Updating deprecated scipy commands + PEP8 guidelines

ai.py

@@ -9,50 +9,53 @@
 from game_control import *
 from keras.models import model_from_json
 
+
 def main():
     # Get Model:
-    model_file = open('Data/Model/model.json', 'r')
+    model_file = open('data/model/model.json', 'r')
     model = model_file.read()
     model_file.close()
     model = model_from_json(model)
-    model.load_weights("Data/Model/weights.h5")
+    model.load_weights('data/model/weights.h5')
 
-    print('AI start now!')
+    print('AI training has begun')
 
     while 1:
         # Get screenshot:
         screen = ImageGrab.grab()
         # Image to numpy array:
         screen = np.array(screen)
         # 4 channel(PNG) to 3 channel(JPG)
-        Y = predict(model, screen)
-        if Y == [0,0,0,0]:
+        y = predict(model, screen)
+        if y == [0, 0, 0, 0]:
             # Not action
             continue
-        elif Y[0] == -1 and Y[1] == -1:
+        elif y[0] == -1 and y[1] == -1:
             # Only keyboard action.
-            key = get_key(Y[3])
-            if Y[2] == 1:
+            key = get_key(y[3])
+            if y[2] == 1:
                 # Press:
                 press(key)
             else:
                 # Release:
                 release(key)
-        elif Y[2] == 0 and Y[3] == 0:
+        elif y[2] == 0 and y[3] == 0:
             # Only mouse action.
-            click(Y[0], Y[1])
+            pass
+            # click(y[0], y[1])
         else:
             # Mouse and keyboard action.
             # Mouse:
-            click(Y[0], Y[1])
+            # click(y[0], y[1])
             # Keyboard:
-            key = get_key(Y[3])
-            if Y[2] == 1:
+            key = get_key(y[3])
+            if y[2] == 1:
                 # Press:
                 press(key)
             else:
                 # Release:
                 release(key)
 
+
 if __name__ == '__main__':
     main()

create_dataset.py

@@ -1,41 +1,59 @@
 # Arda Mavi
 import os
+import cv2
 import sys
 import platform
 import numpy as np
 from time import sleep
 from PIL import ImageGrab
 from game_control import *
 from predict import predict
-from scipy.misc import imresize
 from game_control import get_id
 from get_dataset import save_img
 from multiprocessing import Process
 from keras.models import model_from_json
-from pynput.mouse import Listener as mouse_listener
-from pynput.keyboard import Listener as key_listener
+from pynput.mouse import Listener as Mouse_Listener
+from pynput.keyboard import Listener as Key_Listener
+
 
 def get_screenshot():
     img = ImageGrab.grab()
-    img = np.array(img)[:,:,:3] # Get first 3 channel from image as numpy array.
-    img = imresize(img, (150, 150, 3)).astype('float32')/255.
+    img = np.array(img)[:, :, :3]  # Get first 3 channel from image as numpy array.
+
+    """
+    The ratio is r. The new image will
+    have a height of 150 pixels. To determine the ratio of the new
+    height to the old height, we divide 150 by the old height.
+    """
+
+    r = 150.0 / img.shape[0]
+    dim = (int(img.shape[1] * r), 150)
+
+    img = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
+    print(img)
+
     return img
 
+
 def save_event_keyboard(data_path, event, key):
     key = get_id(key)
     data_path = data_path + '/-1,-1,{0},{1}'.format(event, key)
     screenshot = get_screenshot()
     save_img(data_path, screenshot)
     return
 
+
+"""
+
 def save_event_mouse(data_path, x, y):
     data_path = data_path + '/{0},{1},0,0'.format(x, y)
     screenshot = get_screenshot()
     save_img(data_path, screenshot)
     return
 
+
 def listen_mouse():
-    data_path = 'Data/Train_Data/Mouse'
+    data_path = 'data/train_data/mouse'
     if not os.path.exists(data_path):
         os.makedirs(data_path)
 
@@ -44,15 +62,17 @@ def on_click(x, y, button, pressed):
 
     def on_scroll(x, y, dx, dy):
         pass
-    
+
     def on_move(x, y):
         pass
 
-    with mouse_listener(on_move=on_move, on_click=on_click, on_scroll=on_scroll) as listener:
+    with Mouse_Listener(on_move=on_move, on_click=on_click, on_scroll=on_scroll) as listener:
         listener.join()
+"""
+
 
 def listen_keyboard():
-    data_path = 'Data/Train_Data/Keyboard'
+    data_path = 'data/train_data/keyboard'
     if not os.path.exists(data_path):
         os.makedirs(data_path)
 
@@ -62,18 +82,20 @@ def on_press(key):
     def on_release(key):
         save_event_keyboard(data_path, 2, key)
 
-    with key_listener(on_press=on_press, on_release=on_release) as listener:
+    with Key_Listener(on_press=on_press, on_release=on_release) as listener:
         listener.join()
 
+
 def main():
-    dataset_path = 'Data/Train_Data/'
+    dataset_path = 'data/train_data/'
     if not os.path.exists(dataset_path):
         os.makedirs(dataset_path)
 
     # Start to listening mouse with new process:
-    Process(target=listen_mouse, args=()).start()
+    # Process(target=listen_mouse, args=()).start()
     listen_keyboard()
     return
 
+
 if __name__ == '__main__':
     main()

database_process.py

@@ -2,33 +2,41 @@
 import os
 import sqlite3
 
+
 def set_sql_connect(database_name):
     return sqlite3.connect(database_name)
+
+
 def set_sql_cursor(database_connect):
     return database_connect.cursor()
 
+
 def close_connect(vt):
     if vt:
         vt.commit()
         vt.close
 
+
 def set_connect_and_cursor(path='Data/database.sqlite'):
     vt = set_sql_connect(path)
     db = set_sql_cursor(vt)
     return vt, db
 
+
 def create_table(table_name, columns):
     vt, db = set_connect_and_cursor()
     db.execute("CREATE TABLE IF NOT EXISTS {0} ({1})".format(table_name, columns))
     close_connect(vt)
 
+
 def get_data(sql_command):
     vt, db = set_connect_and_cursor()
     db.execute(sql_command)
     gelen_veri = db.fetchall()
     close_connect(vt)
     return gelen_veri
 
+
 def add_data(table, adding):
     vt, db = set_connect_and_cursor()
     db.execute("INSERT INTO '{0}' VALUES ({1})".format(table, adding))

game_control.py

@@ -2,37 +2,52 @@
 from pynput.mouse import Button, Controller as Mouse
 from pynput.keyboard import Controller as Keyboard
 
+keyboard = Keyboard()
+
+mouse = Mouse()
+
+
 # For encoding keyboard keys:
 def get_keys():
-    return ['\t', '\n', '\r', ' ', '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', '`', 'a', 'b', 'c', 'd', 'e','f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '{', '|', '}', '~', 'accept', 'add', 'alt', 'altleft', 'altright', 'apps', 'backspace', 'browserback', 'browserfavorites', 'browserforward', 'browserhome', 'browserrefresh', 'browsersearch', 'browserstop', 'capslock', 'clear', 'convert', 'ctrl', 'ctrlleft', 'ctrlright', 'decimal', 'del', 'delete', 'divide', 'down', 'end', 'enter', 'esc', 'escape', 'execute', 'f1', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f2', 'f20', 'f21', 'f22', 'f23', 'f24', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'final', 'fn', 'hanguel', 'hangul', 'hanja', 'help', 'home', 'insert', 'junja', 'kana', 'kanji', 'launchapp1', 'launchapp2', 'launchmail', 'launchmediaselect', 'left', 'modechange', 'multiply', 'nexttrack', 'nonconvert', 'num0', 'num1', 'num2', 'num3', 'num4', 'num5', 'num6', 'num7', 'num8', 'num9', 'numlock', 'pagedown', 'pageup', 'pause', 'pgdn', 'pgup', 'playpause', 'prevtrack', 'print', 'printscreen', 'prntscrn', 'prtsc', 'prtscr', 'return', 'right', 'scrolllock', 'select', 'separator', 'shift', 'shiftleft', 'shiftright', 'sleep', 'space', 'stop', 'subtract', 'tab', 'up', 'volumedown', 'volumemute', 'volumeup', 'win', 'winleft', 'winright', 'yen', 'command', 'option', 'optionleft', 'optionright']
+    return ['a', 'd', 's', 'enter', 'return', 'space', 'right', 'left', 'up', 'down']
+
+
+def get_key(key_id):
+    return get_keys()[key_id]
 
-def get_key(id):
-    return get_keys()[id]
 
 def get_id(key):
-    return get_keys().index(key)
+    print('key', key)
+    print(get_keys(), 'index')
+    return
+    # return get_keys().index(key)
+
+
+# Keyboard:
+def press(key):
+    keyboard.press(key)
+    return
+
+
+def release(key):
+    keyboard.release(key)
+    return
 
-keyboard = Keyboard()
-mouse = Mouse()
 
 # Mouse:
+"""
+
 def move(x, y):
     mouse.position = (x, y)
     return
 
+
 def scroll(x, y):
     mouse.scroll(x, y)
     return
 
+
 def click(x, y):
     mouse.press(Button.left)
     return
-
-# Keyboard:
-def press(key):
-    keyboard.press(key)
-    return
-
-def release(key):
-    keyboard.release(key)
-    return
+"""

get_dataset.py

@@ -1,47 +1,69 @@
 # Arda Mavi
 import os
 import numpy as np
+import cv2
 from keras.utils import to_categorical
-from scipy.misc import imread, imresize, imsave
+import imageio
+from skimage import *
+import skimage.io
 from sklearn.model_selection import train_test_split
 
+
 def get_img(data_path):
     # Getting image array from path:
-    img = imread(data_path)
-    img = imresize(img, (150, 150, 3))
+    img = skimage.io.imread(data_path)
+    """
+    The ratio is r. The new image will
+    have a height of 150 pixels. To determine the ratio of the new
+    height to the old height, we divide 150 by the old height.
+    """
+
+    r = 150.0 / img.shape[0]
+    dim = (int(img.shape[1] * r), 150)
+
+    img = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
+    # img = imresize(img, (150, 150, 3)).astype('float32') / 255.
+
     return img
 
+
 def save_img(img, path):
-    imsave(path, img)
+    sk_img = skimage.io.imread(path)
+    sk_img = sk_img.transpose(1, 0, 2).reshape(130, -1)
+    image = img_as_ubyte(sk_img)
+    skimage.io.imsave(path, image)
+    skimage.io.imsave(path, img)
+    # skimage.io.imsave(path, img_as_uint(img))
     return
 
-def get_dataset(dataset_path='Data/Train_Data'):
+
+def get_dataset(dataset_path='data\\train_data'):
     # Getting all data from data path:
     try:
-        X = np.load('Data/npy_train_data/X.npy')
-        Y = np.load('Data/npy_train_data/Y.npy')
-    except:
-        labels = os.listdir(dataset_path) # Geting labels
-        X = []
-        Y = []
-        count_categori = [-1,''] # For encode labels
+        x = np.load('data\\npy_train_data\\x.npy')
+        y = np.load('data\\npy_train_data\\y.npy')
+    except AttributeError:
+        labels = os.listdir(dataset_path)  # getting labels
+        x = []
+        y = []
+        count_category = [-1, '']  # For encode labels
         for label in labels:
-            datas_path = dataset_path+'/'+label
-            for data in os.listdir(datas_path):
-                img = get_img(datas_path+'/'+data)
-                X.append(img)
+            datas_path = dataset_path + '/' + label
+            for d_data in os.listdir(datas_path):
+                img = get_img(datas_path + '/' + d_data)
+                x.append(img)
                 # For encode labels:
-                if data != count_categori[1]:
-                    count_categori[0] += 1
-                    count_categori[1] = data.split(',')
-                Y.append(count_categori[0])
+                if d_data != count_category[1]:
+                    count_category[0] += 1
+                    count_category[1] = d_data.split(',')
+                y.append(count_category[0])
         # Create dateset:
-        X = np.array(X).astype('float32')/255.
-        Y = np.array(Y).astype('float32')
-        Y = to_categorical(Y, count_categori[0]+1)
-        if not os.path.exists('Data/npy_train_data/'):
-            os.makedirs('Data/npy_train_data/')
-        np.save('Data/npy_train_data/X.npy', X)
-        np.save('Data/npy_train_data/Y.npy', Y)
-    X, X_test, Y, Y_test = train_test_split(X, Y, test_size=0.1, random_state=42)
-    return X, X_test, Y, Y_test
+        x = np.array(x).astype('float32') / 255.
+        y = np.array(y).astype('float32')
+        y = to_categorical(y, count_category[0] + 1)
+        if not os.path.exists('data\\npy_train_data/'):
+            os.makedirs('data\\npy_train_data/')
+        np.save('data\\npy_train_data/x.npy', x)
+        np.save('data\\npy_train_data/y.npy', y)
+    x, x_test, y, y_test = train_test_split(x, y, test_size=0.1, random_state=42)
+    return x, x_test, y, y_test