YOLO物体检测中如何获取bounding box的坐标？

Question

我需要使用YOLO对象检测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

如果您要在 python 中实现它，我在 here 中创建了这个小型 python 包装器。按照ReadMe文件安装即可。安装起来会很容易。

之后按照这个 example code 了解如何检测物体。
如果你的检测是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，可以通过以下方式获取中点：

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

希望这对您有所帮助..

Answer 3

对于 windows 中的 python 用户：

首先...，做几项设置工作：

在环境路径中设置 python 暗网文件夹的路径：

PYTHONPATH = 'YOUR DARKNET FOLDER'
通过添加将 PYTHONPATH 添加到 Path 值：

%PYTHONPATH%
编辑 cfg folder 中的文件 coco.data，将 names 文件夹变量更改为您的 coco.names 文件夹，在我的例子中：

names = D:/core/darknetAB/data/coco.names

使用此设置，您可以从任何文件夹调用 darknet.py（来自 alexeyAB\darknet 存储库）作为您的 python 模块。

开始编写脚本：

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 - (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)

    elif len(test) == 1:
        item, confidence_rate, imagedata = test[0]
        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

使用方法：

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

获取坐标：

如果只有 1 个结果：

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

如果结果很多：

for x in checking:
    item = x[0]
    x1, y1, x2, y2 = x[2]
    print(item)
    print(x1, y1, x2, y2)

Answer 4

灵感来自@Wahyu 上面的回答。几乎没有更改、修改和错误修复，并通过单对象检测和多对象检测进行了测试。

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


def detect(img_path):
    ''' this script if you want only want get the coord '''
    picpath = img_path
    # change this if you want use different config
    cfg = '/home/saggi/Documents/saggi/prabin/darknet/cfg/yolo-obj.cfg'
    coco = '/home/saggi/Documents/saggi/prabin/darknet/obj.data'  # you can change this too
    # and this, can be change by you
    data = '/home/saggi/Documents/saggi/prabin/darknet/backup/yolo-obj_last.weights'
    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 = []

    # For multiple Detection
    if len(test) >= 2:
        for x in test:
            item, confidence_rate, imagedata = x
            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)

    # For Single Detection
    elif len(test) == 1:
        item, confidence_rate, imagedata = test[0]
        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


if __name__ == "__main__":
    # Multiple detection image test
    # table = '/home/saggi/Documents/saggi/prabin/darknet/data/26.jpg'
    # Single detection image test
    table = '/home/saggi/Documents/saggi/prabin/darknet/data/1.jpg'
    detections = detect(table)

    # Multiple detection
    if len(detections) > 1:
        for detection in detections:
            print(' ')
            print('========================================================')
            print(' ')
            print('All Parameter of Detection: ', detection)

            print(' ')
            print('========================================================')
            print(' ')
            print('Detected label: ', detection[0])

            print(' ')
            print('========================================================')
            print(' ')
            print('Detected object Confidence: ', detection[1])

            x1, y1, x2, y2 = detection[2]
            print(' ')
            print('========================================================')
            print(' ')
            print(
                'Detected object top left and bottom right cordinates (x1,y1,x2,y2):  x1, y1, x2, y2')
            print('x1: ', x1)
            print('y1: ', y1)
            print('x2: ', x2)
            print('y2: ', y2)

            print(' ')
            print('========================================================')
            print(' ')
            print('Detected object width and height: ', detection[3])
            b_width, b_height = detection[3]
            print('Weidth of bounding box: ', math.ceil(b_width))
            print('Height of bounding box: ', math.ceil(b_height))
            print(' ')
            print('========================================================')

    # Single detection
    else:
        print(' ')
        print('========================================================')
        print(' ')
        print('All Parameter of Detection: ', detections)

        print(' ')
        print('========================================================')
        print(' ')
        print('Detected label: ', detections[0][0])

        print(' ')
        print('========================================================')
        print(' ')
        print('Detected object Confidence: ', detections[0][1])

        x1, y1, x2, y2 = detections[0][2]
        print(' ')
        print('========================================================')
        print(' ')
        print(
            'Detected object top left and bottom right cordinates (x1,y1,x2,y2):  x1, y1, x2, y2')
        print('x1: ', x1)
        print('y1: ', y1)
        print('x2: ', x2)
        print('y2: ', y2)

        print(' ')
        print('========================================================')
        print(' ')
        print('Detected object width and height: ', detections[0][3])
        b_width, b_height = detections[0][3]
        print('Weidth of bounding box: ', math.ceil(b_width))
        print('Height of bounding box: ', math.ceil(b_height))
        print(' ')
        print('========================================================')

# Single detections output:
# test value  [('movie_name', 0.9223029017448425, (206.79859924316406, 245.4672393798828, 384.83673095703125, 72.8630142211914))]

# Multiple detections output:
# test value  [('movie_name', 0.9225175976753235, (92.47076416015625, 224.9121551513672, 147.2491912841797, 42.063255310058594)),
#  ('movie_name', 0.4900225102901459, (90.5261459350586, 12.4061279296875, 182.5990447998047, 21.261077880859375))]

Answer 5

如果已接受的答案对您不起作用，这可能是因为您使用的是 AlexyAB's darknet model instead of pjreddie's 暗网模型。

您只需转到 src 文件夹中的 image_opencv.cpp 文件并取消注释以下部分：

            ...

            //int b_x_center = (left + right) / 2;
            //int b_y_center = (top + bot) / 2;
            //int b_width = right - left;
            //int b_height = bot - top;
            //sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);

这将打印 Bbox 中心坐标以及 Bbox 的宽度和高度。进行更改后，请确保在运行 YOLO 之前再次 make 暗网。

Answer 6

如果您在 darknet 框架中使用 yolov4（我指的是直接从 GitHub repo https://github.com/AlexeyAB/darknet 编译的版本）到运行静态图像上的对象检测，类似下面的命令可以在命令行运行获取边界框作为相对坐标：

.\darknet.exe detector test .\cfg\coco.data .\cfg\yolov4.cfg .\yolov4.weights -ext_output .\data\people1.jpg -out result.json

请注意，以上是 Windows 的语法，因此您可能需要将反斜杠更改为正斜杠才能在 macOS 或 Linux 操作系统上运行。另外，请在运行ning 之前确保路径准确无误。在命令中，输入的是根目录下data目录下的people1.jpg文件。输出将存储在名为 result.json 的文件中。随意修改此输出名称，但保留 .json 扩展名以更改其名称。

YOLO物体检测中如何获取bounding box的坐标？

How to get the coordinates of the bounding box in YOLO object detection?

python

object-detection

computer-vision

deep-learning