Question

我正在尝试使用随机森林对图像进行分类。输出图像有三种颜色：白色，黑色和灰色。现在不同的输出图像有不同的颜色same class（水 - >黑色，白色，灰色）

我想assign colors为不同的类别设置black-＆gt; water，white-＆gt;植被，gray-＆gt;构建区域。有什么想法吗？

这是我的代码

import numpy as np
import os
from osgeo import gdal
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from PIL import Image
import cv2
import numpy as np
import matplotlib
import matplotlib.pyplot as plt 
plt.switch_backend('Qt4Agg') 

def rasterizeVector(path_to_vector,cols,rows,geo_transform,projection):
    lblRaster=np.zeros((rows, cols))
    for i, path in enumerate(path_to_vector):
        label = i+1
        # open the input datasource and read content
        inputDS = gdal.OpenEx(path, gdal.OF_VECTOR)
        shpLayer = inputDS.GetLayer(0)
        # Create the destination data source
        driver = gdal.GetDriverByName('MEM') 
        rasterDS = driver.Create('', cols, rows, 1, gdal.GDT_UInt16)
        # Define spatial reference
        rasterDS.SetGeoTransform(geo_transform)
        rasterDS.SetProjection(projection)
        # Rasterize
        gdal.RasterizeLayer(rasterDS, [1], shpLayer, burn_values=[label])
        # Get a raster band
        rBand = rasterDS.GetRasterBand(1)
        lblRaster += rBand.ReadAsArray()
        rasterDS = None
    return lblRaster

def createGeotiff(outRaster, data, geo_transform, projection):
    # Create a GeoTIFF file with the given data
    driver = gdal.GetDriverByName('GTiff')
    rows, cols = data.shape
    rasterDS = driver.Create(outRaster, cols, rows, 1, gdal.GDT_Byte)
    rasterDS.SetGeoTransform(geo_transform)
    rasterDS.SetProjection(projection)
    band = rasterDS.GetRasterBand(1)
    band.WriteArray(data)
    dataset = None

img = Image.open('test7.png')
img.save('test7.tiff','tiff')

inpRaster = "test7.tiff"
outRaster = "randomForest.tiff"
trainData = "/home/madhuka/Desktop/FYP/Automated-Land-Use-Mapping-master/SatelliteClassification/train"

# Open raster dataset
rasterDS = gdal.Open(inpRaster, gdal.GA_ReadOnly)
# Get spatial reference
geo_transform = rasterDS.GetGeoTransform()
projection = rasterDS.GetProjectionRef()

# Extract band's data and transform into a numpy array
bandsData = []
for b in range(1, rasterDS.RasterCount+1):
    band = rasterDS.GetRasterBand(b)
    bandsData.append(band.ReadAsArray())
bandsData = np.dstack(bandsData)
rows, cols, noBands = bandsData.shape

# Read vector data, and rasterize all the vectors in the given directory into a single labelled raster
files = [f for f in os.listdir(trainData) if f.endswith('.shp')]
classes = [f.split('.')[0] for f in files]
shapefiles = [os.path.join(trainData, f) for f in files if f.endswith('.shp')]
lblRaster = rasterizeVector(shapefiles, rows, cols, geo_transform, projection)

# Prepare training data (set of pixels used for training) and labels
isTrain = np.nonzero(lblRaster)
trainingLabels = lblRaster [isTrain]
trainingData = bandsData[isTrain]

# Train a Random Forest classifier
classifier = RandomForestClassifier(n_jobs=4, n_estimators=10)
classifier.fit(trainingData, trainingLabels)

# Predict class label of unknown pixels
noSamples = rows*cols
flat_pixels = bandsData.reshape((noSamples, noBands))
result = classifier.predict(flat_pixels)
classification = result.reshape((rows, cols))

# Create a GeoTIFF file with the given data
createGeotiff(outRaster, classification, geo_transform, projection)

img = Image.open('randomForest.tiff')
img.save('randomForest.png','png')

#img = cv2.imread('randomForest.png')

gray_image = cv2.imread('randomForest.png')
cv2.imwrite('gray_image.png',gray_image)

hist,bins = np.histogram(gray_image.flatten(),256,[0,256])
cdf = hist.cumsum()

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')

img2 = cdf[img]
image_enhanced=img2
cv2.imwrite('randomForestEnhanced.png',image_enhanced)

#recalculate cdf
hist,bins = np.histogram(image_enhanced.flatten(),256,[0,256])
cdf = hist.cumsum()
cdf_normalized = cdf * hist.max()/ cdf.max()

plt.plot(cdf_normalized, color = 'b')
plt.hist(image_enhanced.flatten(),256,[0,256], color = 'r')
plt.xlim([0,256])
plt.legend(('cdf','histogram'), loc = 'upper left')
plt.savefig('histogram_enhanced_2.png')
plt.show()

Answer 1

您可以使用枕头库

类似的东西：

from PIL import Image
im = Image.open('exemple.jpg')
for pixel in im.getdata():
    if pixel == (0,0,0):
        pixel = (255, 0, 09

img.show()

看评论确实如果表现是一个问题，你应该选择以下内容：

image = Image.open("exemple.jpg")
image = np.array(image)           // get all pixels into a numpy array

image[np.where((image==[0,0,0]).all(axis=2))] = [255,0,0]
img = Image.fromarray(image)

Answer 2

使用pip安装枕头库。以下操作很简单您已发布的图片上逐像素颜色转换。如果它不是你提到的颜色之一，只留下原件颜色：

from PIL import Image
im = Image.open('RD2B0.png')

# input colors
blk=(0,0,0)
wh=(255,255,255)
gr=(128,128,128)

# converted colors
r=(255,0,0)
g=(0,255,0)
blu=(0,0,255)

imageW = im.size[0]
imageH = im.size[1]
for y in range(0, imageH):
  for x in range(0, imageW):
    pixel=im.getpixel((x,y))
    if pixel == blk:
      im.putpixel((x, y), r)
    elif pixel == wh:
      im.putpixel((x, y), g)
    elif pixel == gr:
      im.putpixel((x, y), blu)
im.show()

或使用numpy包，你可以更简洁地做到：

from PIL import Image
from numpy import array, where
im = Image.open('RD2B0.png')

img = array(im)

img[where((img==[0,0,0]).all(axis=2))] = [255,0,0]
img[where((img==[255,255,255]).all(axis=2))] = [0,255,0]
img[where((img==[128,128,128]).all(axis=2))] = [0,0,255]

im = Image.fromarray(img)     
im.show()

但是从结果图像中可以看出，您有其他阴影颜色不仅仅是你提到的颜色。

如何在python中使用Randomforest对图像进行分类

2 个答案: