选择正确的上下HSV边界以使用`cv :: inRange`(OpenCV)进行颜色检测 [英] Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)

问题描述

我有一个想要找到橙色盖子位置的咖啡罐图像. 这是.

I have an image of a coffee can with an orange lid position of which I want to find. Here is it .

gcolor2实用程序显示盖中心的HSV为(22，59，100). 问题是如何选择颜色的界限呢?我尝试了min =(18，40，90)和max =(27，255，255)，但是遇到了意外的

gcolor2 utility shows HSV at the center of the lid to be (22, 59, 100). The question is how to choose the limits of the color then? I tried min = (18, 40, 90) and max = (27, 255, 255), but have got unexpected

这是Python代码:

Here is the Python code:

import cv

in_image = 'kaffee.png'
out_image = 'kaffee_out.png'
out_image_thr = 'kaffee_thr.png'

ORANGE_MIN = cv.Scalar(18, 40, 90)
ORANGE_MAX = cv.Scalar(27, 255, 255)
COLOR_MIN = ORANGE_MIN
COLOR_MAX = ORANGE_MAX

def test1():
    frame = cv.LoadImage(in_image)
    frameHSV = cv.CreateImage(cv.GetSize(frame), 8, 3)
    cv.CvtColor(frame, frameHSV, cv.CV_RGB2HSV)
    frame_threshed = cv.CreateImage(cv.GetSize(frameHSV), 8, 1)
    cv.InRangeS(frameHSV, COLOR_MIN, COLOR_MAX, frame_threshed)
    cv.SaveImage(out_image_thr, frame_threshed)

if __name__ == '__main__':
    test1()

推荐答案

问题1:不同的应用程序对HSV使用不同的比例.例如，gimp使用H = 0-360, S = 0-100 and V = 0-100.但是OpenCV使用H: 0-179, S: 0-255, V: 0-255.在这里，我的色相值为gimp 22.所以我取了一半，即11，并为此定义了范围.即(5,50,50) - (15,255,255).

Problem 1 : Different applications use different scales for HSV. For example gimp uses H = 0-360, S = 0-100 and V = 0-100. But OpenCV uses H: 0-179, S: 0-255, V: 0-255. Here i got a hue value of 22 in gimp. So I took half of it, 11, and defined range for that. ie (5,50,50) - (15,255,255).

问题2:而且，OpenCV使用BGR格式，而不是RGB.因此，请更改将RGB转换为HSV的代码，如下所示:

Problem 2: And also, OpenCV uses BGR format, not RGB. So change your code which converts RGB to HSV as follows:

cv.CvtColor(frame, frameHSV, cv.CV_BGR2HSV)

现在运行它.我得到的输出如下:

Now run it. I got an output as follows:

希望这就是您想要的.有一些错误的检测，但是它们很小，所以您可以选择最大的轮廓作为盖子.

Hope that is what you wanted. There are some false detections, but they are small, so you can choose biggest contour which is your lid.

正如 Karl Philip 在他的评论中所说，添加新代码会很好.但是只有一行是变化的.因此，我想添加在新的cv2模块中实现的相同代码，以便用户可以比较新的cv2模块的简便性和灵活性.

As Karl Philip told in his comment, it would be good to add new code. But there is change of only a single line. So, I would like to add the same code implemented in new cv2 module, so users can compare the easiness and flexibility of new cv2 module.

import cv2
import numpy as np

img = cv2.imread('sof.jpg')

ORANGE_MIN = np.array([5, 50, 50],np.uint8)
ORANGE_MAX = np.array([15, 255, 255],np.uint8)

hsv_img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

frame_threshed = cv2.inRange(hsv_img, ORANGE_MIN, ORANGE_MAX)
cv2.imwrite('output2.jpg', frame_threshed)

它给出与上面相同的结果.但是代码要简单得多.

It gives the same result as above. But code is much more simpler.

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