#Utils.showImage(transtd)
blurred = transtd.copy()
blurred[:, :, 0] = gaussian(blurred[:, :, 0], sigma, preserve_range=True)
print("blurred=" + str(np.mean(blurred[:, :, 0:3])))
#Utils.showImage(blurred)
resized = resize(blurred, (ratio * blurred.shape[0], ratio * blurred.shape[1]),
anti_aliasing=True,
preserve_range=True)
print("resized=" + str(np.mean(resized[:, :, 0:3])))
#Utils.showImage(resized)
print("Treating file: " + newFile)
attributes = pd.DataFrame(
PreProcessor.preprocessImage(resized).reshape(1,
TRAIN_WIDTH * TRAIN_HEIGHT))
attributes["class"] = number
print(type(attributes))
print(attributes.shape)
print(attributes)
###################
from DigitPositionDetector import DigitPositionDetector
dpd = DigitPositionDetector(resized)
dpd.detect()
detectedDigits = dpd.getDetectedDigits()
for dd in detectedDigits:
dd.display()
def generate(self):
if self._dataSet == None:
# Create outdirs
if not os.path.exists(Generator.DIGITS_OUTDIR):
os.makedirs(Generator.DIGITS_OUTDIR)
if not os.path.exists(Generator.TRAINSET_OUTDIR):
os.makedirs(Generator.TRAINSET_OUTDIR)
# Treat all digit images found in source dir
files = Generator.listFiles(Generator.DIGITS_SRCDIR)
outDF = pd.DataFrame(data=[], columns=self._dataColumns+self._classColumns)
for imgFile in files:
outDF1Nb = pd.DataFrame(data=[], columns=self._dataColumns+self._classColumns)
number = Generator.extractNumber(imgFile)
img = Utils.readImage(Generator.DIGITS_SRCDIR + "/" + imgFile);
# double image canvas and center digit
transform = AffineTransform(scale=(2,2), translation=(-img.shape[1]/2,-img.shape[0]/2))
img = warp(img, transform, output_shape=img.shape, mode='edge')
#imgSize = img.shape
# Apply the modifications/augmentations on the digit image
for tilt in Generator.tiltRange:
tiltTform = AffineTransform(shear=tilt, translation=(215*tilt/4, 0))
for angle in Generator.angleRange:
for xtransl in Generator.xTranslRange:
for ytransl in Generator.yTranslRange:
transTform= AffineTransform(translation=(xtransl,ytransl))
for sigma in Generator.blurRange:
for ratio in Generator.zoomRange:
#print(np.mean(img))
zoomTform = AffineTransform(scale=(ratio,ratio), translation=(img.shape[1]/ratio/3-img.shape[1]/3,img.shape[0]/ratio/3-img.shape[0]/3))
tilted = warp(img, tiltTform, output_shape=img.shape, mode='edge')
#print(np.mean(tilted))
rotated = rotate(tilted, angle, mode='edge');
#print(np.mean(rotated))
transtd = warp(rotated, transTform, output_shape=img.shape, mode='edge')
#print(np.mean(transtd))
blured = transtd.copy()
blured[:, :, 0] = gaussian(blured[:, :, 0], sigma, preserve_range = True)
#print(np.mean(blured))
resized =- warp(blured, zoomTform, output_shape=img.shape, mode='edge')
print("resized="+str(np.mean(resized)))
resized = -1*resized ## TODO: why does resize invert all the values?
# Save the new image
newFile = Generator.DIGITS_OUTDIR + "/" + str(number) + \
"_t" + str(tilt) + "_r" + str(angle) + "_t" + str(xtransl) + "_" + str(ytransl) + \
"_b" + str(sigma) + "_s" + str(ratio) + ".png"
print("Writing image file: " + newFile)
#Utils.showImage(resized);
Utils.writeImage(resized, newFile)
print("Treating file: " + newFile)
attributes = pd.DataFrame(PreProcessor.preprocessImage(resized).reshape(1,PreProcessor.TRAIN_WIDTH*PreProcessor.TRAIN_HEIGHT))
attributes["class"] = number
# print(attributes)
# print(type(attributes))
# print(attributes.shape)
attributes.columns=outDF1Nb.columns
outDF1Nb = outDF1Nb.append(attributes) # , ignore_index=True
# Save the partial dataframe for the current digits (in case of crash, allows to restarts only on untreated digits)
outDF1Nb.to_csv(Generator.TRAINSET_OUTDIR + "/trainset_" + number + ".csv");
outDF = outDF.append(outDF1Nb, ignore_index=True)
# Save the whole dataframe for all digits
outDF.to_csv(Generator.TRAINSET_OUTDIR + "/trainset_full.csv")
self._dataSet = outDF
