1. collect_data.py

# https://github.com/Phillyclause89/Skittles-AI/wiki/1.-collect_data.py

import numpy as np
from grabscreen import grab_screen
import cv2
import time
from getkeys import key_check
import os

#    W  S  A  D  WA WD SA SD NK
w = [1, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 1, 0, 0, 0, 0]
wd = [0, 0, 0, 0, 0, 1, 0, 0, 0]
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]

path = "D:/training_data/raw_data/taxi_training_data"
starting_value = 1
data_cap = 25000

game_width = 1920
game_height = 1080
title_bar = 40

width = 480
height = 270

while True:
    file_name = '{}-{}.npy'.format(path, starting_value)

    if os.path.isfile(file_name):
        print('File exists, moving along', starting_value)
        starting_value += 1
    else:
        print('File does not exist, starting fresh!', starting_value)

        break


def keys_to_output(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.
    '''
    output = [0, 0, 0, 0, 0, 0, 0, 0, 0]

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


def main(file_name, starting_value):
    file_name = file_name
    starting_value = starting_value
    training_data = []
    for i in list(range(4))[::-1]:
        print(i + 1)
        time.sleep(1)

    last_time = time.time()
    paused = False
    print('STARTING!!!')
    while (True):

        if not paused:
            screen = grab_screen(region=(0, title_bar, game_width, (game_height+title_bar)))
            # resize to something a bit more acceptable for a CNN
            screen = cv2.resize(screen, (width, height))
            # run a color convert:
            screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)

            keys = key_check()
            output = keys_to_output(keys)
            print(output)
            training_data.append([screen,  output])

            # print('loop took {} seconds'.format(time.time()-last_time))
            #last_time = time.time()
            ##            cv2.imshow('window',cv2.resize(screen,(640,360)))
            ##            if cv2.waitKey(25) & 0xFF == ord('q'):
            ##                cv2.destroyAllWindows()
            ##                break

            if len(training_data) % 100 == 0:
                print(len(training_data))

                if len(training_data) == data_cap:
                    print('SAVING..')
                    np.save(file_name, training_data)
                    print('SAVED')
                    training_data = []
                    starting_value += 1
                    file_name = '{}-{}.npy'.format(path, starting_value)

        keys = key_check()
        if 'T' in keys:
            if paused:
                paused = False
                print('unpaused!')
                time.sleep(1)
            else:
                print('Pausing!')
                paused = True
                time.sleep(1)
                print('SAVING..')
                np.save(file_name, training_data)
                print('SAVED')


main(file_name, starting_value)