我将尝试循环遍历一个包含像素数据的nparray。 我希望对每个像素值执行均衡化并将其作为直方图显示。
我已经通过以下方式达成了我的目标：
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
但出于学习目的，我真的希望能够在不使用预定义函数的情况下完成这项任务。
因此，我尝试了以下方法：
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
但是当我调用绘图函数时，像这样：
plot(newImgPixelList, int(darkestValueStratch), int(whitestValueStratch))
绘制的直方图完全没有被均衡化。它看起来几乎和我未均衡化的直方图完全相同，因此一定有什么问题。
如果有人能帮我解决这个问题，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))
stratch_contrast函数存在问题。公式中使用的darkestValue和whitestValue是从哪里得来的？它们与darkestValueStratch和whitestValueStratch相同吗？ - Sunreefcv.equalizeHist，它应用直方图均衡化。第二个函数应用线性映射，并剪切范围外的值。如果你认为这两个操作除了有点关联之外没有任何关系，那么你需要回到你的教科书里看看。 - Cris Luengo