python - 如何使用 openCV 和 numpy 均衡图像并将其绘制为直方图
问题描述
我正在尝试遍历包含像素数据的 nparray。我想对每个像素值执行均衡并将它们显示为直方图。
我已经通过执行以下操作实现了我的目标:
def stratch_contrast(img):
hist,bins = np.histogram(img.flatten(),256,[0,256])
cdf = hist.cumsum()
cdf_normalized = cdf * hist.max()/ cdf.max()
cdf_m = np.ma.masked_equal(cdf,0)
cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
cdf = np.ma.filled(cdf_m,0).astype('uint8')
img = cdf[img]
plt.hist(img.flatten(),256,[0,256], color = 'black')
plt.xlim([0,256])
plt.legend(('cdf','histogram'), loc = 'upper left')
plt.show()
img = cv2.imread(name,0)
equ = cv2.equalizeHist(img)
res = np.hstack((img,equ)) #stacking images side-by-side
cv2.imwrite('res.png',res)
return
但我真的很想在不使用预定义函数进行学习的情况下做到这一点。
所以我尝试了以下操作:
def stratch_contrast(img, darkestValue, whitestValue):
newImgPixelList = []
h = img.shape[0] #number of pixels in the hight
w = img.shape[1] #number of piexels in the weight
darkestValueStratch = 256 #opposite so it can get darker while loop
whitestValueStratch = 0 #opposite so it can get lighter while loop
for y in range(0, w):
for x in range(0, h):
newImg[x][y] = (img[x][y]-darkestValue)*256/(whitestValue-darkestValue)
pxStratch = newImg[x][y]
newImgPixelList.append(pxStratch)
if darkestValueStratch > pxStratch:
darkestValueStratch = pxStratch
if whitestValueStratch < pxStratch:
whitestValueStratch = pxStratch
return newImgPixelList, darkestValueStratch, whitestValueStratch
但是当我调用我的绘图函数时,就像这样:
plot(newImgPixelList, int(darkestValueStratch), int(whitestValueStratch))
绘制的直方图根本没有均衡。它看起来几乎完全相同,就像我的未均衡直方图一样,所以一定有问题。如果有人可以帮助我,我将非常感激!
我的完整代码:
import matplotlib.pyplot as plt
import numpy as np
import cv2
np.seterr(over='ignore')
name = 'puppy.jpg'
img = cv2.imread(name, cv2.IMREAD_GRAYSCALE) #import image
newImg = np.zeros((img.shape))
def get_histo_scope(img):
imgPixelList = [] #array which later can save the pixel values of the image
h = img.shape[0] #number of pixels in the hight
w = img.shape[1] #number of piexels in the weight
darkestValue = 256 #opposite so it can get darker while loop
whitestValue = 0 #opposite so it can get lighter while loop
for y in range(0, w):
for x in range(0, h):
px = img[x][y] #reads the pixel which is a npndarray [][][]
imgPixelList.append(px) #saves the pixel data of every pixel we loop so we can use it later to plot the histogram
if darkestValue > px: #identifies the darkest pixel value
darkestValue = px
if whitestValue < px: #identifies the whitest pixel value
whitestValue = px
return darkestValue, whitestValue, imgPixelList
def plot(imgPixelList, darkestValue, whitestValue):
values = range(darkestValue, whitestValue, 1) #creates and array with all data from whitesValue to darkestValue
bin_edges = values
plt.hist(imgPixelList, bins=bin_edges, color='black')
plt.xlabel('Color Values')
plt.ylabel('Number of Poxels')
plt.show()
return
def stratch_contrast(img, darkestValue, whitestValue):
#hist,bins = np.histogram(img.flatten(),256,[0,256])
#cdf = hist.cumsum()
#cdf_normalized = cdf * hist.max()/ cdf.max()
#Comment out to remove Equalization
#cdf_m = np.ma.masked_equal(cdf,0)
#cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
#cdf = np.ma.filled(cdf_m,0).astype('uint8')
#img = cdf[img]
#plt.hist(img.flatten(),256,[0,256], color = 'black')
#plt.xlim([0,256])
#plt.legend(('cdf','histogram'), loc = 'upper left')
#plt.show()
#img = cv2.imread(name,0)
#equ = cv2.equalizeHist(img)
#res = np.hstack((img,equ)) #stacking images side-by-side
#cv2.imwrite('res.png',res)
newImgPixelList = []
h = img.shape[0] #number of pixels in the hight
w = img.shape[1] #number of piexels in the weight
darkestValueStratch = 256 #oposite so it can get darker while loop
whitestValueStratch = 0 #oposite so it can get lighter while loop
for y in range(0, w):
for x in range(0, h):
newImg[x][y] = (img[x][y]-darkestValue)*256/(whitestValue-darkestValue)
pxStratch = newImg[x][y]
newImgPixelList.append(pxStratch)
if darkestValueStratch > pxStratch: #identifies the darkest pixel value
darkestValueStratch = pxStratch
if whitestValueStratch < pxStratch: #identifies the whitest pixel value
whitestValueStratch = pxStratch
return newImgPixelList, darkestValueStratch, whitestValueStratch
darkestValue, whitestValue, imgPixelList = get_histo_scope(img) #get scope and pixel values from the img data
plot(imgPixelList, darkestValue, whitestValue) #plot the collected pixel values
newImgPixelList, darkestValueStratch, whitestValueStratch = stratch_contrast(img, darkestValue, whitestValue)
plot(newImgPixelList, int(darkestValueStratch), int(whitestValueStratch))
解决方案
我认为您误解了对比度拉伸算法。
该算法的目标是线性缩放像素值,以便您的图像使用可用的完整动态范围,即min(I) = 0
和max(I) = 255
。
为此,您必须在遍历像素并缩放它们之前min(I)
找到当前和。只需循环遍历整个图像,同时跟踪每个通道的最大值和最小值(RGB 图像为 3 个通道)。然后使用这些值使用公式缩放像素。独立处理每个 R、G 和 B 通道。max(I)
newValue = 255 * (oldValue - minimum) / (maximum - minimum)
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