Histogram-2 (add 50 pixels and subtract 50 pixels and grayscale histogram mask for each pixel value in the picture)

1、 Increase the value of each pixel in the picture 50 Pixels and subtraction 50 Pixel

# 1  Import library 
import cv2
import matplotlib.pyplot as plt
import numpy as np

# 2  Method ： display picture 
def show_image(image, title, pos):
    #   Sequential conversion ：BGR TO RGB
    image_RGB = image[:, :, ::-1] # shape : (height, width, channel)
    #  Show title 
    plt.title(title)
    plt.subplot(2, 3, pos) #  location 
    plt.imshow(image_RGB)

# 3  Method ： Display the gray histogram of the picture 
def show_histogram(hist, title, pos, color):
    #  Show title 
    plt.title(title)
    plt.subplot(2, 3, pos) #  Positioning pictures 
    plt.xlabel("Bins") #  Horizontal axis information 
    plt.ylabel("Pixels") #  Vertical axis information 
    plt.xlim([0, 256]) #  Range 
    plt.plot(hist, color=color) #  Draw histogram 


# 4  The main function  main()
def main():
    # 5  Create a canvas 
    plt.figure(figsize=(15, 6)) #  Canvas size 
    plt.suptitle("Gray Image Histogram", fontsize=14, fontweight="bold") #  Set the title form 

    # 6  Loading pictures 
    img = cv2.imread("children.jpg")

    # 7  Gray scale conversion 
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 8  Calculate the histogram of gray image 
    hist_img = cv2.calcHist([img_gray], [0], None, [256], [0, 256])

    # 9  Display gray histogram 
    #  Gray scale image is converted into BGR Format picture 
    img_BGR = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR)
    show_image(img_BGR, "BGR image", 1)
    show_histogram(hist_img, "gray image histogram", 4, "m")

    # 10  Increase the value of each pixel in the picture 50 Pixel 
    M = np.ones(img_gray.shape, np.uint8) * 50 #  Build the matrix 
    # np.ones() Function returns a new array of the given shape and data type , Where the value of the element is set to 1.   multiply 50

    added_img = cv2.add(img_gray, M)
    add_img_hist = cv2.calcHist([added_img], [0], None, [256], [0, 256]) #  Calculate the histogram    List elements 
    added_img_BGR = cv2.cvtColor(added_img, cv2.COLOR_GRAY2BGR)  #BGR
    show_image(added_img_BGR, "added image", 2)
    show_histogram(add_img_hist, "added image hist", 5, "m")

    # 11  Subtract... From each pixel value in the picture 50 Pixel 
    subtract_img = cv2.subtract(img_gray, M)
    subtract_img_hist = cv2.calcHist([subtract_img], [0], None, [256], [0, 256]) #  Calculate the histogram 
    subtract_img_BGR = cv2.cvtColor(subtract_img, cv2.COLOR_GRAY2BGR)
    show_image(subtract_img_BGR, "subtracted image", 3)
    show_histogram(subtract_img_hist, "subtracted image hist", 6, "m")

    plt.show()

#  Call the main function 
if __name__ == '__main__':
    main()

2、 Gray histogram ——mask

mask ： Extract the region of interest .

# 1  Import library 
import cv2
import matplotlib.pyplot as plt
import numpy as np

# 2  Method ： display picture 
def show_image(image, title, pos):
    img_RGB = image[:, :, ::-1]  # BGR to RGB
    plt.title(title)
    plt.subplot(2, 2, pos)
    plt.imshow(img_RGB)

# 3  Method ： Displays a grayscale histogram 
def show_histogram(hist, title, pos, color):
    plt.subplot(2, 2, pos)
    plt.title(title)
    plt.xlim([0, 256])
    plt.plot(hist, color=color)

# 4  The main function 
def main():
    # 5  Create a canvas 
    plt.figure(figsize=(12, 7))
    plt.suptitle("Gray Image and Histogram with mask", fontsize=16, fontweight="bold")

    # 6  Read the picture and convert it into gray , Calculate the histogram , Show 
    img_gray = cv2.imread("children.jpg", cv2.COLOR_BGR2GRAY) #  Read and perform grayscale conversion 
    img_gray_hist = cv2.calcHist([img_gray], [0], None, [256], [0, 256]) #  Calculate the histogram 
    show_image(img_gray, "image gray", 1)
    show_histogram(img_gray_hist, "image gray histogram", 2, "m")

    # 7  establish mask, Calculate bitmap , Histogram 
    mask = np.zeros(img_gray.shape[:2], np.uint8)
    mask[130:500, 600:1400] = 255 #  obtain mask, And give color 
    img_mask_hist = cv2.calcHist([img_gray], [0], mask, [256], [0, 256])  #  Calculation mask Histogram 
    #  Return to a given shape and type 0 Filled array ;zeros(shape, dtype=float, order=‘C’)
    # shape:  shape   dtype:  data type , Optional parameters , Default numpy.float64  order:  Optional parameters ,c Representative and c Language is similar to , Line first ;F Delegate priority 

    # 8  Bit operation （ And operation ） Calculation with mask Gray scale image of 
    mask_img = cv2.bitwise_and(img_gray, img_gray, mask = mask)

    # 9  Display with mask Pictures and histograms of 
    show_image(mask_img, "gray image with mask", 3)
    show_histogram(img_mask_hist, "histogram with masked gray image", 4, "m")

    plt.show()
if __name__ == '__main__':
    main()