# Draw it on image
rect = self._frame_rect(box)
blob = Blob(f"{self.name}:{str(i)}", rect, fps=self.fps.fps())
blobs.append(blob)
i += 1
except Exception as e:
print(f"model {self.name}: tracking exit Exception: {e}")
self.blobs.track_blobs(blobs)
self.fps.update()
def clear(self):
super(CamShift, self).clear()
# clear selected tracing boxes
self.boxes.clear()
self.boxes_hist.clear()
if __name__ == '__main__':
cam = Camera(device=0).start()
model = CamShift()
cam.register_models(model)
# test Model instance
cam.test()
87: 'scissors',
88: 'teddy bear',
89: 'hair drier',
90: 'toothbrush'
}
def detect(self, image):
# grab the frame dimensions and convert it to a blob
image = image if len(image.shape) == 3 else cv2.cvtColor(
image, cv2.COLOR_GRAY2BGR)
roi = self._init_roi(image)
h, w = roi.shape[:2]
# pass the blob through the webcam and obtain the detections and predictions
blob = cv2.dnn.blobFromImage(roi, size=(300, 300), swapRB=True)
self.net.setInput(blob)
blobs = self.net_blobs(h, w)
self.blobs.track_blobs(blobs)
self.fps.update()
if __name__ == '__main__':
cam = Camera(device=1, sync=False).start()
cam_plus = CamPlus(camera=cam)
model = Caffe()
# test Model instance
cam_plus.register_models(model)
cam_plus.test()
self.hog = cv2.HOGDescriptor()
self.hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
def detect(self, image):
blobs = []
image = image if len(image.shape) == 3 else cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
roi = self._init_roi(image)
roi_rectangles, w = self.hog.detectMultiScale(roi, winStride=(8, 8), padding=(32, 32), scale=1.05)
for i, roi_rect in enumerate(roi_rectangles):
rect = self._frame_rect(roi_rect)
blob = Blob(f"{self.name}:{str(i)}", rect, fps=self.fps.fps())
blobs.append(blob)
self.blobs.track_blobs(blobs)
self.fps.update()
if __name__ == '__main__':
video = ["airport.mp4", "weco.mp4", "count1.mp4", "overpass.mp4", "cars.mp4", "walk.avi", "run.mp4"]
v0 = f"{CWD}//data//Sample//videos//{video[5]}"
cam = Camera(device=v0).start()
cam_plus = CamPlus(camera=cam)
model = HOGDescriptor()
# test Model instance
cam_plus.register_models(model)
cam_plus.test()
# for nose detect
# if abs(w / 2 - x2 - w2 / 2) < 20:
rect = self._frame_rect(eye)
blob = Blob(f"eye:{str(j)}", rect, fps=self.fps.fps())
blobs.append(blob)
self.blobs.track_blobs(blobs)
self.fps.update()
# face-mask detect and sound alert
count = []
for blob in self.blobs.blobs:
if 'eye' in blob.label:
count.append(blob.age)
self.bool_alert = True if len(count) < 2 else False
if __name__ == '__main__':
# camera instance, started before assigning to Model
cam = Camera(device=1).start()
cam_plus = CamPlus(camera=cam)
# assigning camera to Model
model = CascadeClassifier()
# test Model instance
cam_plus.register_models(model)
cam_plus.test()