Goal
----
-- In this tutorial, you will learn Simple thresholding, Adaptive thresholding, Otsu's thresholding
- etc.
-- You will learn these functions : **cv.threshold**, **cv.adaptiveThreshold** etc.
+- In this tutorial, you will learn Simple thresholding, Adaptive thresholding and Otsu's thresholding.
+- You will learn the functions **cv.threshold** and **cv.adaptiveThreshold**.
Simple Thresholding
-------------------
-Here, the matter is straight forward. If pixel value is greater than a threshold value, it is
-assigned one value (may be white), else it is assigned another value (may be black). The function
-used is **cv.threshold**. First 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. Different types are:
+Here, the matter is straight forward. For every pixel, the same threshold value is applied.
+If the pixel value is smaller than the threshold, it is set to 0, otherwise it is set to a maximum value.
+The function **cv.threshold** is used to apply the thresholding.
+The first argument is the source image, which **should be a grayscale image**.
+The second argument is the threshold value which is used to classify the pixel values.
+The third argument is the maximum value which is assigned to pixel values exceeding the threshold.
+OpenCV provides different types of thresholding which is given by the fourth parameter of the function.
+Basic thresholding as described above is done by using the type cv.THRESH_BINARY.
+All simple thresholding types are:
- cv.THRESH_BINARY
- cv.THRESH_BINARY_INV
- cv.THRESH_TOZERO
- cv.THRESH_TOZERO_INV
-Documentation clearly explain what each type is meant for. Please check out the documentation.
+See the documentation of the types for the differences.
-Two outputs are obtained. First one is a **retval** which will be explained later. Second output is
-our **thresholded image**.
+The method returns two outputs.
+The first is the threshold that was used and the second output is the **thresholded image**.
-Code :
+This code compares the different simple thresholding types:
@code{.py}
import cv2 as cv
import numpy as np
plt.show()
@endcode
-@note To plot multiple images, we have used plt.subplot() function. Please checkout Matplotlib docs
-for more details.
+@note To plot multiple images, we have used the plt.subplot() function. Please checkout the matplotlib docs for more details.
-Result is given below :
+The code yields this result:
Adaptive Thresholding
---------------------
-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.
+In the previous section, we used one global value as a threshold.
+But this might not be good in all cases, e.g. if an image has different lighting conditions in different areas.
+In that case, adaptive thresholding thresholding can help.
+Here, the algorithm determines the threshold for a pixel based on a small region around it.
+So we get different thresholds for different regions of the same image which gives better results for images with varying illumination.
-It has three ‘special’ input params and only one output argument.
+Additionally to the parameters described above, the method cv.adaptiveThreshold three input parameters:
-**Adaptive Method** - It decides how thresholding value is calculated.
- - cv.ADAPTIVE_THRESH_MEAN_C : threshold value is the mean of neighbourhood area.
- - cv.ADAPTIVE_THRESH_GAUSSIAN_C : threshold value is the weighted sum of neighbourhood
- values where weights are a gaussian window.
+The **adaptiveMethod** decides how the threshold value is calculated:
+ - cv.ADAPTIVE_THRESH_MEAN_C: The threshold value is the mean of the neighbourhood area minus the constant **C**.
+ - cv.ADAPTIVE_THRESH_GAUSSIAN_C: The threshold value is a gaussian-weighted sum of the neighbourhood
+ values minus the constant **C**.
-**Block Size** - It decides the size of neighbourhood area.
+The **blockSize** determines the size of the neighbourhood area and **C** is a constant that is subtracted from the mean or weighted sum of the neighbourhood pixels.
-**C** - It is just a constant which is subtracted from the mean or weighted mean calculated.
-
-Below piece of code compares global thresholding and adaptive thresholding for an image with varying
+The code below compares global thresholding and adaptive thresholding for an image with varying
illumination:
@code{.py}
import cv2 as cv
plt.xticks([]),plt.yticks([])
plt.show()
@endcode
-Result :
+Result:
-Otsu’s Binarization
+Otsu's Binarization
-------------------
-In the first section, I told you there is a second parameter **retVal**. Its use comes when we go
-for Otsu’s Binarization. So 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 words, 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 images which are not bimodal, binarization
-won’t be accurate.)
-
-For this, our cv.threshold() function is used, but pass an extra flag, cv.THRESH_OTSU. **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. In first case, I applied global thresholding
-for a value of 127. In second case, I applied Otsu’s thresholding directly. In third case, I
-filtered image with a 5x5 gaussian kernel to remove the noise, then applied Otsu thresholding. See
-how noise filtering improves the result.
+In global thresholding, we used an arbitrary chosen value as a threshold.
+In contrast, Otsu's method avoids having to choose a value and determines it automatically.
+
+Consider an image with only two distinct image values (*bimodal image*), where the histogram would only consist of two peaks.
+A good threshold would be in the middle of those two values.
+Similarly, Otsu's method determines an optimal global threshold value from the image histogram.
+
+In order to do so, the cv.threshold() function is used, where cv.THRESH_OTSU is passed as an extra flag.
+The threshold value can be chosen arbitrary.
+The algorithm then finds the optimal threshold value which is returned as the first output.
+
+Check out the example below.
+The input image is a noisy image.
+In the first case, global thresholding with a value of 127 is applied.
+In the second case, Otsu's thresholding is applied directly.
+In the third case, the image is first filtered with a 5x5 gaussian kernel to remove the noise, then Otsu thresholding is applied.
+See how noise filtering improves the result.
@code{.py}
import cv2 as cv
import numpy as np
plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])
plt.show()
@endcode
-Result :
+Result:
-### How Otsu's Binarization Works?
+### How does Otsu's Binarization work?
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 :
+minimizes the **weighted within-class variance** given by the relation:
\f[\sigma_w^2(t) = q_1(t)\sigma_1^2(t)+q_2(t)\sigma_2^2(t)\f]
\f[q_1(t) = \sum_{i=1}^{t} P(i) \quad \& \quad q_2(t) = \sum_{i=t+1}^{I} P(i)\f]\f[\mu_1(t) = \sum_{i=1}^{t} \frac{iP(i)}{q_1(t)} \quad \& \quad \mu_2(t) = \sum_{i=t+1}^{I} \frac{iP(i)}{q_2(t)}\f]\f[\sigma_1^2(t) = \sum_{i=1}^{t} [i-\mu_1(t)]^2 \frac{P(i)}{q_1(t)} \quad \& \quad \sigma_2^2(t) = \sum_{i=t+1}^{I} [i-\mu_2(t)]^2 \frac{P(i)}{q_2(t)}\f]
It actually finds a value of t which lies in between two peaks such that variances to both classes
-are minimum. It can be simply implemented in Python as follows:
+are minimal. It can be simply implemented in Python as follows:
@code{.py}
img = cv.imread('noisy2.png',0)
blur = cv.GaussianBlur(img,(5,5),0)
ret, otsu = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
print( "{} {}".format(thresh,ret) )
@endcode
-*(Some of the functions may be new here, but we will cover them in coming chapters)*
Additional Resources
--------------------
projects / platform / upstream / opencv.git / commitdiff