Tag Archives: image thresholding

Thresholding using cv2.inRange() function

In the previous blogs, we discussed various thresholding methods such as Otsu, adaptive, BHT, etc. In this blog, we will learn how to segment out a particular region or color from an image. This is naively equivalent to multiple thresholding where we assign a particular value to the region falling in between the two thresholds. Remaining region is assigned a different value. OpenCV provides an inbuilt function for this as shown below

cv2.inRange(src, lowerb, upperb)

1	cv2.inRange(src, lowerb, upperb)

Here, src is the input image. ‘lowerb’ and ‘upperb’ denotes the lower and upper boundary of the threshold region. A pixel is set to 255 if it lies within the boundaries specified otherwise set to 0. This way it returns the thresholded image.

A nice way to understand any method is to play with the arguments and for that, trackbars come very handily. Let’s segment the image based on the color as any color (and its shades) mostly covers some range of intensity values. Thus for segmentation any color this function will be very useful. Below is the code where I have created trackbars to segment any color in a live webcam feed.

import cv2
import numpy as np
 
def nothing(x):
    pass

# Open the camera
cap = cv2.VideoCapture(0) 
 
# Create a window
cv2.namedWindow('image')
 
# create trackbars for color change
cv2.createTrackbar('lowH','image',0,179,nothing)
cv2.createTrackbar('highH','image',179,179,nothing)
 
cv2.createTrackbar('lowS','image',0,255,nothing)
cv2.createTrackbar('highS','image',255,255,nothing)
 
cv2.createTrackbar('lowV','image',0,255,nothing)
cv2.createTrackbar('highV','image',255,255,nothing)
 
while(True):
    ret, frame = cap.read()
 
    # get current positions of the trackbars
    ilowH = cv2.getTrackbarPos('lowH', 'image')
    ihighH = cv2.getTrackbarPos('highH', 'image')
    ilowS = cv2.getTrackbarPos('lowS', 'image')
    ihighS = cv2.getTrackbarPos('highS', 'image')
    ilowV = cv2.getTrackbarPos('lowV', 'image')
    ihighV = cv2.getTrackbarPos('highV', 'image')
    
    # convert color to hsv because it is easy to track colors in this color model
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    lower_hsv = np.array([ilowH, ilowS, ilowV])
    higher_hsv = np.array([ihighH, ihighS, ihighV])
    # Apply the cv2.inrange method to create a mask
    mask = cv2.inRange(hsv, lower_hsv, higher_hsv)
    # Apply the mask on the image to extract the original color
    frame = cv2.bitwise_and(frame, frame, mask=mask)
    cv2.imshow('image', frame)
    # Press q to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

import cv2

import numpy as np

def nothing(x):

pass

# Open the camera

cap = cv2.VideoCapture(0)

# Create a window

cv2.namedWindow('image')

# create trackbars for color change

cv2.createTrackbar('lowH','image',0,179,nothing)

cv2.createTrackbar('highH','image',179,179,nothing)

cv2.createTrackbar('lowS','image',0,255,nothing)

cv2.createTrackbar('highS','image',255,255,nothing)

cv2.createTrackbar('lowV','image',0,255,nothing)

cv2.createTrackbar('highV','image',255,255,nothing)

while(True):

ret, frame = cap.read()

# get current positions of the trackbars

ilowH = cv2.getTrackbarPos('lowH', 'image')

ihighH = cv2.getTrackbarPos('highH', 'image')

ilowS = cv2.getTrackbarPos('lowS', 'image')

ihighS = cv2.getTrackbarPos('highS', 'image')

ilowV = cv2.getTrackbarPos('lowV', 'image')

ihighV = cv2.getTrackbarPos('highV', 'image')

# convert color to hsv because it is easy to track colors in this color model

hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

lower_hsv = np.array([ilowH, ilowS, ilowV])

higher_hsv = np.array([ihighH, ihighS, ihighV])

# Apply the cv2.inrange method to create a mask

mask = cv2.inRange(hsv, lower_hsv, higher_hsv)

# Apply the mask on the image to extract the original color

frame = cv2.bitwise_and(frame, frame, mask=mask)

cv2.imshow('image', frame)

# Press q to exit

if cv2.waitKey(1) & 0xFF == ord('q'):

break

cap.release()

cv2.destroyAllWindows()

Play around with the trackbars to get a feel of cv2.inRange function. 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.

Adaptive Thresholding

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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.

Balanced histogram thresholding

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In the previous blogs, we discussed different methods for automatically finding the global threshold for an image. For instance, the iterative method, Otsu’s method, etc. In this blog, we will discuss another very simple approach for automatic thresholding – Balanced histogram thresholding. As clear from the name, this method tries to automatically find the threshold by balancing the image histogram. Let’s understand this method in detail.

Note: This method assumes that the image histogram is bimodal and a reasonable contrast ratio exists between the background and the region of interest.

Concept

Suppose you have a perfectly balanced histogram i.e. a histogram where the distribution of the background and the roi is the same. If you place such a histogram over the lever, it will be balanced. And the optimum threshold will be at the center of the lever as shown in the figure below

This is the main idea behind the Balanced Histogram Thresholding. This method tries to balance the image histogram and then infer the threshold value from that.

But in real-life situations, we don’t encounter images with such perfectly balanced histograms. So, let’s see how this method balances the unbalanced histograms.

First, it places the histogram over the lever and calculates the center point.
Then this calculates the left side and right side weights from the center point.
Removes weight from the heavier side and adjust the center.
Repeat the above two steps until the starting and the endpoints are equal to the center.

The whole procedure can be summed up in the below gif (taken from Wikipedia)

**Credits: By Power3d – Own work, CC BY-SA 3.0**

Below is the python code for this. Here, i_s, i_e are the starting and the endpoints of the histogram and i_m is the center

def balanced_hist_thresholding(b):
    # Starting point of histogram
    i_s = np.min(np.where(b[0]>0))
    # End point of histogram
    i_e = np.max(np.where(b[0]>0))
    # Center of histogram
    i_m = (i_s + i_e)//2
    # Left side weight
    w_l = np.sum(b[0][0:i_m+1])
    # Right side weight
    w_r = np.sum(b[0][i_m+1:i_e+1])
    # Until starting point not equal to endpoint
    while (i_s != i_e):
        # If right side is heavier
        if (w_r > w_l):
            # Remove the end weight
            w_r -= b[0][i_e]
            i_e -= 1
            # Adjust the center position and recompute the weights
            if ((i_s+i_e)//2) < i_m:
                w_l -= b[0][i_m]
                w_r += b[0][i_m]
                i_m -= 1
        else:
            # If left side is heavier, remove the starting weight
            w_l -= b[0][i_s]
            i_s += 1
            # Adjust the center position and recompute the weights
            if ((i_s+i_e)//2) >= i_m:
                w_l += b[0][i_m+1]
                w_r -= b[0][i_m+1]
                i_m += 1
    return i_m

def balanced_hist_thresholding(b):

# Starting point of histogram

i_s = np.min(np.where(b[0]>0))

# End point of histogram

i_e = np.max(np.where(b[0]>0))

# Center of histogram

i_m = (i_s + i_e)//2

# Left side weight

w_l = np.sum(b[0][0:i_m+1])

# Right side weight

w_r = np.sum(b[0][i_m+1:i_e+1])

# Until starting point not equal to endpoint

while (i_s != i_e):

# If right side is heavier

if (w_r > w_l):

# Remove the end weight

w_r -= b[0][i_e]

i_e -= 1

# Adjust the center position and recompute the weights

if ((i_s+i_e)//2) < i_m:

w_l -= b[0][i_m]

w_r += b[0][i_m]

i_m -= 1

else:

# If left side is heavier, remove the starting weight

w_l -= b[0][i_s]

i_s += 1

# Adjust the center position and recompute the weights

if ((i_s+i_e)//2) >= i_m:

w_l += b[0][i_m+1]

w_r -= b[0][i_m+1]

i_m += 1

return i_m

The above function takes the image histogram as the input and returns the optimum threshold. Let’s take an example to check how this works.

import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(7)

# Create a sample image
img = np.random.normal(40,10,size=(500,500)).astype('uint8')
img[img>100]=40
img[100:400,100:400] = np.random.normal(150,20,size=(300,300)).astype('uint8')

# Plot the histogram
b1 = plt.hist(img.ravel(),256,[0,256])
plt.show()

import cv2

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

np.random.seed(7)

# Create a sample image

img = np.random.normal(40,10,size=(500,500)).astype('uint8')

img[img>100]=40

img[100:400,100:400] = np.random.normal(150,20,size=(300,300)).astype('uint8')

# Plot the histogram

b1 = plt.hist(img.ravel(),256,[0,256])

plt.show()

Below is the histogram of the image constructed.

Now, let’s apply the Balanced Histogram thresholding method to check what threshold value this outputs.

thresh_value = balanced_hist_thresholding(b1)
>>> 87

1 2	thresh_value = balanced_hist_thresholding(b1) >>> 87

87 looks like a reasonable threshold, check the image histogram above. So, that’s all for this time. 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.

Optimum Global Thresholding using Otsu’s Method

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In the previous blog, we discussed global thresholding and how to find the global threshold using the iterative approach. In this blog, we will discuss Otsu’s method, named after Nobuyuki Otsu, that automatically finds the global threshold. So, let’s discuss this method in detail.

Note: This method assumes that the image histogram is bimodal and a reasonable contrast ratio exists between the background and the region of interest.

In simple terms, Otsu’s method tries to find a threshold value which minimizes the weighted within-class variance. Since Variance is the spread of the distribution about the mean. Thus, minimizing the within-class variance will tend to make the classes compact.

Let’s say we threshold a histogram at a value “t”. This produces two regions – left and right of “t” whose variance is given by σ²₀ and σ²₁. Then the weighted within-class variance is given by

where w₀(t) and w₁(t) are the weights given to each class. Weights are total pixels in a thresholded region (left or right) divided by the total image pixels. Let’s take a simple example to understand how to calculate these.

Suppose we have the following histogram and we want to find the weighted within-class variance corresponding to threshold value 1.

Below are the weights and the variances calculated for left and the right regions obtained after thresholding at value 1.

Similarly, we will iterate over all the possible threshold values, calculate the weighted within-class variance for each of the thresholds. The optimum threshold will be the one with the minimum within-class variance.

Now, let’s see how to do this using python.

import cv2
import matplotlib.pyplot as plt
import numpy as np

# Create a sample image
np.random.seed(7)
img = np.random.normal(40,10,size=(500,500)).astype('uint8')
img[img>100]=40
img[100:400,100:400] = np.random.normal(150,20,size=(300,300)).astype('uint8')

# plot the histogram
hist = plt.hist(img.ravel(),256,[0,256])
plt.show()

import cv2

import matplotlib.pyplot as plt

import numpy as np

# Create a sample image

np.random.seed(7)

img = np.random.normal(40,10,size=(500,500)).astype('uint8')

img[img>100]=40

img[100:400,100:400] = np.random.normal(150,20,size=(300,300)).astype('uint8')

# plot the histogram

hist = plt.hist(img.ravel(),256,[0,256])

plt.show()

The image histogram is shown below

Now, let’s calculate the within-class variance using the steps which we discussed earlier.

# Set minimum value to infinity
final_min = np.inf
# total pixels in an image
total = np.sum(hist[0])
for i in range(256):
    # Split regions based on threshold
    left, right = np.hsplit(hist[0],[i])
    # Splt intensity values based on threshold
    left_bins, right_bins = np.hsplit(hist[1],[i])
    # Only perform thresholding if neither side empty
    if np.sum(left) !=0 and np.sum(right) !=0:
        # Calculate weights on left and right sides
        w_0 = np.sum(left)/total
        w_1 = np.sum(right)/total
        # Calculate the mean for both sides
        mean_0 = np.dot(left,left_bins)/np.sum(left)
        mean_1 = np.dot(right,right_bins[:-1])/np.sum(right)  # right_bins[:-1] because matplotlib has uses 1 bin extra
        # Calculate variance of both sides
        var_0 = np.dot(((left_bins-mean_0)**2),left)/np.sum(left)
        var_1 = np.dot(((right_bins[:-1]-mean_1)**2),right)/np.sum(right)
        # Calculate final within class variance
        final = w_0*var_0 + w_1*var_1
        # if variance minimum, update it
        if final<final_min:
            final_min = final
            thresh = i
        
print(thresh) # 95

# Set minimum value to infinity

final_min = np.inf

# total pixels in an image

total = np.sum(hist[0])

for i in range(256):

# Split regions based on threshold

left, right = np.hsplit(hist[0],[i])

# Splt intensity values based on threshold

left_bins, right_bins = np.hsplit(hist[1],[i])

# Only perform thresholding if neither side empty

if np.sum(left) !=0 and np.sum(right) !=0:

# Calculate weights on left and right sides

w_0 = np.sum(left)/total

w_1 = np.sum(right)/total

# Calculate the mean for both sides

mean_0 = np.dot(left,left_bins)/np.sum(left)

mean_1 = np.dot(right,right_bins[:-1])/np.sum(right) # right_bins[:-1] because matplotlib has uses 1 bin extra

# Calculate variance of both sides

var_0 = np.dot(((left_bins-mean_0)**2),left)/np.sum(left)

var_1 = np.dot(((right_bins[:-1]-mean_1)**2),right)/np.sum(right)

# Calculate final within class variance

final = w_0*var_0 + w_1*var_1

# if variance minimum, update it

if final<final_min:

final_min = final

thresh = i

print(thresh) # 95

The gif below shows how the within-class variance (blue dots) varies with the threshold value for the above histogram. The optimum threshold value is the one where the within-class variance is minimum.

OpenCV also provides a builtin function to calculate the threshold using this method.

OpenCV

You just need to pass an extra flag, cv2.THRESH_OTSU in the cv2.threshold() function which we discussed in the previous blog. The optimum threshold value will be returned by this along with the thresholded image. Let’s see how to use this.

gray = cv2.imread('kang.jpg',0)
retval, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

1 2	gray = cv2.imread('kang.jpg',0) retval, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

A Faster Approach

We all know that minimizing within-class variance is equivalent to maximizing between-class variance. This maximization operation can be implemented recursively and is faster than the earlier method. The expression for between-class variance is given by

Below are the steps to calculate recursively between-class variance.

Calculate the histogram of the image.
Set up weights and means corresponding to the “0” threshold value.
Loop through all the threshold values
1. Update the weights and the mean
2. Calculate the between-class variance
The optimum threshold will be the one with the max variance.

Below is the code in Python that implements the above steps.

# Calculate the histogram
hist = plt.hist(img1.ravel(),256,[0,256])
# Total pixels in the image
total = np.sum(hist[0])
# calculate the initial weights and the means
left, right = np.hsplit(hist[0],[0])
left_bins, right_bins = np.hsplit(hist[1],[0])
# left weights
w_0 = 0.0
# Right weights
w_1 = np.sum(right)/total
# Left mean
mean_0 = 0.0
weighted_sum_0 = 0.0
# Right mean
weighted_sum_1 = np.dot(right,right_bins[:-1])
mean_1 = weighted_sum_1/np.sum(right)
def recursive_otsu1(hist, w_0=w_0, w_1=w_1, weighted_sum_0=weighted_sum_0, weighted_sum_1=weighted_sum_1, thres=1, fn_max=-np.inf, thresh=0, total=total):
    if thres<=255:
        # To pass the division by zero warning
        if np.sum(hist[0][:thres+1]) !=0 and np.sum(hist[0][thres+1:]) !=0:
            # Update the weights
            w_0 += hist[0][thres]/total
            w_1 -= hist[0][thres]/total
            # Update the mean
            weighted_sum_0 += (hist[0][thres]*hist[1][thres])
            mean_0 = weighted_sum_0/np.sum(hist[0][:thres+1])
            weighted_sum_1 -= (hist[0][thres]*hist[1][thres])
            if thres == 255:
                mean_1 = 0.0
            else:
                mean_1 = weighted_sum_1/np.sum(hist[0][thres+1:])
            # Calculate the between-class variance
            out = w_0*w_1*((mean_0-mean_1)**2)
            # # if variance maximum, update it
            if out>fn_max:
                fn_max = out
                thresh = thres
        return recursive_otsu1(hist, w_0=w_0, w_1=w_1, weighted_sum_0=weighted_sum_0, weighted_sum_1=weighted_sum_1, thres=thres+1, fn_max=fn_max, thresh=thresh, total=total)
    # Stopping condition
    else:
        return fn_max,thresh
    
    
# Check the results
var_value, thresh_value = recursive_otsu1(hist, w_0=w_0, w_1=w_1, weighted_sum_0=weighted_sum_0, weighted_sum_1=weighted_sum_1, thres=1, fn_max=-np.inf, thresh=0, total=total)
print(var_value, thresh_value)

# Calculate the histogram

hist = plt.hist(img1.ravel(),256,[0,256])

# Total pixels in the image

total = np.sum(hist[0])

# calculate the initial weights and the means

left, right = np.hsplit(hist[0],[0])

left_bins, right_bins = np.hsplit(hist[1],[0])

# left weights

w_0 = 0.0

# Right weights

w_1 = np.sum(right)/total

# Left mean

mean_0 = 0.0

weighted_sum_0 = 0.0

# Right mean

weighted_sum_1 = np.dot(right,right_bins[:-1])

mean_1 = weighted_sum_1/np.sum(right)

def recursive_otsu1(hist, w_0=w_0, w_1=w_1, weighted_sum_0=weighted_sum_0, weighted_sum_1=weighted_sum_1, thres=1, fn_max=-np.inf, thresh=0, total=total):

if thres<=255:

# To pass the division by zero warning

if np.sum(hist[0][:thres+1]) !=0 and np.sum(hist[0][thres+1:]) !=0:

# Update the weights

w_0 += hist[0][thres]/total

w_1 -= hist[0][thres]/total

# Update the mean

weighted_sum_0 += (hist[0][thres]*hist[1][thres])

mean_0 = weighted_sum_0/np.sum(hist[0][:thres+1])

weighted_sum_1 -= (hist[0][thres]*hist[1][thres])

if thres == 255:

mean_1 = 0.0

else:

mean_1 = weighted_sum_1/np.sum(hist[0][thres+1:])

# Calculate the between-class variance

out = w_0*w_1*((mean_0-mean_1)**2)

# # if variance maximum, update it

if out>fn_max:

fn_max = out

thresh = thres

return recursive_otsu1(hist, w_0=w_0, w_1=w_1, weighted_sum_0=weighted_sum_0, weighted_sum_1=weighted_sum_1, thres=thres+1, fn_max=fn_max, thresh=thresh, total=total)

# Stopping condition

else:

return fn_max,thresh

# Check the results

var_value, thresh_value = recursive_otsu1(hist, w_0=w_0, w_1=w_1, weighted_sum_0=weighted_sum_0, weighted_sum_1=weighted_sum_1, thres=1, fn_max=-np.inf, thresh=0, total=total)

print(var_value, thresh_value)

This is how you can implement otsu’s method recursively if you consider maximizing between-class variance. Now, let’s discuss what are the limitations of this method.

Limitations

Otsu’s method is only guaranteed to work when

The histogram should be bimodal.
Reasonable contrast ratio exists between the background and the roi.
Uniform lighting conditions are there.
Image is not affected by noise.
Size of the background and the roi should be comparable.

There are many modifications done to the original Otsu’s algorithm to address these limitations such as two-dimensional Otsu’s method etc. We will discuss some of these modifications in the following blogs.

In the following blogs, we will also discuss how to counter these limitations so as to get satisfactory results with 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.

Improving Global Thresholding

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In the previous blog, we discussed otsu’s method for automatic image thresholding. Then we also discussed the limitations of the otsu’s method. In this blog, we will discuss how to handle these limitations so as to produce satisfactory thresholding results. So, let’s get started.

Case-1: When the noise is present in the image

If the noise is present in the image, then this tends to change the modality of the histogram. The sharp valleys between the peaks of the bimodal histogram start degrading. In that case, the otsu’s method or any other global thresholding method will fail. So, in order to find the global threshold, one should first remove the noise using any smoothing filters like Gaussian, etc. and then apply any automatic thresholding method like otsu, etc.

Case-2: When the object area is small compared to the background area

In this case, the image histogram will be dominated by a large background area. This will increase the probability of any pixel belonging to the background. So, the histogram will no longer exhibit bimodality and thus otsu will result in segmentation error. To prevent this, one should only consider pixels that lie on or near the edges between the objects and the background. Doing so will result in an image histogram with peaks of approximately the same size. Then we can apply any automatic thresholding method like otsu, etc. Below are the steps to implement the above procedure.

Calculate the edge image using any high pass filter like Sobel, Laplacian, etc.
Select any threshold value (T).
Threshold the above edge image to produce a binary mask.
Apply the mask image on the input image using any bitwise operations or any other method.
This results in only those pixels where the mask image was white.
Compute the histogram of only those pixels
Finally, apply any automatic global thresholding method like otsu, etc.

Case-3: When the image is taken under non-uniform illumination conditions

In this case, the histogram no longer remains bimodal and thus we will not be able to segment the image satisfactorily. One of the simplest approaches is to subdivide the image into non-overlapping images/rectangles. The size of these rectangles is chosen such that the illumination is nearly constant in each of these rectangles. Then we will apply any global thresholding technique like otsu for each of these rectangles.

The above procedure only works when the size of the object and the background are comparable in the rectangle. This is quite intuitive as only then we will have a bimodal histogram. Taking care of the background and the object sizes in each rectangle is a tedious task.

So, in the next blog, we will discuss adaptive thresholding that works pretty well for the above conditions. That’s all for this blog. 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

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Image Segmentation is the process of subdividing an image into its constituent regions or objects. In many computer vision applications, image segmentation is very useful to detect the region of interest. For instance, in medical imaging where we have to locate tumors, or in object detection like self-driving cars have to detect pedestrians, traffic signals, etc or for video surveillance, etc. There are a number of methods available to perform image segmentation. For instance, thresholding, clustering methods, graph partitioning methods, and convolutional methods to mention a few.

In this blog, we will discuss Image Thresholding which is one of the simplest methods for image segmentation. In this, we partition the images directly into regions based on the intensity values. So, let’s discuss image thresholding in greater detail.

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: image thresholding

Thresholding using cv2.inRange() function

Adaptive Thresholding

Balanced histogram thresholding

Concept

Optimum Global Thresholding using Otsu’s Method

OpenCV

A Faster Approach

Limitations

Improving Global Thresholding

Case-1: When the noise is present in the image

Case-2: When the object area is small compared to the background area

Case-3: When the image is taken under non-uniform illumination conditions

Image Thresholding

Concept

Applicability Condition