ubuntu22.04@laptop OpenCV Get Started: 007_color

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1. 源由

在本章中，将了解计算机视觉中使用的流行颜色空间，并将其用于基于颜色的分割。

不同颜色空间定义
基于颜色图像分割

记得曾经有人谈到，什么是科学? 或者说对于我们来说科学的定义是什么?

Science is a rigorous, systematic endeavor that builds and organizes knowledge in the form of testable explanations and predictions about the world.

这里话题稍微展开一些，因为，从工程技术的角度来说，个人感觉国内目前更多偏于浮躁，注重功利性。因为，目前工程技术在企业、社会上更多的认知是解决问题。

从个人的观点来看，其实科学的定义在wiki百科上讲的非常清楚，换言之，讲的是逻辑和道理。

知识体系
可解释
可预测

因为企业的逐利性，社会的导向性，过于表面的注重解决实际应用问题，而忽略了逻辑和道理。导致很多表现上只要解决问题，就觉得好了，成功了。而真正的本质没有讲清楚或者深入研究清楚，进而无法将科学更好的应用于生产力。

通常也是大家可以看到，很多人似乎能解决问题，但是无法用言语表达清楚，甚至用纸笔记录下来，因为这些问题的解决是一种表象，内部实质问题没有了解清楚。

我可能多说了很多“废话”，希望国内这种浮躁的科学技术作风能有所改善！

2. 颜色空间

颜色空间可以简单的理解为色彩在不同坐标系下的展开方式。

正交坐标系统，通常理解为不同单位向量之间是解耦的关系。对于非正交系统来说，单位向量有松耦合，甚至紧耦合的关系。而颜色在不同坐标系统下的展开也会影响到对于特征的判断。

在计算机视觉里面也有类似的问题，这是一个物理到计算机语言的表达过程。这里就凸显基础学科的重要性，通过定义，基本原理和逻辑来给出解决方案。当然应用方面，“拿来主义”也能出色的完成工作，但是背后的逻辑思路，以及遇到复杂问题的分析能力将会在后续的研发、研究上阻碍技术的发展。

希望通过这些点滴思考，能够对于当下社会浮躁研发氛围有所触发和讨论。当然，总的方向是好的，只不过。。。。

大体上计算机视觉上有以下颜色空间：

RGB颜色空间
LAB颜色空间
YCrCb颜色空间
HSV颜色空间

2.1 RGB颜色空间

定义：

A linear combination of Red, Green, and Blue values.
The three channels are correlated by the amount of light hitting the surface.

下面是同一个物体在不同光照条件下的对比分析：

ubuntu22.04@laptop OpenCV Get Started: 007_color_spaces,Linux,opencv,人工智能,计算机视觉

显著的感知不一致性
色度（颜色相关信息）和亮度（强度相关信息）数据的混合

换句话说：其坐标系是非解耦的，有相关性；当亮度变化的时候，RGB都会发生变化。

默认读入的文件数据就是BGR格式：

C++:

//C++
bright = cv::imread('cube1.jpg')
dark = cv::imread('cube8.jpg')

Python:

#python
bright = cv2.imread('cube1.jpg')
dark = cv2.imread('cube8.jpg')

2.2 LAB颜色空间

定义：

L – Lightness ( Intensity ).
a – color component ranging from Green to Magenta.
b – color component ranging from Blue to Yellow.

下面是同一个物体在不同光照条件下的对比分析：
ubuntu22.04@laptop OpenCV Get Started: 007_color_spaces,Linux,opencv,人工智能,计算机视觉

感知均匀的颜色空间，近似于我们感知颜色的方式
独立于设备（捕捉或显示）
在Adobe Photoshop中广泛使用
通过一个复杂的变换方程与RGB颜色空间相关的。

BGR转换：
C++:

//C++
cv::cvtColor(bright, brightLAB, cv::COLOR_BGR2LAB);
cv::cvtColor(dark, darkLAB, cv::COLOR_BGR2LAB);

Python:

#python
brightLAB = cv2.cvtColor(bright, cv2.COLOR_BGR2LAB)
darkLAB = cv2.cvtColor(dark, cv2.COLOR_BGR2LAB)

2.3 YCrCb颜色空间

定义：

Y – Luminance or Luma component obtained from RGB after gamma correction.
Cr = R – Y ( how far is the red component from Luma ).
Cb = B – Y ( how far is the blue component from Luma ).

下面是同一个物体在不同光照条件下的对比分析：
ubuntu22.04@laptop OpenCV Get Started: 007_color_spaces,Linux,opencv,人工智能,计算机视觉

亮度与LAB类似的
与LAB相比，即使在室外图像中，红色和橙色之间的感知差异也较小
白色的所有三个组成部分都发生了变化

BGR转换：
C++:

//C++
cv::cvtColor(bright, brightYCB, cv::COLOR_BGR2YCrCb);
cv::cvtColor(dark, darkYCB, cv::COLOR_BGR2YCrCb);

Python:

#python
brightYCB = cv2.cvtColor(bright, cv2.COLOR_BGR2YCrCb)
darkYCB = cv2.cvtColor(dark, cv2.COLOR_BGR2YCrCb)

2.4 HSV颜色空间

定义：

H – Hue ( Dominant Wavelength ).
S – Saturation ( Purity / shades of the color ).
V – Value ( Intensity ).

下面是同一个物体在不同光照条件下的对比分析：
ubuntu22.04@laptop OpenCV Get Started: 007_color_spaces,Linux,opencv,人工智能,计算机视觉

H分量在两个图像中都非常相似，这表明即使在照明变化的情况下颜色信息也是完整的
S分量在两幅图像中也非常相似
V分量捕捉落在其上的光量，因此它会因照明变化而变化
室外和室内图像的红色部分的值之间存在巨大差异。这是因为色调表示为一个圆形，而红色处于起始角度。因此，它可以取介于[300360]和[0,60]之间的值

BGR转换：
C++:

//C++
cv::cvtColor(bright, brightHSV, cv::COLOR_BGR2HSV);
cv::cvtColor(dark, darkHSV, cv::COLOR_BGR2HSV);

Python:

#python
brightHSV = cv2.cvtColor(bright, cv2.COLOR_BGR2HSV)
darkHSV = cv2.cvtColor(dark, cv2.COLOR_BGR2HSV)

3 代码工程结构

007_color_spaces是OpenCV基于颜色空间分割区域的示例程序。

3.1 C++应用Demo

C++应用Demo工程结构：

007_color_spaces/CPP$ tree . -L 1
.
├── CMakeLists.txt
├── interactive_color_detect.cpp
├── interactive_color_segment.cpp
├── images
└── pieces

2 directories, 3 files

C++示例编译前，确认OpenCV安装路径：

$ find /home/daniel/ -name "OpenCVConfig.cmake"
/home/daniel/OpenCV/installation/opencv-4.9.0/lib/cmake/opencv4/
/home/daniel/OpenCV/opencv/build/OpenCVConfig.cmake
/home/daniel/OpenCV/opencv/build/unix-install/OpenCVConfig.cmake


$ export OpenCV_DIR=/home/daniel/OpenCV/installation/opencv-4.9.0/lib/cmake/opencv4/

C++应用Demo工程编译执行：

$ mkdir build
$ cd build
$ cmake ..
$ cmake --build . --config Release
$ cd ..
$ ./build/interactive_color_detect
$ ./build/interactive_color_segment

3.2 Python应用Demo

Python应用Demo工程结构：

007_color_spaces/Python$ tree . -L 1
.
├── data_analysis.py
├── interactive_color_detect.py
├── interactive_color_segment.py
├── images
└── pieces

2 directories, 3 files

Python应用Demo工程执行：

$ sudo apt-get install tcl-dev tk-dev python-tk python3-tk
$ workoncv-4.9.0
$ pip install PyQt5 PySide2
$ python interactive_color_detect.py
$ python interactive_color_segment.py
$ python data_analysis.py

4. 重点分析

4.1 interactive_color_detect

cvtColor(src, dst, code)

获取图像数据中一个点的色彩坐标数据：

C++:

Vec3b bgrPixel(img.at<Vec3b>(y, x));
        
Mat3b hsv,ycb,lab;
// Create Mat object from vector since cvtColor accepts a Mat object
Mat3b bgr (bgrPixel);
        
//Convert the single pixel BGR Mat to other formats
cvtColor(bgr, ycb, COLOR_BGR2YCrCb);
cvtColor(bgr, hsv, COLOR_BGR2HSV);
cvtColor(bgr, lab, COLOR_BGR2Lab);
        
//Get back the vector from Mat
Vec3b hsvPixel(hsv.at<Vec3b>(0,0));
Vec3b ycbPixel(ycb.at<Vec3b>(0,0));
Vec3b labPixel(lab.at<Vec3b>(0,0));

Python:

# get the value of pixel from the location of mouse in (x,y)
bgr = img[y,x]

# Convert the BGR pixel into other colro formats
ycb = cv2.cvtColor(np.uint8([[bgr]]),cv2.COLOR_BGR2YCrCb)[0][0]
lab = cv2.cvtColor(np.uint8([[bgr]]),cv2.COLOR_BGR2Lab)[0][0]
hsv = cv2.cvtColor(np.uint8([[bgr]]),cv2.COLOR_BGR2HSV)[0][0]

4.2 interactive_color_segment

inRange(src, lowerb, upperb, dst )
bitwise_and(src1, src2, dst, mask)

使用mask过滤图像数据：

C++:

// Get values from the BGR trackbar
BMin = getTrackbarPos("BMin", "SelectBGR");
GMin = getTrackbarPos("GMin", "SelectBGR");
RMin = getTrackbarPos("RMin", "SelectBGR");

BMax = getTrackbarPos("BMax", "SelectBGR");
GMax = getTrackbarPos("GMax", "SelectBGR");
RMax = getTrackbarPos("RMax", "SelectBGR");

minBGR = Scalar(BMin, GMin, RMin);
maxBGR = Scalar(BMax, GMax, RMax);

// Get values from the HSV trackbar
HMin = getTrackbarPos("HMin", "SelectHSV");
SMin = getTrackbarPos("SMin", "SelectHSV");
VMin = getTrackbarPos("VMin", "SelectHSV");

HMax = getTrackbarPos("HMax", "SelectHSV");
SMax = getTrackbarPos("SMax", "SelectHSV");
VMax = getTrackbarPos("VMax", "SelectHSV");

minHSV = Scalar(HMin, SMin, VMin);
maxHSV = Scalar(HMax, SMax, VMax);

// Get values from the LAB trackbar
LMin = getTrackbarPos("LMin", "SelectLAB");
aMin = getTrackbarPos("AMin", "SelectLAB");
bMin = getTrackbarPos("BMin", "SelectLAB");

LMax = getTrackbarPos("LMax", "SelectLAB");
aMax = getTrackbarPos("AMax", "SelectLAB");
bMax = getTrackbarPos("BMax", "SelectLAB");

minLab = Scalar(LMin, aMin, bMin);
maxLab = Scalar(LMax, aMax, bMax);

// Get values from the YCrCb trackbar
YMin = getTrackbarPos("YMin", "SelectYCB");
CrMin = getTrackbarPos("CrMin", "SelectYCB");
CbMin = getTrackbarPos("CbMin", "SelectYCB");

YMax = getTrackbarPos("YMax", "SelectYCB");
CrMax = getTrackbarPos("CrMax", "SelectYCB");
CbMax = getTrackbarPos("CbMax", "SelectYCB");

minYCrCb = Scalar(YMin, CrMin, CbMin);
maxYCrCb = Scalar(YMax, CrMax, CbMax);

// Convert the BGR image to other color spaces
original.copyTo(imageBGR);
cvtColor(original, imageHSV, COLOR_BGR2HSV);
cvtColor(original, imageYCrCb, COLOR_BGR2YCrCb);
cvtColor(original, imageLab, COLOR_BGR2Lab);

// Create the mask using the min and max values obtained from trackbar and apply bitwise and operation to get the results
inRange(imageBGR, minBGR, maxBGR, maskBGR);
resultBGR = Mat::zeros(original.rows, original.cols, CV_8UC3);
bitwise_and(original, original, resultBGR, maskBGR);

inRange(imageHSV, minHSV, maxHSV, maskHSV);
resultHSV = Mat::zeros(original.rows, original.cols, CV_8UC3);
bitwise_and(original, original, resultHSV, maskHSV);

inRange(imageYCrCb, minYCrCb, maxYCrCb, maskYCrCb);
resultYCrCb = Mat::zeros(original.rows, original.cols, CV_8UC3);
bitwise_and(original, original, resultYCrCb, maskYCrCb);

inRange(imageLab, minLab, maxLab, maskLab);
resultLab = Mat::zeros(original.rows, original.cols, CV_8UC3);
bitwise_and(original, original, resultLab, maskLab);

// Show the results
imshow("SelectBGR", resultBGR);
imshow("SelectYCB", resultYCrCb);
imshow("SelectLAB", resultLab);
imshow("SelectHSV", resultHSV);

Python:

# Get values from the BGR trackbar
BMin = cv2.getTrackbarPos('BGRBMin','SelectBGR')
GMin = cv2.getTrackbarPos('BGRGMin','SelectBGR')
RMin = cv2.getTrackbarPos('BGRRMin','SelectBGR')
BMax = cv2.getTrackbarPos('BGRBMax','SelectBGR')
GMax = cv2.getTrackbarPos('BGRGMax','SelectBGR')
RMax = cv2.getTrackbarPos('BGRRMax','SelectBGR')
minBGR = np.array([BMin, GMin, RMin])
maxBGR = np.array([BMax, GMax, RMax])

# Get values from the HSV trackbar
HMin = cv2.getTrackbarPos('HMin','SelectHSV')
SMin = cv2.getTrackbarPos('SMin','SelectHSV')
VMin = cv2.getTrackbarPos('VMin','SelectHSV')
HMax = cv2.getTrackbarPos('HMax','SelectHSV')
SMax = cv2.getTrackbarPos('SMax','SelectHSV')
VMax = cv2.getTrackbarPos('VMax','SelectHSV')
minHSV = np.array([HMin, SMin, VMin])
maxHSV = np.array([HMax, SMax, VMax])

# Get values from the LAB trackbar
LMin = cv2.getTrackbarPos('LABLMin','SelectLAB')
AMin = cv2.getTrackbarPos('LABAMin','SelectLAB')
BMin = cv2.getTrackbarPos('LABBMin','SelectLAB')
LMax = cv2.getTrackbarPos('LABLMax','SelectLAB')
AMax = cv2.getTrackbarPos('LABAMax','SelectLAB')
BMax = cv2.getTrackbarPos('LABBMax','SelectLAB')
minLAB = np.array([LMin, AMin, BMin])
maxLAB = np.array([LMax, AMax, BMax])

# Get values from the YCrCb trackbar
YMin = cv2.getTrackbarPos('YMin','SelectYCB')
CrMin = cv2.getTrackbarPos('CrMin','SelectYCB')
CbMin = cv2.getTrackbarPos('CbMin','SelectYCB')
YMax = cv2.getTrackbarPos('YMax','SelectYCB')
CrMax = cv2.getTrackbarPos('CrMax','SelectYCB')
CbMax = cv2.getTrackbarPos('CbMax','SelectYCB')
minYCB = np.array([YMin, CrMin, CbMin])
maxYCB = np.array([YMax, CrMax, CbMax])

# Convert the BGR image to other color spaces
imageBGR = np.copy(original)
imageHSV = cv2.cvtColor(original,cv2.COLOR_BGR2HSV)
imageYCB = cv2.cvtColor(original,cv2.COLOR_BGR2YCrCb)
imageLAB = cv2.cvtColor(original,cv2.COLOR_BGR2LAB)

# Create the mask using the min and max values obtained from trackbar and apply bitwise and operation to get the results         
maskBGR = cv2.inRange(imageBGR,minBGR,maxBGR)
resultBGR = cv2.bitwise_and(original, original, mask = maskBGR)         

maskHSV = cv2.inRange(imageHSV,minHSV,maxHSV)
resultHSV = cv2.bitwise_and(original, original, mask = maskHSV)

maskYCB = cv2.inRange(imageYCB,minYCB,maxYCB)
resultYCB = cv2.bitwise_and(original, original, mask = maskYCB)         

maskLAB = cv2.inRange(imageLAB,minLAB,maxLAB)
resultLAB = cv2.bitwise_and(original, original, mask = maskLAB)         

# Show the results
cv2.imshow('SelectBGR',resultBGR)
cv2.imshow('SelectYCB',resultYCB)
cv2.imshow('SelectLAB',resultLAB)
cv2.imshow('SelectHSV',resultHSV)