_, img = vc.read()
if img is None:
break
current_frame_position += 1
img = cv2.resize(
img,
(int(img.shape[1] * scale_factor), int(img.shape[0] * scale_factor)))
# detect objects in frame
bboxes = detector.detect(img)
detections = [] # our list of Detections objects
bb = None
current_time = datetime.datetime.now()
if bboxes is not None and len(bboxes) > 0:
for i, bb in enumerate(bboxes):
det = Detection()
det.bbox = np.array([bb[0], bb[1], bb[2], bb[3]])
det.frame_id = current_frame_position
detections.append(det)
# DRAW DETECTIONS IN GREEN (B, G, R) = (0, 255, 0)
cv2.rectangle(img, (bb[0], bb[1]), (bb[2], bb[3]), (0, 255, 0), 2)
# log the data, recall headers = ['time', 'id', 'x1', 'y1', 'x2', 'y2']
bb_id = i
data = [current_time, bb_id, bb[0], bb[1], bb[2], bb[3]]
logger.log(data=data)
tracker.update_tracks(detections=detections,
frame_id=current_frame_position)
tracker.draw_tracks(img)
cv2.imshow('img', img)
key = cv2.waitKey(1)
# if key == 27:
def run():
# create access to settings object
settings = Settings()
# initiate logging
if settings.ENABLE_LOGGING is True:
if not os.path.exists(settings.LOGGING_DIRECTORY):
os.mkdir(settings.LOGGING_DIRECTORY)
log_filename = os.path.join(settings.LOGGING_DIRECTORY,
settings.OUTPUT_VIDEO_FILENAME + '.csv')
# [current_frame_position, current_time_string, uid, latest_bbox, lidar_distance]
headers = [
'frame#', 'time', 'uid', 'x1', 'y1', 'x2', 'y2', 'lidar_d(ft)',
'grnd_angle_d(ft)'
]
data_logger = DataLogger(filename=log_filename, headers=headers)
vc = cv2.VideoCapture()
if settings.SIMULATION_MODE is True:
vc.open(settings.INPUT_VIDEO_FILE)
else:
vc.open(settings.WEBCAM_ID)
if settings.RECORD_VIDEO is True:
if not os.path.exists(settings.OUTPUT_VIDEO_FOLDER):
os.mkdir(settings.OUTPUT_VIDEO_FOLDER)
# get first frame of video to get recording size
_, frame = vc.read()
# declare video writer obj
fourcc = cv2.VideoWriter_fourcc(*'XVID')
vw = cv2.VideoWriter(
os.path.join(settings.OUTPUT_VIDEO_FOLDER,
settings.OUTPUT_VIDEO_FILENAME + '.avi'), fourcc,
20.0, (frame.shape[1], frame.shape[0]))
detector = CarDetector()
tracker = MultipleObjectTracker()
if settings.SIMULATION_MODE is True:
lidar = LidarSensor(mode='simulation',
simulation_data_file=settings.SIMULATION_FILE)
else:
lidar = LidarSensor()
distance_predictor = DistancePredictor()
scale_factor = 0.75 # reduce frame by this size
tracked_bbox = [] # a list of past bboxes
current_frame_position = 0
while True:
# take a snapshot of the current time
current_time = datetime.datetime.now()
current_time_string = current_time.strftime(
'%Y-%m-%d %H:%M:%S.%f')[:-3]
# first get lidar distance
lidar_distance = lidar.get_distance(
frame_number=current_frame_position)
# now acquire image and get detections
_, img = vc.read()
if img is None:
print 'null image retrieved! frame={}'.format(
current_frame_position)
break
current_frame_position += 1
img = cv2.resize(img, (int(
img.shape[1] * scale_factor), int(img.shape[0] * scale_factor)))
# detect objects in frame
bboxes = detector.detect(img)
detections = [] # our list of Detections objects
bb = None
if bboxes is not None and len(bboxes) > 0:
for i, bb in enumerate(bboxes):
det = Detection()
det.bbox = np.array([bb[0], bb[1], bb[2], bb[3]])
det.frame_id = current_frame_position
detections.append(det)
# DRAW DETECTIONS IN GREEN (B, G, R) = (0, 255, 0)
cv2.rectangle(img, (bb[0], bb[1]), (bb[2], bb[3]), (0, 255, 0),
2)
# log the data, recall headers = ['time', 'id', 'x1', 'y1', 'x2', 'y2']
bb_id = i
data = [current_time, bb_id, bb[0], bb[1], bb[2], bb[3]]
tracker.update_tracks(detections=detections,
frame_id=current_frame_position)
# create a list of the current row for logging
logging_data = []
# get the "head" of each track
current_tracks = tracker.get_track_heads()
for current_track in current_tracks:
latest_bbox = current_track.get_latest_bb()
# get predicted distances from bounding box
predicted_distances = distance_predictor.predict(latest_bbox)
uid = current_track.uid
logging_row = [
current_frame_position, current_time_string, uid,
latest_bbox[0], latest_bbox[1], latest_bbox[2], latest_bbox[3],
lidar_distance, predicted_distances[0]
]
logging_data.append(logging_row)
if settings.ENABLE_LOGGING is True:
data_logger.log(logging_data)
tracker.draw_tracks(img)
# draw lidar distance
cv2.putText(img, 'lidar_dist = ' + str(int(lidar_distance)) + 'ft',
(0, 25), 1, 1.5, (255, 0, 0), 2)
cv2.imshow('img', img)
key = cv2.waitKey(1)
if key == 27:
break
if settings.RECORD_VIDEO is True:
# rescale image back to original shape
img = cv2.resize(img, (int(img.shape[1] / scale_factor),
int(img.shape[0] / scale_factor)))
vw.write(img)