Algorithm-Repo/SourceCode/MachineLearningUtils/ObjectDetection/YOLO_V4/darknettoonnx.py

import sys
import onnx
import os
import argparse
import numpy as np
import cv2
import onnxruntime
import multiprocessing

from tool.utils import *
from tool.darknet2onnx import *

cfg_file = 'C:\\Users\\VINNO\\Desktop\\新建文件夹 (2)\\pytorch-YOLOv4-master\\yolov4-tiny-breast-anchors--3l---20210901.cfg'
weight_file = 'C:\\Users\\VINNO\\Desktop\\新建文件夹 (2)\\pytorch-YOLOv4-master\\yolov4-tiny-breast-anchors--3l---20210901_last.weights'
batch_size = 0

if batch_size <= 0:
    onnx_path_demo = transform_to_onnx(cfg_file, weight_file, batch_size)
else:
    # Transform to onnx as specified batch size
    transform_to_onnx(cfg_file, weight_file, batch_size)
    # Transform to onnx as demo
    onnx_path_demo = transform_to_onnx(cfg_file, weight_file, 1)
session = onnxruntime.InferenceSession(onnx_path_demo)


cpuCount = os.cpu_count()
print("Number of CPUs in the system:", cpuCount)



# file_name = 'C:\\Users\\VINNO\\Desktop\\新建文件夹 (2)\\新建文本文档.txt'
# dest_name = 'C:\\Users\\VINNO\\Desktop\\新建文件夹 (2)\\1.txt'
# # 以二进制打开和保存数据
# with open(dest_name, "a", encoding="utf-8") as data:
#     with open(file_name, "a", encoding="utf-8") as s:
#         data.writelines(s.readlines())