如何在 python 中使用 Randomforest 对图像进行分类

Question

我正在尝试使用随机森林对图像进行分类。输出图像具有三种颜色：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，白，灰）

我想assign colors到不同的类黑->水，白->植被，灰->建成区。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 Pillow 库

类似于：

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()

查看评论确实是，如果性能是一个问题，您应该选择以下内容：

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pillow 库。下面做一个简单的对您发布的图像进行逐像素颜色转换。如果它不是你提到的颜色之一它只是离开原来的颜色：

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()

或者使用 numpy 包，更简洁的做法是：

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()

但是正如您从生成的图像中看到的那样，您还有其他阴影颜色比你提到的那些要多。

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