plot_2d_images.py

from keras.models import load_model, Sequential
from keras import layers
from keras.layers import Layer
from keras import models
import numpy as np
import matplotlib.pyplot as plt
import sys

x = np.load("/datasets/10zlevels.npy")
y = 1000*np.expand_dims(np.load("/datasets/1980-2016/full_tp_1980_2016.npy"), axis=3)
print "data loaded"
print x.shape

levels = [0,2,6]

for i in range(5000,5050):
    plt.imsave('in_{:04d}.png'.format(i), x[i,:,:,0], cmap='jet')
    plt.imsave('out_{:04d}.png'.format(i), y[i,:,:,0], cmap='Blues')

sys.exit(0)

vgg16 = load_model('/datasets/vgg16.h5')
#segnet = load_model('/datasets/segnet.h5')
unet = load_model('/datasets/unet.h5')

t_mae_unet = 0
t_mae_vgg16 = 0
for i in range(40000, 40100):
    print(i)
    geop = x[i][:,:,levels]
    geop = geop[np.newaxis, ...]
    print geop.shape
    print "rain max", y[i,:,:,0].max()
    print "rain sum", y[i,:,:,0].sum()
    plt.imsave('in_{}.png'.format(i), x[i,:,:,0], cmap='jet')
    plt.imsave('out_{}.png'.format(i), y[i,:,:,0], cmap='Blues')
    rain = vgg16.predict(geop)#/1000
    print "vgg16 max", rain.max()
    print "vgg16 sum", rain.sum()
    mae_vgg16 = np.mean(np.absolute((rain[0,:,:,0] - y[i,:,:,0])))
    print "vgg16 mae", mae_vgg16
    t_mae_vgg16 += mae_vgg16
    #plt.imsave('vgg16_{}.png'.format(i), rain[0,:,:,0], cmap='Blues')
    #rain = segnet.predict(geop)/1000
    #plt.imsave('segnet_{}.png'.format(i), rain[0,:,:,0], cmap='Blues')
    rain = unet.predict(geop)#/1000
    print "unet max", rain.max()
    print "unet sum", rain.sum()
    mae_unet = np.mean(np.absolute((rain[0,:,:,0] - y[i,:,:,0])))
    print "unet mae", mae_unet
    t_mae_unet += mae_unet
    #plt.imsave('unet_{}.png'.format(i), rain[0,:,:,0], cmap='Blues')

print t_mae_vgg16/100.0
print t_mae_unet/100.0