add the ability to mask the standing location of a person

This commit is contained in:
blakeblackshear 2019-04-14 11:58:02 -05:00
parent 3e803b6a03
commit 4dacf02ef9
3 changed files with 17 additions and 21 deletions

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config/back-mask.bmp Normal file

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@ -13,27 +13,7 @@ cameras:
# values that begin with a "$" will be replaced with environment variable # values that begin with a "$" will be replaced with environment variable
password: $RTSP_PASSWORD password: $RTSP_PASSWORD
path: /cam/realmonitor?channel=1&subtype=2 path: /cam/realmonitor?channel=1&subtype=2
regions: mask: back-mask.bmp
- size: 350
x_offset: 0
y_offset: 300
min_person_area: 5000
- size: 400
x_offset: 350
y_offset: 250
min_person_area: 2000
- size: 400
x_offset: 750
y_offset: 250
min_person_area: 2000
back2:
rtsp:
user: viewer
host: 10.0.10.10
port: 554
# values that begin with a "$" will be replaced with environment variable
password: $RTSP_PASSWORD
path: /cam/realmonitor?channel=1&subtype=2
regions: regions:
- size: 350 - size: 350
x_offset: 0 x_offset: 0

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@ -171,6 +171,13 @@ class Camera:
mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects) mqtt_publisher = MqttObjectPublisher(self.mqtt_client, self.mqtt_topic_prefix, self.objects_parsed, self.detected_objects)
mqtt_publisher.start() mqtt_publisher.start()
# load in the mask for person detection
if 'mask' in self.config:
self.mask = cv2.imread("/config/{}".format(self.config['mask']), cv2.IMREAD_GRAYSCALE)
else:
self.mask = np.zeros((self.frame_shape[0], self.frame_shape[1], 1), np.uint8)
self.mask[:] = 255
def start(self): def start(self):
self.capture_process.start() self.capture_process.start()
# start the object detection prep threads # start the object detection prep threads
@ -207,6 +214,15 @@ class Camera:
if region and region['min_person_area'] > person_area: if region and region['min_person_area'] > person_area:
continue continue
# compute the coordinates of the person and make sure
# the location isnt outide the bounds of the image (can happen from rounding)
y_location = min(int(obj['ymax']), len(self.mask)-1)
x_location = min(int((obj['xmax']-obj['xmin'])/2.0), len(self.mask[0])-1)
# if the person is in a masked location, continue
if self.mask[y_location][x_location] == [0]:
continue
self.detected_objects.append(obj) self.detected_objects.append(obj)
with self.objects_parsed: with self.objects_parsed: