satnogs-wut/wut-ml

#!/usr/bin/python3
# wut-ml
#
# Vet a SatNOGS image using machine learning (guessing).
# It will vet the image located at test/unvetted/waterfall.png.
#
# Note, there is an issue to fix where it will vet everything
# under the data/test directory, so fix that. For now, just delete
# everything else. :)
#
# Usage:
# wut-ml
# Example:
# wut-ml

import os
import numpy as np
import tensorflow.python.keras
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from tensorflow.python.keras import optimizers
from tensorflow.python.keras.preprocessing import image
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.preprocessing.image import load_img
from tensorflow.python.keras.preprocessing.image import img_to_array

# XXX
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Input, concatenate
#from tensorflow.python.keras.optimizers import Adam


# XXX Plot
from tensorflow.python.keras.utils import plot_model
from tensorflow.python.keras.callbacks import ModelCheckpoint
## for visualizing 
import matplotlib.pyplot as plt, numpy as np
from sklearn.decomposition import PCA

# https://keras.io/preprocessing/image/
# TODO:
# * Pre-process image
print("datagen")
datagen = ImageDataGenerator(
	featurewise_center=False,
	samplewise_center=False,
	featurewise_std_normalization=False,
	samplewise_std_normalization=False,
	zca_whitening=False,
	zca_epsilon=1e-06,
	rescale=1./255,
	shear_range=0.0,
	zoom_range=0.0,
	rotation_range=0,
	width_shift_range=0.0,
	height_shift_range=0.0,
	brightness_range=None,
	channel_shift_range=0.0,
	fill_mode='nearest',
	cval=0.0,
	horizontal_flip=False,
	vertical_flip=False,
	preprocessing_function=None,
	data_format='channels_last',
	validation_split=0.0,
	dtype='float32')

print("datagen.flow")
train_it = datagen.flow_from_directory('/srv/satnogs/data/train/', class_mode='binary')
val_it = datagen.flow_from_directory('/srv/satnogs/data/val/', class_mode='binary')
test_it = datagen.flow_from_directory('/srv/satnogs/data/test/', class_mode='binary')

print("train_it.next()")
trainX, trainY = train_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (trainX.shape, trainX.min(), trainX.max()))
valX, valY = val_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (valX.shape, valX.min(), valX.max()))
testX, testY = test_it.next()
print('Batch shape=%s, min=%.3f, max=%.3f' % (testX.shape, testX.min(), testX.max()))

print("input shape")
input_shape=trainX.shape[1:]
print(input_shape)

#img_width=823
#img_height=1606
img_width=256
img_height=256
print("Height", img_height, "Width", img_width)

# https://keras.io/models/sequential/
# https://keras.io/getting-started/sequential-model-guide/
print("Sequential")
model = Sequential()

print("add")
# Other data to consider adding:
# * JSON metadata
# * TLE
# * Audio File (ogg)
#   https://www.tensorflow.org/io/api_docs/python/tfio/ffmpeg/AudioDataset
# * Decoded Data (HEX, ASCII, PNG)
# Data from external sources to consider adding:
# * Weather

print("convolution 2 deeeee")
# https://keras.io/layers/convolutional/
#model.add(Convolution2D(32, 3, 3, input_shape=trainX.shape[1:]))
#model.add(Convolution2D(32, 3, 3, input_shape=(255,255,3)))
model.add(Convolution2D(32, 3, 3, input_shape=(img_width, img_height,3)))
# https://keras.io/activations/
print("Activation relu")
model.add(Activation('relu'))
# https://keras.io/layers/pooling/
print("Pooling")
model.add(MaxPooling2D(pool_size=(2, 2)))
print("Convolution2D")
model.add(Convolution2D(32, 3, 3))
print("Activation relu")
model.add(Activation('relu'))
print("Pooling")
model.add(MaxPooling2D(pool_size=(2, 2)))
print("Convolution2D")
model.add(Convolution2D(64, 3, 3))
print("Activation relu")
model.add(Activation('relu'))
print("Pooling")
model.add(MaxPooling2D(pool_size=(2, 2)))
# https://keras.io/layers/core/
print("Flatten")
model.add(Flatten())
# https://keras.io/layers/core/
print("Dense")
model.add(Dense(64))
print("Activation relu")
model.add(Activation('relu'))
# https://keras.io/layers/core/
print("Dropout")
model.add(Dropout(0.5))
print("Dense")
model.add(Dense(1))
print("Activation softmax")
model.add(Activation('softmax'))

# https://keras.io/models/sequential/
print("compile")
model.compile(
	loss='categorical_crossentropy',
	loss_weights=None,
	sample_weight_mode=None,
	weighted_metrics=None,
	target_tensors=None,	
	optimizer='rmsprop',
	metrics=['accuracy'])



# https://keras.io/models/sequential/
print("fit")
model.fit(
	x=train_it,
	y=None,
	batch_size=None,
	epochs=1,
	verbose=2,
	callbacks=None,
	validation_split=0.0,
	validation_data=val_it,
	shuffle=True,
	class_weight=None,
	sample_weight=None,
	initial_epoch=0,
	steps_per_epoch=None,
	validation_steps=None,
	validation_freq=1,
	max_queue_size=10,
	workers=16,
	use_multiprocessing=True)

# https://keras.io/models/sequential/
# evaluate(x=None, y=None, batch_size=None, verbose=1, sample_weight=None, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)

# TODO:
# * Generate output to visualize training/validating/testing.
# Plot, fail
#print("plot")
#plot_model(test_it, to_file='data/wut-plot.png', show_shapes=True, show_layer_names=True)

# https://keras.io/models/sequential/
print("predict")
prediction = model.predict(
	x=test_it,
	batch_size=None,
	verbose=2,
	steps=None,
	callbacks=None,
	max_queue_size=10,
	workers=16,
	use_multiprocessing=True)

print(prediction)

if prediction[0][0] == 1:
  rating = 'bad'
else:
  rating = 'good'
print('Observation: %s' % (rating))