Windows 编译cython nms_cython——nms

目录

编译python库：

python编译通用库：用vs的native命令窗口

pytorch nms

---

https://github.com/pjl1995/CTracker/blob/master/model.py

调用：

from lib.cython_nms.cpu_nms import soft_nms as cython_soft_nms_wrapper

demo:

import numpy as np
 
import cpu_nms
# from lib.cython_nms import cpu_nms import cython_soft_nms_wrapper
x = np.ones([11,10], dtype =np.float32)
 
aa= cpu_nms.soft_nms(x,0.7)
 
print(aa)

编译python库：

python setup_cpu.py build_ext --inplace

python编译通用库：用vs的native命令窗口

python setup.py build_ext --inplace

有时报错，需要用vs的native命令窗口

（2）打开vs2019（或者vs2017）专用的命令行窗口

 因为我是64位的系统，因此选择的x64 native tools command prompt for vs2019

from setuptools import setup, Extension
 
import numpy as np
from Cython.Build import cythonize
from Cython.Distutils import build_ext
 
 
# Obtain the numpy include directory.  This logic works across numpy versions.
try:
    numpy_include = np.get_include()
except AttributeError:
    numpy_include = np.get_numpy_include()
 
# extensions
ext_args = dict(
    include_dirs=[numpy_include],
    language='c++',
)
ext_modules = [
 
    Extension(
        "bbox",
        ["bbox.pyx"],
        **ext_args
    ),
    Extension(
        "cpu_nms",
        ["cpu_nms.pyx"],
        **ext_args
    ),
    ]
 
setup(
    name='frcnn_cython',
    ext_modules=cythonize(ext_modules),
    # inject our custom trigger
    cmdclass={'build_ext': build_ext},
)

pytorch nms

import torch
def nms(boxes, scores, overlap=0.7, top_k=200):
    """
    输入:
        boxes: 存储一个图片的所有预测框。[num_positive,4].
        scores:置信度。如果为多分类则需要将nms函数套在一个循环内。[num_positive].
        overlap: nms抑制时iou的阈值.
        top_k: 先选取置信度前top_k个框再进行nms.
    返回:
        nms后剩余预测框的索引.
    """
 
    keep = scores.new(scores.size(0)).zero_().long()
    # 保存留下来的box的索引 [num_positive]
    # 函数new(): 构建一个有相同数据类型的tensor
 
    # 如果输入box为空则返回空Tensor
    if boxes.numel() == 0:
        return keep
 
    x1 = boxes[:, 0]  # x1 坐标
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    area = torch.mul(x2 - x1, y2 - y1)  # 并行化计算所有框的面积
    v, idx = scores.sort(0)  # 升序排序
    idx = idx[-top_k:]  # 前top-k的索引，从小到大
    xx1 = boxes.new()
    yy1 = boxes.new()
    xx2 = boxes.new()  # new() 无参数，创建 相同类型的空值；
    yy2 = boxes.new()
    w = boxes.new()
    h = boxes.new()
 
    count = 0
    while idx.numel() > 0:
        i = idx[-1]  # 目前最大score对应的索引  # 选取得分最大的框索引；
        keep[count] = i  # 存储在keep中
        count += 1
        if idx.size(0) == 1:  # 跳出循环条件：box被筛选完了
            break
        idx = idx[:-1]  # 去掉最后一个
 
        # 剩下boxes的信息存储在xx，yy中
        torch.index_select(x1, 0, idx, out=xx1)  # 从x1中再维度0选取索引为idx 数据 输出到xx1中；
        torch.index_select(y1, 0, idx, out=yy1)  # torch.index_select（） # 从tensor中按指定维度和索引 取值；
        torch.index_select(x2, 0, idx, out=xx2)
        torch.index_select(y2, 0, idx, out=yy2)
 
        # 计算当前最大置信框与其他剩余框的交集，不知道clamp的同学确实容易被误导
        xx1 = torch.clamp(xx1, min=x1[i])  # max(x1,xx1)  # x1 y1 的最大值
        yy1 = torch.clamp(yy1, min=y1[i])  # max(y1,yy1)
        xx2 = torch.clamp(xx2, max=x2[i])  # min(x2,xx2)  # x2 x3 最小值；
        yy2 = torch.clamp(yy2, max=y2[i])  # min(y2,yy2)
        w.resize_as_(xx2)
        h.resize_as_(yy2)
        w = xx2 - xx1  # w=min(x2,xx2)−max(x1,xx1)
        h = yy2 - yy1  # h=min(y2,yy2)−max(y1,yy1)
        w = torch.clamp(w, min=0.0)  # max(w,0)
        h = torch.clamp(h, min=0.0)  # max(h,0)
        inter = w * h
 
        # 计算当前最大置信框与其他剩余框的IOU
        # IoU = i / (area(a) + area(b) - i)
        rem_areas = torch.index_select(area, 0, idx)  # 剩余的框的面积
        union = rem_areas + area[i] - inter  # 并集
        IoU = inter / union  # 计算iou
 
        # 选出IoU <= overlap的boxes(注意le函数的使用)
        idx = idx[IoU.le(overlap)]  # le: 小于等于 返回的bool ， 去除大于overlap的值；
    return keep, count