Create collecting-data-for-larger-fpv-model.py

submitting a data collection file for people who wish to contribute data.

collecting-data-for-larger-fpv-model.py

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+'''
+This file is meant to collect data for the latest model.
+
+The data should be first person view data with the *HOOD CAMERA* in an armored Karuma. 
+
+I mainly train during day, but I would like more data from other times of day/weather, so feel free to submit whatever you like. 
+
+I will check all data for fitment to AI (basically how close does my AI predict the data you submit) to validate 
+against people trying to submit bad data. 
+
+When you have some data files, host them to google docs or something of that sort and share with 
+[email protected]
+'''
+# create_training_data.py
+
+import numpy as np
+from grabscreen import grab_screen
+import cv2
+import time
+from getkeys import key_check
+import os
+
+
+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]
+
+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
+
+
+file_name = 'training_data.npy'
+
+if os.path.isfile(file_name):
+    print('File exists, loading previous data!')
+    training_data = list(np.load(file_name))
+else:
+    print('File does not exist, starting fresh!')
+    training_data = []
+
+
+def main():
+
+    for i in list(range(4))[::-1]:
+        print(i+1)
+        time.sleep(1)
+
+    paused = False
+    while(True):
+
+        if not paused:
+            # 800x600 windowed mode
+            screen = grab_screen(region=(0,40,800,640))
+            last_time = time.time()
+            screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
+            screen = cv2.resize(screen, (160,120))
+            # resize to something a bit more acceptable for a CNN
+            keys = key_check()
+            output = keys_to_output(keys)
+            training_data.append([screen,output])
+
+            if len(training_data) % 1000 == 0:
+                print(len(training_data))
+                np.save(file_name,training_data)
+
+        keys = key_check()
+        if 'T' in keys:
+            if paused:
+                paused = False
+                print('unpaused!')
+                time.sleep(1)
+            else:
+                print('Pausing!')
+                paused = True
+                time.sleep(1)
+
+main()