Image thresholding

import cv2
from matplotlib import pyplot as plt
 
def showimage(myimage, figsize=[10,10]):
    if (myimage.ndim>2):  #This only applies to RGB or RGBA images (e.g. not to Black and White images)
        myimage = myimage[:,:,::-1] #OpenCV follows BGR order, while matplotlib likely follows RGB order
 
    fig, ax = plt.subplots(figsize=figsize)
    ax.imshow(myimage, cmap = 'gray', interpolation = 'bicubic')
    plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
    plt.show()
 
img_originale = cv2.imread('inputCoins.jpg', 1)
showimage(img_originale)
 
img_gray = cv2.imread('inputCoins.jpg', 0)
showimage(img_gray)
 
(retVal, newImg) = cv2.threshold(img_gray, 130, 255, cv2.THRESH_BINARY)
showimage(newImg)
 
newImg = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 15)
showimage(newImg)
 

# Python program to illustrate
# Otsu thresholding type on an image
 
# organizing imports
import cv2
from matplotlib import pyplot as plt
 
def showimage(myimage, figsize=[10,10]):
    if (myimage.ndim>2):  #This only applies to RGB or RGBA images (e.g. not to Black and White images)
        myimage = myimage[:,:,::-1] #OpenCV follows BGR order, while matplotlib likely follows RGB order
 
    fig, ax = plt.subplots(figsize=figsize)
    ax.imshow(myimage, cmap = 'gray', interpolation = 'bicubic')
    plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
    plt.show()
 
# path to input image is specified and
# image is loaded with imread command
image_originale = cv2.imread('inputCoins.jpg')
showimage(image_originale)
 
# cv2.cvtColor is applied over the
# image input with applied parameters
# to convert the image in grayscale
img = cv2.cvtColor(image_originale, cv2.COLOR_BGR2GRAY)
showimage(img)
 
# applying Otsu thresholding
# as an extra flag in binary 
# thresholding     
ret, thresh1 = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
 
# the window showing output image         
# with the corresponding thresholding         
# techniques applied to the input image    
showimage(thresh1)
 

# Python program to illustrate 
# adaptive thresholding type on an image
 
# organizing imports 
import cv2 
from matplotlib import pyplot as plt
 
def showimage(myimage, figsize=[10,10]):
    if (myimage.ndim>2):  #This only applies to RGB or RGBA images (e.g. not to Black and White images)
        myimage = myimage[:,:,::-1] #OpenCV follows BGR order, while matplotlib likely follows RGB order
 
    fig, ax = plt.subplots(figsize=figsize)
    ax.imshow(myimage, cmap = 'gray', interpolation = 'bicubic')
    plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
    plt.show()
 
# path to input image is specified and  
# image is loaded with imread command 
image_originale = cv2.imread('inputCoins.jpg')
showimage(image_originale)
 
# cv2.cvtColor is applied over the
# image input with applied parameters
# to convert the image in grayscale 
img = cv2.cvtColor(image_originale, cv2.COLOR_BGR2GRAY)
 
# applying different thresholding 
# techniques on the input image
thresh1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY, 199, 5)
 
thresh2 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 199, 5)
 
# the window showing output images
# with the corresponding thresholding 
# techniques applied to the input image
showimage(thresh1)
showimage(thresh2)
 

# Python program to illustrate
# Otsu thresholding type on an image
 
# organizing imports
import cv2
from matplotlib import pyplot as plt
 
def showimage(myimage, figsize=[10,10]):
    if (myimage.ndim>2):  #This only applies to RGB or RGBA images (e.g. not to Black and White images)
        myimage = myimage[:,:,::-1] #OpenCV follows BGR order, while matplotlib likely follows RGB order
 
    fig, ax = plt.subplots(figsize=figsize)
    ax.imshow(myimage, cmap = 'gray', interpolation = 'bicubic')
    plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
    plt.show()
 
# path to input image is specified and
# image is loaded with imread command
image_originale = cv2.imread('inputCoins.jpg')
showimage(image_originale)
 
# cv2.cvtColor is applied over the
# image input with applied parameters
# to convert the image in grayscale
img = cv2.cvtColor(image_originale, cv2.COLOR_BGR2GRAY)
showimage(img)
 
# applying Otsu thresholding
# as an extra flag in binary 
# thresholding     
th, thresh1 = cv2.threshold(img, 50, 255, cv2.THRESH_TRUNC)
 
# the window showing output image         
# with the corresponding thresholding         
# techniques applied to the input image    
showimage(thresh1)
 
# applying Otsu thresholding
# as an extra flag in binary 
# thresholding     
th, thresh1 = cv2.threshold(img, 100, 255, cv2.THRESH_TRUNC)
 
# the window showing output image         
# with the corresponding thresholding         
# techniques applied to the input image    
showimage(thresh1)
 
# applying Otsu thresholding
# as an extra flag in binary 
# thresholding     
th, thresh1 = cv2.threshold(img, 150, 255, cv2.THRESH_TRUNC)
 
# the window showing output image         
# with the corresponding thresholding         
# techniques applied to the input image    
showimage(thresh1)
 
# applying Otsu thresholding
# as an extra flag in binary 
# thresholding     
th, thresh1 = cv2.threshold(img, 200, 255, cv2.THRESH_TRUNC)
 
# the window showing output image         
# with the corresponding thresholding         
# techniques applied to the input image    
showimage(thresh1)