def upload_file():
if request.method == 'POST':
file = request.files['file']
if file and allowed_file(file.filename):
filename = file.filename
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
cnn.test()
return redirect(url_for('uploaded_file', filename=filename))
return render_template("index.html")
def do_POST(self):
response_code = 200
response = ""
var_len = int(self.headers.get('Content-Length'))
content = self.rfile.read(var_len)
payload = json.loads(content)
print(payload)
if 'train' in self.path:
print('train')
start_training()
elif 'test' in self.path:
print('test')
response = test(payload['image'], payload['label'])
else:
response_code = 400
b = io.BytesIO(json.dumps(response).encode('utf-8'))
view = b.getbuffer()
self.send_response(response_code)
self.send_header('Content-Type', 'application/json')
self.send_header('Access-Control-Allow-Origin', '*')
self.end_headers()
self.wfile.write(view)
def result():
if request.method == 'POST':
f = request.files['file']
f.save(secure_filename(f.filename))
shutil.move(
f.filename, "os.getcwd/static" + f.filename
) # as the file we save will be in directory we need to move to static
#make sure that cnn.py is in the same directory created
import cnn
result = cnn.test(f.filename)
print(result)
return render_template('result.html',
value=result,
filename=str("../static/" + f.filename))
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# Train Module
# Module to train a CNN model on character data.
import numpy as np
import cnn
import utility
input_arguments = utility.parse_input_arguments(module="train")
images, labels = utility.load_data(input_arguments.image_path)
class_count = len(np.unique(labels))
images, labels = utility.preprocess(images, labels)
images, labels = utility.shuffle(images, labels)
x_train, x_test, y_train, y_test = utility.split(images, labels, test_size=0.2)
cnn = cnn.CNN(x_train.shape[1], x_train.shape[2], x_train.shape[3], class_count)
cnn.summary()
cnn.train(x_train, y_train, epochs=input_arguments.epochs, batch_size=input_arguments.batch_size,
validation_split=input_arguments.validation_split, output_path=input_arguments.output_path)
cnn.test(x_test, y_test, output_path=input_arguments.output_path)
from keras.datasets import mnist from conv import Conv2d from maxpool import MaxPooling2d from softmax import Softmax from cnn import training_loop, test (train_images, train_labels), (test_images, test_labels) = mnist.load_data() train_images = train_images[:1000] train_labels = train_labels[:1000] test_images = test_images[:1000] test_labels = test_labels[:1000] conv2d = Conv2d(8, 3) maxpool2d = MaxPooling2d(2) softmax = Softmax(13*13*8, 10) training_loop(3, train_images, train_labels, conv2d, maxpool2d, softmax) test(test_images, test_labels, conv2d, maxpool2d, softmax)