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 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():
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 = './ssd_mobilenet_v1_coco_people/{}_trt.pb'.format(args.model)
log_path = './logs/{}_trt'.format(args.model)
print(log_path)
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('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)
od_type = 'ssd'
logger.info('opening cameras')
cam1 = cv2.VideoCapture(0)
cam2 = cv2.VideoCapture(1)
logger.info('starting to loop and detect')
vis = BBoxVisualization(cls_dict)
show_fps1 = True
fps1 = 0.0
show_fps2 = True
fps2 = 0.0
tic1 = time.time()
tic2 = time.time()
# grab images and do object detections (until stopped by user)
while True:
ret1, frame1 = cam1.read()
ret2, frame2 = cam2.read()
if ret1 and ret2:
box1, conf1, cls1 = detect(frame1,
tf_sess,
args.conf_th,
od_type=od_type)
box2, conf2, cls2 = detect(frame2,
tf_sess,
args.conf_th,
od_type=od_type)
frame1 = vis.draw_bboxes(frame1, box1, conf1, cls1)
frame2 = vis.draw_bboxes(frame2, box2, conf2, cls2)
if show_fps1:
frame1 = draw_help_and_fps(frame1, fps1)
cv2.imshow(WINDOW_NAME1, frame1)
toc1 = time.time()
curr_fps1 = 1.0 / (toc1 - tic1)
# calculate an exponentially decaying average of fps number
fps1 = curr_fps1 if fps1 == 0.0 else (fps1 * 0.9 + curr_fps1 * 0.1)
tic1 = toc1
if show_fps2:
frame2 = draw_help_and_fps(frame2, fps2)
cv2.imshow(WINDOW_NAME2, frame2)
toc2 = time.time()
curr_fps2 = 1.0 / (toc2 - tic2)
# calculate an exponentially decaying average of fps number
fps2 = curr_fps2 if fps2 == 0.0 else (fps2 * 0.9 + curr_fps2 * 0.1)
tic2 = toc2
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
elif key == ord('F') or key == ord('f'): # Toggle fps
show_fps1 = not show_fps1
logger.info('cleaning up and closing cameras')
tf_sess.close()
cam1.release()
cam2.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
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 = './ssd_mobilenet_v1_coco_people/{}_trt.pb'.format(args.model)
log_path = './logs/{}_trt'.format(args.model)
print(log_path)
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')
#cam1 = Camera(args)
#cam1.open()
#if not cam1.is_opened:
# sys.exit('Failed to open camera #1!')
#cam1.start() # ask the camera to start grabbing images
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)
od_type = 'ssd'
cam2 = cv2.VideoCapture(1)
logger.info('starting to loop and detect')
vis = BBoxVisualization(cls_dict)
show_fps1 = True
fps1 = 0.0
tic1 = time.time()
while True:
ret2, frame2 = cam2.read()
if ret2:
box, conf, cls = detect(frame2, tf_sess, args.conf_th, od_type=od_type)
frame2 = vis.draw_bboxes(frame2, box, conf, cls)
if show_fps1:
frame2 = draw_help_and_fps(frame2, fps1)
cv2.imshow(WINDOW_NAME2, frame2)
toc1 = time.time()
curr_fps1 = 1.0 / (toc1 - tic1)
# calculate an exponentially decaying average of fps number
fps1 = curr_fps1 if fps1 == 0.0 else (fps1*0.9 + curr_fps1*0.1)
tic1 = toc1
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
elif key == ord('H') or key == ord('h'): # Toggle help/fps
show_fps1 = not show_fps1
# grab image and do object detection (until stopped by user)
#open_display_window(cam1.img_width, cam1.img_height)
#open_display_window(args.image_width, args.image_height)
#loop_and_detect(cam1, tf_sess, args.conf_th, vis, od_type=od_type)
#loop_and_detect(cam2, tf_sess, args.conf_th, vis, od_type=od_type)
logger.info('cleaning up')
#cam1.stop() # terminate the sub-thread in camera
tf_sess.close()
#cam1.release()
cam2.release()
cv2.destroyAllWindows()
