![]() ![]() To visualize what your stroke-based drawings look like when rendered as aīitmap, you can run the following utility function: sf = build_strokes_sframe() More information about the preprocessing done under the hood. Stroke-based drawings into bitmaps as part of preprocessing so the Toolkit either at train or inference time, the toolkit converts the When stroke-based drawing data is given as input to the Drawing Classifier Sf.save(os.path.join(sframes_dir, "stroke_square_triangle.sframe")) Sframes_dir = os.path.join(quickdraw_dir, 'sframes')Ĭlass_data = np.load(os.path.join(bitmaps_dir, class_name + npy_ext))Ĭlass_data_selected = class_dataĬlass_data_selected = class_data_selected.reshape(Ĭlass_data_selected.shape, 28, 28, 1)įor np_pixel_data in class_data_selected:īitmap = tc.Image(_image_data = np_pixel_data.tobytes(), Random_state = np.random.RandomState( 100)īitmaps_dir = os.path.join(quickdraw_dir, 'bitmaps') Here is a snippet to sample 100 examples per class into an SFrame for bitmap data as the input format: import turicreate as tc # Make directories with the dataĪfter running the above script, you should have a directory structure like this: quickdraw/įor both bitmap and stroke-based drawing input formats, we will sample 100 examples from each of the classes and turn it into an SFrame. The following script (run from your terminal) should get you started. We start by dowloading a few examples of "square" and "triangle" shapes - around 120,000 examples each, both as bitmaps and as stroke-based drawings. ![]() ![]() Make a drawing classifier for squares and triangles. In this section, we will setup a subset of Quick, Draw! 1. ![]()
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