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)