conv_to_bin_mat.py

from PIL import Image
import numpy as np
import os
import scipy.misc



class ConvBinMap:

	@staticmethod
	def map_to_binary_image():
		""" Converts an image stored in the 'SourceMaps' directory
		    into a black and white image (0 = black, 255 = white) that is 
		    then stored in the 'BinaryMaps' directory.

		    User is prompted to enter the filename of the chosen map first (e.g. MD_0.png)
		    Output file is named the same thing as the input file name, but with '_binary' 
		    appended to the end.
		"""
		source_im = raw_input("Enter the path of your file: ")
		# print source_im

		col = Image.open("SourceMaps/" + source_im)
		gray = col.convert('L')

		# converting pixels to pure black or white
		bw = np.asarray(gray).copy()

		# Pixel range is 0...255, 256/2 = 128
		bw[bw < 128] = 0    # Black
		bw[bw >= 128] = 255 # White

		# Now we put it back in Pillow/PIL land
		imfile = Image.fromarray(bw)

		source_im_wo_ext = os.path.splitext(source_im)[0]
		out_im = source_im_wo_ext + "_binary.png"
		# print out_im

		imfile.save("BinaryMaps/" + out_im)


	@staticmethod
	def map_to_mat(map_image_path):
		""" Uses a black and white png image produced by map_to_binary_image() and converts it 
			into a matrix of the same dimensions as the image (there is a one-to-one mapping 
			of pixels to matrix elements).

			The white pixels (color value of 255) are converted into '0' in the new matrix.
			The black pixels (color value of 0) are converted into '1' in the new matrix.

			1 means obstacle cell
			0 means empty cell

			Takes a string as an argument (the name of the black and white image).
			Returns a numpy matrix of zeros and ones, indexed as: map_matrix[rows][cols]
			
		"""
		map_matrix = scipy.misc.imread(map_image_path)

		# map_matrix[map_matrix > 200] = 0 # no obstacle
		# map_matrix[map_matrix < 50] = 1 # obstacle
			
		for (x,y), value in np.ndenumerate(map_matrix):
			if map_matrix[x][y] == 255:
				map_matrix[x][y] = 0  # no obstacle
			elif map_matrix[x][y] == 0:
				map_matrix[x][y] = 1  # obstacle

		return map_matrix


# For testing with the terminal:
if __name__ == '__main__':
	ConvBinMap.map_to_binary_image()
	# ConvBinMap.map_to_mat('BinaryMaps/MD_0_binary.png')