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Wood Heart Shape Mosaic Blank with chain.

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It's completely possible to use a categorizer for the prediction. show () HBox(children=(IntProgress(value=0, max=32), HTML(value=''))) imshow ( abs ( prediction - prediction_with_reflection ), interpolation = 'bilinear', vmin = 0, vmax = 1 ) plt. imshow ( abs ( fused_prediction_without_overlap - prediction ), interpolation = 'bilinear', vmin = 0, vmax = 1 ) plt. imshow ( prediction_with_reflection, interpolation = 'bilinear', vmin = 0, vmax = 1 ) plt. import os import matplotlib.pyplot as plt import numpy as np import PIL.Image from keras.models import load_model from MightyMosaic import MightyMosaic face_filename = '3578641 0.png' full_filename = '_original_drawn_by_itou_onsoku_tassha_sample-f9c6557ccec993c08627d33e49cf4524.jpg' model_filename = 'open_eyes_saliency_256.h5' model = load_model ( model_filename ) print ( f 'The model input shape is ' ) plt. Let's begin by importing the library that we need and define the filename for both the images and the model that we will use.ītw, credits for itout (pixiv69581) and Akae Neo (pixiv17014316) for drawing the pictures that we used as examples. RequirementsĪs we use f-string (in the asserts), it is necessary to have, at least, python3.6.įor running this jupyter notebook (which should have been converted to markdown at some point), we also need the following library: The MightyMosaic allow the overlapping of tiles, which is necessary to avoid discrepancies between adjacent tiles. The class is implemented so that we can apply functions on the tiles (usually the prediction function of a neural network, which has a fixed input shape) and fuse the result. The goal of the library is to create a 'mosaic', which is a sublass of np.ndarray, dividing images of arbitrary shape into a 4d or 5d array.

Mosaic shapes base