Python matplolib ListedColormap 将所有图像绘制为黄色但几乎完全相同的图像绘制正确

Question

偶然发现一个非常奇怪的问题 problem.I 从 agro 创建一个 tgi 图像 images.Althought 我有两个非常类似的 agro 图像，颜色几乎相同，其中一个 tif 文件生成了一个很好的 tgi 图像但是另一个生成完整的 tgi 图像 yellow.I 应用 matplolib.ListedColormap 我认为我的代码错误：

import cv2
import numpy as np
from PIL import Image
from matplotlib import colors




HD_rgb_tgi =  cv2.imread('good_tif.tif',cv2.IMREAD_UNCHANGED)
red = HD_rgb_tgi[:,:,0].astype('float64')
green = HD_rgb_tgi[:,:,1].astype('float64')
blue = HD_rgb_tgi[:,:,2].astype('float64')
tgi=(-1) * 0.5 * ((200*(red-green))-(100 * (red-blue)))
tgi_normalized = ((tgi - np.amin(tgi)) / (np.amax(tgi) - np.amin(tgi)))
tgi_f = Image.fromarray(np.uint8(colors.ListedColormap([ '#C90000', '#CF630A'  , '#EBE42D', '#27FF0F', '#0C750C'])(tgi_normalized) * 255))
tgi_f = tgi_f.convert('RGBA')
tgi_f=cv2.cvtColor(np.float32(tgi_f), cv2.COLOR_RGBA2BGRA)
cv2.imwrite("tgi_good.png",tgi_f)





HD_rgb_tgi =  cv2.imread('bad_tif.tif',cv2.IMREAD_UNCHANGED)
red = HD_rgb_tgi[:,:,0].astype('float64')
green = HD_rgb_tgi[:,:,1].astype('float64')
blue = HD_rgb_tgi[:,:,2].astype('float64')
tgi=(-1) * 0.5 * ((200*(red-green))-(100 * (red-blue)))
tgi_normalized = ((tgi - np.amin(tgi)) / (np.amax(tgi) - np.amin(tgi)))
tgi_f = Image.fromarray(np.uint8(colors.ListedColormap([ '#C90000', '#CF630A'  , '#EBE42D', '#27FF0F','#0C750C'])(tgi_normalized) * 255))
tgi_f = tgi_f.convert('RGBA')
tgi_f=cv2.cvtColor(np.float32(tgi_f), cv2.COLOR_RGBA2BGRA)
cv2.imwrite("tgi_bad.png",tgi_f)

link 到 2 个 tif 图片：

https://mega.nz/file/2J0EzbKQ#wPFPr4onmRCwEvdOHyBCwLQnBMqPQYIsZucA8bNERBw https://mega.nz/file/DF0i2BTJ#HuzKxWI60-R0DjAB7lwuqkhOiO5Y8bgEFXUHkHfmwNE

抱歉，外部 links 但我无法将问题重现到其他图像。最好的问候

Answer 1

即使不可见，显然照片的直方图也不相同。将 cv2.equalizeHist 应用于不良图像似乎可以解决问题。

import cv2
import numpy as np
from PIL import Image
from matplotlib import colors


HD_rgb_tgi =  cv2.imread('good_tif.tif',cv2.IMREAD_UNCHANGED)#odm_orthophoto
red = HD_rgb_tgi[:,:,0].astype('float64')
green = HD_rgb_tgi[:,:,1].astype('float64')
blue = HD_rgb_tgi[:,:,2].astype('float64')
tgi=(-1) * 0.5 * ((200*(red-green))-(100 * (red-blue)))
tgi_normalized = ((tgi - np.amin(tgi)) / (np.amax(tgi) - np.amin(tgi)))
tgi_f = Image.fromarray(np.uint8(colors.ListedColormap([ '#C90000', '#CF630A'  , '#EBE42D', '#27FF0F', '#0C750C'])(tgi_normalized) * 255))
tgi_f = tgi_f.convert('RGBA')
tgi_f=cv2.cvtColor(np.float32(tgi_f), cv2.COLOR_RGBA2BGRA)
cv2.imwrite("tgi_good.png",tgi_f)


def run_histogram_equalization(image_path):
    rgb_img = cv2.imread(image_path)
    # convert from RGB color-space to YCrCb
    ycrcb_img = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2YCrCb)
    # equalize the histogram of the Y channel
    ycrcb_img[:, :, 0] = cv2.equalizeHist(ycrcb_img[:, :, 0])
    # convert back to RGB color-space from YCrCb
    equalized_img = cv2.cvtColor(ycrcb_img, cv2.COLOR_YCrCb2BGR)
    cv2.imwrite('res.png',equalized_img)

run_histogram_equalization('bad_tif.tif')


D_rgb_tgi =  cv2.imread('res.png',cv2.IMREAD_UNCHANGED)#odm_orthophoto
red = HD_rgb_tgi[:,:,0].astype('float64')
green = HD_rgb_tgi[:,:,1].astype('float64')
blue = HD_rgb_tgi[:,:,2].astype('float64')
tgi=(-1) * 0.5 * ((200*(red-green))-(100 * (red-blue)))
tgi_normalized = ((tgi - np.amin(tgi)) / (np.amax(tgi) - np.amin(tgi)))
tgi_f = Image.fromarray(np.uint8(colors.ListedColormap([ '#C90000', '#CF630A'  , '#EBE42D', '#27FF0F', '#0C750C'])(tgi_normalized) * 255))
tgi_f = tgi_f.convert('RGBA')
tgi_f=cv2.cvtColor(np.float32(tgi_f), cv2.COLOR_RGBA2BGRA)
cv2.imwrite("tgi_bad.png",tgi_f)

Python matplolib ListedColormap 将所有图像绘制为黄色但几乎完全相同的图像绘制正确

Python matplolib ListedColormap paints all image yellow but identical almost image paints correctly

python

opencv

matplotlib

python-imaging-library