Balanced histogram thresholding

Algorithm

intBHThreshold(int[]histogram){
i_m=(int)((i_s+i_e)/2.0f);// center of the weighing scale I_m
w_l=get_weight(i_s,i_m+1,histogram);// weight on the left W_l
w_r=get_weight(i_m+1,i_e+1,histogram);// weight on the right W_r
while(i_s<=i_e){
if(w_r>w_l){// right side is heavier
w_r-=histogram[i_e--];
if(((i_s+i_e)/2)<i_m){
w_r+=histogram[i_m];
w_l-=histogram[i_m--];
}
}elseif(w_l>=w_r){// left side is heavier
w_l-=histogram[i_s++];
if(((i_s+i_e)/2)>=i_m){
w_l+=histogram[i_m+1];
w_r-=histogram[i_m+1];
i_m++;
}
}
}
returni_m;
}

defbalanced_histogram_thresholding(histogram, minimum_bin_count: int = 5, jump: int = 1) -> int:
"""
 Determines an optimal threshold by balancing the histogram of an image, 
 focusing on significant histogram bins to segment the image into two parts.
 Args:
 histogram (list): The histogram of the image as a list of integers, 
 where each element represents the count of pixels 
 at a specific intensity level.
 minimum_bin_count (int): Minimum count for a bin to be considered in the 
 thresholding process. Bins with counts below this 
 value are ignored, reducing the effect of noise.
 jump (int): Step size for adjusting the threshold during iteration. Larger values 
 speed up convergence but may skip the optimal threshold.
 Returns:
 int: The calculated threshold value. This value represents the intensity level 
 (i.e. the index of the input histogram) that best separates the significant
 parts of the histogram into two groups, which can be interpreted as foreground
 and background. 
 If the function returns -1, it indicates that the algorithm was unable to find 
 a suitable threshold within the constraints (e.g., all bins are below the 
 minimum_bin_count).
 """
 # Find the start and end indices where the histogram bins are significant
 start_index = 0
 while start_index < len(histogram) and histogram[start_index] < minimum_bin_count:
 start_index += 1
 
 end_index = len(histogram) - 1
 while end_index >= 0 and histogram[end_index] < minimum_bin_count:
 end_index -= 1
 # Check if no valid bins are found
 if start_index >= end_index:
 return -1 # Indicates an error or non-applicability
 # Initialize threshold
 threshold = (start_index + end_index) // 2
 # Iteratively adjust the threshold
 while start_index <= end_index:
 # Calculate weights on both sides of the threshold
 weight_left = sum(histogram[start_index:threshold])
 weight_right = sum(histogram[threshold:end_index + 1])
 # Adjust the threshold based on the weights
 if weight_left > weight_right:
 start_index += jump
 elif weight_left < weight_right:
 end_index -= jump
 else: # Equal weights; move both indices
 start_index += jump
 end_index -= jump
 # Calculate the new threshold
 threshold = (start_index + end_index) // 2
 return threshold

References

External links