42 lines
2.1 KiB
Markdown
42 lines
2.1 KiB
Markdown
the steps taken so far, which lead to a successfull detection of an image
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- train the model defined in mnist_siamese_example, which uses the 'siamese.py' model to
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create a siamese keras model.
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- in this mnist siamese example, the data collection has been updated form the mnist drawing
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sample to the fruit sample. Lots of work went into setting the arrays up correctly, because the
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example from towards data science did not correctly seperate the classes. He had originally used
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91 classes for teching and the rest for testing, where I now use images of every class for
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teaching _and_ training.
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- The images were shrunken down to 28 x 28 so the model defined in the siamese example could be used
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without adaption
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- in this example, there is two teachings going on, once he trains the siamese model (which is saved under
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'siamese_checkpoint' and then he reteaches a new model based on this one, with some additonal layers ontop
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I'm not yet sure what these do [todo] but 'I'll figure it out.
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- after you've successfully trained the model, it's now saved to 'model_checkpoint' or 'siamese_checkpoint'
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- note that the current model design has removed the second training layer, it now only creates 'siamese_checkpoint'
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- The following steps can be used to classify two images:
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Note, that it was so far only tested using images in a 'pdb' shell from the mnist_siamese_example script
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```
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import tensorflow.keras as keras
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from PIL import image
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model = keras.models.load_model('./siamese_checkpoint')
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image1 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_254_100.jpg').convert('RGB').resize((28, 28))) / 255
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image2 = np.asarray(Image.open('../towards/data/fruits-360/Training/Avocado/r_250_100.jpg').convert('RGB').resize((28, 28))) / 255
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# note that the double division through 255 is only because the model bas taught with this double division, depends on
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# the input numbers of course
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output = model.predict([np.array([image2]), np.array([image1])])
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# Note here, that the cast to np.array is nencessary - otherwise the input vector is malformed
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print(output)
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```
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