def store_prediction(self, sess, batch_x, batch_y, name):
"""
calculate stats on verification data
:param sess:
:param batch_x: test_x
:param batch_y: test_y
:param name: save prediction result as name
:return:
"""
prediction = sess.run(self.net.predicter, feed_dict={self.net.x: batch_x,
self.net.y: batch_y,
self.net.keep_prob: 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.})
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
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.
})
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.
})
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
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.
})
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.
})
logging.info("Verification error= {:.1f}%, loss= {:.4f}".format(
error_rate(prediction,
util.crop_to_shape(batch_y, prediction.shape)), loss))
print("batch_x", batch_x.shape)
print("batch_y", batch_y.shape)
print("prediction", prediction.shape)
if batch_x.shape[-1] != batch_y.shape[-1]:
logging.warn("image and label have different number of channels")
return pred_shape
img = util.combine_img_prediction(batch_x, batch_y, prediction)
util.save_image(img, "%s/%s.jpg" % (self.prediction_path, name))
return pred_shape
def launch(data_root, output_path, training_iters, epochs, restore, layers, features_root):
print("Using data from: %s"%data_root)
data_provider = DataProvider(600, glob.glob(data_root+"/*"))
net = unet.Unet(channels=data_provider.channels,
n_class=data_provider.n_class,
layers=layers,
features_root=features_root,
add_regularizers=True,
# filter_size=5
)
path = output_path if restore else create_training_path(output_path)
trainer = unet.Trainer(net, optimizer="momentum", opt_kwargs=dict(momentum=0.2))
path = trainer.train(data_provider, path,
training_iters=training_iters,
epochs=epochs,
dropout=0.5,
display_step=2,
restore=restore)
x_test, y_test = data_provider(1)
prediction = net.predict(path, x_test)
print("Testing error rate: {:.2f}%".format(unet.error_rate(prediction, util.crop_to_shape(y_test, prediction.shape))))
# import numpy as np
# np.save("prediction", prediction[0, ..., 1])
img = util.combine_img_prediction(x_test, y_test, prediction)
util.save_image(img, "prediction.jpg")
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.
})
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.
})
logging.info("Verification error= {:.1f}%, loss= {:.4f}".format(
error_rate(prediction,
util.crop_to_shape(batch_y, prediction.shape)), loss))
#filename = '/Users/imke/Downloads/tf_unet/epoch'+str(epoch)+'.txt'
#with open (filename, 'a') as fileobj:
# fileobj.write("Verification error= {:.1f}%, loss= {:.4f}\n".format(error_rate(prediction,
# util.crop_to_shape(batch_y,
# prediction.shape))))
img = util.combine_img_prediction(batch_x, batch_y, prediction)
util.save_image(img, "%s/%s.jpg" % (self.prediction_path, name))
return pred_shape
def launch(data_root, output_path, training_iters, epochs, restore, layers, features_root):
generator = Generator(572, data_root)
data, label = generator(1)
weights = None#(1/3) / (label.sum(axis=2).sum(axis=1).sum(axis=0) / data.size)
net = unet.Unet(channels=generator.channels,
n_class=generator.n_class,
layers=layers,
features_root=features_root,
add_regularizers=True,
class_weights=weights,
# filter_size=5
)
path = output_path if restore else create_training_path(output_path)
# trainer = unet.Trainer(net, optimizer="momentum", opt_kwargs=dict(momentum=0.2))
trainer = unet.Trainer(net, optimizer="adam", opt_kwargs=dict(beta1=0.91))
path = trainer.train(generator, path,
training_iters=training_iters,
epochs=epochs,
dropout=0.5,
display_step=2,
restore=restore)
prediction = net.predict(path, data)
print("Testing error rate: {:.2f}%".format(unet.error_rate(prediction, util.crop_to_shape(label, prediction.shape))))
# import numpy as np
# np.save("prediction", prediction[0, ..., 1])
img = util.combine_img_prediction(data, label, prediction)
util.save_image(img, "prediction.jpg")
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.
})
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.
})
logging.info("Verification error= {:.1f}%, loss= {:.4f}".format(
error_rate(prediction,
util.crop_to_shape(batch_y, prediction.shape)), loss))
#print ('in store_prediction')
#print ('prediction: ', prediction.shape)
#print ('batch_x: ', type(batch_x),batch_x.shape)
#print ('batch_y: ', type(batch_y),batch_y.shape)
#adjust for multiple class
#adjust for multiple channels
img = util.combine_img_prediction(batch_x, batch_y, prediction)
#print ('in store_pred2: ', img.shape)
util.save_image(img, "%s/%s.jpg" % (self.prediction_path, name))
return pred_shape
def test_VDSR_with_sess(
epoch,
ckpt_path,
data_path,
sess,
):
folder_list = glob.glob(os.path.join(data_path))
print 'folder_list', folder_list
saver.restore(sess, ckpt_path)
psnr_dict = {}
for folder_path in folder_list:
psnr_list = []
img_list = get_img_list(folder_path)
for i in range(len(img_list)):
(input_list, gt_list, scale_list) = get_image_batch(img_list, i, 2)
input_y = input_list
gt_y = gt_list
# print input_y[0].shape
start_t = time.time()
# img_vdsr_y = sess.run([output_tensor],
# feed_dict={input_tensor: np.resize(input_y,
# (1, input_y.shape[0],
# input_y.shape[1], 1))})
img_vdsr_y = sess.run([output_tensor],
feed_dict={input_tensor: input_y})
print np.asarray(img_vdsr_y).shape
# img_vdsr_y = np.resize(img_vdsr_y, (2, input_y.shape[1],
# input_y.shape[2],1))
end_t = time.time()
print 'end_t', end_t, 'start_t', start_t
print 'time consumption', end_t - start_t
print 'image_size', input_y.shape
img_vdsr_y = np.asarray(img_vdsr_y[0])
print 'img_vdsr_y', img_vdsr_y.shape
print 'input_y', input_y.shape
print 'gt_y', gt_y.shape
# misc.toimage(np.resize(img_vdsr_y[0],(input_y.shape[1], input_y.shape[2]) ),
# cmin=0.0,cmax=1.0).save('outfile_%d.jpg' % i)
img = util.combine_img_prediction(input_y, gt_y, img_vdsr_y)
util.save_image(img, 'outfile%d.jpg' % i)
scipy.io.savemat('outfile%d' % i, mdict={'img': img_vdsr_y})
# misc.toimage(img_vdsr_y).save('outfile%d.jpg' %i)
psnr_bicub = psnr(input_y, gt_y, scale_list)
psnr_vdsr = psnr(img_vdsr_y, gt_y, scale_list)
print 'PSNR: bicubic %f\tVDSR %f' % (psnr_bicub, psnr_vdsr)
psnr_list.append([psnr_bicub, psnr_vdsr, scale_list])
psnr_dict[os.path.basename(folder_path)] = psnr_list
with open('psnr/%s' % os.path.basename(ckpt_path), 'wb') as f:
pickle.dump(psnr_dict, f)
def store_prediction(self,
sess,
eval_iters,
eval_data_provider,
border_size,
patch_size,
input_size,
name,
prediction_path,
verification_batch_size,
combine=False,
hard_prediction=False):
for i in range(eval_iters):
patches = eval_data_provider.get_patches(get_coordinates=True)
if combine:
image = np.zeros(
(verification_batch_size, input_size, input_size, 3))
label = np.zeros(
(verification_batch_size, input_size, input_size, 2))
prediction = np.zeros((verification_batch_size, input_size,
input_size, self.n_class))
for patch in patches:
pred = sess.run(
(self.predicter),
feed_dict={
self.x: patch[0],
self.y: patch[1],
self.keep_prob: 1.0,
self.is_training: False
})
x, y = patch[2]
prediction[:, x:x + patch_size, y:y + patch_size, ...] = pred
if combine:
offset = border_size
image[:, x:x + patch_size, y:y + patch_size,
...] = patch[0][:, offset:-offset, offset:-offset,
...]
label[:, x:x + patch_size, y:y + patch_size,
...] = patch[1]
pred_shape = prediction.shape
if hard_prediction:
argmax = np.argmax(prediction, axis=3)
prediction = np.stack([1 - argmax, argmax], axis=3)
if combine:
img = util.combine_img_prediction(image, label, prediction)
else:
img = util.to_rgb(prediction[...,
1].reshape(-1, input_size, 1))
util.save_image(img, "%s/%s_%s.jpg" % (prediction_path, name, i))
return pred_shape
def main():
dp = DataProvider(batchSize=BATCH_SIZE, validationSize=VALIDATION_SIZE)
dp.readData()
print("DONE READING DATA")
# calculate num of iterations
iters = dp.getTrainSize() // BATCH_SIZE
# unet
net = unet.Unet(channels = 1, n_class = 2, layers = 3,\
features_root = 16, cost="cross_entropy", cost_kwargs={})
# # trainer
# options = {"momentum":0.2, "learning_rate":0.2,"decay_rate":0.95}
# trainer = unet.Trainer(net, optimizer="momentum",plotter = plot, opt_kwargs=options )
# # train model
# path = trainer.train(dp, OUTPUT_PATH,training_iters = iters,epochs=EPOCHS,\
# dropout=DROPOUT_KEEP_PROB, display_step = DISPLAY_STEP,restore = restore)
path = os.getcwd() + "/retinaModel/model.cpkt"
x_test, y_test = dp.getTestData(3, crop=False)
prediction = net.predict(path, x_test)
# # sanity check
# fig, ax = plt.subplots(3, 3)
# ax[0][0].imshow(x_test[0,:,:,0],cmap=plt.cm.gray)
# ax[0][1].imshow(y_test[0,:,:,1],cmap=plt.cm.gray)
# ax[0][2].imshow(np.argmax(prediction[0,...],axis =2),cmap=plt.cm.gray)
# ax[1][0].imshow(x_test[1,:,:,0],cmap=plt.cm.gray)
# ax[1][1].imshow(y_test[1,:,:,1],cmap=plt.cm.gray)
# ax[1][2].imshow(np.argmax(prediction[1,...],axis =2),cmap=plt.cm.gray)
# ax[2][0].imshow(x_test[2,:,:,0],cmap=plt.cm.gray)
# ax[2][1].imshow(y_test[2,:,:,1],cmap=plt.cm.gray)
# ax[2][2].imshow(np.argmax(prediction[2,...],axis =2),cmap=plt.cm.gray)
# plt.show()
# save test result as image
# check for path
if not os.path.lexists(OUTPUT_PATH):
os.makedirs(OUTPUT_PATH)
sampleSize = 3
img = util.combine_img_prediction(x_test[0:sampleSize,...], y_test[0:sampleSize,...]\
, prediction[0:sampleSize,...])
util.save_image(
img, "%s/%s.jpg" % (os.getcwd() + "/" + "testResults", "testSample"))
print("Testing error rate: {:.2f}%".format(
unet.error_rate(prediction, util.crop_to_shape(y_test,
prediction.shape))))
def _store_prediction(sess, batch_x, batch_y, name, predicter, prediction_path, cost, x, y, keep_prob):
prediction = sess.run(predicter,
feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
pred_shape = prediction.shape
loss = sess.run(cost, feed_dict={x: batch_x,
y: util.crop_to_shape(batch_y, pred_shape), keep_prob: 1.})
logging.info("Verification error= {:.1f}%, loss= {:.4f}".format(
Trainer._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"%(prediction_path, name))
return pred_shape
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.
})
pred_shape = prediction.shape
if len(batch_x.shape) == 5:
loss = sess.run(self.net.cost,
feed_dict={
self.net.x: batch_x,
self.net.y: batch_y,
self.net.keep_prob: 1.
})
logging.info("Verification error= {:.1f}%, loss= {:.4f}".format(
error_rate(prediction, batch_y), loss))
img = np.concatenate(
(batch_x[..., 0], batch_y[..., 0], prediction[..., 0]), axis=3)
util.save_image(
util.to_rgb(img[0, 0, ...]),
"%s/%s.jpg" % (self.prediction_path, name + '_0_0'))
# for i in range(0,img.shape[0]):
# for j in range(0,img.shape[1]):
# util.save_image(util.to_rgb(img[i,0,...]), "%s/%s.jpg" % (self.prediction_path, name+'_'+str(i)+'_'+str(j)))
else:
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.
})
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
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.})
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.})
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
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.
})
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.
})
img = util.combine_img_prediction(batch_x, batch_y, prediction)
util.save_image(img, "%s/%s.jpg" % (self.prediction_path, name))
return pred_shape
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.})
pred_shape = prediction.shape
# print("pred shape = ", pred_shape)
# fig, ax = plt.subplots(1, 5)
# ax[0].imshow(batch_x[0,:,:,0],cmap=plt.cm.gray)
# ax[1].imshow(prediction[0,:,:,0],cmap=plt.cm.gray)
# ax[2].imshow(prediction[0,:,:,1],cmap=plt.cm.gray)
# ax[3].imshow(batch_y[0,:,:,0],cmap=plt.cm.gray)
# ax[4].imshow(batch_y[0,:,:,1],cmap=plt.cm.gray)
# plt.show()
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.})
logging.info("Verification error= {:.1f}%, loss= {:.4f}".format(error_rate(prediction,
util.crop_to_shape(batch_y,
prediction.shape)),
loss))
diceScore = dice_score(prediction,util.crop_to_shape(batch_y,prediction.shape))
logging.info("Dice score= {:.2f}".format(diceScore))
# add data to plotter
self.plotter.updateLogger(diceScore,"dice score")
self.plotter.updateLogger(loss,"validation loss")
er = error_rate(prediction,util.crop_to_shape(batch_y,prediction.shape))
self.plotter.updateLogger(er,"validation error")
#
# smple from validation data
sampleSizeValid = 10
img = util.combine_img_prediction(batch_x[0:sampleSizeValid,...],\
batch_y[0:sampleSizeValid,...], prediction[0:sampleSizeValid,...])
util.save_image(img, "%s/%s.jpg"%(self.prediction_path, name))
return pred_shape
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.})
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.})
logging.info("Verification loss= {:.4f}".format(loss))
# img = util.combine_img_prediction(batch_x, batch_y, prediction)
# util.save_image(img, "%s/%s.jpg"%(self.prediction_path, name))
img_0, img_1, img_2, img_3, img_4, img_5 = util.combine_img_prediction(batch_x, batch_y, prediction)
util.save_image(img_0, "%s/%s_1.jpg" % (self.prediction_path, name))
util.save_image(img_1, "%s/%s_2.jpg" % (self.prediction_path, name))
util.save_image(img_2, "%s/%s_3.jpg" % (self.prediction_path, name))
util.save_image(img_3, "%s/%s_4.jpg" % (self.prediction_path, name))
util.save_image(img_4, "%s/%s_5.jpg" % (self.prediction_path, name))
util.save_image(img_5, "%s/%s_6.jpg" % (self.prediction_path, name))
# return pred_shape
# By XY
return pred_shape, prediction
# In[9]:
trainer = Trainer(net, optimizer="momentum", opt_kwargs=dict(momentum=0.2))
# In[8]:
data_provider = image_util.ImageDataProvider(
"C:/Users/orange/Desktop/data/*.tif")
# In[9]:
path = trainer.train(data_provider,
"C:/Users/orange/Desktop/out",
training_iters=4,
epochs=4,
display_step=2)
# In[10]:
x_test, y_test = data_provider(13)
# In[11]:
prediction = net.predict("C:/Users/orange/Desktop/out/model.cpkt", x_test)
# In[12]:
error_rate(prediction, util.crop_to_shape(y_test, prediction.shape))
img = util.combine_img_prediction(x_test, y_test, prediction)
util.save_image(img, "C:/Users/orange/Desktop/out/prediction.jpg")
ny = 572
training_iters = 20
epochs = 100
dropout = 0.75 # Dropout, probability to keep units
display_step = 2
restore = False
generator = image_gen.get_image_gen_rgb(nx, ny, cnt=20)
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=training_iters,
epochs=epochs,
dropout=dropout,
display_step=display_step,
restore=restore)
x_test, y_test = generator(4)
prediction = net.predict(path, x_test)
print("Testing error rate: {:.2f}%".format(unet.error_rate(prediction, util.crop_to_shape(y_test, prediction.shape))))
import numpy as np
np.savetxt("prediction.txt", prediction[..., 1].reshape(-1, prediction.shape[2]))
img = util.combine_img_prediction(x_test, y_test, prediction)
util.save_image(img, "prediction.jpg")
print("Error rate = %f" % unet.error_rate(
predictions, util.crop_to_shape(y_tests, predictions.shape)))
indexes = range(x_tests.shape[0])
for idx in indexes:
print("\tProcessing %d-th test image..." % idx)
x_test = x_tests[idx].reshape(1, x_tests[idx].shape[0],
x_tests[idx].shape[1], x_tests[idx].shape[2])
y_test = y_tests[idx].reshape(1, y_tests[idx].shape[0],
y_tests[idx].shape[1], y_tests[idx].shape[2])
prediction = prediction[idx].reshape(1, prediction[idx].shape[0],
prediction[idx].shape[1],
prediction[idx].shape[2])
img = util.combine_img_prediction(x_test, y_test, prediction)
util.save_image(img,
prediction_dir_path + "/prediction" + str(idx) + ".jpg")
#indexes = range(x_tests.shape[0])
#for idx in indexes:
# print("Processing %d-th test image..." % idx)
#
# x_test = x_tests[idx].reshape(1, x_tests[idx].shape[0], x_tests[idx].shape[1], x_tests[idx].shape[2])
# y_test = y_tests[idx].reshape(1, y_tests[idx].shape[0], y_tests[idx].shape[1], y_tests[idx].shape[2])
#
# prediction = net.predict(model_filepath, x_test)
# #Image.fromarray(prediction[0,:,:,0], mode='F').show() % Error: not correct.
# #Image.fromarray(prediction[0,:,:,1], mode='F').show() % Error: not correct.
#
# print("Error rate = %f" % unet.error_rate(prediction, util.crop_to_shape(y_test, prediction.shape)))
#
# img = util.combine_img_prediction(x_test, y_test, prediction)
opt,
loss,
acc,
train_output,
learning_rate,
global_step,
],
feed_dict=feed_dict)
# del input_data, gt_data, cbcr_data
print output.shape
img = util.combine_img_prediction(input_data, gt_data,
output + input_data)
name = 'epoch_%s' % epoch
util.save_image(img, '%s/%s.jpg' % (prediction_path, name))
print '[epoch %2.4f] loss %.4f\t acc %.4f\t lr %.7f' \
% (epoch + float(step) * BATCH_SIZE
/ len(train_list), np.sum(l), accuracy, lr)
psnr_bicub = psnr(input_data, gt_data, 0)
psnr_vdsr = psnr(output + input_data, gt_data, 0)
print 'PSNR: bicubic %f\U-NET %f' % (psnr_bicub, psnr_vdsr)
# print "[epoch %2.4f] loss %.4f\t lr %.7f"%(epoch+(float(step)*BATCH_SIZE/len(train_list)), np.sum(l)/BATCH_SIZE, lr)
# saver.save(sess, "./checkpoints/VDSR_const_clip_0.01_epoch_%03d.ckpt" % epoch ,global_step=global_step)
saver.save(sess, ckpt_path)
# test_VDSR(epoch, ckpt_path, TEST_DATA_PATH)
def main():
# input training and test datasets
train_data = image_util.ImageDataProvider(
search_path='RoadDetection_Train_Images')
test_data = image_util.ImageDataProvider(
search_path='RoadDetection_Test_Images')
#
# # train u-net
# net = unet.Unet(layers=5, n_class=train_data.n_class, channels=train_data.channels, features_root=64, cost='dice_coefficient', cost_kwargs=dict(regularizer=0.01))
net = unet.Unet(layers=4,
n_class=train_data.n_class,
channels=train_data.channels,
features_root=48,
cost='dice_coefficient',
cost_kwargs={
'regularizer': 0.01,
'class_weights': [0.1777, 0.8222]
})
x_test, y_test = test_data(10)
# save prediction masks in TIFF format
data_files = [d for d in test_data.data_files if d.split('.')[-1] == 'jpg']
data_files.sort()
for i, name in enumerate(data_files):
file_name = name.split('\\')[1].split('.')[0]
prediction = net.predict(model_path="./unet_trained/model.ckpt",
x_test=x_test[i].reshape(-1, 600, 400, 3))
ny = prediction.shape[2]
img = to_rgb(prediction[..., 1])
im.fromarray(img[0].round().astype(np.uint8)).save(
r'RoadDetection_Test_Predictions\\{}_mask.tif'.format(file_name),
'TIFF',
dpi=[300, 300],
quality=90)
# save prediction masks in JPEG format
data_files = [d for d in test_data.data_files if d.split('.')[-1] == 'jpg']
data_files.sort()
for i, name in enumerate(data_files):
file_name = name.split('\\')[1].split('.')[0]
prediction = net.predict(model_path="./unet_trained/model.ckpt",
x_test=x_test[i].reshape(-1, 600, 400, 3))
ny = prediction.shape[2]
img = to_rgb(prediction[..., 1])
im.fromarray(img[0].round().astype(np.uint8)).save(
r'RoadDetection_Test_Predictions\\{}_mask.jpg'.format(file_name),
'JPEG',
dpi=[300, 300],
quality=90)
# predict mask from training data for presentation
x_val, y_val = train_data(1)
prediction = net.predict(model_path="./unet_trained/model.ckpt",
x_test=x_val)
fig, ax = plt.subplots(1, 3, figsize=(12, 5))
ax[0].imshow(x_val[0, ..., 0], aspect="auto")
ax[1].imshow(y_val[0, ..., 1], aspect="auto")
pred = np.squeeze(prediction[0, ..., 1])
ax[2].imshow(pred, aspect="auto")
ax[0].set_title("Input")
ax[1].set_title("Ground truth")
ax[2].set_title("Prediction")
fig.tight_layout()
# predict mask from test data for presentation
x_val, y_val = test_data(1)
prediction = net.predict(model_path="./unet_trained/model.ckpt",
x_test=x_val)
fig, ax = plt.subplots(1, 3, figsize=(12, 5))
ax[0].imshow(x_val[0, ..., 0], aspect="auto")
ax[1].imshow(y_val[0, ..., 1], aspect="auto")
pred = np.squeeze(prediction[0, ..., 1])
ax[2].imshow(pred, aspect="auto")
ax[0].set_title("Input")
ax[1].set_title("Ground truth")
ax[2].set_title("Prediction")
fig.tight_layout()
# presentation method 2
x_test, y_test = test_data(10)
img = util.combine_img_prediction(x_test, y_test, prediction)
util.save_image(img, "test_pred_image.jpg")
util.plot_prediction(x_test=x_train, y_test=x_train, prediction=validation)
print('Process completed.')
def TestData(net , Test_Path , Train_Path , padSize):
TestImageNum = 7
Trained_Model_Path = Train_Path + 'model/model.cpkt'
TestResults_Path = Test_Path + 'results/'
try:
os.stat(TestResults_Path)
except:
os.makedirs(TestResults_Path)
AllImage_logical = np.zeros((1924,1924))
AllImage = np.zeros((1924,1924))
trainer = unet.Trainer(net)
TestData = image_util.ImageDataProvider( Test_Path + '*.tif' , shuffle_data=False)
L = len(TestData.data_files)
DiceCoefficient = np.zeros(L)
LogLoss = np.zeros(L)
# BB_Cord = np.zeros(L,3)
BB_Cord = np.zeros((L,2))
aa = TestData.data_files
for BB_ind in range(L):
# BB_ind = 1
bb = aa[BB_ind]
d = bb.find('/img')
cc = bb[d:len(bb)-4]
dd = cc.split('_')
# imageName = int(dd[0])
xdim = int(dd[1])
ydim = int(dd[2])
# BB_Cord[ BB_ind , : ] = [xdim,ydim,imageName]
BB_Cord[ BB_ind , : ] = [xdim,ydim]
Data , Label = TestData(L)
szD = Data.shape
szL = Label.shape
data = np.zeros((1,szD[1],szD[2],szD[3]))
label = np.zeros((1,szL[1],szL[2],szL[3]))
shiftFlag = 0
for BB_ind in range(L):
data[0,:,:,:] = Data[BB_ind,:,:,:].copy()
label[0,:,:,:] = Label[BB_ind,:,:,:].copy()
if shiftFlag == 1:
shiftX = 0
shiftY = 0
data = np.roll(data,[0,shiftX,shiftY,0])
label = np.roll(label,[0,shiftX,shiftY,0])
prediction = net.predict( Trained_Model_Path, data)
PredictedSeg = prediction[0,...,1] > 0.2
# ix, iy, ImgNum = BB_Cord[ BB_ind , : ]
ix, iy = BB_Cord[ BB_ind , : ]
ix = int(148*ix)
iy = int(148*iy)
# AllImage[148*ix:148*(ix+1) , 148*iy:148*(iy+1) ,ImgNum] = prediction[0,...,1]
# AllImage_logical[148*ix:148*(ix+1) , 148*iy:148*(iy+1) ,ImgNum] = PredictedSeg
AllImage[ix:148+ix , iy:148+iy] = prediction[0,...,1]
AllImage_logical[ix:148+ix , iy:148+iy] = PredictedSeg
# unet.error_rate(prediction, util.crop_to_shape(label, prediction.shape))
sz = label.shape
A = (padSize/2)
imgCombined = util.combine_img_prediction(data, label, prediction)
DiceCoefficient[BB_ind] = DiceCoefficientCalculator(PredictedSeg,label[0,A:sz[1]-A,A:sz[2]-A,1]) # 20 is for zero padding done for input
util.save_image(imgCombined, TestResults_Path+"prediction_slice"+ str(BB_Cord[BB_ind]) + ".jpg")
Loss = unet.error_rate(prediction,label[:,A:sz[1]-A,A:sz[2]-A,:])
LogLoss[BB_ind] = np.log10(Loss+eps)
np.savetxt(TestResults_Path+'DiceCoefficient.txt',DiceCoefficient)
np.savetxt(TestResults_Path+'LogLoss.txt',LogLoss)
im = Image.fromarray(np.uint8(AllImage))
msk = Image.fromarray(np.uint8(AllImage_logical))
im.save( TestResults_Path + 'PredictionSeg_'+str(TestImageNum)+'.tif')
msk.save(TestResults_Path + 'PredictionSeg_'+str(TestImageNum)+'_Logical.tif')
return AllImage , AllImage_logical
# Basic usage. from tf_unet import unet, util, image_util dataset_home_path = "/home/sangwook/my_dataset/life_science/isbi" train_dataset_path = dataset_home_path + "/train-volume.tif" test_dataset_path = dataset_home_path + "/test-volume.tif" model_output_path = dataset_home_path + "/output" # Prepare data loading. train_data_provider = image_util.ImageDataProvider(train_dataset_path) #-- Setup & train. net = unet.Unet(layers=3, features_root=64, channels=1, n_class=2) trainer = unet.Trainer(net) path = trainer.train(train_data_provider, model_output_path, training_iters=32, epochs=100) #-- Veriry. #-- Test. #test_data_provider = image_util.ImageDataProvider(test_dataset_path) #x_test, y_test = test_data_provider(1) x_test, y_test = train_data_provider(1) prediction = net.predict(path, x_test) unet.error_rate(prediction, util.crop_to_shape(y_test, prediction.shape)) img = util.combine_img_prediction(x_test, y_test, prediction) util.save_image(img, dataset_home_path + "/prediction.jpg")