python计算输出图像性能指标

1、图像信息熵

这里信息熵公式不做过分说明。

图像信息熵也是图像一维熵。

import cv2
import numpy as np
import math
tmp = []
for i in range(256):
    tmp.append(0)
val = 0
k = 0
res = 0
image = cv2.imread('图像位置',0)
img = np.array(image)
for i in range(len(img)):
    for j in range(len(img[i])):
        val = img[i][j]
        tmp[val] = float(tmp[val] + 1)
        k = float(k + 1)
for i in range(len(tmp)):
    tmp[i] = float(tmp[i] / k)
for i in range(len(tmp)):
    if(tmp[i]==0):
        res = res
    else:
        res = float(res - tmp[i] * (math.log(tmp[i]) / math.log(2.0)))
print(res)

2、图像对比度

from cv2 import cv2
import numpy as np


def contrast(img0):
    img1 = cv2.cvtColor(img0, cv2.COLOR_BGR2GRAY)  # 彩色转为灰度图片
    m, n = img1.shape
    # 图片矩阵向外扩展一个像素
    img1_ext = cv2.copyMakeBorder(img1, 1, 1, 1, 1, cv2.BORDER_REPLICATE)
    rows_ext, cols_ext = img1_ext.shape
    b = 0.0
    for i in range(1, rows_ext - 1):
        for j in range(1, cols_ext - 1):
            b += ((img1_ext[i, j] - img1_ext[i, j + 1]) ** 2 + (img1_ext[i, j] - img1_ext[i, j - 1]) ** 2 +
            (img1_ext[i, j] - img1_ext[i + 1, j]) ** 2 + (img1_ext[i, j] - img1_ext[i - 1, j]) ** 2)

    cg = b / (4 * (m - 2) * (n - 2) + 3 * (2 * (m - 2) + 2 * (n - 2)) + 2 * 4)  # 对应上面48的计算公式
    print(cg)

img0 = cv2.imread('图像位置')
contrast(img0)

3、图像清晰度

'计算laplacian绝对值的方差,输出越大，清晰度越好'
import cv2


def variance_of_laplacian(image):
    
    return cv2.Laplacian(image, cv2.CV_64F).var()

image = cv2.imread("ring_4.bmp")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
fm = variance_of_laplacian(gray)
text = "Not Blurry"

if fm < 100:
    text = "Blurry"
# show the image
cv2.putText(image, "{}: {:.2f}".format(text, fm), (10, 30),
            cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)
cv2.imshow("Image", image)
key = cv2.waitKey(0)

4、图像亮度检测

import cv2
import numpy as np
# 把图片转换为单通道的灰度图
gray_img = cv2.imread("ring_4.bmp", 0)

# 获取形状以及长宽
img_shape = gray_img.shape
height, width = img_shape[0], img_shape[1]
size = gray_img.size
# 灰度图的直方图
hist = cv2.calcHist([gray_img], [0], None, [256], [0, 256])

# 计算灰度图像素点偏离均值(128)程序
a = 0
ma = 0
reduce_matrix = np.full((height, width), 128)
shift_value = gray_img - reduce_matrix
shift_sum = sum(map(sum, shift_value))

da = shift_sum / size

# 计算偏离128的平均偏差
for i in range(256):
    ma += (abs(i - 128 - da) * hist[i])
m = abs(ma / size)
# 亮度系数
k = abs(da) / m
print(k)
if k[0] > 1:
    # 过亮
    if da > 0:
        print("过亮")
    else:
        print("过暗")
else:
    print("亮度正常")