def detect(q1, q2):
model_config = parser.parse_args()
print("Loading detector")
model = "ssd_mobilenet_v2_coco"
conf_th = 0.3
INPUT_HW = (300, 300)
cls_dict = get_cls_dict("coco")
vis = BBoxVisualization(cls_dict)
trt_ssd = TrtSSD(model, INPUT_HW)
print("Loading detector complete")
if model_config.ui == 1:
cv2.startWindowThread()
cv2.namedWindow("window")
while 1:
try:
frame, frame_time = q1.get()
delay = time.time() - frame_time
if delay > 0.4:
print("Skipping frame")
continue
boxes, confs, clss = trt_ssd.detect(frame, conf_th)
print([get_cls_dict("coco")[c] for c in clss])
if model_config.ui == 1:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img = vis.draw_bboxes(frame, boxes, confs, clss)
cv2.imshow('window', img[..., ::-1])
except Exception as e:
traceback.print_exc()
def main():
args = parse_args()
#YOLO INIT
#cls_dict = get_cls_dict('coco')
cls_dict = get_cls_dict('deepfamily')
print("classes count : ", len(cls_dict))
yolo_dim = int(args.model.split('-')[-1]) # 416 or 608
print("yolo_dim : ", yolo_dim)
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim))
#CAMERA
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cam.start()
#CAM-WINDOW
open_window(WINDOW_NAME, args.image_width, args.image_height,
'DEEPFAMILY PROJECT - TensorRT YOLOv3')
vis = BBoxVisualization(cls_dict)
#DETECT-LOOP
loop_and_detect(cam, trt_yolov3, conf_th=0.95, vis=vis)
#loop_and_detect(cam, trt_yolov3, conf_th=0.95)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict('coco')
yolo_dim = int(args.model.split('-')[-1]) # 416 or 608
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim))
cam.start()
# open_window(WINDOW_NAME, args.image_width, args.image_height,
# 'Camera TensorRT YOLOv3 Demo')
vis = BBoxVisualization(cls_dict)
# for video
# loop_and_detect(cam, trt_yolov3, conf_th=0.3, vis=vis)
# for single file
detect_demo(cam, trt_yolov3, conf_th=0.3, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
global THREAD_RUNNING
args = parse_args()
cap = open_cam_rtsp(args.cam_name, args.cam_password, args.cam_ip)
if not cap.isOpened():
sys.exit('Failed to open camera!')
#抓取图像子进程
THREAD_RUNNING = True
th = threading.Thread(target=grab_img, args=(cap, ))
th.start()
#目标识别
cls_dict = get_cls_dict('coco')
yolo_dim = int(args.model.split('-')[-1]) # 416 or 608
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim))
vis = BBoxVisualization(cls_dict)
#考虑对roi区域裁剪并resize
loop_and_detect(trt_yolov3, conf_th=0.3, vis=vis)
#关闭图像子进程
THREAD_RUNNING = False
th.join()
cap.release()
cv2.destroyAllWindows()
def __init__(self, input_stream: cv.VideoCapture, camera_gps: np.ndarray):
# todo: add verbosity/display mode flag
"""Constructor for ClassName"""
self.input_stream = input_stream
self.camera_gps = camera_gps
self.normalized_gps_df = pd.DataFrame(dtype=np.float64)
self.sampling_time = 0.05 # todo: change to use FPS
# initialize constants
self.SCALE_FACTOR = 1000.0
# yolo model specific constants
self.target_name_dict = {0: 'TargetShip'}
self.vis = BBoxVisualization(self.target_name_dict)
self.conf_th = 0.5
self.display_mode = False # verbosity flag
self.model = 'yolov4-416'
self.model_height = 416
self.model_width = 416
self.model_shape = (self.model_height, self.model_width)
self.category_number = 80
self.trt_yolo = TrtYOLO(self.model, self.model_shape,
self.category_number)
self.a_lim = 720.0
self.b_lim = 1280.0 / 2.0
# todo: load the models here and save them as constants
super(AitasInterface, self).__init__()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
if not os.path.isfile('yolo/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolo/%s.trt) not found!' % args.model)
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
cls_dict = get_cls_dict(args.category_num)
yolo_dim = args.model.split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
w, h = int(dim_split[0]), int(dim_split[1])
else:
h = w = int(yolo_dim)
if h % 32 != 0 or w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num)
open_window(WINDOW_NAME, 'Camera TensorRT YOLO Demo', 640, 480)
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_yolo, conf_th=0.3, vis=vis)
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
if not os.path.isfile('yolo/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolo/%s.trt) not found!' % args.model)
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
cls_dict = get_cls_dict(args.category_num)
vis = BBoxVisualization(cls_dict)
h, w = get_input_shape(args.model)
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num, args.letter_box)
open_window(WINDOW_NAME, 'Camera TensorRT YOLO Demo', cam.img_width,
cam.img_height)
msg_queue = Queue(maxsize=100)
# msg_queue.put("0,0,0,-1".encode())
Thread(target=serArd, args=(msg_queue, )).start()
loop_and_detect(cam, trt_yolo, msg_queue, conf_th=0.7, vis=vis)
while True:
pass
cam.release()
cv2.destroyAllWindows()
def main():
print(f"{datetime.datetime.now().isoformat()} start!", flush=True)
args = parse_args()
if args.category_num <= 0:
raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
if not os.path.isfile('yolo/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolo/%s.trt) not found!' % args.model)
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
cls_dict = get_cls_dict(args.category_num)
yolo_dim = args.model.split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
w, h = int(dim_split[0]), int(dim_split[1])
else:
h = w = int(yolo_dim)
if h % 32 != 0 or w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num)
# open_window(
# WINDOW_NAME, 'Camera TensorRT YOLO Demo',
# cam.img_width, cam.img_height)
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_yolo, conf_th=0.3, vis=vis)
cam.release()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit('Bad category_num: %d!' % args.category_num)
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict(args.category_num)
yolo_dim = int(args.model.split('-')[-1])
if yolo_dim not in (288, 416, 608):
raise SystemExit('Bad yolo_dim: %d!\nPlease make sure the model file name contains the correct dimension...' % yolo_dim)
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim), args.category_num)
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT YOLOv3 Demo')
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_yolov3, conf_th=0.3, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
if not os.path.isfile('yolo/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolo/%s.trt) not found!' % args.model)
cap = cv2.VideoCapture(args.video_name)
if (cap.isOpened() == False):
print("unable to read read source video feed")
cls_dict = get_cls_dict(args.category_num)
yolo_dim = args.model.split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
w, h = int(dim_split[0]), int(dim_split[1])
else:
h = w = int(yolo_dim)
if h % 32 != 0 or w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num)
vis = BBoxVisualization(cls_dict)
loop_and_detect(cap, trt_yolo, args.result_video, conf_th=0.3, vis=vis)
cv2.destroyAllWindows()
def main():
args = parse_args()
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict(args.model)
trt_ssd = TrtSSD(args.model, INPUT_HW)
cam.start()
if args.use_console:
loop_and_detect_console(cam,
trt_ssd,
conf_th=0.3,
loop=args.loop,
cls_dict=cls_dict)
else:
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT SSD Demo for Jetson Nano')
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_ssd, conf_th=0.3, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
global THREAD_RUNNING
cuda.init() # init pycuda driver
args = parse_args()
cap = open_cam_rtsp(args.cam_name, args.cam_password, args.cam_ip)
if not cap.isOpened():
sys.exit('Failed to open camera!')
#抓取图像子进程
THREAD_RUNNING = True
th = threading.Thread(target=grab_img, args=(cap, ))
th.start()
#目标识别
cls_dict = get_cls_dict(args.model.split('_')[-1])
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT SSD Demo for Jetson Nano')
vis = BBoxVisualization(cls_dict)
condition = threading.Condition()
global IMG_HANDLE
trt_thread = TrtThread(condition, IMG_HANDLE, args.model, conf_th=0.3)
trt_thread.start() # start the child thread
loop_and_display(condition, vis)
trt_thread.stop() # stop the child thread
#关闭图像子进程
THREAD_RUNNING = False
th.join()
cap.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
if not os.path.isfile('yolo/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolo/%s.trt) not found!' % args.model)
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
cls_dict = get_cls_dict(args.category_num)
vis = BBoxVisualization(cls_dict)
h, w = get_input_shape(args.model)
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num, args.letter_box)
mjpeg_server = MjpegServer(port=args.mjpeg_port)
print('MJPEG server started...')
try:
loop_and_detect(cam,
trt_yolo,
conf_th=0.3,
vis=vis,
mjpeg_server=mjpeg_server)
except Exception as e:
print(e)
finally:
mjpeg_server.shutdown()
cam.release()
def main():
args = parse_args()
cls_dict = get_cls_dict('coco')
yolo_dim = int(args.model.split('-')[-1]) # 416 or 608
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim))
vis = BBoxVisualization(cls_dict)
check(trt_yolov3, conf_th=0.3, vis=vis)
def main():
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Ask tensorflow logger not to propagate logs to parent (which causes
# duplicated logging)
logging.getLogger('tensorflow').propagate = False
args = parse_args()
logger.info('called with args: %s' % args)
# build the class (index/name) dictionary from labelmap file
logger.info('reading label map')
cls_dict = read_label_map(args.labelmap_file)
pb_path = './data/{}_trt.pb'.format(args.model)
log_path = './logs/{}_trt'.format(args.model)
if args.do_build:
logger.info('building TRT graph and saving to pb: %s' % pb_path)
build_trt_pb(args.model, pb_path)
logger.info('opening camera device/file')
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
logger.info('loading TRT graph from pb: %s' % pb_path)
trt_graph = load_trt_pb(pb_path)
logger.info('starting up TensorFlow session')
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
#tf_sess = tf.Session(config=tf_config, graph=trt_graph)
tf_sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True),graph=trt_graph)
if args.do_tensorboard:
logger.info('writing graph summary to TensorBoard')
write_graph_tensorboard(tf_sess, log_path)
logger.info('warming up the TRT graph with a dummy image')
od_type = 'faster_rcnn' if 'faster_rcnn' in args.model else 'ssd'
dummy_img = np.zeros((720, 1280, 3), dtype=np.uint8)
_, _, _ = detect(dummy_img, tf_sess, conf_th=.3, od_type=od_type)
cam.start() # ask the camera to start grabbing images
# grab image and do object detection (until stopped by user)
logger.info('starting to loop and detect')
vis = BBoxVisualization(cls_dict)
open_display_window(cam.img_width, cam.img_height)
loop_and_detect(cam, tf_sess, args.conf_th, vis, od_type=od_type)
logger.info('cleaning up')
cam.stop() # terminate the sub-thread in camera
tf_sess.close()
cam.release()
cv2.destroyAllWindows()
def init_yolo(self):
""" Initialises yolo parameters required for trt engine """
if self.model.find('-') == -1:
self.model = self.model + "-" + self.input_shape
yolo_dim = self.model.split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
self.w, self.h = int(dim_split[0]), int(dim_split[1])
else:
self.h = self.w = int(yolo_dim)
if self.h % 32 != 0 or self.w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
cls_dict = get_cls_dict(self.category_num)
self.vis = BBoxVisualization(cls_dict)
def main():
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Ask tensorflow logger not to propagate logs to parent (which causes
# duplicated logging)
logging.getLogger('tensorflow').propagate = False
args = parse_args()
logger.info('called with args: %s' % args)
# build the class (index/name) dictionary from labelmap file
logger.info('reading label map')
cls_dict = read_label_map(args.labelmap_file)
pb_path = './data/{}_trt.pb'.format(args.model)
log_path = './logs/{}_trt'.format(args.model)
if args.do_build:
logger.info('building TRT graph and saving to pb: %s' % pb_path)
build_trt_pb(args.model, pb_path)
logger.info('opening camera device/file')
infile=args.filename
cam=cv2.VideoCapture(infile)
size=(int(cam.get(cv2.CAP_PROP_FRAME_WIDTH)),int(cam.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fps=cam.get(cv2.CAP_PROP_FPS)
writer=cv2.VideoWriter('_res.'.join([f for f in infile.split('.')]),cv2.VideoWriter_fourcc(*'mp4v'), fps, size)
logger.info('loading TRT graph from pb: %s' % pb_path)
trt_graph = load_trt_pb(pb_path)
logger.info('starting up TensorFlow session')
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
tf_sess = tf.Session(config=tf_config, graph=trt_graph)
if args.do_tensorboard:
logger.info('writing graph summary to TensorBoard')
write_graph_tensorboard(tf_sess, log_path)
logger.info('warming up the TRT graph with a dummy image')
od_type = 'faster_rcnn' if 'faster_rcnn' in args.model else 'ssd'
dummy_img = np.zeros((720, 1280, 3), dtype=np.uint8)
_, _, _ = detect(dummy_img, tf_sess, conf_th=.3, od_type=od_type)
# grab image and do object detection (until stopped by user)
logger.info('starting to loop and detect')
vis = BBoxVisualization(cls_dict)
#open_display_window(cam.img_width, cam.img_height)
loop_and_detect(cam, tf_sess, 0.2, vis, od_type=od_type,writer=writer)
logger.info('cleaning up')
tf_sess.close()
cam.release()
writer.release()
def main_one():
filename = "../../1.jpg"
result_file_name = str(filename)
img = cv2.imread(filename)
cls_dict = get_cls_dict()
model_name = "../../TrafficCamNet/trafficnet_int8_m1b1.engine"
traCamNet = TrtTrafficCamNet(model_name, INPUT_HW)
vis = BBoxVisualization(cls_dict)
print("start detection!")
detect_one(img, traCamNet, conf_th=0.30, vis=vis)
print("finish!")
def create_detect_result():
#filename = "test_face.jpg"
#result_file_name = str(filename)
dir = "val/images"
#img = cv2.imread(filename)
cls_dict = get_cls_dict("ssd_mobilenet_v2_signs".split('_')[-1])
print(cls_dict)
model_name = "ssd_mobilenet_v2_signs"
trt_ssd = TrtSSD(model_name, INPUT_HW)
vis = BBoxVisualization(cls_dict)
print("start detection!")
detect_dir(dir, trt_ssd, conf_th=0.2, vis=vis)
cv2.destroyAllWindows()
print("finish!")
def yolo_detection():
# dev = cuda.Device(0)
# ctx = dev.make_context()
args = parse_args()
print(args)
"""
config assert
"""
if args.category_num <= 0:
raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
if not os.path.isfile('yolo/darknet/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolo/darknet/%s.trt) not found!' %
args.model)
cls_dict = get_cls_dict(args.category_num)
yolo_dim = args.model.split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
w, h = int(dim_split[0]), int(dim_split[1])
else:
h = w = int(yolo_dim)
if h % 32 != 0 or w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
"""
capture the image
"""
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
"""
deploy the yolo model
"""
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num)
# open_window(
# WINDOW_NAME, 'Camera TensorRT YOLO Demo',
# cam.img_width, cam.img_height)
"""
detect the insulator using model
"""
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_yolo, conf_th=0.3, vis=vis)
"""
release the image
"""
cam.release()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit(f'ERROR: bad category_num ({args.category_num})!')
if not os.path.isfile(args.model):
raise SystemExit(f'ERROR: file {args.model} not found!')
# Process valid coco json file
process_valid_json(args.valid_coco)
if args.write_images:
if not os.path.exists(args.image_output): os.mkdir(args.image_output)
# Create camera for video/image input
cam = Camera(args)
if not cam.get_is_opened():
raise SystemExit('ERROR: failed to open camera!')
class_dict = get_cls_dict(args.category_num)
yolo_dim = (args.model.replace(".trt", "")).split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit(f'ERROR: bad yolo_dim ({yolo_dim})!')
w, h = int(dim_split[0]), int(dim_split[1])
else:
h = w = int(yolo_dim)
if h % 32 != 0 or w % 32 != 0:
raise SystemExit(f'ERROR: bad yolo_dim ({yolo_dim})!')
# Create yolo
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num)
if args.activate_display:
open_window(WINDOW_NAME, 'Camera TensorRT YOLO Demo', cam.img_width,
cam.img_height)
visual = BBoxVisualization(class_dict)
# Run detection
loop_and_detect(cam,
trt_yolo,
args,
confidence_thresh=args.confidence_threshold,
visual=visual)
# Clean up
cam.release()
if args.activate_display:
cv2.destroyAllWindows()
def main():
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Ask tensorflow logger not to propagate logs to parent (which causes
# duplicated logging)
logging.getLogger('tensorflow').propagate = False
# build the class (index/name) dictionary from labelmap file
logger.info('reading label map')
cls_dict = read_label_map(DEFAULT_LABELMAP)
pb_path = './data/{}_trt.pb'.format(DEFAULT_MODEL)
log_path = './logs/{}_trt'.format(DEFAULT_MODEL)
logger.info('opening camera device/file')
url1 = 'http://pi1.local:8000/stream.mjpg'
url2 = 'http://pi2.local:8000/stream.mjpg' #'http://raspi3bp.local:4000/stream.mjpg'
url3 = 'http://pi3.local:8000/stream.mjpg' #'http://picamblack.local:5000/stream.mjpg'
url4 = 'http://barcodepi.local:8000/stream.mjpg' #'http://picam201902.local:3000/stream.mjpg'
threaded = True
stream_handler = VideoStreamHandler([url1, url2, url3, url4],
threaded=threaded,
resolution=(360, 640))
logger.info('loading TRT graph from pb: %s' % pb_path)
trt_graph = load_trt_pb(pb_path)
logger.info('starting up TensorFlow session')
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
tf_sess = tf.Session(config=tf_config, graph=trt_graph)
logger.info('warming up the TRT graph with a dummy image')
od_type = 'ssd'
dummy_img = np.zeros((720, 1280, 3), dtype=np.uint8)
_, _, _ = detect(dummy_img, tf_sess, conf_th=.3, od_type=od_type)
# grab image and do object detection (until stopped by user)
logger.info('starting to loop and detect')
vis = BBoxVisualization(cls_dict)
open_display_window(1280, 720)
#if threaded:
#stream_handler.start()
loop_and_detect(stream_handler, tf_sess, 0.2, vis, od_type=od_type)
stream_handler.close()
logger.info('cleaning up')
tf_sess.close()
stream_handler.join_streams()
cv2.destroyAllWindows()
def main():
model = "yolov3-tiny-covid"
category_num = 5
conf_th = 0.3
input_size = 608
cls_dict = get_cls_dict(category_num)
vis = BBoxVisualization(cls_dict)
#args = parse_args()
detector = yoloRT(model, input_size, vis, conf_th, category_num)
pub = rospy.Publisher('people_detection', Bool, queue_size=5)
rospy.init_node('people_detector', anonymous=True)
sub = rospy.Subscriber('front_camera/color/image_raw', Image,
detector.detect)
#pipeline = camera_config(sys.argv)
try:
rospy.spin()
except KeyboardInterrupt:
print("Shutting down")
cls_dict = get_cls_dict(category_num)
yolo_dim = model.split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
w, h = int(dim_split[0]), int(dim_split[1])
else:
h = w = int(608)
if h % 32 != 0 or w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
trt_yolov3 = TrtYOLOv3(model, (h, w), category_num)
vis = BBoxVisualization(cls_dict)
loop_and_detect(pub, pipeline, trt_yolov3, conf_th=0.3, vis=vis)
pipeline.stop()
def main():
# Parse arguments and get input
args = parse_args()
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
# Create NN1 and NN2 models and load into memory
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
mtcnn = TrtMtcnn()
# Create Preview Window
open_window(WINDOW_NAME, 'Camera Preview', cam.img_width, cam.img_height)
vis = BBoxVisualization(cls_dict)
# Enter Detection Mode
while True:
# Get Image
img = cam.read()
out.write(img)
nn1_results = []
# Run Neural Networks
img, nn1_results, nn2_results, nn3_results = loop_and_detect(
img, mtcnn, args.minsize, trt_ssd, conf_th=0.3, vis=vis)
# Communicate to Arduino
if (nn1_results != []):
img = robot_drive(img, nn1_results)
else:
serial_port.write("N".encode())
print("N")
# Display and save output
cv2.imshow(WINDOW_NAME, img)
outNN.write(img)
# User/Keyboard Input
key = cv2.waitKey(1)
if key == ord('q'):
out.release()
outNN.release()
break
# Clean up and exit
cam.release()
cv2.destroyAllWindows()
serial_port.close()
def main():
args = parse_args()
cam = Camera(args)
if not cam.get_is_opened():
raise SystemExit('ERROR: failed to open camera!')
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
open_window(WINDOW_NAME, 'Camera TensorRT SSD Demo', cam.img_width,
cam.img_height)
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_ssd, conf_th=0.3, vis=vis)
cam.release()
cv2.destroyAllWindows()
def main_loop():
filename = "../../test-video2.mp4"
result_file_name = str(filename)
video = cv2.VideoCapture(filename)
cls_dict = get_cls_dict()
model_name = "../../TrafficCamNet/trafficnet_int8_m1b1.engine"
trt = TrtTrafficCamNet(model_name, INPUT_HW)
vis = BBoxVisualization(cls_dict)
print("start detection!")
detect_video(video,
trt,
conf_th=0.3,
vis=vis,
result_file_name=result_file_name)
video.release()
cv2.destroyAllWindows()
print("\nfinish!")
def main():
# Parse arguments and get input
args = parse_args()
# Create NN1 and NN2 models
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
mtcnn = TrtMtcnn()
# Create Preview Window
vis = BBoxVisualization(cls_dict)
imageNum = 10
# Enter Detection Mode
while True:
# Get Image
imageName = "/home/nvidia/Pictures/test13.jpg"
#imageName = "/media/E76F-73E0/Faces/1 (" + str(imageNum) + ").jpg"
#imageName = "/home/nvidia/Pictures/Benchmarks/Pedestrians/PennPed000" + str(imageNum) + ".png"
imageNum = imageNum + 1
#print(imageName)
img = cv2.imread(imageName)
cv2.imshow(WINDOW_NAME, img)
# Run Neural Networks
img, nn1_results, nn2_results, nn3_results = loop_and_detect(
img, mtcnn, args.minsize, trt_ssd, conf_th=0.3, vis=vis)
# Display Results
cv2.imshow(WINDOW_NAME, img)
#cv2.waitKey(0)
# User/Keyboard Input
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
# Clean up and exit
cv2.destroyAllWindows()
serial_port.close()
def main(args, cam):
# args = parse_args()
is_open = up()
time.sleep(60)
# cam = Camera(args)
if is_open:
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
cam.start()
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_ssd, conf_th=0.9, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
if not os.path.isfile('yolo/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolo/%s.trt) not found!' % args.model)
client = init_mqtt(args.host, args.port)
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
cls_dict = get_cls_dict(args.category_num)
print("cls_dict:", cls_dict)
#print(cls_dict[3])
yolo_dim = args.model.split('-')[-1]
print("yolo_dim:", yolo_dim)
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
w, h = int(dim_split[0]), int(dim_split[1])
else:
h = w = int(yolo_dim)
print('w:{0}, h:{1}'.format(w, h))
if h % 32 != 0 or w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num, args.letter_box)
open_window(WINDOW_NAME, 'Camera TensorRT YOLO Demo', cam.img_width,
cam.img_height)
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_yolo, conf_th=0.3, vis=vis, \
cls_dict=cls_dict, client=client, topic=args.topic)
cam.release()
cv2.destroyAllWindows()
client.disclose()
def main():
args = parse_args()
cam = Camera(args)
if not cam.get_is_opened():
raise SystemExit('ERROR: failed to open camera!')
cuda.init() # init pycuda driver
cls_dict = get_cls_dict(args.model.split('_')[-1])
open_window(WINDOW_NAME, 'Camera TensorRT SSD Demo', cam.img_width,
cam.img_height)
vis = BBoxVisualization(cls_dict)
condition = threading.Condition()
trt_thread = TrtThread(condition, cam, args.model, conf_th=0.3)
trt_thread.start() # start the child thread
loop_and_display(condition, vis)
trt_thread.stop() # stop the child thread
cam.release()
cv2.destroyAllWindows()