def classify():
if request.method == 'POST':
input_file = request.files[
'file'] # werkzeug.datastructures.FileStorage instance
# if user does not select file, submit a empty part without filename
if input_file.filename == '': # file name (without path)
print "no selected file! ", input_file, input_file.filename #flash('No selected file')
return redirect(request.url)
elif input_file and allowed_file(input_file.filename):
#filename = secure_filename(input_file.filename)
#create_path_if_doesnt_exist(UPLOAD_FOLDER)
#input_file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
dc = DigitClassifier("CNN")
# Load trained model
model = dc.load_model()
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# evaluate loaded model on test data
#filename = 'data/test/4391.png'
predicted_label = dc.predict_image(input_file, model)
print "Predictions with loaded model for image: ", input_file.filename, type(
input_file.filename), ": ", predicted_label
return 'Predicted number for input image: %s ' % (predicted_label)
#return redirect(url_for('uploaded_file', filename=filename))
else:
return "file input format not allowed or was empty"
else:
return "ok" # a function or a string must be return
def upload():
image_path = request.files['file']
dc = DigitClassifier("CNN")
# Load trained model
model = dc.load_model()
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# evaluate loaded model on test data
#image_path = 'data/test/4391.png'
predicted_label = dc.predict_image(image_path, model)
print "Prediction with loaded model for image: ", image_path, " is: ", predicted_label, request.files[
'file']
return 'predict: %s ' % (predicted_label[0])
def main():
my01path = "C:/DigitProject/DigitDetector/binaryFolder/digit_data/"
files = [f for f in listdir(my01path) if isfile(join(my01path, f))]
matches = [re.search("^input_[0-9]+_[0-9]+_[0-9]+\.json$", i) for i in files]
jsonFiles = [i.group(0) for i in matches if i]
partitions = kCross(10, jsonFiles)
DigitClassifier(partitions, my01path, 'neural')
def __initData(self):
self.__paintBoard = PaintBoard(self)
self.__model = DigitClassifier()
class MainWidget(QWidget):
def __init__(self, Parent=None):
super().__init__(Parent)
self.__result = -1
self.__initData()
self.__initView()
def __initData(self):
self.__paintBoard = PaintBoard(self)
self.__model = DigitClassifier()
def __initView(self):
self.setFixedSize(600, 400)
self.setWindowTitle('Application')
main_layout = QHBoxLayout(self)
main_layout.setSpacing(10)
main_layout.addWidget(self.__paintBoard)
sub_layout = QVBoxLayout()
sub_layout.setContentsMargins(10, 10, 10, 10)
sub_layout.setSpacing(30)
self.__btn_Clear = QPushButton('clear')
self.__btn_Clear.setParent(self)
self.__btn_Clear.clicked.connect(self.__paintBoard.clear)
sub_layout.addWidget(self.__btn_Clear)
self.__btn_Predict = QPushButton('predict')
self.__btn_Predict.setParent(self)
self.__btn_Predict.clicked.connect(self.predict)
sub_layout.addWidget(self.__btn_Predict)
self.__btn_Quit = QPushButton('quit')
self.__btn_Quit.setParent(self)
self.__btn_Quit.clicked.connect(self.quit)
sub_layout.addWidget(self.__btn_Quit)
self.__lb_Result_Tip = QLabel()
font = QFont()
font.setPointSize(24)
self.__lb_Result_Tip.setFont(font)
self.__lb_Result_Tip.setText('result')
self.__lb_Result_Tip.setParent(self)
sub_layout.addWidget(self.__lb_Result_Tip)
self.__lb_Result = QLabel()
font = QFont()
font.setPointSize(30)
self.__lb_Result.setFont(font)
self.__lb_Result.setParent(self)
self.__lb_Result.setAlignment(Qt.AlignHCenter)
sub_layout.addWidget(self.__lb_Result)
main_layout.addLayout(sub_layout)
def quit(self):
self.close()
def predict(self):
image = self.__paintBoard.getImage()
pil_img = ImageQt.fromqimage(image)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
# pil_img.save('./images/test66.png')
# pil_img.show()
img_array = np.array(pil_img.convert('L')).reshape(784)
img_array = np.hstack([img_array, [1.0]]).reshape((1, 785))
# display image
# plt.imshow(img_array.reshape(28, 28), cmap="binary")
# plt.imshow(pil_img, cmap="binary")
# plt.show()
# fig = plt.figure(figsize=(6, 6))
# fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
# # 绘制数字：每张图像8*8像素点
# for i in range(64):
# ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])
# ax.imshow(self.xtest[i].reshape(28, 28), cmap=plt.cm.binary, interpolation='nearest')
# # 用目标值标记图像
# ax.text(0, 7, str(self.ytest[i]))
# plt.show()
self.__result = self.__model.predict(img_array)
print("result: %d" % self.__result)
self.__lb_Result.setText("%d" % self.__result)
}
if len(sys.argv) > 1: #TODO check + controllo games
if sys.argv[1] is not None:
info['game'] = sys.argv[1]
if sys.argv[2] is not None:
info['GRID_LEN'] = int(sys.argv[2])
if sys.argv[3] is not None:
info['SQUARE_LEN'] = int(sys.argv[3])
puzzle_detected = False
puzzle_analyzed = False
puzzle_solved = False
detector = PuzzleDetector(info)
classifier = DigitClassifier()
solver = Solver(info)
REAL_TIME = True
# 1. Board detection phase
#if REAL_TIME:
# cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
# counter = 0
# while not puzzle_detected:
# _, frame = cap.read()
# detector.detectGameBoard(frame)
# counter += 1
# if counter % 10 == 0 and detector.grid_digit_images is not None:
class MainWidget(QWidget):
def __init__(self, Parent=None):
super().__init__(Parent)
self.__result = -1
self.__initData()
self.__initView()
def __initData(self):
self.__paintBoard = PaintBoard(self)
self.__model = DigitClassifier()
def __initView(self):
self.setFixedSize(600, 400)
self.setWindowTitle('Application')
main_layout = QHBoxLayout(self)
main_layout.setSpacing(10)
main_layout.addWidget(self.__paintBoard)
sub_layout = QVBoxLayout()
sub_layout.setContentsMargins(10, 10, 10, 10)
sub_layout.setSpacing(30)
self.__btn_Clear = QPushButton('clear')
self.__btn_Clear.setParent(self)
self.__btn_Clear.clicked.connect(self.__paintBoard.clear)
sub_layout.addWidget(self.__btn_Clear)
self.__btn_Predict = QPushButton('predict')
self.__btn_Predict.setParent(self)
self.__btn_Predict.clicked.connect(self.predict)
sub_layout.addWidget(self.__btn_Predict)
self.__btn_Quit = QPushButton('quit')
self.__btn_Quit.setParent(self)
self.__btn_Quit.clicked.connect(self.quit)
sub_layout.addWidget(self.__btn_Quit)
self.__lb_Result_Tip = QLabel()
font = QFont()
font.setPointSize(24)
self.__lb_Result_Tip.setFont(font)
self.__lb_Result_Tip.setText('result')
self.__lb_Result_Tip.setParent(self)
sub_layout.addWidget(self.__lb_Result_Tip)
self.__lb_Result = QLabel()
font = QFont()
font.setPointSize(30)
self.__lb_Result.setFont(font)
self.__lb_Result.setParent(self)
self.__lb_Result.setAlignment(Qt.AlignHCenter)
sub_layout.addWidget(self.__lb_Result)
main_layout.addLayout(sub_layout)
def quit(self):
self.close()
def predict(self):
image = self.__paintBoard.getImage()
pil_img = ImageQt.fromqimage(image)
pil_img = pil_img.resize((28, 28), Image.ANTIALIAS)
# pil_img.save('./images/test66.png')
# pil_img.show()
img_array = np.array(pil_img.convert('L')).reshape(784)
# display image
plt.imshow(img_array.reshape(28, 28), cmap="binary")
# plt.imshow(pil_img, cmap="binary")
plt.show()
img_array = np.hstack([img_array, [1.0]]).reshape((1, 785))
# img_array = np.hstack([img_array, [1.0]])
# print(img_array.shape) # (785,)
# img_array = np.reshape(img_array, (img_array.shape[0], -1))
# print(img_array.shape) # (785, 1)
self.__result = self.__model.predict(img_array)
print("result: %d" % self.__result)
self.__lb_Result.setText("%d" % self.__result)
from Downloader import Downloader
from DigitClassifier import DigitClassifier
import numpy as np
from TheBlueAllianceAPI import get_event_match_keys_with_vidoes, get_event_match_outcomes
from MatchProcessing import MatchProcessing
from DataBaseWorker import DataBaseWorker
from MatchProcessingWorker import MatchProcessingWorker
import time
score_classifier = DigitClassifier(
'C:\\Users\\darkd\\Documents\\ScoreProject\\knntrain\\', (15, 20),
np.array([200, 200, 200]), np.array([255, 255, 255]))
time_classifier = DigitClassifier(
'C:\\Users\\darkd\\Documents\\ScoreProject\\knntraintime\\', (14, 20),
np.array([0, 0, 0]), np.array([120, 120, 120]))
d = Downloader('https://www.youtube.com/watch?v=hbLME8QLdeU',
'C:\\Users\\darkd\\Documents\\ScoreProject\\', 'testmilian')
d.download()
match = MatchProcessing(d.name, score_classifier, time_classifier)
states = match.process_match()
print(states)
# db = DataBaseWorker('C:\\Users\\darkd\\Documents\\ScoreProject\\timeseriesdb.db')
# db.start()
# event_name = ['2018mibel', '2018milan', '2018miwmi', '2018miesc', '2018migay', '2018migul', '2018milin', '2018mimid']
# thread_list = []
# for event in event_name:
# matches_and_vidoes = get_event_match_keys_with_vidoes(event)