def hello(): from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets(os.environ['HOME'] + util.data_dir, one_hot=True) image = mnist.test.images[1] image = util.img2binary(image) image = util.index2ary(util.img_size * util.img_size, image) ret = rec.do(image) return str(ret)
def main(): from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets(os.environ['HOME'] + util.data_dir, one_hot=True) image = mnist.test.images[1] image = util.img2binary(image) image = util.index2ary(util.img_size * util.img_size, image) rec = DeepRecognizer() print(rec.do(image))
def create():
raw_data = request.form['image']
label = request.form['label']
data = map(lambda x: int(x), raw_data.split(','))
data = util.index2ary(util.img_size * util.img_size, data)
data_str = "".join(map(lambda x: x == 0 and '\xff' or '\x00', data))
buf = bytes(data_str)
img = Image.frombuffer('L', (util.img_size, util.img_size), buf, 'raw', 'L', 0, 1)
img.save(os.environ['HOME'] + util.img_dir + str(label) + "/" + str(uuid.uuid1()) + ".bmp")
return 1
def digit():
raw_data = request.form['image']
data = map(lambda x: int(x), raw_data.split(','))
ret = rec.do(util.index2ary(util.img_size * util.img_size, data))
return str(ret)
