Datasets:
Tasks:
Text Classification
Formats:
text
Languages:
Karo (Brazil)
Size:
10K - 100K
Tags:
driving
License:
Upload 18 files
Browse files- .gitattributes +2 -0
- captured_image.png +3 -0
- main.py +65 -0
- output.png +3 -0
- trainingData_1713658458.txt +0 -0
- trainingData_1713658647.txt +3 -0
- trainingData_1713658834.txt +0 -0
- trainingData_1713658890.txt +3 -0
- trainingData_1713658970.txt +0 -0
- trainingData_1713659021.txt +0 -0
- trainingData_1713659082.txt +0 -0
- trainingData_1713659108.txt +0 -0
- trainingData_1713659138.txt +0 -0
- trainingData_1713659167.txt +0 -0
- trainingData_1713659190.txt +0 -0
- trainingData_1713659245.txt +0 -0
- trainingData_1713659328.txt +0 -0
- trainingData_1713659397.txt +0 -0
- trainingData_1713659443.txt +0 -0
.gitattributes
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@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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trainingData_1713658647.txt filter=lfs diff=lfs merge=lfs -text
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trainingData_1713658890.txt filter=lfs diff=lfs merge=lfs -text
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captured_image.png
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Git LFS Details
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main.py
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import time
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import numpy as np
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from PIL import ImageGrab
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import keyboard
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def capture_screen():
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start_time = 0
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filename = ''
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while True:
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if keyboard.is_pressed('F7') and start_time == 0: # Check if F7 is pressed to start capturing
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start_time = int(time.time()) # Record start time
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filename = 'trainingData_{}.txt'.format(start_time)
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break
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while True:
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try:
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if keyboard.is_pressed('F8'): # Check if F7 is pressed to stop capturing
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break
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# Capture entire screen
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screenshot = ImageGrab.grab()
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# Resize to 128x128
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screenshot = screenshot.resize((128, 64))
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# Convert to grayscale
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grayscale_img = screenshot.convert('L')
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# Convert image to numpy array
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grayscale_array = np.array(grayscale_img)
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# Normalize pixel values between 0 and 1 and round to 2 decimal places
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grayscale_array = np.round(grayscale_array / 255.0, 1)
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# Flatten array to 1D
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flattened_array = grayscale_array.flatten()
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# Save the image with two decimal place precision
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screenshot.save('captured_image.png')
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# Capture WASD keys
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keys = [
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int(keyboard.is_pressed(key)) for key in ['w', 'a', 's', 'd']
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]
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# Append to training data file with Unix time
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with open(filename, 'a') as f:
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f.write("{data}\n")
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f.write(str(flattened_array.tolist()) + '\n')
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f.write("{action}\n")
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f.write(str(keys) + '\n')
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# Wait for 0.1 seconds
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time.sleep(0.1)
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except KeyboardInterrupt:
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print("Stopping screen capture.")
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break
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if __name__ == "__main__":
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while True:
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capture_screen()
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output.png
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Git LFS Details
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trainingData_1713658458.txt
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trainingData_1713658647.txt
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version https://git-lfs.github.com/spec/v1
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oid sha256:aa81df1205904b51b7e0affbf1302ffd770070ee40c5d68e69b2e55c0f559f9a
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size 12298200
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trainingData_1713658834.txt
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trainingData_1713658890.txt
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version https://git-lfs.github.com/spec/v1
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oid sha256:93e79757316b89957b325685992bbfd4c7ff1f30c6815622f526736b3b0bfa02
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size 11560308
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trainingData_1713658970.txt
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trainingData_1713659021.txt
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trainingData_1713659082.txt
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trainingData_1713659108.txt
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trainingData_1713659138.txt
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trainingData_1713659167.txt
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trainingData_1713659190.txt
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trainingData_1713659245.txt
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trainingData_1713659328.txt
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trainingData_1713659397.txt
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trainingData_1713659443.txt
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