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2023/3/6更新
很久没搞图像这块了，这个以后再改吧。全灰是因为读图像的时候就是读的灰的。。 img = cv2.imread(source, cv2.IMREAD_GRAYSCALE)
哪天想起来再改这篇吧。
所有代码已测试通过。
import cv2
import numpy as np
def gamma(source,out):
img = cv2.imread(source, cv2.IMREAD_GRAYSCALE)
# 归1
Cimg = img / 255
# 伽玛变换
gamma = 0.7
O = np.power(Cimg, gamma)
O = O * 255
# 效果
cv2.imwrite(out, O, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
def hist(source):
img = cv2.imread(source, cv2.IMREAD_GRAYSCALE)
# 求出img 的最大最小值
Maximg = np.max(img)
Minimg = np.min(img)
# 输出最小灰度级和最大灰度级
Omin, Omax = 0, 255
# 求 a, b
a = float(Omax - Omin) / (Maximg - Minimg)
b = Omin - a * Minimg
# 线性变换
O = a * img + b
O = O.astype(np.uint8)
cv2.imshow('enhance', O)
#cv2.imwrite('hist.png', O, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
cv2.waitKey(0)
cv2.destroyAllWindows()
def hist_auto(source):
img = cv2.imread(source, 0)
img = cv2.resize(img, None, fx=0.5, fy=0.5)
# 创建CLAHE对象
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
# 限制对比度的自适应阈值均衡化
dst = clahe.apply(img)
# 使用全局直方图均衡化
equa = cv2.equalizeHist(img)
# 分别显示原图，CLAHE，HE
#cv.imshow("img", img)
#cv2.imshow("dst", dst)
cv2.imwrite('hist_auto.png', dst, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
def calcGrayHist(I):
# 计算灰度直方图
h, w = I.shape[:2]
grayHist = np.zeros([256], np.uint64)
for i in range(h):
for j in range(w):
grayHist[I[i][j]] += 1
return grayHist
def equalHist(img):
import math
# 灰度图像矩阵的高、宽
h, w = img.shape
# 第一步：计算灰度直方图
grayHist = calcGrayHist(img)
# 第二步：计算累加灰度直方图
zeroCumuMoment = np.zeros([256], np.uint32)
for p in range(256):
if p == 0:
zeroCumuMoment[p] = grayHist[0]
else:
zeroCumuMoment[p] = zeroCumuMoment[p - 1] + grayHist[p]
# 第三步：根据累加灰度直方图得到输入灰度级和输出灰度级之间的映射关系
outPut_q = np.zeros([256], np.uint8)
cofficient = 256.0 / (h * w)
for p in range(256):
q = cofficient * float(zeroCumuMoment[p]) - 1
if q >= 0:
outPut_q[p] = math.floor(q)
else:
outPut_q[p] = 0
# 第四步：得到直方图均衡化后的图像
equalHistImage = np.zeros(img.shape, np.uint8)
for i in range(h):
for j in range(w):
equalHistImage[i][j] = outPut_q[img[i][j]]
return equalHistImage
def linear(source):
img = cv2.imread(source, 0)
# 使用自己写的函数实现
equa = equalHist(img)
cv2.imshow("equa", equa)
cv2.imwrite('temp.png', equa, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
cv2.waitKey()
source = "raw.jpg"
#gamma(source)
hist(source)
#hist_auto(source)
#linear(source)
#gamma('temp.png','g2.png')
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