Tag Archives: adaptive thresholding

Adaptive Thresholding

In the previous blog, we discussed how global thresholding can be a tedious task when dealing with images having non-uniform illumination. This is because you need to ensure that while subdividing an image, each sub-image histogram is bimodal. Otherwise, the segmentation task will fail.

In this blog, we will discuss adaptive thresholding that works well for varying conditions like non-uniform illumination, etc. In this, the threshold value is calculated separately for each pixel using some statistics obtained from its neighborhood. This way we will get different thresholds for different image regions and thus tackles the problem of varying illumination.

The whole procedure can be summed up as:

For each pixel in the image
- Calculate the statistics (such as mean, median, etc.) from its neighborhood. This will be the threshold value for that pixel.
- Compare the pixel value with this threshold

Now, let’s discuss the OpenCV function for adaptive thresholding.

cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C)

1	cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C)

src: 8-bit greyscale image
thresholdType: This tells us what value to assign to pixels greater/less than the threshold. Must be either THRESH_BINARY or THRESH_BINARY_INV. (You can read more about it here).
maxValue: This is the value assigned to the pixels after thresholding. This depends on the thresholding type. If the type is cv2.THRESH_BINARY, all the pixels greater than the threshold are assigned this maxValue.
adaptiveMethod: This tells us how the threshold is calculated from the pixel neighborhood. This currently supports two methods:
- cv2.ADAPTIVE_THRESH_MEAN_C: In this, the threshold value is the mean of the neighborhood area.
- cv2.ADAPTIVE_THRESH_GAUSSIAN_C: In this, the threshold value is the weighted sum of the neighborhood area. This uses Gaussian weights computed using getGaussiankernel() method. You can read more about it here.
blockSize: This is the neighborhood size.
C: a constant which is subtracted from the threshold.

As discussed OpenCV only provides mean and weighted mean to serve as the threshold. But don’t limit yourself to these two statistics. Try other statistics like standard deviation, median, etc. by writing your own helper function. Let’s see how to use this.

import cv2
# Load the image
img1 = cv2.imread("D:/downloads/adap1.jpg",0)
# Apply Otsu method
ret, thres = cv2.threshold(img2,127,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Apply adaptive threshold
th3 = cv2.adaptiveThreshold(img2,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,5,2)
# Display the result
cv2.imshow('original',img1)
cv2.imshow('otsu', thres)
cv2.imshow('adaptive', th3)
cv2.waitKey(0)

import cv2

# Load the image

img1 = cv2.imread("D:/downloads/adap1.jpg",0)

# Apply Otsu method

ret, thres = cv2.threshold(img2,127,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Apply adaptive threshold

th3 = cv2.adaptiveThreshold(img2,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,5,2)

# Display the result

cv2.imshow('original',img1)

cv2.imshow('otsu', thres)

cv2.imshow('adaptive', th3)

cv2.waitKey(0)

See how effective adaptive thresholding is in the case of non-uniform illumination. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Global Thresholding

OpenCV

cv2.threshold(src, thresh, maxval, type) → retval, dst

1	cv2.threshold(src, thresh, maxval, type) → retval, dst

This function returns the thresholded image(dst) and the threshold value(retval). Its arguments are

src: input greyscale image (8-bit or 32-bit floating point)
thresh: global threshold value
type: Different types that decide “val_high” and “val_low“. In other words, these types decide what value to assign for pixels greater than and less than the threshold. Below figure shows different thresholding types available.
maxval: maximum value to be used with THRESH_BINARY and THRESH_BINARY_INV. Check the below image.

To specify the thresholding type, write “cv2.” as the prefix. For instance, write cv2.THRESH_BINARY if you want to use this type. Let’s take an example

import cv2
# Load an image in the greyscale
img = cv2.imread('D:/downloads/opencv_logo1.PNG',cv2.IMREAD_GRAYSCALE)
# threshold the image
ret, thresh = cv2.threshold(img,75,255,cv2.THRESH_BINARY)

import cv2

# Load an image in the greyscale

img = cv2.imread('D:/downloads/opencv_logo1.PNG',cv2.IMREAD_GRAYSCALE)

# threshold the image

ret, thresh = cv2.threshold(img,75,255,cv2.THRESH_BINARY)

Similarly, you can apply other thresholding types to check how they work. Till now we discussed how to threshold an image using a global threshold value. But we didn’t discuss how to get this threshold value. So, in the next section, let’s discuss this.

How to choose the threshold value?

As already discussed, that global thresholding is a suitable approach only when intensity distributions of the background and the ROI are sufficiently distinct. In other words, there is a clear valley between the peaks of the histogram. We can easily select the threshold value in that situation. But what if we have a number of images. In that case, we don’t manually want to first check the image histogram and then deciding the threshold value. We want something that can automatically estimate the threshold value for each image. Below is the algorithm that can be used for this purpose.

Below is the code for the above algorithm.

def thres_finder(img, thres=20,delta_T=1.0):
    
    # Step-2: Divide the images in two parts
    x_low, y_low = np.where(img<=thres)
    x_high, y_high = np.where(img>thres)
    
    # Step-3: Find the mean of two parts
    mean_low = np.mean(img[x_low,y_low])
    mean_high = np.mean(img[x_high,y_high])
    
    # Step-4: Calculate the new threshold
    new_thres = (mean_low + mean_high)/2
    
    # Step-5: Stopping criteria, otherwise iterate
    if abs(new_thres-thres)< delta_T:
        return new_thres
    else:
        return thres_finder(img, thres=new_thres,delta_T=1.0)

def thres_finder(img, thres=20,delta_T=1.0):

# Step-2: Divide the images in two parts

x_low, y_low = np.where(img<=thres)

x_high, y_high = np.where(img>thres)

# Step-3: Find the mean of two parts

mean_low = np.mean(img[x_low,y_low])

mean_high = np.mean(img[x_high,y_high])

# Step-4: Calculate the new threshold

new_thres = (mean_low + mean_high)/2

# Step-5: Stopping criteria, otherwise iterate

if abs(new_thres-thres)< delta_T:

return new_thres

else:

return thres_finder(img, thres=new_thres,delta_T=1.0)

Now, let’s take an example to check how’s this working.

import cv2
# Load an image in the greyscale
img = cv2.imread('D:/downloads/opencv_logo1.PNG',cv2.IMREAD_GRAYSCALE)
# apply threshold finder
vv1 = thres_finder1(img, thres=30,delta_T=1.0)
# threshold the image
ret, thresh = cv2.threshold(img,vv1,255,cv2.THRESH_BINARY)
# Display the image side by side
out = cv2.hconcat([img,thresh])
cv2.imshow('threshold',out)
cv2.waitKey(0)

import cv2

# Load an image in the greyscale

img = cv2.imread('D:/downloads/opencv_logo1.PNG',cv2.IMREAD_GRAYSCALE)

# apply threshold finder

vv1 = thres_finder1(img, thres=30,delta_T=1.0)

# threshold the image

ret, thresh = cv2.threshold(img,vv1,255,cv2.THRESH_BINARY)

# Display the image side by side

out = cv2.hconcat([img,thresh])

cv2.imshow('threshold',out)

cv2.waitKey(0)

That’s all for this blog. In the next blog, we will discuss how to perform optimum global thresholding using Otsu’s method. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Image Thresholding

Concept

If the pixel value is greater than a threshold value, it is assigned one value (maybe white), else it is assigned another value (maybe black).

In other words, if f(x,y) is the input image then the segmented image g(x,y) is given by

If the threshold value T remains constant over the entire image, then this is known as global thresholding. When the value of T changes over the entire image or depends upon the pixel neighborhood, then this is known as adaptive thresholding. We will cover both these types in greater detail in the following blogs.

Applicability Condition

Thresholding is only guaranteed to work when a good contrast ratio between the region of interest and the background exists. Otherwise, the thresholding will not be able to fully detect the region of interest. Let’s understand this by an example.

Suppose we have two images from which we want to segment the square region (our region of interest) from the background.

Let’s plot the histogram of these two images.

Clearly as expected for “A“, the histogram is showing two peaks corresponding to the square and the background. The separation between the peaks shows that the background and ROI have a good contrast ratio. By choosing a threshold value between the peaks, we will be able to segment out the ROI. While for “B”, the intensity distribution of the ROI and the background is not that distinct. Thus we may not be able to fully segment the ROI.

Thresholded images are shown below (How to choose a threshold value will be discussed in the next blog).

So, always plot the image histogram to check the contrast ratio between the background and the ROI. Only if the contrast ratio is good, choose the thresholding method for image segmentation. Otherwise, look for other methods.

In the next blog, we will discuss global thresholding and how to choose the threshold value using the iterative method. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

TheAILearner

Mastering Artificial Intelligence

Tag Archives: adaptive thresholding

Adaptive Thresholding

Global Thresholding

OpenCV

How to choose the threshold value?

Image Thresholding

Concept

Applicability Condition