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()
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
trt_ssd = TrtSSD(args.model)
cam.start()
# initialize bot
logger.info('initialize robot')
robot = Robot()
logger.info('starting to loop and detect')
loop_and_detect(cam=cam,
trt_ssd=trt_ssd,
conf_th=0.3,
robot=robot,
model=args.model)
logger.info('cleaning up')
robot.stop()
cam.stop()
cam.release()
def main():
args = parse_args()
cam = Camera(args)
cam.open()
# import pdb
# pdb.set_trace()
if not cam.is_opened:
sys.exit('[INFO] 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))
print('[INFO] Camera: starting')
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'TensorRT YOLOv3 Detector')
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam,
args.runtime,
trt_yolov3,
conf_th=0.3,
vis=vis,
window_name=WINDOW_NAME)
print('[INFO] Program: stopped')
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)
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)
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()
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():
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():
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():
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():
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
global args
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) -- Vincent
#Solve : "unable to satfisfy explicit device /dev/CPU:0 -- Vincent
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_height, cam.img_width)
result = 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 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():
args = parse_args()
cam = Camera(args)
if not cam.get_is_opened():
raise SystemExit('ERROR: failed to open camera!')
mtcnn = TrtMtcnn()
open_window(
WINDOW_NAME, 'Camera TensorRT MTCNN Demo for Jetson Nano',
cam.img_width, cam.img_height)
loop_and_detect(cam, mtcnn, args.minsize)
cam.release()
cv2.destroyAllWindows()
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():
args = parse_args()
labels = np.loadtxt('googlenet/synset_words.txt', str, delimiter='\t')
cam = Camera(args)
if not cam.get_is_opened():
raise SystemExit('ERROR: failed to open camera!')
# initialize the tensorrt googlenet engine
net = PyTrtGooglenet(DEPLOY_ENGINE, ENGINE_SHAPE0, ENGINE_SHAPE1)
open_window(WINDOW_NAME, 'Camera TensorRT GoogLeNet Demo', cam.img_width,
cam.img_height)
loop_and_classify(cam, net, labels, args.crop_center)
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
labels = np.loadtxt('googlenet/synset_words.txt', str, delimiter='\t')
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
open_window(WINDOW_NAME, 'Camera TensorRT GoogLeNet Demo', cam.img_width,
cam.img_height)
condition = threading.Condition()
trt_thread = TrtGooglenetThread(condition, cam, labels, args.crop_center)
trt_thread.start() # start the child thread
loop_and_display(condition)
trt_thread.stop() # stop the child thread
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!')
mtcnn = TrtMtcnn()
cam.start()
open_window(WINDOW_NAME, width=640, height=480, title='MTCNN Window')
detect_faces(cam, mtcnn)
cam.stop()
cam.release()
cv2.destroyAllWindows()
del mtcnn
def main():
args = parse_args()
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
mtcnn = TrtMtcnn()
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT MTCNN Demo for Jetson TX2')
loop_and_detect(cam, mtcnn, args.minsize)
cam.stop()
cam.release()
cv2.destroyAllWindows()
del (mtcnn)
def main():
args = parse_args()
labels = np.loadtxt('googlenet/synset_words.txt', str, delimiter='\t')
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cam.start() # let camera start grabbing frames
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT GoogLeNet Demo for Jetson Nano')
condition = threading.Condition()
trt_thread = TrtGooglenetThread(condition, cam, labels, args.crop_center)
trt_thread.start() # start the child thread
loop_and_display(condition)
trt_thread.stop() # stop the child thread
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()
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)
trt_yolo = TrtYOLO(args.model, args.category_num, args.letter_box)
open_window(WINDOW_NAME, 'Camera TensorRT YOLO Demo', cam.img_width,
cam.img_height)
loop_and_detect(cam, trt_yolo, conf_th=0.3, vis=vis)
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
cam = Camera(args)
is_open = up()
#time.sleep(60)
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()
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=0.9, 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)
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)
vis = BBoxVisualization(cls_dict)
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()
if not os.path.isfile('retinaface/%s.trt' % args.model):
raise SystemExit('ERROR: file (retinaface/%s.trt) not found!' % args.model)
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
cfg = cfg_mnet
input_size = args.model.split('-')[-1]
input_shape = (int(input_size), int(input_size))
priorbox = PriorBox(cfg, input_shape)
priors = priorbox.forward()
trt_retinaface = TRT_RetinaFace(args.model, input_shape)
open_window(
WINDOW_NAME, 'Camera TensorRT Face Detection Demo',
cam.img_width, cam.img_height)
loop_and_detect(cam, trt_retinaface, priors, cfg)
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)
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)
load_weight_start = time.time()
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num)
load_weights_time = datetime.timedelta(seconds=time.time() -
load_weight_start)
print('Load weights Time: %s' % (load_weights_time))
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)
reporter = MemReporter()
reporter.report()
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(args.model.split('_')[-1])
cuda.init() # init pycuda driver
cam.start() # let camera start grabbing frames
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT SSD Demo for Jetson Nano')
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.stop()
cam.release()
cv2.destroyAllWindows()
def loop_and_detect(cam, tf_sess, conf_th, vis, od_type):
"""Loop, grab images from camera, and do object detection.
# Arguments
cam: the camera object (video source).
tf_sess: TensorFlow/TensorRT session to run SSD object detection.
conf_th: confidence/score threshold for object detection.
vis: for visualization.
"""
show_fps = True
full_scrn = False
fps = 0.0
tic = time.time()
tracks = []
global rects, ct, temp, args
frame_buff = 0
none_buff = 0
restart_flag = False
backup_label = None
while True:
#if cv2.getWindowProperty(WINDOW_NAME, 0) < 0:
# Check to see if the user has closed the display window.
# If yes, terminate the while loop.
# break
if (restart_flag == True):
cam = Camera(args)
cam.open()
cam.start()
#pb_path = './data/{}_trt.pb'.format(args.model)
#log_path = './logs/{}_trt'.format(args.model)
#trt_graph = load_trt_pb(pb_path)
#tf_config = tf.ConfigProto()
#tf_config.gpu_options.allow_growth = True
#tf_sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True),graph=trt_graph)
#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)
restart_flag = False
rects = []
img = cam.read()
optical_flow_image = img
if img is not None:
box, conf, cls = detect(img, tf_sess, conf_th, od_type=od_type)
img = vis.draw_bboxes(img, box, conf, cls)
objects = ct.update(rects)
cv2.rectangle(img, (0, 980), (1920, 1075), (0, 0, 0), -1)
for (objectID, centroid) in objects.items():
# draw both the ID of the object and the centroid of the
# object on the output frame
text = "ID {}".format(objectID)
cv2.putText(img, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2)
cv2.circle(img, (centroid[0], centroid[1]), 4, (255, 0, 255),
-1)
backup_label = str(objectID)
cv2.putText(img, backup_label, (330, 1035),
cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 255), 2,
cv2.LINE_AA)
sys_clock = str(
datetime.datetime.now()) + " Frame_buff=" + str(frame_buff)
print(sys_clock)
cv2.putText(img, sys_clock, (20, 950), cv2.FONT_HERSHEY_TRIPLEX, 1,
(0, 0, 255), 2, cv2.LINE_AA)
cv2.putText(img, "Traffic Counter: ", (20, 1035),
cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0, 255), 2,
cv2.LINE_AA)
cv2.putText(img, "Detector Type: Human", (400, 1035),
cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0, 255), 2,
cv2.LINE_AA)
cv2.putText(img, "Real Time Optical Trace :", (900, 1035),
cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0, 255), 2,
cv2.LINE_AA)
cv2.putText(img, "OFF", (1380, 1035), cv2.FONT_HERSHEY_TRIPLEX, 1,
(255, 255, 255), 2, cv2.LINE_AA)
"""
if(frame_buff == 2000):
print("[SYSTEM] VSTARCAMERA Restart")
cam.stop() # terminate the sub-thread in camera
#tf_sess.close()
#tf.reset_default_graph()
#tf.contrib.keras.backend.clear_session()
cam.release()
restart_flag = True
frame_buff = 0
img = None
cv2.destroyAllWindows()
frame_buff += 1
"""
if (restart_flag == False):
if show_fps:
img = draw_help_and_fps(img, fps)
#set_full_screen(full_scrn)
cv2.moveWindow(WINDOW_NAME, 0, 0)
cv2.imshow(WINDOW_NAME, img)
toc = time.time()
curr_fps = 1.0 / (toc - tic)
# calculate an exponentially decaying average of fps number
fps = curr_fps if fps == 0.0 else (fps * 0.9 + curr_fps * 0.1)
tic = toc
else:
print("None Image --> None Buff = {}".format(none_buff))
none_buff += 1
if (none_buff == 500):
print("[SYSTEM] VSTARCAMERA Restart")
cam.stop() # terminate the sub-thread in camera
#tf_sess.close()
#tf.reset_default_graph()
#tf.contrib.keras.backend.clear_session()
cam.release()
restart_flag = True
none_buff = 0
img = None
cv2.destroyAllWindows()
if (restart_flag == False):
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
elif key == ord('H') or key == ord('h'): # Toggle help/fps
show_fps = not show_fps
elif key == ord('F') or key == ord('f'): # Toggle fullscreen
full_scrn = not full_scrn
set_full_screen(full_scrn)
