def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'YOLO model on Jetson')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('-n',
'--num_points',
type=int,
default=50,
help='Number of interpolated points.')
parser.add_argument('-c',
'--category_num',
type=int,
default=1,
help='number of object categories [80]')
parser.add_argument('-d',
'--display_mode',
type=bool,
default=False,
help=('to turn display_mode on: --display_mode True'))
parser.add_argument('-lat',
'--latitude',
type=float,
required=True,
help=('Please provide latitude in decimal degrees'))
parser.add_argument('-lon',
'--longitude',
type=float,
required=True,
help=('Please provide longitude in decimal degrees'))
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('capture Video input file and save BBoxed Video'
'object detection with TensorRT optimized '
'YOLO model on Jetson')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('-v',
'--video_name',
type=str,
required=True,
help='You need to put input video')
parser.add_argument('-o',
'--result_video',
type=str,
required=True,
help='You need to put output video name')
parser.add_argument('-c',
'--category_num',
type=int,
default=15,
help='number of object categories [15]')
parser.add_argument(
'-m',
'--model',
type=str,
required=True,
help=('[yolov3|yolov3-tiny|yolov3-spp|yolov4|yolov4-tiny]-'
'[{dimension}], where dimension could be a single '
'number (e.g. 288, 416, 608) or WxH (e.g. 416x256)'))
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('This script captures and displays live camera video, '
'and does real-time object detection with TF-TRT model '
'on Jetson TX2/TX1/Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--model', dest='model',
help='tf-trt object detecion model '
'[{}]'.format(DEFAULT_MODEL),
default=DEFAULT_MODEL, type=str)
parser.add_argument('--build', dest='do_build',
help='re-build TRT pb file (instead of using'
'the previously built version)',
action='store_true')
parser.add_argument('--tensorboard', dest='do_tensorboard',
help='write optimized graph summary to TensorBoard',
action='store_true')
parser.add_argument('--labelmap', dest='labelmap_file',
help='[{}]'.format(DEFAULT_LABELMAP),
default=DEFAULT_LABELMAP, type=str)
parser.add_argument('--num-classes', dest='num_classes',
help='(deprecated and not used) number of object '
'classes', type=int)
parser.add_argument('--confidence', dest='conf_th',
help='confidence threshold [0.3]',
default=0.3, type=float)
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = 'MJPEG version of trt_yolo'
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('-c',
'--category_num',
type=int,
default=80,
help='number of object categories [80]')
parser.add_argument(
'-m',
'--model',
type=str,
required=True,
help=('[yolov3|yolov3-tiny|yolov3-spp|yolov4|yolov4-tiny]-'
'[{dimension}], where dimension could be a single '
'number (e.g. 288, 416, 608) or WxH (e.g. 416x256)'))
parser.add_argument('-p',
'--mjpeg_port',
type=int,
default=8080,
help='MJPEG server port [8080]')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = 'MJPEG version of trt_yolo'
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('-c',
'--category_num',
type=int,
default=80,
help='number of object categories [80]')
parser.add_argument(
'-m',
'--model',
type=str,
required=True,
help=('[yolov3-tiny|yolov3|yolov3-spp|yolov4-tiny|yolov4|'
'yolov4-csp|yolov4x-mish]-[{dimension}], where '
'{dimension} could be either a single number (e.g. '
'288, 416, 608) or 2 numbers, WxH (e.g. 416x256)'))
parser.add_argument('-l',
'--letter_box',
action='store_true',
help='inference with letterboxed image [False]')
parser.add_argument('-p',
'--mjpeg_port',
type=int,
default=8080,
help='MJPEG server port [8080]')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'YOLO model on Jetson')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('-c',
'--category_num',
type=int,
default=80,
help='number of object categories [80]')
parser.add_argument(
'-m',
'--model',
type=str,
required=True,
help=('[yolov3|yolov3-tiny|yolov3-spp|yolov4|yolov4-tiny]-'
'[{dimension}], where dimension could be a single '
'number (e.g. 288, 416, 608) or WxH (e.g. 416x256)'))
parser.add_argument('-l',
'--letter_box',
action='store_true',
help='inference with letterboxed image [False]')
parser.add_argument('--host', \
type=str, default='localhost', metavar='MQTT_HOST', \
help='MQTT remote broker IP address')
parser.add_argument('--topic', \
type=str, default='yolo', metavar='MQTT_TOPIC', \
help='MQTT topic to be published on')
parser.add_argument('--port', \
type=int, default=1883, metavar='MQTT_PORT', \
help='MQTT port number')
args = parser.parse_args()
return args
def parse_args():
desc = 'Capture and display live camera video'
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--minsize', type=int, default=40, help='minsize (in pixels) for detection [40]')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'YOLO model on Jetson')
'''description = python3 trt_yolo.py --usb 0 -m yolov4-tiny-288'''
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('-c',
'--category_num',
type=int,
default=80,
help='number of object categories [80]')
parser.add_argument(
'-m',
'--model',
type=str,
required=True,
help=('[yolov3|yolov3-tiny|yolov3-spp|yolov4|yolov4-tiny]-'
'[{dimension}], where dimension could be a single '
'number (e.g. 288, 416, 608) or WxH (e.g. 416x256)'))
parser.add_argument('-l',
'--letter_box',
action='store_true',
help='inference with letterboxed image [False]')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time face detection with TrtMtcnn on Jetson '
'Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'YOLOv3 model on Jetson Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--model', type=str, default='yolov3-416')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time face detection with TrtMtcnn on Jetson '
'Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--minsize', type=int, default=40,
help='minsize (in pixels) for detection [40]')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'SSD model on Jetson Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--model', type=str, default='ssd_mobilenet_v2_coco',
choices=SUPPORTED_MODELS)
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = 'Follow cats with SSD model on Jetson Nano'
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--model',
type=str,
default='ssd_mobilenet_v1_coco',
choices=SUPPORTED_MODELS)
args = parser.parse_args()
return args
def parse_args():
'''parse args'''
parser = argparse.ArgumentParser()
parser = add_camera_args(parser)
parser.add_argument('--model', type=str, default='ssd_mobilenet_v1_digger')
parser.add_argument('--image_resize', default=300, type=int)
parser.add_argument('--det_conf_thresh', default=0.8, type=float)
parser.add_argument('--seq_dir', default="sequence/")
parser.add_argument('--sort_max_age', default=5, type=int)
parser.add_argument('--sort_min_hit', default=3, type=int)
return parser.parse_args()
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time image classification with TrtGooglenet '
'on Jetson Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--crop', dest='crop_center',
help='crop center square of image for '
'inferencing [False]',
action='store_true')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'YOLOv3 model on Jetson Family')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--model', type=str, default='yolov3-416',
choices=['yolov3-288', 'yolov3-416', 'yolov3-608',
'yolov3-tiny-288', 'yolov3-tiny-416'])
parser.add_argument('--runtime', action='store_true',
help='display detailed runtime')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time face detection with TensorRT optimized '
'retinaface model on Jetson')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument(
'-m', '--model', type=str, required=True,
help=('[retinaface]-'
'[{dimension}], where dimension could be a single '
'number (e.g. 320, 640)'))
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'YOLOv3 model on Jetson Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--model', type=str, default='yolov3-416',
choices=['yolov3-288', 'yolov3-416', 'yolov3-608',
'yolov3-tiny-288', 'yolov3-tiny-416'])
parser.add_argument('--category_num', type=int, default=80,
help='number of object categories [80]')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'YOLO model on Jetson')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('-c',
'--category_num',
type=int,
default=80,
help='number of object categories [80]')
parser.add_argument(
'-m',
'--model',
type=str,
required=True,
help=('[yolov3|yolov3-tiny|yolov3-spp|yolov4|yolov4-tiny]-'
'[{dimension}], where dimension could be a single '
'number (e.g. 288, 416, 608) or WxH (e.g. 416x256)'))
parser.add_argument('-v',
'--valid_coco',
type=str,
help='Path to the valid coco json file')
parser.add_argument(
'--write_images',
action="store_true",
help='Write images with detected bounding boxes to output directory')
parser.add_argument("--image_output",
type=str,
default="/home/out/images",
help="Output directory for images with bounding boxes")
parser.add_argument("--result_json",
type=str,
default="/home/out/result.json",
help="Output file for annotations")
parser.add_argument("--confidence_threshold",
type=float,
default=0.3,
help="Output file for annotations")
parser.add_argument("--activate_display",
action="store_true",
help="Output file for annotations")
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time face detection with TrtMtcnn on Jetson '
'Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--minsize',
type=int,
default=40,
help='minsize (in pixels) for detection [40]')
parser.add_argument('-m',
'--model',
type=str,
default='ssd_mobilenet_v1_coco',
choices=SUPPORTED_MODELS)
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'YOLO model on Jetson')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument(
'--model',
type=str,
required=True,
help=('[yolov3|yolov3-tiny|yolov3-spp|yolov4|yolov4-tiny]-'
'[{dimension}], where dimension could be a single '
'number (e.g. 288, 416, 608) or WxH (e.g. 416x256)'))
parser.add_argument('--category_num',
type=int,
default=80,
help='number of object categories [80]')
args = parser.parse_args()
return args
def parse_args():
"""Parse input arguments."""
desc = ('Capture and display live camera video, while doing '
'real-time object detection with TensorRT optimized '
'SSD model on Jetson Nano')
parser = argparse.ArgumentParser(description=desc)
parser = add_camera_args(parser)
parser.add_argument('--model',
type=str,
default='ssd_mobilenet_v2_coco',
choices=SUPPORTED_MODELS)
parser.add_argument('--console',
dest='use_console',
help='write output to console',
action='store_true')
parser.add_argument('--once',
dest='loop',
help='Run model once',
default=True,
action='store_false')
args = parser.parse_args()
return args
