def store_prediction(self, sess, batch_x, batch_y, name):
prediction = sess.run(self.net.predicter,
feed_dict={
self.net.x: batch_x,
self.net.y: batch_y,
self.net.keep_prob: 1.,
self.net.block_size: 1
})
pred_shape = prediction.shape
loss = sess.run(self.net.cost,
feed_dict={
self.net.x: batch_x,
self.net.y:
util.crop_to_shape(batch_y, pred_shape),
self.net.keep_prob: 1.,
self.net.block_size: 1
})
logging.info("Verification error= {:.1f}%, loss= {:.4f}".format(
error_rate(prediction,
util.crop_to_shape(batch_y, prediction.shape)), loss))
img = util.combine_img_prediction(batch_x, batch_y, prediction)
util.save_image(img, "%s/%s.jpg" % (self.prediction_path, name))
return pred_shape
import numpy as np
data_provider = image_util.ImageDataProvider("DRIVE700/test/*",
data_suffix="_test.tif",
mask_suffix='_manual1.png',
n_class=2)
net = unet.Unet(layers=4, features_root=8, channels=3, n_class=2)
test_x, test_y = data_provider(1)
prediction = net.predict("mode/drive100_0.92_700/model.ckpt", test_x)
prediction = util.crop_to_shape(prediction, (20, 584, 565, 2))
AUC_ROC = metrics.roc_Auc(prediction,
util.crop_to_shape(test_y, prediction.shape))
print("auc", AUC_ROC)
acc = metrics.acc(prediction, util.crop_to_shape(test_y, prediction.shape))
print("acc:", acc)
precision = metrics.precision(prediction,
util.crop_to_shape(test_y, prediction.shape))
print("ppv:", precision)
sen = metrics.sen(prediction, util.crop_to_shape(test_y, prediction.shape))
print("TPR:", sen)
TNR = metrics.TNR(prediction, util.crop_to_shape(test_y, prediction.shape))
print("tnr:", TNR)
f1 = metrics.f1score2(prediction, util.crop_to_shape(test_y, prediction.shape))
print("f1:", f1)
img = util.combine_img_prediction(test_x, test_y, prediction)
util.save_image(img, "19.jpg")
