from person_vehicle_monitoring.config import TRITON_SERVER_URL
from person_vehicle_monitoring.tools import httpclient
try:
http_triton_client = httpclient.InferenceServerClient(
url=TRITON_SERVER_URL, verbose=False)
except Exception as e:
raise e
right,
cv2.BORDER_CONSTANT,
value=color) # add border
return img, ratio, (dw, dh)
def post_process(self, boxes, ratio, pad):
raw_boxes = boxes.copy()
raw_boxes[:, 0] = (boxes[:, 0] - pad[0]) / ratio[0]
raw_boxes[:, 1] = (boxes[:, 1] - pad[1]) / ratio[1]
raw_boxes[:, 2] = (boxes[:, 2] - pad[0]) / ratio[0]
raw_boxes[:, 3] = (boxes[:, 3] - pad[1]) / ratio[1]
return raw_boxes
if __name__ == "__main__":
triton_client = httpclient.InferenceServerClient(url='10.20.5.9:9911')
yolov5 = Yolov5TRT()
img = cv2.imread("/models/xu.jpg")
t1 = time.time()
for i in range(100):
dets = yolov5.inference(img, triton_client)
t2 = time.time()
print('infer={}ms'.format((t2 - t1) * 1000 / 100))
colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0)]
for det in dets:
x1 = int(det[0])
y1 = int(det[1])
x2 = int(det[2])
y2 = int(det[3])
conf = det[4]
cls = int(det[5])
from person_vehicle_monitoring.tools import httpclient from person_vehicle_monitoring.config import TRITON_HTTP_SERVER_URL CLIENT = httpclient.InferenceServerClient(url='172.30.2.18:9911', verbose=False) CLIENT_DECORATE = httpclient.InferenceServerClient(url='172.30.2.18:9922', verbose=False)