There are more than color-space conversion methods available in OpenCV. For color conversion, we use the function cv2. For BGR Gray conversion we use the flags cv2. To get other flags, just run following commands in your Python terminal :.
Different softwares use different scales. So if you are comparing OpenCV values with them, you need to normalize these ranges. In our application, we will try to extract a blue colored object.
So here is the method:. There are some noises in the image. We will see how to remove them in later chapters. This is the simplest method in object tracking. Once you learn functions of contours, you can do plenty of things like find centroid of this object and use it to track the object, draw diagrams just by moving your hand in front of camera and many other funny stuffs.
This is a common question found in stackoverflow. It is very simple and you can use the same function, cv2. Instead of passing an image, you just pass the BGR values you want. OpenCV-Python Tutorials latest. In addition to that, we will create an application which extracts a colored object in a video You will learn following functions : cv2. Note There are some noises in the image. Note This is the simplest method in object tracking. How to find HSV values to track?Almost all the operations in this section are mainly related to Numpy rather than OpenCV.
A good knowledge of Numpy is required to write better optimized code with OpenCV. You can access a pixel value by its row and column coordinates. For grayscale image, just corresponding intensity is returned.
Numpy is an optimized library for fast array calculations. So simply accessing each and every pixel value and modifying it will be very slow and it is discouraged.
Image properties include number of rows, columns, and channels; type of image data; number of pixels; etc. The shape of an image is accessed by img.
It returns a tuple of the number of rows, columns, and channels if the image is color :. Sometimes, you will have to play with certain regions of images. For eye detection in images, first face detection is done over the entire image. When a face is obtained, we select the face region alone and search for eyes inside it instead of searching the whole image.
It improves accuracy because eyes are always on faces :D and performance because we search in a small area. ROI is again obtained using Numpy indexing. Here I am selecting the ball and copying it to another region in the image:. Sometimes you will need to work separately on the B,G,R channels of an image.
In this case, you need to split the BGR image into single channels. In other cases, you may need to join these individual channels to create a BGR image.
You can do this simply by:. Suppose you want to set all the red pixels to zero - you do not need to split the channels first.
Numpy indexing is faster:. So use it only if necessary. Otherwise go for Numpy indexing. If you want to create a border around an image, something like a photo frame, you can use cv.
But it has more applications for convolution operation, zero padding etc. This function takes following arguments:. See the result below. Image is displayed with matplotlib.In this tutorial, we will learn about popular colorspaces used in Computer Vision and use it for color based segmentation. Inthe Hungarian Patent HU introduced a puzzle with just one right solution out of 43,, 43 quintillion possibilities. He was trying to use color segmentation to find the current state of the cube.
While his color segmentation code worked pretty well during evenings in his room, it fell apart during daytime outside his room! He asked me for help and I immediately understood where he was going wrong. Like many other amateur computer vision enthusiasts, he was not taking into account the effect of different lighting conditions while doing color segmentation.
We face this problem in many computer vision applications involving color based segmentation like skin tone detection, traffic light recognition etc.
Color Detection in Python with OpenCV
In this section, we will cover some important color spaces used in computer vision. Instead, we will develop a basic intuition and learn some important properties which will be useful in making decisions later on. It will get loaded in BGR format by default. We can convert between different colorspaces using the OpenCV function cvtColor as will be shown later.
The first image is taken under outdoor conditions with bright sunlight, while the second is taken indoor with normal lighting conditions.
Let us split the two images into their R, G and B components and observe them to gain more insight into the color space. If you look at the blue channel, it can be seen that the blue and white pieces look similar in the second image under indoor lighting conditions but there is a clear difference in the first image. This kind of non-uniformity makes color based segmentation very difficult in this color space.
Below we have summarized the inherent problems associated with the RGB Color space:. The Lab color space is quite different from the RGB color space. In RGB color space the color information is separated into three channels but the same three channels also encode brightness information. On the other hand, in Lab color space, the L channel is independent of color information and encodes brightness only.
The other two channels encode color. Now that we have got some idea about the different color spaces, lets first try to use them to detect the Green color from the cube. Find the approximate range of values of green color for each color space. Extract all pixels from the image which have values close to that of the green pixel.
Lets see some more results. Doing the same experiment to detect the yellow color gives the following results. But why is it that the results are so bad? This is because we had taken a wild guess of 40 for the threshold.
I made another interactive demo where you can play with the values and try to find one that works for all the images. Check out the screenshot. We cannot just take some threshold by trial and error blindly. We are not using the power of the color spaces by doing so.
I have collected 10 images of the cube under varying illumination conditions and separately cropped every color to get 6 datasets for the 6 different colors. You can see how much change the colors undergo visually. The density plot or the 2D Histogram gives an idea about the variations in values for a given color.
For example, Ideally the blue channel of a blue colored image should always have the value of But practically, it is distributed between 0 to It can be seen that under similar lighting conditions all the plots are very compact.Matrix ExpressionsabsdiffconvertScaleAbs.
Calculates the per-element absolute difference between two arrays or between an array and a scalar. Absolute difference between an array and a scalar when the second array is constructed from Scalar or has as many elements as the number of channels in src1 :. Absolute difference between a scalar and an array when the first array is constructed from Scalar or has as many elements as the number of channels in src2 :. In case of multi-channel arrays, each channel is processed independently.
You may even get a negative value in the case of overflow. Sum of two arrays when both input arrays have the same size and the same number of channels:. Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of elements as src1. Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of elements as src2. The input arrays and the output array can all have the same or different depths.
For example, you can add a bit unsigned array to a 8-bit signed array and store the sum as a bit floating-point array. Depth of the output array is determined by the dtype parameter. In the second and third cases above, as well as in the first case, when src1. In this case, the output array will have the same depth as the input array, be it src1src2 or both.
You may even get result of an incorrect sign in the case of overflow. The function addWeighted calculates the weighted sum of two arrays as follows:.
Two arrays when src1 and src2 have the same size:. An array and a scalar when src2 is constructed from Scalar or has the same number of elements as src1. A scalar and an array when src1 is constructed from Scalar or has the same number of elements as src2. In case of floating-point arrays, their machine-specific bit representations usually IEEEcompliant are used for the operation.
In the second and third cases above, the scalar is first converted to the array type. In case of a floating-point input array, its machine-specific bit representation usually IEEEcompliant is used for the operation.
Color spaces in OpenCV (C++ / Python)
In the 2nd and 3rd cases above, the scalar is first converted to the array type. The covariance matrix will be nsamples x nsamples. Such an unusual covariance matrix is used for fast PCA of a set of very large vectors see, for example, the EigenFaces technique for face recognition. The functions calcCovarMatrix calculate the covariance matrix and, optionally, the mean vector of the set of input vectors. PCAmulTransposedMahalanobis. The function cartToPolar calculates either the magnitude, angle, or both for every 2D vector x I ,y I :.
The angles are calculated with accuracy about 0. For the point 0,0the angle is set to 0. SobelScharr. The functions checkRange check that every array element is neither NaN nor infinite.
If some values are out of range, position of the first outlier is stored in pos when pos! Elements of two arrays when src1 and src2 have the same size:. Elements of src1 with a scalar src2 when src2 is constructed from Scalar or has a single element:. When the comparison result is true, the corresponding element of output array is set to The comparison operations can be replaced with the equivalent matrix expressions:. The function completeSymm copies the lower half of a square matrix to its another half.
The matrix diagonal remains unchanged:.Use Mat::splitwhich splits multi-channel image into several single-channel arrays. Your answer definitely helped me, but I think there is a tiny mistake in the first line. The Mat should be called src, since afterwards you use src in split. The code really helped me. I just want to ask about end results of channels, they are really red, green and blue channels images like in matlab or in grayscale three same images but different ray level?
I am asking this because I get the color of channels iin grayscale instead of red, green and blue channel image respectively, thanks in advance. You will get three grayscale images representing the colors in the original images.
They should definitly not be the same images except if the original image was grayscale itself. Asked: What's the best way to segment different coloured blobs? Counting the number of colours in an image. How to split cv2. OpenCV VideoCapture. Problem creating Mat from camera buffers edited.
Ask Your Question.NOTE: Are you interested in machine learning? In this tutorial, we will be doing basic color detection in OpenCV version 2. Note that you will also need to install NumPy to run the code in this article. On a computer, color can be represented in many formats. With BGR, a pixel is represented by 3 parameters, blue, green, and red. Each parameter usually has a value from 0 — For example, a pure blue pixel on your computer screen would have a B value ofa G value of 0, and a R value of 0.
This pixel is parts blue, 0 parts green, and 0 parts red. Instead, it uses hue, which is the color or shade of the pixel. The saturation is the intensity of the color. A saturation of 0 is white, and a saturation of is maximum intensity. Another way to think about it is to imagine saturation as the colorfulness of a certain pixel.
Value is the simplest of the three, as it is just how bright or dark the color is. First, copy the following code into your favorite text editor and save it as converter.
The lower and upper bound part will be explained later. Now, we need an image to do color detection on. Download the image below and place it in the same directory as converter. The lower range is the minimum shade of red that will be detected, and the upper range is the maximum shade of red that will be detected.
To do so, we will need to obtain the RGB numbers for the red circle. I personally prefer Gimp, so I will be using that for the color picker feature. Simply use the color picker and click on the red circle, and you will have copied it. Now, click on the red shade that you copied. We can see that red equalsgreen equals 28, and blue equals Almost all the operations in this section is mainly related to Numpy rather than OpenCV.
A good knowledge of Numpy is required to write better optimized code with OpenCV. Examples will be shown in Python terminal since most of them are just single line codes.
You can access a pixel value by its row and column coordinates. For grayscale image, just corresponding intensity is returned. Numpy is a optimized library for fast array calculations.
So simply accessing each and every pixel values and modifying it will be very slow and it is discouraged. Above mentioned method is normally used for selecting a region of array, say first 5 rows and last 3 columns like that. For individual pixel access, Numpy array methods, array.
But it always returns a scalar. So if you want to access all B,G,R values, you need to call array. Image properties include number of rows, columns and channels, type of image data, number of pixels etc.
Shape of image is accessed by img. It returns a tuple of number of rows, columns and channels if image is color :. If image is grayscale, tuple returned contains only number of rows and columns. So it is a good method to check if loaded image is grayscale or color image. Total number of pixels is accessed by img. Image datatype is obtained by img. Sometimes, you will have to play with certain region of images. For eye detection in images, first perform face detection over the image until the face is found, then search within the face region for eyes.
This approach improves accuracy because eyes are always on faces :D and performance because we search for a small area. ROI is again obtained using Numpy indexing.
Here I am selecting the ball and copying it to another region in the image:. The B,G,R channels of an image can be split into their individual planes when needed. Then, the individual channels can be merged back together to form a BGR image again. This can be performed by:. Suppose, you want to make all the red pixels to zero, you need not split like this and put it equal to zero. You can simply use Numpy indexing which is faster. Numpy indexing is much more efficient and should be used if possible.
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If you want to create a border around the image, something like a photo frame, you can use cv2. But it has more applications for convolution operation, zero padding etc. This function takes following arguments:. See the result below.