OpenCV+Tensorflow 人工智能图像处理（三十四）—— 图像直方图源码

#本质：统计每个像素灰度出现概率 0-255
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('car.jpg', 1)   #读取图片
imgInfo = img.shape  #维度信息
height = imgInfo[0]
width = imgInfo[1]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)   #转灰度
count = np.zeros(256, np.float)
for i in range(0, height):
    for j in range(0, width):
        pixel = gray[i, j]   #获取每一个像素
        index = int(pixel)
        count[index] = count[index] + 1  #灰度统计
for i in range(0, 255):
    count[i] = count[i]/(height * width)
x = np.linspace(0, 255, 256)   #0-255共256个
y = count
plt.bar(x, y, 0.9, alpha=1, color='b')
plt.show()
cv2.waitKey(0)

#本质：统计每个像素灰度出现概率 0-255
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('car.jpg', 1)   #读取图片
imgInfo = img.shape  #维度信息
height = imgInfo[0]
width = imgInfo[1]
#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)   #转灰度
count_b = np.zeros(256, np.float)
count_g = np.zeros(256, np.float)
count_r = np.zeros(256, np.float)

for i in range(0, height):
    for j in range(0, width):
        (b, g, r) = img[i, j]   #获取每一个像素
        index_b = int(b)
        index_g = int(g)
        index_r = int(r)
        count_b[index_b] = count_b[index_b] + 1  #颜色统计
        count_g[index_g] = count_g[index_g] + 1
        count_r[index_r] = count_r[index_r] + 1
for i in range(0, 255):
    count_b[i] = count_b[index_b]/(height * width)
    count_g[i] = count_g[index_g] / (height * width)
    count_r[i] = count_r[index_r] / (height * width)
x = np.linspace(0, 255, 256)   #0-255共256个
y1 = count_b
plt.figure()
plt.bar(x, y1, 0.9, alpha=1, color='b')
y2 = count_g
plt.figure()
plt.bar(x, y2, 0.9, alpha=1, color='g')
y3 = count_r
plt.figure()
plt.bar(x, y3, 0.9, alpha=1, color='r')
plt.show()
cv2.waitKey(0)

 

 

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