from PIL import Image
from lib_threshold import identifyLetters, makeWhiteOnBlack, matrixToImage, imageToNPMatrix, thresholdMatrix
from lib_LetterBBox import fitTo28x28
from lib_dataset import nnetResultToChar, topNResults, chars
import numpy
# first open image
print "Loading image file..."
image = Image.open('./test-text.png')
print "Extracting letters..."
# extract letter data
letters = identifyLetters(image.convert('L'))
# flatten to array inputs and clamp between 0 and 1
letters_processed = [fitTo28x28(matrixToImage(img)) for (pos, img) in letters]
# show us what they look like
for (letter, num) in zip(letters_processed, range(0, len(letters_processed))):
letter.save('input-' + str(num) + '.png')
# create the inputs for the nnet
nnetInputs = [
imageToNPMatrix(fitTo28x28(matrixToImage(img))).flatten()
for (pos, img) in letters
]
# create the outputs for the nnet
nnetOutputs = [net.activate(input) for input in nnetInputs]
arr[chars.index(char)] = 1
return arr
for fontName in fontNames:
for size in range(24, 28, 2):
font = ImageFont.truetype(fontName, size)
img = Image.new("L", (1000, 40), (0, 0, 0))
draw = ImageDraw.Draw(img)
draw.text((0, 0), string, (255, 255, 255), font=font)
img.save(fontName + '.png')
letters = identifyLetters(img)
_28x28Images = [(symbol,
imageToNPMatrix(fitTo28x28(matrixToImage(letter[1]))),
charToOutputs(symbol))
for (symbol, letter) in zip(chars, letters)]
_28x28Images = [(symbol, image, outputs)
for (symbol, image, outputs) in _28x28Images]
if isFirst:
isFirst = False
for (symbol, matrix, outputs) in _28x28Images:
printMatrix(matrix)
matrixToImage(matrix).save(fontName + '-' + symbol + '.png')
datums = [(symbol, map0255to01(list(matrix.flatten())), outputs)
for (symbol, matrix, outputs) in _28x28Images]
for d in datums:
dataset.append(d)
'''
def charToOutputs(char):
arr = [0] * len(chars)
arr[chars.index(char)] = 1
return arr
for fontName in fontNames:
for size in range(24, 28, 2):
font = ImageFont.truetype(fontName, size)
img = Image.new("L", (1000, 40), (0, 0, 0))
draw = ImageDraw.Draw(img)
draw.text((0, 0), string, (255,255,255), font=font)
img.save(fontName + '.png')
letters = identifyLetters(img)
_28x28Images = [(symbol, imageToNPMatrix(fitTo28x28(matrixToImage(letter[1]))), charToOutputs(symbol)) for (symbol, letter) in zip(chars, letters)]
_28x28Images = [(symbol, image, outputs) for (symbol, image, outputs) in _28x28Images]
if isFirst:
isFirst = False
for (symbol, matrix, outputs) in _28x28Images:
printMatrix(matrix)
matrixToImage(matrix).save(fontName + '-' + symbol + '.png')
datums = [(symbol, map0255to01(list(matrix.flatten())), outputs) for (symbol, matrix, outputs) in _28x28Images]
for d in datums:
dataset.append(d)
'''
for fontName in fontNames:
from PIL import Image
from lib_threshold import identifyLetters, makeWhiteOnBlack, matrixToImage, imageToNPMatrix, thresholdMatrix
from lib_LetterBBox import fitTo28x28
from lib_dataset import nnetResultToChar, topNResults, chars
import numpy
# first open image
print "Loading image file..."
image = Image.open("./test-text.png")
print "Extracting letters..."
# extract letter data
letters = identifyLetters(image.convert("L"))
# flatten to array inputs and clamp between 0 and 1
letters_processed = [fitTo28x28(matrixToImage(img)) for (pos, img) in letters]
# show us what they look like
for (letter, num) in zip(letters_processed, range(0, len(letters_processed))):
letter.save("input-" + str(num) + ".png")
# create the inputs for the nnet
nnetInputs = [imageToNPMatrix(fitTo28x28(matrixToImage(img))).flatten() for (pos, img) in letters]
# create the outputs for the nnet
nnetOutputs = [net.activate(input) for input in nnetInputs]
def prePostProcessNnetOutput(nnetOutputs):
res = []
for output in nnetOutputs: