Initialize variables for obstruction detection

obstruct_initialize_var.py

# initialize the first frame in the video stream
# firstFrame = None
firstFrame = None
status_list = [None, None]
times = []
timer = []
df = pd.DataFrame(columns = ["Start","End", "duration_in_sec"])

fps_start_time = datetime.now()
fps = 0
total_frames = 0

object_id_list = []
dtime = dict()
dwell_time = dict()
allowable_duration = 10.0 # stipulated allowable duration for static obstruction in seconds

timestr = time.strftime("%H%M%S")
t = int(timestr)
filename = "sample_camera/file_%a.avi" % t
filename_d = "sample_camera/file_d_%a.avi" % t
fourcc = cv2.VideoWriter_fourcc(*'DIVX')

# variables for drawing ROI

points = [(919.2771084337348,417.8554216867469),
          (1472,1079),
          (818,1079),
          (787.9518072289156,884.1204819277108),
          (762.6506024096385,428.69879518072287)]

points_roi = polys
print(f'ROIS LOADED: \n{points_roi}')
"""
points = [(12.668639053254438, 281.6568047337278),
          (115.62721893491124, 197.0414201183432),
          (396.10059171597635, 193.49112426035504),
          (519.1775147928994, 485.79881656804736),
          (12.668639053254438, 495.85798816568047)]
"""
points_resized = (np.int32(np.int32(points)/2)) # rescale points as the frame is resized from 1920 px to 960 px

polygon = [np.int32(points)]



# initialize our centroid tracker and frame dimensions -- added for centroid tracker
ct = CentroidTracker()
(H, W) = (None, None)

# initialize colors
GREEN = (0 , 255, 0)
RED = (0, 0, 255)
BLUE = (255, 0 ,0)
BLACK = (0, 0, 0)
WHITE = (255, 255, 255)

# Define the codec and create VideoWriter object.The output is stored in 'output.avi' file.
out = cv2.VideoWriter(filename, fourcc, 25.0, (960, 540))
out_det = cv2.VideoWriter(filename_d, fourcc, 25.0, (960, 540))