Uploading mini dataset

mini/README.md

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+### Info
+- Image Resolution : 270, 480
+- Mode : RGB
+- Dimension : (270, 480, 3)
+- File Count : 01
+- Size : 1.81 GB
+
+### Data Count
+```
+ 'W':  [1, 0, 0, 0, 0, 0, 0, 0, 0] : 3627
+ 'S':  [0, 1, 0, 0, 0, 0, 0, 0, 0] : 50
+ 'A':  [0, 0, 1, 0, 0, 0, 0, 0, 0] : 104
+ 'D':  [0, 0, 0, 1, 0, 0, 0, 0, 0] : 106
+ 'WA': [0, 0, 0, 0, 1, 0, 0, 0, 0] : 364
+ 'WD': [0, 0, 0, 0, 0, 1, 0, 0, 0] : 416
+ 'SA': [0, 0, 0, 0, 0, 0, 1, 0, 0] : 35
+ 'SD': [0, 0, 0, 0, 0, 0, 0, 1, 0] : 47
+ 'NK': [0, 0, 0, 0, 0, 0, 0, 0, 1] : 248
+  NONE : 3 
+```
+
+### 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

mini/training_data-mini.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:489bbaecce52d879c1f6b629d7e49171db26a2cbbd81ca754da4a79cde7377e6
+size 1944328936

mini/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-mini.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-mini.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_101-200.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(1,2)
+    get_count_choices_per_file(1,2)
+    #display_training_data('mini')
+    print(f'Elapsed time: {time.time() - start_time} seconds')
+
+# Output:
+'''
+'W':  [1, 0, 0, 0, 0, 0, 0, 0, 0] : 3627
+'S':  [0, 1, 0, 0, 0, 0, 0, 0, 0] : 50
+'A':  [0, 0, 1, 0, 0, 0, 0, 0, 0] : 104
+'D':  [0, 0, 0, 1, 0, 0, 0, 0, 0] : 106
+'WA': [0, 0, 0, 0, 1, 0, 0, 0, 0] : 364
+'WD': [0, 0, 0, 0, 0, 1, 0, 0, 0] : 416
+'SA': [0, 0, 0, 0, 0, 0, 1, 0, 0] : 35
+'SD': [0, 0, 0, 0, 0, 0, 0, 1, 0] : 47
+'NK': [0, 0, 0, 0, 0, 0, 0, 0, 1] : 248
+NONE : 3 
+'''