TensorRT-Demo/trt_yolo.py

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"""trt_yolo.py
This script demonstrates how to do real-time object detection with
TensorRT optimized YOLO engine.
"""
import os
import time
import argparse
import cv2
import pycuda.autoinit # This is needed for initializing CUDA driver
from utils.yolo_classes import get_cls_dict
from utils.camera import add_camera_args, Camera
from utils.display import open_window, set_display, show_fps
from utils.visualization import BBoxVisualization
from utils.yolo_with_plugins import TrtYOLO
WINDOW_NAME = 'TrtYOLODemo'
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(
'-t', '--conf_thresh', type=float, default=0.3,
help='set the detection confidence threshold')
parser.add_argument(
'-m', '--model', type=str, required=True,
help=('[yolov3-tiny|yolov3|yolov3-spp|yolov4-tiny|yolov4|'
'yolov4-csp|yolov4x-mish|yolov4-p5]-[{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]')
args = parser.parse_args()
return args
def loop_and_detect(cam, trt_yolo, conf_th, vis):
"""Continuously capture images from camera and do object detection.
# Arguments
cam: the camera instance (video source).
trt_yolo: the TRT YOLO object detector instance.
conf_th: confidence/score threshold for object detection.
vis: for visualization.
"""
full_scrn = False
fps = 0.0
tic = time.time()
while True:
if cv2.getWindowProperty(WINDOW_NAME, 0) < 0:
break
img = cam.read()
if img is None:
break
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# 获取trt推理结果
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boxes, confs, clss = trt_yolo.detect(img, conf_th)
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# 绘制识别框
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img = vis.draw_bboxes(img, boxes, confs, clss)
img = show_fps(img, fps)
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*0.95 + curr_fps*0.05)
tic = toc
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
elif key == ord('F') or key == ord('f'): # Toggle fullscreen
full_scrn = not full_scrn
set_display(WINDOW_NAME, full_scrn)
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!')
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# 加载COCO数据集标签
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cls_dict = get_cls_dict(args.category_num)
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# 加载框绘制器
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vis = BBoxVisualization(cls_dict)
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# 创建推理引擎
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trt_yolo = TrtYOLO(args.model, args.category_num, args.letter_box)
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# 打开窗口
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open_window(
WINDOW_NAME, 'Camera TensorRT YOLO Demo',
cam.img_width, cam.img_height)
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# 开始推理
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loop_and_detect(cam, trt_yolo, args.conf_thresh, vis=vis)
cam.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
main()