual object detection
def process_frames(shared_arr, object_queue, shared_frame_time, frame_lock, frame_ready, def process_frames(shared_arr, object_queue, shared_frame_time, frame_lock, frame_ready,
motion_detected, frame_shape, region_size, region_x_offset, region_y_offset, motion_detected, frame_shape, region_size, region_x_offset, region_y_offset,
debug): min_person_area, debug):
# shape shared input array into frame for processing # shape shared input array into frame for processing
arr = tonumpyarray(shared_arr).reshape(frame_shape) arr = tonumpyarray(shared_arr).reshape(frame_shape)
@ -441,6 +443,9 @@ def process_frames(shared_arr, object_queue, shared_frame_time, frame_lock, fram
# do the object detection # do the object detection
objects = detect_objects(cropped_frame_rgb, sess, detection_graph, region_size, region_x_offset, region_y_offset, debug) objects = detect_objects(cropped_frame_rgb, sess, detection_graph, region_size, region_x_offset, region_y_offset, debug)
for obj in objects: for obj in objects:
# ignore persons below the size threshold
if obj['name'] == 'person' and (obj['xmax']-obj['xmin'])*(obj['ymax']-obj['ymin']) < min_person_area:
continue
obj['frame_time'] = frame_time obj['frame_time'] = frame_time
object_queue.put(obj) object_queue.put(obj)