Training Data 1-100

Training Data(1-100)/README.md

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+### Info
+- Image Resolution : 270, 480
+- Mode : RGB
+- Dimension : (270, 480, 3)
+- File Count : 100
+- Size : 1.81 GB/file
+- Total Size : 181 GB
+- Total Frames : 500,000
+
+### Data Count (1-100)
+```
+ 'W':  [1, 0, 0, 0, 0, 0, 0, 0, 0] : 353725
+ 'S':  [0, 1, 0, 0, 0, 0, 0, 0, 0] : 2243
+ 'A':  [0, 0, 1, 0, 0, 0, 0, 0, 0] : 14303
+ 'D':  [0, 0, 0, 1, 0, 0, 0, 0, 0] : 13114
+ 'WA': [0, 0, 0, 0, 1, 0, 0, 0, 0] : 30877
+ 'WD': [0, 0, 0, 0, 0, 1, 0, 0, 0] : 29837
+ 'SA': [0, 0, 0, 0, 0, 0, 1, 0, 0] : 1952
+ 'SD': [0, 0, 0, 0, 0, 0, 0, 1, 0] : 1451
+ 'NK': [0, 0, 0, 0, 0, 0, 0, 0, 1] : 52256
+  NONE : 242
+```
+  
+### Graphics Details
+- Original Resolution : 800 x 600
+- Aspect Ratio : 16:10
+- All Video Settings : Low
+
+### Camera Details
+- Camera : Hood Cam
+- Vehical Camera Height : Low
+- First Person Vehical Auto-Center : On
+- First Person Head Bobbing : Off
+
+### Other Details
+- Vehical : Michael's Car
+- Vehical Mods : All Max 
+- Cv2 Mask : None
+- Way Point : Enabled/Following
+- Weather Conditions : Mostly Sunny
+- Time of Day : Day, Night
+- Rain : Some
+
+### Note
+- Remove `NONE` while processing the data
+- Check `training_data_count_001-100.csv` for detailed count
+- Check `training_data_stats.py` for more info

Training Data(1-100)/training_data_count_001-100.csv

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+File,W,S,A,D,WA,WD,SA,SD,NK,NONE
+training_data-1.npy,3442,0,165,197,348,233,0,0,615,0
+training_data-2.npy,3566,48,125,90,373,379,0,0,418,1
+training_data-3.npy,3310,0,214,163,272,269,0,0,771,1
+training_data-4.npy,3263,54,219,295,632,365,3,0,167,2
+training_data-5.npy,3411,46,174,163,267,397,46,0,495,1
+training_data-6.npy,3143,92,187,113,336,421,23,58,625,2
+training_data-7.npy,3035,122,247,185,296,406,11,3,694,1
+training_data-8.npy,3440,47,194,114,326,467,0,1,406,5
+training_data-9.npy,3259,10,125,92,257,159,61,120,916,1
+training_data-10.npy,4190,0,151,10,263,243,0,0,140,3
+training_data-11.npy,4203,0,57,21,235,366,0,0,117,1
+training_data-12.npy,4213,0,29,47,235,244,0,0,231,1
+training_data-13.npy,3294,59,166,153,281,248,0,0,797,2
+training_data-14.npy,3814,0,18,53,477,348,0,0,289,1
+training_data-15.npy,3157,46,192,313,264,393,0,97,536,2
+training_data-16.npy,3234,90,143,232,509,304,56,29,400,3
+training_data-17.npy,3398,122,172,207,309,323,0,27,441,1
+training_data-18.npy,3839,0,47,68,267,318,0,0,460,1
+training_data-19.npy,2809,79,415,153,399,242,18,0,884,1
+training_data-20.npy,3955,24,78,46,161,335,0,0,400,1
+training_data-21.npy,2731,45,231,210,516,397,8,174,685,3
+training_data-22.npy,3164,6,460,353,229,337,0,1,450,0
+training_data-23.npy,2536,5,346,524,376,692,9,0,511,1
+training_data-24.npy,3680,0,192,200,266,395,0,0,266,1
+training_data-25.npy,4054,19,42,26,221,381,0,0,255,2
+training_data-26.npy,3792,84,70,65,307,283,2,118,278,1
+training_data-27.npy,3348,109,193,124,242,376,6,12,589,1
+training_data-28.npy,3753,0,135,143,237,393,0,0,338,1
+training_data-29.npy,2868,2,362,410,344,271,3,60,677,3
+training_data-30.npy,3463,3,167,215,344,249,0,37,521,1
+training_data-31.npy,3485,69,242,137,486,261,72,92,137,19
+training_data-32.npy,4295,0,28,29,214,259,0,0,174,1
+training_data-33.npy,3394,10,356,146,222,306,0,0,563,3
+training_data-34.npy,4285,16,67,15,189,242,0,0,184,2
+training_data-35.npy,4098,30,112,15,221,193,27,0,304,0
+training_data-36.npy,4089,0,71,31,303,240,0,0,262,4
+training_data-37.npy,3343,13,96,159,231,414,93,0,644,7
+training_data-38.npy,4022,0,61,79,300,284,0,0,251,3
+training_data-39.npy,3351,27,249,207,258,252,31,87,538,0
+training_data-40.npy,3303,56,254,164,316,373,0,0,529,5
+training_data-41.npy,4054,28,87,72,219,249,43,0,246,2
+training_data-42.npy,2914,62,380,187,420,426,0,0,610,1
+training_data-43.npy,3338,13,95,155,473,507,0,25,393,1
+training_data-44.npy,3728,0,95,104,352,277,0,0,443,1
+training_data-45.npy,3911,3,101,86,272,209,45,0,368,5
+training_data-46.npy,3874,0,113,62,340,343,0,0,268,0
+training_data-47.npy,3841,24,59,95,258,186,0,2,532,3
+training_data-48.npy,4386,0,17,4,282,197,0,0,112,2
+training_data-49.npy,3380,79,123,116,295,238,51,66,651,1
+training_data-50.npy,4165,0,57,40,291,250,0,0,197,0
+training_data-51.npy,3684,15,100,74,392,267,32,29,403,4
+training_data-52.npy,3495,78,148,94,374,391,10,4,405,1
+training_data-53.npy,3721,0,77,123,231,290,0,0,557,1
+training_data-54.npy,3344,28,147,195,274,316,58,0,636,2
+training_data-55.npy,4144,0,27,14,273,256,0,0,286,0
+training_data-56.npy,3993,0,29,12,292,241,0,0,431,2
+training_data-57.npy,3559,13,94,55,367,453,68,0,389,2
+training_data-58.npy,3329,2,228,225,317,338,64,3,493,1
+training_data-59.npy,3675,24,136,81,349,329,0,0,403,3
+training_data-60.npy,3284,0,122,155,363,230,18,29,794,5
+training_data-61.npy,3662,0,115,14,178,157,0,0,872,2
+training_data-62.npy,3535,9,81,54,328,332,114,0,545,2
+training_data-63.npy,3118,49,148,170,246,227,0,0,1039,3
+training_data-64.npy,3170,0,287,150,252,326,0,0,809,6
+training_data-65.npy,3257,42,161,254,430,359,0,0,496,1
+training_data-66.npy,4023,0,30,22,208,284,0,0,433,0
+training_data-67.npy,4176,0,7,26,274,183,0,0,333,1
+training_data-68.npy,3648,0,121,103,264,238,0,0,625,1
+training_data-69.npy,3859,0,64,64,247,222,0,0,544,0
+training_data-70.npy,3391,26,127,145,322,234,0,0,754,1
+training_data-71.npy,2771,0,345,462,293,229,0,0,899,1
+training_data-72.npy,2930,7,258,173,300,265,109,0,951,7
+training_data-73.npy,3797,18,174,79,204,244,8,0,473,3
+training_data-74.npy,3621,0,27,110,320,209,0,0,713,0
+training_data-75.npy,3635,0,60,6,125,128,0,0,1044,2
+training_data-76.npy,2755,0,228,244,356,352,0,0,1059,6
+training_data-77.npy,3649,26,155,211,322,378,0,13,243,3
+training_data-78.npy,3334,0,97,125,370,326,115,0,632,1
+training_data-79.npy,4224,0,0,6,246,301,0,0,223,0
+training_data-80.npy,4034,0,75,57,153,160,0,0,520,1
+training_data-81.npy,3851,21,96,44,218,320,0,0,445,5
+training_data-82.npy,3682,0,67,70,266,271,0,0,640,4
+training_data-83.npy,3429,47,40,122,482,260,59,0,560,1
+training_data-84.npy,3179,1,192,98,298,344,46,0,838,4
+training_data-85.npy,2804,0,299,249,536,421,65,65,555,6
+training_data-86.npy,3188,38,157,279,513,254,90,71,403,7
+training_data-87.npy,4184,0,5,16,211,291,0,0,292,1
+training_data-88.npy,3988,41,21,42,214,255,0,0,436,3
+training_data-89.npy,3255,5,160,90,390,290,139,0,665,6
+training_data-90.npy,3777,0,62,200,243,241,0,0,475,2
+training_data-91.npy,3398,153,30,56,262,298,11,121,665,6
+training_data-92.npy,4025,0,3,42,161,210,0,0,554,5
+training_data-93.npy,3908,0,71,37,212,216,0,0,553,3
+training_data-94.npy,3276,0,96,102,295,219,0,0,1010,2
+training_data-95.npy,2747,6,282,254,361,205,85,0,1057,3
+training_data-96.npy,2792,0,328,189,307,281,11,28,1058,6
+training_data-97.npy,3309,4,155,188,419,344,180,71,326,4
+training_data-98.npy,3668,14,121,52,376,378,20,0,371,0
+training_data-99.npy,2485,34,284,279,459,257,42,8,1148,4
+training_data-100.npy,3343,0,187,314,453,277,0,0,423,3

Training Data(1-100)/training_data_stats.py

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+# training_data_stats.py
+import cv2
+import numpy as np
+import time
+from collections import Counter
+import pandas as pd
+
+def get_count_choices(a,b):
+    total_count_choices = Counter()
+    for i in range(a,b):
+        training_data = np.load(f'training_data-{i}.npy', allow_pickle=True)
+        choices = [str(data[1]) for data in training_data]
+
+        total_count_choices.update(choices)
+    count_choices_dict = dict(total_count_choices)
+    print(count_choices_dict)
+
+def get_count_choices_per_file(a,b):
+    df = pd.DataFrame(columns=['File','W','S','A','D','WA','WD','SA','SD','NK','NONE'])
+    choice_to_column = {'[1, 0, 0, 0, 0, 0, 0, 0, 0]':'W',
+                        '[0, 1, 0, 0, 0, 0, 0, 0, 0]':'S',
+                        '[0, 0, 1, 0, 0, 0, 0, 0, 0]':'A',
+                        '[0, 0, 0, 1, 0, 0, 0, 0, 0]':'D',
+                        '[0, 0, 0, 0, 1, 0, 0, 0, 0]':'WA',
+                        '[0, 0, 0, 0, 0, 1, 0, 0, 0]':'WD',
+                        '[0, 0, 0, 0, 0, 0, 1, 0, 0]':'SA',
+                        '[0, 0, 0, 0, 0, 0, 0, 1, 0]':'SD',
+                        '[0, 0, 0, 0, 0, 0, 0, 0, 1]':'NK',        
+                        'None':'NONE'}
+    for i in range(a,b):
+        training_data = np.load(f'training_data-{i}.npy', allow_pickle=True)
+        choice = [str(data[1]) for data in training_data]
+        count_choices = Counter(choice)
+        count_choices_dict = dict(count_choices)
+        df = df.append({'File': f'training_data-{i}.npy'}, ignore_index=True)
+        for key in count_choices_dict:
+            #print(key,':',count_choices_dict[key])
+            if key == None:
+                df.loc[i-a,'NONE'] = count_choices_dict['NONE']
+            else:
+                df.loc[i-a,choice_to_column[key]] = count_choices_dict[key]
+    #print(df)
+    df.replace(np.nan, 0, inplace=True)
+    df.to_csv('training_data_count_001-100.csv', index=False)
+
+def roi(img, vertices):
+    # Applies ROI Mask to Image
+    mask = np.zeros_like(img)   
+    cv2.fillPoly(mask, vertices, color=[255,255,255])
+    masked = cv2.bitwise_and(img, mask)
+    return masked
+
+def display_training_data(n):
+    '''
+    Displays training data
+    '''
+    training_data = np.load(f'training_data-{n}.npy', allow_pickle=True)
+    mask = False #True
+
+    if mask:
+        # Masking Region of Interest
+        vertices = np.array([[0,25],[0,270],[100,270],[100,200],[430,200],[430,270],[480,270],[480,25],], np.int32)
+
+    for data in training_data:
+        img = data[0]
+        choice = data[1]
+
+        if mask:
+            img = roi(img, [vertices])
+
+        cv2.imshow('screen', img)
+        print(choice)
+        print(img.shape)
+        
+        if cv2.waitKey(25) & 0xFF == ord('q'):
+            cv2.destroyAllWindows()
+            break
+
+if __name__ == "__main__":
+    start_time = time.time()
+    #get_count_choices(1,101)
+    get_count_choices_per_file(1,101)    
+    #display_training_data(74)
+    print(f'Elapsed time: {time.time() - start_time} seconds')
+
+# Output:
+'''
+ Image Resolution : (270, 480, 3)
+ 'W':  [1, 0, 0, 0, 0, 0, 0, 0, 0] : 353725
+ 'S':  [0, 1, 0, 0, 0, 0, 0, 0, 0] : 2243
+ 'A':  [0, 0, 1, 0, 0, 0, 0, 0, 0] : 14303
+ 'D':  [0, 0, 0, 1, 0, 0, 0, 0, 0] : 13114
+ 'WA': [0, 0, 0, 0, 1, 0, 0, 0, 0] : 30877
+ 'WD': [0, 0, 0, 0, 0, 1, 0, 0, 0] : 29837
+ 'SA': [0, 0, 0, 0, 0, 0, 1, 0, 0] : 1952
+ 'SD': [0, 0, 0, 0, 0, 0, 0, 1, 0] : 1451
+ 'NK': [0, 0, 0, 0, 0, 0, 0, 0, 1] : 52256
+  NONE : 242 
+  '''
+# Elapsed time: 181.86165976524353 seconds