def main(): args = argparse.ArgumentParser() add_camera_opener_options(args) args.add_argument("-d", "--device", type=str, default='MYRIAD', help='on Device') args.add_argument("-m", "--mode", type=int, default=1, help="ModeNo.") args.add_argument("-n", "--name", type=str, default='MODE_5_45FPS_500', help="ModeName.") args.add_argument("-q", "--queues", type=int, default=33 * 5, help="Depth of Queue.") args.add_argument("-ud", "--usedepth", action='store_true', help="full highlight for objects") args = args.parse_args() flexx(args)
def main (): platformhelper = PlatformHelper() parser = argparse.ArgumentParser (usage = __doc__) add_camera_opener_options (parser) parser.add_argument ("--frames", type=int, required=True, help="duration to capture data (number of frames)") parser.add_argument ("--output", type=str, required=True, help="filename to record to") parser.add_argument ("--skipFrames", type=int, default=0, help="frameSkip argument for the API method") parser.add_argument ("--skipMilliseconds", type=int, default=0, help="msSkip argument for the API method") options = parser.parse_args() opener = CameraOpener (options) cam = opener.open_camera () print_camera_info (cam) l = MyListener() cam.registerRecordListener(l) cam.startCapture() cam.startRecording (options.output, options.frames, options.skipFrames, options.skipMilliseconds); seconds = options.frames * (options.skipFrames + 1) / cam.getFrameRate() if options.skipMilliseconds: timeForSkipping = options.frames * options.skipMilliseconds / 1000 seconds = int (max (seconds, timeForSkipping)) print ("Capturing with the camera running at {rate} frames per second".format (rate=cam.getFrameRate())) print ("This is expected to take around {seconds} seconds".format (seconds=seconds)) l.waitForStop() cam.stopCapture()
def main(): platformhelper = PlatformHelper() parser = argparse.ArgumentParser(usage=__doc__) add_camera_opener_options(parser) parser.add_argument("-s", "--seconds", type=int, default=15, help="duration to capture data") parser.add_argument("-n", "--name", type=str, default='MODE_5_45FPS_500', help="mode name") options = parser.parse_args() opener = CameraOpener(options) cam = opener.open_camera() cam.setUseCase(options.name) print_camera_info(cam) print("isConnected", cam.isConnected()) print("getFrameRate", cam.getFrameRate()) # we will use this queue to synchronize the callback with the main # thread, as drawing should happen in the main thread q = queue.Queue() xq = queue.Queue() yq = queue.Queue() l = MyListener(q, xq, yq) cam.registerDataListener(l) cam.startCapture() # create a loop that will run for a time (default 15 seconds) process_event_queue(q, xq, yq, l, options.seconds) cam.stopCapture()
def main (): try: rospy.init_node('pico_flexx') r = rospy.Rate(10) parser = argparse.ArgumentParser (usage = __doc__) add_camera_opener_options (parser) parser.add_argument ("--seconds", type=int, default=15, help="duration to capture data") options = parser.parse_args() opener = CameraOpener (options) cam = opener.open_camera () print_camera_info (cam) print("isConnected", cam.isConnected()) print("getFrameRate", cam.getFrameRate()) # we will use this queue to synchronize the callback with the main # thread, as drawing should happen in the main thread q = queue.Queue(maxsize=0) l = MyListener(q) cam.registerDataListener(l) cam.startCapture() while not rospy.is_shutdown(): r.sleep() cam.stopCapture() except rospy.ROSInterruptException: pass
def get_parser_options(proj_desc: str): """Returns an argument parser than can be used to parse command-line arguments""" parser = argparse.ArgumentParser(description=proj_desc, usage=__doc__) add_camera_opener_options(parser) parser.add_argument("--external_trig", '-e', action="store_true") return parser
def __init__(self, disp_parent=None, file: str = ""): super().__init__(disp_parent, file) self.platform = roypy_platform_utils.PlatformHelper() parser = argparse.ArgumentParser() roypy_sample_utils.add_camera_opener_options(parser) self.options = parser.parse_args("") self.manager = roypy_sample_utils.CameraOpener(self.options) self.frame_q = deque() self.depth_q = deque() self.listener = DepthListener(self.frame_q, self.depth_q) self.exposure = 80 self.mode = 'MODE_5_45FPS_500'
def main(): # PICOFLEXX parser1 = argparse.ArgumentParser(usage=__doc__) add_camera_opener_options(parser1) # parser1.add_argument("--seconds", type=int, default=15, help="duration to capture data") options = parser1.parse_args() opener = CameraOpener(options) cam = opener.open_camera() cam.setUseCase("MODE_5_45FPS_500") # print_camera_info(cam) # print("isConnected", cam.isConnected()) # print("getFrameRate", cam.getFrameRate()) # LEAP MOTION controller = Leap.Controller() controller.set_policy_flags(Leap.Controller.POLICY_IMAGES) run(controller, cam)
def main(): # PICOFLEXX parser1 = argparse.ArgumentParser(usage=__doc__) add_camera_opener_options(parser1) # parser1.add_argument("--seconds", type=int, default=15, help="duration to capture data") options = parser1.parse_args() opener = CameraOpener(options) cam = opener.open_camera() cam.setUseCase("MODE_5_45FPS_500") # print_camera_info(cam) # print("isConnected", cam.isConnected()) # print("getFrameRate", cam.getFrameRate()) # # LEAP MOTION # controller = Leap.Controller() # controller.set_policy_flags(Leap.Controller.POLICY_IMAGES) # CARICO RETI use_cuda = torch.cuda.is_available() device = torch.device('cuda' if use_cuda else 'cpu') if use_cuda: torch.cuda.manual_seed(1994) torch.backends.cudnn.deterministic = True # DEPTH model_depth = models.densenet161(pretrained=True) # for params in model_depth.parameters(): # params.requires_grad = False model_depth.features._modules['conv0'] = nn.Conv2d(in_channels=args.n_frames, out_channels=96, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)) model_depth.classifier = nn.Linear(in_features=2208, out_features=12, bias=True) # carico i pesi model_depth.load_state_dict(torch.load(args.depth_model_path, map_location=device)['state_dict']) model_depth = model_depth.to(device) model_depth.eval() # IR model_ir = models.densenet161(pretrained=True) # for params in model_ir.parameters(): # params.requires_grad = False model_ir.features._modules['conv0'] = nn.Conv2d(in_channels=args.n_frames, out_channels=96, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)) model_ir.classifier = nn.Linear(in_features=2208, out_features=12, bias=True) model_ir.load_state_dict(torch.load(args.ir_model_path, map_location=device)['state_dict']) model_ir = model_ir.to(device) model_ir.eval() # create CROSS CONVNET # model = CrossConvNet(n_classes=12, depth_model=model_depth, ir_model=None, rgb_model=None, # mode='depth_ir', cross_mode='avg').to(device) # model.eval() # carico media e std npzfile = np.load("weights/mean_std_depth_z.npz") mean_depth = npzfile['arr_0'] std_depth = npzfile['arr_1'] print('mean, std depth_z loaded.') npzfile = np.load("weights/mean_std_depth_ir.npz") mean_ir = npzfile['arr_0'] std_ir = npzfile['arr_1'] print('mean, std depth_ir loaded.') run(controller=None, cam=cam, model=model_depth, device=device, mean_depth=mean_depth, std_depth=std_depth, mean_ir=mean_ir, std_ir=std_ir)