Binary images python
Here, the matter is straight forward. If pixel value is greater than a threshold value, it is assigned one value may be whitebinary images python it is assigned another value may be black. The function used is cv2. Binary images python argument is the source image, which should be a grayscale image.
Second argument is the threshold value which is used to classify the pixel values. Third argument is the maxVal which represents the value to be given if pixel value is more than sometimes less than the threshold value.
OpenCV provides different styles of thresholding and it is decided by the fourth parameter of the function. Two outputs are obtained. First one is a retval which will be explained later. Second output is our thresholded image. In the previous section, we used a global value as threshold value. But it may not be good in all the conditions where image has different lighting conditions in different areas.
In that case, we go for adaptive thresholding. In this, the algorithm calculate the threshold for a small regions of the image. So we get different thresholds for different regions of the same image and it gives us better results for images with varying illumination. Below piece of code compares global thresholding and adaptive thresholding for an image with binary images python illumination:.
In the first section, I told you there is a second parameter retVal. Binary images python what is it? In global thresholding, we used an arbitrary value for threshold value, right? So, how can we know a value we selected is good or not? Answer is, trial and error method. But consider a bimodal image In simple binary images python, bimodal image is an image whose histogram has two peaks.
For that image, we can approximately take a value in the middle of those peaks as threshold value, right? That is what Otsu binarization does. So in simple words, it automatically calculates a threshold value from image histogram for a bimodal image. For this, our cv2. For threshold value, simply pass zero. Then the algorithm finds the optimal threshold value and returns you as the second output, retVal. If Otsu thresholding is not used, retVal is same as the threshold value you used.
Check out below example. Input image is a noisy image. Binary images python first case, I applied global thresholding for a value of In third case, I filtered binary images python with a 5x5 gaussian kernel to remove the noise, then applied Otsu thresholding.
See how noise filtering improves the result. This section demonstrates a Python implementation of Otsu's binarization to show how it works actually. If you are not interested, you can skip this. Since we are working with bimodal images, Otsu's algorithm tries to find a threshold value t which minimizes the weighted within-class variance given by the relation:.
It actually finds a value of t which lies in between binary images python peaks such that variances to both classes are minimum.
It can be simply implemented in Python as follows:. Goal In this tutorial, you will learn Simple thresholding, Adaptive thresholding, Otsu's thresholding etc. You will learn these functions: Simple Thresholding Here, the matter is straight forward.
Please check out the documentation.