def train():
# imgUrl = request.args.get('imageUrl', '')
# splited = imgUrl.split('/')
# imgName = splited[len(splited)-1]
# imgPath = _util.getImagePath(imgName)
session_id = request.args.get('session_id', 0)
imageUrl = request.args.get('imageUrl', '') # only for debugging purposes
pixDeg = request.args.get('pixDeg', 1)
leakgDeg = request.args.get('leakgDeg', 0)
leakdDeg = request.args.get('leakdDeg', 0)
spotMaxNum = request.args.get('spotMaxNum', 15)
spotNumVariance = request.args.get('spotNumVariance', 10)
spotBaseColor = request.args.get('spotBaseColor', '#FF0000')
spotBaseColorRGB = _util.hex_to_rgb(spotBaseColor)
spotColorVariance = request.args.get('spotColorVariance', 20)
spotBlur = request.args.get('spotBlur', 0)
spotNoise = request.args.get('spotNoise', 0)
paperColor = request.args.get('paperColor', '#5C1C56')
paperColorRGB = _util.hex_to_rgb(paperColor)
# Draw Image with actual training text
text = request.args.get('text', trainingText)
drawer = _create_image.ImageDrawer(text, _util.size_training)
path = drawer.drawImage() # Note we re-assign
# Get filename and absolute imgPath
splited = path.split('/')
filename = splited[len(splited)-1]
poorTyper = _process_image.ImagePoortyper(filename, trainingText=trainingText, trainingSize=_util.size_training, isFadeGeneral=False, isFadeShallow=True, isLeakDown=True, leakDeg=leakgDeg, leakHeight=leakdDeg, spotMaxNum=spotMaxNum, spotNumVariance=spotNumVariance, spotBaseColor=spotBaseColorRGB, spotColorVariance=spotColorVariance, spotBlur=spotBlur, spotNoise=spotNoise, paperColor=paperColorRGB)
filename_rendered = poorTyper.render()
pixelator = _process_image.ImagePixelator(filename_rendered)
filename_rendered_pixed = pixelator.pixelate(degree=pixDeg)
imgPath = _util.getImagePath(filename_rendered_pixed)
thisRender = (
session_id,
path, #'imageUrl'
pixDeg, #'pixDeg'
leakgDeg, #'leakgDeg'
leakdDeg, #'leakdDeg'
spotMaxNum, #'spotMaxNum'
spotNumVariance, #'spotNumVariance'
spotBaseColor, #'spotBaseColor'
spotColorVariance, #'spotColorVariance'
spotBlur, #'spotBlur'
spotNoise, #'spotNoise'
paperColor, #'paperColor'
filename, #'filename'
'TRAIN', # 'action'
)
_util.saveRenderToDB(thisRender)
trainer = _train_tesseract.Trainer(imgPath) # absolute path
traineddataPath = trainer.train()
if (traineddataPath is not None):
return jsonify(status='success', result=url_for('static',filename=traineddataPath))
return jsonify(status='fail')
