pytorch_onnx_infer.py
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'''
_*_coding:utf-8 _*_
@Time :2022/1/30 10:28
@Author : qiaofengsheng
@File :pytorch_onnx_infer.py
@Software :PyCharm
'''
import os
import sys
import numpy as np
sys.path.append(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
import cv2
import onnxruntime
import argparse
from PIL import Image, ImageDraw, ImageFont
from torchvision import transforms
import torch
from model.utils import utils
parse = argparse.ArgumentParser(description='onnx model infer!')
parse.add_argument('demo', type=str, help='推理类型支持：image/video/camera')
parse.add_argument('--config_path', type=str, help='配置文件存放地址')
parse.add_argument('--onnx_path', type=str, default=None, help='onnx包存放路径')
parse.add_argument('--image_path', type=str, default='', help='图片存放路径')
parse.add_argument('--video_path', type=str, default='', help='视频路径')
parse.add_argument('--camera_id', type=int, default=0, help='摄像头id')
parse.add_argument('--device', type=str, default='cpu', help='默认设备cpu （暂未完善GPU代码）')
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
def onnx_infer_image(args, config):
ort_session = onnxruntime.InferenceSession(args.onnx_path)
transform = transforms.Compose([transforms.ToTensor()])
image = Image.open(args.image_path)
image_data = utils.keep_shape_resize(image, config['image_size'])
image_data = transform(image_data)
image_data = torch.unsqueeze(image_data, dim=0)
if args.device == 'cpu':
ort_input = {ort_session.get_inputs()[0].name: to_numpy(image_data)}
ort_out = ort_session.run(None, ort_input)
out = np.argmax(ort_out[0], axis=1)
result = config['class_names'][int(out)]
draw = ImageDraw.Draw(image)
font = ImageFont.truetype(r"C:\Windows\Fonts\BRITANIC.TTF", 35)
draw.text((10, 10), result, font=font, fill='red')
image.show()
elif args.device == 'cuda':
pass
else:
exit(0)
def onnx_infer_video(args, config):
ort_session = onnxruntime.InferenceSession(args.onnx_path)
transform = transforms.Compose([transforms.ToTensor()])
cap = cv2.VideoCapture(args.video_path)
while True:
_, frame = cap.read()
if _:
image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_data = Image.fromarray(image_data)
image_data = utils.keep_shape_resize(image_data, config['image_size'])
image_data = transform(image_data)
image_data = torch.unsqueeze(image_data, dim=0)
if args.device == 'cpu':
ort_input = {ort_session.get_inputs()[0].name: to_numpy(image_data)}
ort_out = ort_session.run(None, ort_input)
out = np.argmax(ort_out[0], axis=1)
result = config['class_names'][int(out)]
cv2.putText(frame, result, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), thickness=2)
cv2.imshow('frame', frame)
if cv2.waitKey(24) & 0XFF == ord('q'):
break
elif args.device == 'cuda':
pass
else:
exit(0)
else:
exit(0)
def onnx_infer_camera(args, config):
ort_session = onnxruntime.InferenceSession(args.onnx_path)
transform = transforms.Compose([transforms.ToTensor()])
cap = cv2.VideoCapture(args.camera_id)
while True:
_, frame = cap.read()
if _:
image_data = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_data = Image.fromarray(image_data)
image_data = utils.keep_shape_resize(image_data, config['image_size'])
image_data = transform(image_data)
image_data = torch.unsqueeze(image_data, dim=0)
if args.device == 'cpu':
ort_input = {ort_session.get_inputs()[0].name: to_numpy(image_data)}
ort_out = ort_session.run(None, ort_input)
out = np.argmax(ort_out[0], axis=1)
result = config['class_names'][int(out)]
cv2.putText(frame, result, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), thickness=2)
cv2.imshow('frame', frame)
if cv2.waitKey(24) & 0XFF == ord('q'):
break
elif args.device == 'cuda':
pass
else:
exit(0)
else:
exit(0)
if __name__ == '__main__':
args = parse.parse_args()
config = utils.load_config_util(args.config_path)
if args.demo == 'image':
onnx_infer_image(args, config)
elif args.demo == 'video':
onnx_infer_video(args, config)
elif args.demo == 'camera':
onnx_infer_camera(args, config)
else:
exit(0)