Self-Driving-Car/main_mindata.py at master · sohjag/Self-Driving-Car · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
import numpy as np
from PIL import ImageGrab
import cv2
import time
#import pyautogui
from directkeys import PressKey, W, A, S, D, ReleaseKey
from screengrab import grab_screen
from getkeys import key_check
import os

w = [1,0,0]
#s = [0,1,0,0,0,0,0,0,0]
#a = [0,0,1,0,0,0,0,0,0]
#d = [0,0,0,1,0,0,0,0,0]
wa = [0,1,0]
wd = [0,0,1]
#sa = [0,0,0,0,0,0,1,0,0]
#sd = [0,0,0,0,0,0,0,1,0]
#nk = [0,0,0,0,0,0,0,0,1]


def keys_to_op(keys):
    '''
    Convert keys to a ...multi-hot... array
     0  1  2  3  4   5   6   7    8
    [W, S, A, D, WA, WD, SA, SD, NOKEY] boolean values.
    '''
    op = [0,0,0]

    if 'W' in keys and 'A' in keys:
        op = wa
    elif 'W' in keys and 'D' in keys:
        op = wd
    #elif 'S' in keys and 'A' in keys:
    #    op = sa
    #elif 'S' in keys and 'D' in keys:
    #    op = sd
    elif 'W' in keys:
        op = w
    #elif 'S' in keys:
    #    op = s
    #elif 'A' in keys:
    #    op = a
    #elif 'D' in keys:
    #    op = d
    #else:
    #    op = nk
    return op


file_name_x = 'training_data_x_mindata.npy'
file_name_y = 'training_data_y_mindata.npy'

if os.path.isfile(file_name_x):
    print('File present!')
    training_data_x = list(np.load(file_name_x))
else:
    print('File does not exist, starting fresh')
    training_data_x = []

if os.path.isfile(file_name_y):
    print('File present!')
    training_data_y = list(np.load(file_name_y))
else:
    print('File does not exist, starting fresh')
    training_data_y = []


def process_img(image):
    original_image = image
    # convert to gray
    processed_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # edge detection
    processed_img =  cv2.Canny(processed_img, threshold1 = 200, threshold2=300)
    return processed_img


for i in list(range(4))[::-1]:
    print(i+1)
    time.sleep(1)

'''last_time = time.time()
            while True:
                PressKey(W)
                screen =  np.array(ImageGrab.grab(bbox=(0,40,800,640)))
                #print('Frame took {} seconds'.format(time.time()-last_time))
                last_time = time.time()
                new_screen = process_img(screen)
                cv2.imshow('window', new_screen)
                #cv2.imshow('window',cv2.cvtColor(screen, cv2.COLOR_BGR2RGB))
                 if cv2.waitKey(25) & 0xFF == ord('q'):
                    cv2.destroyAllWindows()
                    break'''


'''for i in list(range(2))[::-1]:
    PressKey(W)
    time.sleep(1)
    ReleaseKey(W)'''

while True:
    curr_view = grab_screen([0,30,800,620])
    #cv2.imshow('frame', curr_view)
    #cv2.imshow('frame',cv2.cvtColor(curr_view, cv2.COLOR_BGR2GRAY))
    #screen = cv2.imshow('frame',cv2.cvtColor(curr_view, cv2.COLOR_BGR2GRAY))
    screen = cv2.cvtColor(curr_view, cv2.COLOR_BGR2GRAY)

    #speed_img = screen[-60:-40, 25:60]
    #cv2.imshow("cropped", speed_img)

    screen = cv2.resize(screen,(60,60))
    cv2.imshow('screen',screen)
    training_data_x.append(screen)

    keys = key_check()
    output = keys_to_op(keys)
    training_data_y.append(output)

    if len(training_data_x) % 500 == 0:
        print(len(training_data_x))
        np.save(file_name_x, training_data_x)
        np.save(file_name_y, training_data_y)

    if cv2.waitKey(25) & 0xFF == ord('q'):
        cv2.destroyAllWindows()
        break