from tf_SDUnet import unet, util, image_util, metrics
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")
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['image.cmap'] = 'gist_earth'
np.random.seed(98765)
from tf_SDUnet import image_gen
from tf_SDUnet import unet
nx = 572
ny = 572
generator = image_gen.GrayScaleDataProvider(nx, ny, cnt=20)
x_test, y_test = generator(1)
fig, ax = plt.subplots(1, 2, sharey=True, figsize=(8, 4))
ax[0].imshow(x_test[0, ..., 0], aspect="auto")
ax[1].imshow(y_test[0, ..., 1], aspect="auto")
net = unet.Unet(channels=generator.channels,
n_class=generator.n_class,
layers=3,
features_root=16)
trainer = unet.Trainer(net,
optimizer="momentum",
opt_kwargs=dict(momentum=0.2))
path = trainer.train(generator,
"./unet_trained",
training_iters=32,
epochs=10,
display_step=2)
x_test, y_test = generator(1)
prediction = net.predict("./unet_trained/model.ckpt", x_test)
fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(12, 5))
ax[0].imshow(x_test[0, ..., 0], aspect="auto")
ax[1].imshow(y_test[0, ..., 1], aspect="auto")
from tf_SDUnet import unet, util, image_util
#preparing data loading
data_provider = image_util.ImageDataProvider("dataset2/train/*.jpg",
data_suffix=".jpg",
mask_suffix='_mask.png',
n_class=2)
output_path = "out_put2"
#setup & training
net = unet.Unet(layers=6, features_root=16, channels=3, n_class=2)
trainer = unet.Trainer(net)
path = trainer.train(data_provider, output_path, training_iters=12, epochs=1)
test_x, test_y = data_provider(1)
print(test_x.shape)
prediction = net.predict(path, test_x)
print(prediction)
unet.error_rate(prediction, util.crop_to_shape(test_y, prediction.shape))
img = util.combine_img_prediction(test_x, test_y, prediction)
util.save_image(img, "voc_prediction.jpg")
n_class=2)
ST = image_util.ImageDataProvider("test2/ST/*.tif",
data_suffix=".tif",
mask_suffix='_binary.tif',
n_class=2)
# data_provider = image_util.ImageDataProvider("Tissue_images/*.tif",data_suffix=".tif", mask_suffix='_binary.tif', n_class=2)
#data_provider = image_util.ImageDataProvider("Kumar_aug/aug/*.tif",data_suffix=".tif", mask_suffix='_binary.tif', n_class=2)
data_provider = image_util.ImageDataProvider("tissue_aug/train/*.tif",
data_suffix=".tif",
mask_suffix='_binary.tif',
n_class=2)
output_path = "model_f8_0.88_dice_100"
#setup & training
net = unet.Unet(layers=4,
features_root=8,
channels=3,
n_class=2,
cost="dice_coefficient")
trainer = unet.Trainer(net,
batch_size=4,
verification_batch_size=4,
optimizer="adam")
path = trainer.train(data_provider,
output_path,
keep_prob=0.88,
block_size=7,
training_iters=64,
epochs=100,
display_step=2,
restore=False)
test_x, test_y = DT(6)