如何在YOLO对象检测中获取边界框的坐标？

Question

我需要使用YOLO对象检测来获取上图中生成的边界框坐标。

Answer 1

一个快速的解决方案是修改image.c文件以打印出边界框信息：

...
if(bot > im.h-1) bot = im.h-1;

// Print bounding box values 
printf("Bounding Box: Left=%d, Top=%d, Right=%d, Bottom=%d\n", left, top, right, bot); 
draw_box_width(im, left, top, right, bot, width, red, green, blue);
...

Answer 2

有一个很好的小蟒蛇（2 - 但只有很少的修改3. [只是将打印和字符串更改为主要的二进制字符串]）程序，你可以在主回购https://github.com/pjreddie/darknet/blob/master/python/darknet.py中使用

注意！给定的坐标是中点，宽度和高度。

Answer 3

如果你打算在python中实现这个，那么我在python创建了这个小的here包装器。按照ReadMe文件进行安装。它很容易安装。

之后跟着这个example code知道如何检测物体。 如果您的检测是det

top_left_x = det.bbox.x
top_left_y = det.bbox.y
width = det.bbox.w
height = det.bbox.h

如果需要，您可以通过以下方式获得中点：

mid_x, mid_y = det.bbox.get_point(pyyolo.BBox.Location.MID)

希望这可以帮助..

Answer 4

对于Windows中的python用户：

首先......，做几个设置工作：

1. 在环境路径中设置darknet文件夹的pythonpath： qazxsw poi
2. 通过添加将PYTHONPATH添加到Path值： PYTHONPATH = 'YOUR DARKNET FOLDER'
3. 在%PYTHONPATH%中编辑文件coco.data，将cfg folder文件夹变量更改为names文件夹，在我的情况下： coco.names

使用此设置，您可以从任何文件夹中调用darknet.py（来自names = D:/core/darknetAB/data/coco.names存储库）作为您的python模块。

开始编写脚本：

alexeyAB\darknet

如何使用它：

from darknet import performDetect as scan #calling 'performDetect' function from darknet.py

def detect(str):
    ''' this script if you want only want get the coord '''
    picpath = str
    cfg='D:/core/darknetAB/cfg/yolov3.cfg' #change this if you want use different config
    coco='D:/core/darknetAB/cfg/coco.data' #you can change this too
    data='D:/core/darknetAB/yolov3.weights' #and this, can be change by you
    test = scan(imagePath=picpath, thresh=0.25, configPath=cfg, weightPath=data, metaPath=coco, showImage=False, makeImageOnly=False, initOnly=False) #default format, i prefer only call the result not to produce image to get more performance

    #until here you will get some data in default mode from alexeyAB, as explain in module.
    #try to: help(scan), explain about the result format of process is: [(item_name, convidence_rate (x_center_image, y_center_image, width_size_box, height_size_of_box))], 
    #to change it with generally used form, like PIL/opencv, do like this below (still in detect function that we create):

    newdata = []
    if len(test) >=2:
        for x in test:
            item, confidence_rate, imagedata = x
            x1, y1, w_size, h_size = imagedata
            x_start = round(x1 - (weight_size/2))
            y_start = round(y1 - (height_size/2))
            x_end = round(x_start + w_size)
            y_end = round(y_start + h_size)
            data = (item, confidence_rate, (x_start, y_start, x_end, y_end), w_size, h_size)
            newdata.append(data)

    elif len(test) == 1:
        item, confidence_rate, imagedata = test
        x1, y1, w_size, h_size = imagedata
        x_start = round(x1 - (w_size/2))
        y_start = round(y1 - (h_size/2))
        x_end = round(x_start + w_size)
        y_end = round(y_start + h_size)
        data = (item, confidence_rate, (x_start, y_start, x_end, y_end), w_size, h_size)
        newdata.append(data)

    else:
        newdata = False

    return newdata

得到坐标：

如果只有1个结果：

table = 'D:/test/image/test1.jpg'
checking = detect(table)'

如果有很多结果：

x1, y1, x2, y2 = checking[2]