使用 PIL 而不是 imagio 库读取图像时，图像无法正确显示

如何解决使用 PIL 而不是 imagio 库读取图像时，图像无法正确显示

我有将图像从 RGB 空间转换为 HSI 空间的代码：

import numpy as np
import imageio
from matplotlib import pyplot as plt


def RGB_TO_HSI(img):
    with np.errstate(divide='ignore',invalid='ignore'):

        rgb = np.float32(img) / 255

        # Separate color channels
        red = rgb[:,:,0]
        green = rgb[:,1]
        blue = rgb[:,2]

        # Calculate Intensity
        def calc_intensity(red,green,blue):
            intensity = (red + green + blue + 0.001) / 3
            return intensity

        # Calculate Saturation
        def calc_saturation(red,blue):
            minimum = np.minimum(np.minimum(red,green),blue)
            saturation = 1 - (minimum / calc_intensity(red,blue))
            return saturation

        # Calculate Hue
        def calc_hue(red,blue):
            hue = np.copy(red)  # Basically have our hue = red for Now; we only need its size/dimensions

            for i in range(0,blue.shape[0]):
                for j in range(0,blue.shape[1]):

                    if blue[i][j] <= green[i][j]:
                        hue[i][j] = np.arccos(0.5 * ((red[i][j] - green[i][j]) + (red[i][j] - blue[i][j])) / (np.sqrt((red[i][j] - green[i][j]) ** 2 + (red[i][j] - blue[i][j]) * (green[i][j] - blue[i][j]))))
                    else:
                        hue[i][j] = 2 * np.pi - np.arccos(0.5 * ((red[i][j] - green[i][j]) + (red[i][j] - blue[i][j])) / (np.sqrt((red[i][j] - green[i][j]) ** 2 + (red[i][j] - blue[i][j]) * (green[i][j] - blue[i][j]))))
            return hue

    # Merge channels into picture and return image
    hsi = np.zeros(img.shape)  # instead of having 3 channels,one for each color (RGB),here we have a channel; for "hue",another for "saturation" and another for "intensity"
    hsi[:,0],hsi[:,1],2] = calc_hue(red,blue),calc_saturation(red,calc_intensity(red,blue)
    return hsi

cat = imageio.imread("Path...")
hsi_cat = RGB_TO_HSI(cat)
plt.imshow(hsi_cat)
plt.show()

但是，将代码中的行调整为：hsi = np.zeros(rgb.shape,dtype=np.uint8)，然后使用 PIL 加载图像，并将其作为参数传递给函数：

cat_p = Image.open("Path...")  # Using PIL Now
his_cat_p = RGB_TO_HSI(cat_p)  # Passing the JPEG image into the function
his_cat_p = Image.fromarray(his_cat_p)  # Converting the result back from an array (return hsi),into JPEG format
his_cat_p.show()  # Black image appears

这个结果是一个黑色的图像，我不太确定为什么！

解决方法

函数 Image.fromarray 仅支持具有典型范围的有限 modes。如果只是想将数据正确渲染为图像，也许最好从 HSI 转换回 RGB 值，然后输出它。类似的东西，

cat_hsi = RGB_TO_HSI(cat)
result = modify(cat_hsi)  # I guess you want to operate on hsi instead of rgb
result_rgb = HSI_TO_RGB(result)
img = Image.fromarray(result_rgb,mode='RGB')
img.show()

---

此外，我对代码进行了一些修改，使其在我的机器上运行。

• 确保将形状为 (X,Y,3) 的 RGB 数组传递给函数
• 在 calc_hue 函数中明确处理可能出现的零除情况。

import numpy as np
from numba import njit  # code gets significantly faster
from matplotlib import pyplot as plt
from PIL import Image


def RGB_TO_HSI(img):
    rgb = np.float32(img) / 255

    red = rgb[:,:,0]
    green = rgb[:,1]
    blue = rgb[:,2]

    def calc_intensity(red,green,blue):
        intensity = (red + green + blue + 0.001) / 3
        return intensity

    def calc_saturation(red,blue):
        minimum = np.minimum(np.minimum(red,green),blue)
        saturation = 1 - (minimum / calc_intensity(red,blue))
        return saturation

    @njit  # use numba to accelerate
    def calc_hue(red,blue):
        hue = np.copy(red)
        for i in range(0,blue.shape[0]):
            for j in range(0,blue.shape[1]):
                denominator = np.sqrt(
                    (red[i][j] - green[i][j]) ** 2 + (red[i][j] - blue[i][j]) * (green[i][j] - blue[i][j])
                )
                if abs(denominator) < 1e-10:  # exliciply handle the possible zero-division cases
                    hue[i][j] = np.nan
                else:
                    if blue[i][j] <= green[i][j]:
                        hue[i][j] = np.arccos(0.5 * ((red[i][j] - green[i][j]) + (red[i][j] - blue[i][j])) / denominator)
                    else:
                        hue[i][j] = 2 * np.pi - np.arccos(0.5 * ((red[i][j] - green[i][j]) + (red[i][j] - blue[i][j])) / denominator)
        return hue

    # Merge channels into picture and return image
    hsi = np.zeros(img.shape)  # instead of having 3 channels,one for each color (RGB),here we have a channel; for "hue",another for "saturation" and another for "intensity"
    hsi[:,0],hsi[:,1],2] = calc_hue(red,blue),calc_saturation(red,calc_intensity(red,blue)
    return hsi

cat = np.array(Image.open("test.png"))
hsi_cat = RGB_TO_HSI(cat[:,:3])  # sometims images are in RGBA format
plt.imshow(hsi_cat)
plt.show()

（numba 很神奇吧？）