将 PIL 图像传递给 OpenCV 导致 TypeError: Expected cv::UMat for argument 'src'

Question

我正在尝试将使用 OpenCV 库的 CLAHE 方法应用于已拆分的绿色通道，但随后出现此错误：

 cl = clahe.apply(multiBands[1])

TypeError: Expected cv::UMat for argument 'src'

我的代码：

# 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 ndarray，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，我认为它会解决你的问题。

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）。