Project update log:
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Begin by adding a mp4 video to the project. Anyone who leverages the project to a CCTV monitoring would replace this with real-time footage.
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Begin by extracting two images from the video, that will help the model to train finding empty spots on the particular site. NOTE: the frames must be rendered from the video after some time interval so sufficient number of cars have changed position during that time. (understand_site.py)
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From the images taken in step 2, import images one by one and create dataset for training and testing the CNN.
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To detect, images = [cv2.imwrite(path) for path in glob.glob()]. shape[0] = rows, shape[1] = columns for i in range(0, rows): for j in range(0, columns): if(image[i][j] == 0): count = count + 1 total_count = total_count + 1 if(count/total_count > 0.85): cv2.imwrite('', image)
Huge shoutout to Priya Dwivedi (https://towardsdatascience.com/find-where-to-park-in-real-time-using-opencv-and-tensorflow-4307a4c3da03) from whose work the current work is inspired.
and Nimish Mishra 2nd year IIIT Kalyani for making things smooth for us and helping in this project
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numpy, pandas, opencv, keras, pillow, matplotlib, glob, os, tkinter.
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Three buttons: understand site, Create training data, predict (edge_detection.py + prediction.py).
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Booking: book_spot.py
understand_site -> create_data -> edge_detection -> cnn_model -> prediction -> book_spot