How to classify image using Randomforest in python

Question

I am trying to classify an image using random forest. The output image has three colors: white, black and gray. Right now different output images have different colors to same class (water->black,white,gray)

I want to assign colors to different classes black->water, white->vegetation, gray->built up area. Any idea?

Here is my code

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

You can use the Pillow library

something like:

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()

looking at comments it is true that if performance is an issue, you should go for something like:

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

Use pip to install the pillow library. The following does a simple pixel by pixel color conversion on the image you've posted. If it isn't one of the colors you've mentioned it just leaves the original color:

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()

or using the numpy package, more succinctly you can just do:

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()

But as you can see from the resulting image you have other shades of colors than just those you mentioned.