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('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():
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():
global THREAD_RUNNING
args = parse_args()
cap = open_cam_rtsp(args.cam_name, args.cam_password, args.cam_ip)
if not cap.isOpened():
sys.exit('Failed to open camera!')
#抓取图像子进程
THREAD_RUNNING = True
th = threading.Thread(target=grab_img, args=(cap, ))
th.start()
#目标识别
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))
vis = BBoxVisualization(cls_dict)
#考虑对roi区域裁剪并resize
loop_and_detect(trt_yolov3, conf_th=0.3, vis=vis)
#关闭图像子进程
THREAD_RUNNING = False
th.join()
cap.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()
check_args(args)
results_file = 'yolov3_onnx/results_%s.json' % args.model
yolo_dim = int(args.model.split('-')[-1]) # 416 or 608
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim))
cocoGt = COCO(args.annotations)
catIds = cocoGt.getCatIds(catNms=['stop sign'])
imgIds_cat = cocoGt.getImgIds(catIds=catIds)
# test = cocoGt.loadImgs(433892)
# print(test)
# print(imgIds_cat)
jpgs = [cocoGt.loadImgs(ids)[0]['file_name'] for ids in imgIds_cat]
# jpgs = [j for j in os.listdir(args.imgs_dir) if j.endswith('.jpg')]
generate_results(trt_yolov3, args.imgs_dir, jpgs, results_file)
# Run COCO mAP evaluation
# Reference: https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
cocoDt = cocoGt.loadRes(results_file)
imgIds = sorted(cocoGt.getImgIds())
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.params.imgIds = imgIds
cocoEval.evaluate()
cocoEval.accumulate()
print(cocoEval.summarize())
def feed_frames(self):
"""
By Guanghao Zhang.
直接将视频帧放入推送队列，不进行行人检测
:return:
"""
global unnormal_send, img_send, dist_time, interval, camera_sta, dist, put_img
num = 0
category_num = 80
model = 'cfg/yolov3-416.trt'
w, h = 416, 416
conf_th = 0.7
cuda.init()
cuda_ctx = cuda.Device(0).make_context() # GPU 0
trt_yolo = TrtYOLOv3(model, (h, w), category_num)
while True:
start = time.time()
frame = self.camera_queue.get()
boxes, confs, clss = trt_yolo.detect(frame, conf_th)
end_lab = change_lab(frame, boxes, clss)
end_img, unnormal_num, distence = warnplay(frame,
np.array(end_lab),
interval)
end = time.time()
print(end - start)
put_img = end_img
# self.frame_queue.put(end_img)
if unnormal_num > unnormal_send:
unnormal_send = unnormal_num
dist = distence
img_send = end_img
num += 1
if int(num) % int(
dist_time * 1000 /
100) == 0 and unnormal_send != 0 and camera_sta == True:
if not os.path.exists(
'/DATA/pedestrian_dist/images/static/' +
datetime.datetime.now().strftime("%Y-%m-%d")):
os.mkdir('/DATA/pedestrian_dist/images/static/' +
datetime.datetime.now().strftime("%Y-%m-%d"))
save_path = '/DATA/pedestrian_dist/images/static/' + datetime.datetime.now(
).strftime("%Y-%m-%d") + '/' + datetime.datetime.now(
).strftime('%Y-%m-%d-%H-%M-%S') + '_' + str(
self.rount) + '.jpg'
# print(save_path)
cv2.imwrite(save_path, img_send)
Post(
self.alarmpost,
RobotLogin(
self.rount,
str(datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S')), str(round(dist, 1)),
str(unnormal_send), str(os.path.basename(save_path)),
str(camera_sta)))
num = 0
unnormal_send = 0
del trt_yolo
cuda_ctx.pop()
del cuda_ctx
def main():
args = parse_args()
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))
vis = BBoxVisualization(cls_dict)
check(trt_yolov3, conf_th=0.3, vis=vis)
def __init__(self, model, size, vis, conf_th, cat_num):
self.model = model
self.size = size
self.vis = vis
self.th = conf_th
self.num = cat_num
self.bridge = CvBridge()
self.yoloRT = TrtYOLOv3(model, (size, size), cat_num)
self.pub = rospy.Publisher("people_detection", Image)
def detect_and_alarm():
WINDOW_NAME = "Robot_YOLOv3_Detector_with_TensorRT"
MODEL = "yolov3-tiny-416"
IMAGE_WIDTH = 640
IMAGE_HEIGHT = 480
RUN_TIME = False
global DETECT_TIME
cam = Camera_for_Robot(video_dev=DETECT_ID,
image_width=IMAGE_WIDTH,
image_height=IMAGE_HEIGHT)
#cam = Camera_for_Robot(video_dev='./test.mp4', image_width=IMAGE_WIDTH, image_height=IMAGE_HEIGHT)
cam.open()
if not cam.is_opened:
print("Capture road opens failure")
#return
cam.start()
open_window(WINDOW_NAME, IMAGE_WIDTH, IMAGE_HEIGHT,
'TensorRT YOLOv3 Detector')
cls_dict = get_cls_dict('coco')
yolo_dim = int(MODEL.split('-')[-1]) # 416 or 608
trt_yolov3 = TrtYOLOv3(model=MODEL, input_shape=(yolo_dim, yolo_dim))
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam,
RUN_TIME,
trt_yolov3,
conf_th=0.3,
vis=vis,
window_name=WINDOW_NAME,
total_time=DETECT_TIME)
cam.stop()
cam.release()
cv2.destroyAllWindows()
print("Detection finishes successfully")
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('yolov3_onnx/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolov3_onnx/%s.trt) not found!' %
args.model)
cam = Camera(args)
cam.open()
if not cam.is_opened:
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_yolov3 = TrtYOLOv3(args.model, (h, w), 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():
model = "yolov3-tiny-covid"
category_num = 5
conf_th = 0.3
input_size = 608
cls_dict = get_cls_dict(category_num)
vis = BBoxVisualization(cls_dict)
#args = parse_args()
detector = yoloRT(model, input_size, vis, conf_th, category_num)
pub = rospy.Publisher('people_detection', Bool, queue_size=5)
rospy.init_node('people_detector', anonymous=True)
sub = rospy.Subscriber('front_camera/color/image_raw', Image,
detector.detect)
#pipeline = camera_config(sys.argv)
try:
rospy.spin()
except KeyboardInterrupt:
print("Shutting down")
cls_dict = get_cls_dict(category_num)
yolo_dim = 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(608)
if h % 32 != 0 or w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
trt_yolov3 = TrtYOLOv3(model, (h, w), category_num)
vis = BBoxVisualization(cls_dict)
loop_and_detect(pub, pipeline, trt_yolov3, conf_th=0.3, vis=vis)
pipeline.stop()
loop_and_detect(cam, trt_yolov3, conf_th=0.3, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
# if __name__ == '__main__':
# main()
args = parse_args()
cap = cv2.VideoCapture(0) #("rtsp://*****:*****@192.168.10.67:554/1/1")
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))
vis = BBoxVisualization(cls_dict)
full_scrn = False
fps = 0.0
tic = time.time()
conf_th = 0.3
while True:
_, frame = cap.read()
time_start = time.time()
boxes, confs, clss = trt_yolov3.detect(frame, conf_th)
print("--------------tf1----------\n",
time.time() - time_start, "\n-------------------------\n")
img = vis.draw_bboxes(frame, boxes, confs, clss)