如何使用 OpenCV 检测图像中的色块? [英] How to detect colored patches in an image using OpenCV?

问题描述

我正在尝试通过移动相机检测房间条件下的图片(黑白草图)是否着色.

I am trying to detect if the picture(black and white sketch) is colored or not in the room conditions by a mobile camera.

我已经能够得到这个结果

I have been able to get this result

使用以下代码

Mat dest = new Mat (sections[i].rows(),sections[i].cols(),CvType.CV_8UC3);
Mat hsv_image = new Mat (sections[i].rows(),sections[i].cols(),CvType.CV_8UC3);

Imgproc.cvtColor (sections[i],hsv_image,Imgproc.COLOR_BGR2HSV);

List <Mat> rgb = new List<Mat> ();
Core.split (hsv_image, rgb);
Imgproc.equalizeHist (rgb [1], rgb [2]);
Core.merge (rgb, sections[i]);
Imgproc.cvtColor (sections[i], dest, Imgproc.COLOR_HSV2BGR);

Core.split (dest, rgb);

我怎样才能成功地找出图像是否有颜色.颜色可以是任意的，它有房间条件.请帮助我，因为我是初学者.

How can I sucessfully find out if the image is colored or not. The color can be any and it has room conditions. Please help me on this as I am beginner to it.

谢谢

推荐答案

在HSV color-space中处理彩色图像是一个很好的方向.我拆分了频道，发现 S 频道很棒.因为是色彩度的饱和度.

To process the colorful image in HSV color-space is a good direction. And I split the channels and find the S channel is great. Because S is Saturation(饱和度) of color.

然后将 S 阈值设为 100，你会得到这个.

Then threshold the S with thresh of 100, you will get this.

很容易在脱粒后的二值图像中分离彩色区域.

It will be easy to separate the colorful region in the threshed binary image.

正如@Mark 所建议的，我们可以使用除固定阈值之外的自适应阈值.因此，在标志中添加 THRESH_OTSU.

As @Mark suggest, we can use adaptive thresh other than the fixed one. So, add THRESH_OTSU in the flags.

核心python代码如下:

Core python code is presented as follow:

##(1) read into  bgr-space
img = cv2.imread("test.png")

##(2) convert to hsv-space, then split the channels
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)

##(3) threshold the S channel using adaptive method(`THRESH_OTSU`)  
th, threshed = cv2.threshold(s, 100, 255, cv2.THRESH_OTSU|cv2.THRESH_BINARY)

##(4) print the thresh, and save the result
print("Thresh : {}".format(th))
cv2.imwrite("result.png", threshed)


## >>> Thresh : 85.0

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