将 PIL 图像传递给 OpenCV 会导致类型错误：参数 'src' 的预期 cv：：UMat

Question

我试图使用OpenCV库将CLAHE方法应用于已分割的绿色通道，但随后出现此错误:

cl = clahe.apply(multiBands[1])
TypeError: Expected cv::UMat for argument 'src'

我代码:

# Example Python Program for contrast stretching
from PIL import Image
import cv2 as cv

# Method to process the green band of the image
def normalizeGreen(intensity):
iI      = intensity
minI    = 90
maxI    = 225
minO    = 0
maxO    = 255
iO      = (iI-minI)*(((maxO-minO)/(maxI-minI))+minO)
return iO
# Create an image object
imageObject     = Image.open("input/IDRiD_02.jpg")
# Split the red, green and blue bands from the Image
multiBands      = imageObject.split()
# create a CLAHE object (Arguments are optional)
clahe = cv.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
cl = clahe.apply(multiBands[1])
# Apply point operations that does contrast stretching on each color band
normalizedGreenBand    = cl.point(normalizeGreen)
# Display the image before contrast stretching
imageObject.show()

# Display the image after contrast stretching
normalizedGreenBand.show()

Answer 1

PIL和OpenCV都有不同格式的图像对象，但它们可以互换，例如，OpenCV接受narray，您可以使用NumPy将PIL图像更改为数组。同样，您可以使用PIL.Image.fromarray()方法从numpy数组中获取PIL图像对象。我已经修改了你的代码，我想它会解决你的问题。

from PIL import Image
import cv2 as cv
import numpy as np
# Method to process the green band of the image
def normalizeGreen(intensity):
minI, maxI = 90, 225
minO, maxO = 0, 255
iO = (intensity-minI)*(((maxO-minO)/(maxI-minI))+minO)
return iO
# Create an image object
# imageObject = Image.open("<path-to-image>")
imageObject = Image.open("images/13/img-r1-000.jpg")
# Split the red, green and blue bands from the Image
r, g, b = imageObject.split()
# Apply point operations that does contrast stretching on each color band
normalizedGreenBand = g.point(normalizeGreen)
# convert the Pil Image object to OpenCv image object
normalizedGreenBand = np.array(normalizedGreenBand)
# create a CLAHE object (Arguments are optional)
clahe = cv.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
normalizedGreenBand = clahe.apply(normalizedGreenBand)
# Display the image before contrast stretching
imageObject.show()
# convert the OpenCv image object to Pil Image object
normalizedGreenBand = Image.fromarray(normalizedGreenBand)
# Display the image after contrast stretching
normalizedGreenBand.show()

我首先在绿色通道上应用point()方法，然后将归一化的绿色通道转换为NumPy数组，以便应用cv.createCLAHE()操作。最后，我将NumPy数组重新转换为PIL图像对象进行显示(我们可以使用cv2.imshow()而不转换为PIL对象)。