def __init__(self, data, densnet_unet_config, compression_coefficient,
growth_rate, sample_no, validation_samples,
no_sample_per_each_itr, train_tag, validation_tag, test_tag,
img_name, label_name, torso_tag, log_tag, tumor_percent,
other_percent, Logs, fold, server_path):
settings.init()
# ==================================
# densenet_unet parameters:
self.densnet_unet_config = densnet_unet_config
self.compression_coefficient = compression_coefficient
self.growth_rate = growth_rate
# ==================================
self.train_tag = train_tag
self.validation_tag = validation_tag
self.test_tag = test_tag
self.img_name = img_name
self.label_name = label_name
self.torso_tag = torso_tag
self.data = data
# ==================================
self.learning_decay = .95
self.learning_rate = 1E-5
self.beta_rate = 0.05
self.img_padded_size = 519
self.seg_size = 505
self.GTV_patchs_size = 49 # 61#33 # 61
self.patch_window = 63 ##87#79#57#47 73
self.sample_no = sample_no
self.batch_no = 7
self.batch_no_validation = 30
self.validation_samples = validation_samples
self.display_step = 100
self.display_validation_step = 1
self.total_epochs = 10
self.loss_instance = _loss_func()
self.server_path = server_path
self.dropout_keep = .5
self.no_sample_per_each_itr = no_sample_per_each_itr
# self.test_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/test/'
self.log_ext = ''.join(map(str, self.densnet_unet_config)) + '_' + str(
self.compression_coefficient) + '_' + str(
self.growth_rate) + log_tag
self.LOGDIR = server_path + Logs + self.log_ext + '/'
self.train_acc_file = server_path + '/Accuracy/' + self.log_ext + '.txt'
self.train_acc_file = server_path + '/Accuracy/' + self.log_ext + '.txt'
self.validation_acc_file = server_path + '/Accuracy/' + self.log_ext + '.txt'
self.chckpnt_dir = server_path + Logs + self.log_ext + '/densenet_unet_checkpoints/'
self.out_path = server_path + '/Outputs/' + self.log_ext + '/'
self.x_hist = 0
self.tumor_percent = tumor_percent
self.other_percent = other_percent
self.fold = fold
def __init__(self, data, densnet_unet_config, compression_coefficient,
growth_rate, sample_no, validation_samples,
no_sample_per_each_itr, train_tag, validation_tag, test_tag,
img_name, label_name, torso_tag, log_tag, tumor_percent, Logs,
fold, server_path):
settings.init()
# densenet_unet parameters
self.densnet_unet_config = densnet_unet_config
self.compression_coefficient = compression_coefficient
self.growth_rate = growth_rate
self.train_tag = train_tag
self.validation_tag = validation_tag
self.test_tag = test_tag
self.img_name = img_name
self.label_name = label_name
self.torso_tag = torso_tag
self.data = data
self.learning_decay = .95
self.learning_rate = 1E-5
self.beta_rate = 0.05
self.img_padded_size = 519
self.seg_size = 505
self.GTV_patchs_size = 49 #output patch
self.patch_window = 63 #input patch
self.sample_no = sample_no
self.batch_no = 7
self.batch_no_validation = 30
self.validation_samples = validation_samples
self.display_step = 100
self.display_validation_step = 1
self.total_epochs = 10
self.loss_instance = _loss_func(
) #an instance from the class of loss functions
self.server_path = server_path
self.dropout_keep = .5
self.no_sample_per_each_itr = no_sample_per_each_itr
self.log_ext = ''.join(map(str, self.densnet_unet_config)) + '_' + str(
self.compression_coefficient) + '_' + str(
self.growth_rate) + log_tag
self.LOGDIR = server_path + Logs + self.log_ext + '/'
self.train_acc_file = server_path + '/Accuracy/' + self.log_ext + '.txt'
self.train_acc_file = server_path + '/Accuracy/' + self.log_ext + '.txt'
self.validation_acc_file = server_path + '/Accuracy/' + self.log_ext + '.txt'
self.chckpnt_dir = server_path + Logs + self.log_ext + '/densenet_unet_checkpoints/'
self.out_path = server_path + '/Outputs/' + self.log_ext + '/'
self.x_hist = 0
self.tumor_percent = tumor_percent
self.fold = fold
def test_all_nets(fold, out_dir, Log):
# densnet_unet_config = [3, 4, 4, 4, 3]
compression_coefficient = .75
growth_rate = 4
# pad_size = 14
# ext = ''.join(map(str, densnet_unet_config)) # +'_'+str(compression_coefficient)+'_'+str(growth_rate)
data = 2
# sample_no=2280000
# validation_samples=5700
# no_sample_per_each_itr=3420
# train_tag='train/'
# validation_tag='validation/'
# test_tag='test/'
# img_name='CT_padded.mha'
# label_name='GTV_CT_padded.mha'
# torso_tag='CT_padded_Torso.mha'
train_tag = 'train/'
validation_tag = 'validation/'
test_tag = 'Esophagus/'
# img_name='CTpadded.mha'
# label_name='GTV_CTpadded.mha'
# torso_tag='Torsopadded.mha'
img_name = ''
label_name = ''
torso_tag = ''
_rd = _read_data(data=data,
train_tag=train_tag,
validation_tag=validation_tag,
test_tag=test_tag,
img_name=img_name,
label_name=label_name)
test_path = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Data-01/' + test_tag
chckpnt_dir = '/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log + log_tag + '/densenet_unet_checkpoints/'
# test_CTs, test_GTVs ,test_Torsos= _rd.read_imape_path(test_path)
train_CTs, train_GTVs, train_Torso, train_penalize, \
validation_CTs, validation_GTVs, validation_Torso, validation_penalize, \
test_CTs, test_GTVs, test_Torso, test_penalize = _rd.read_data_path(fold=fold)
# test_CTs=train_CTs
# test_GTVs=train_GTVs
# test_Torso=train_Torso
# test_penalize=train_penalize
# test_CTs=np.sort(test_CTs)
# test_GTVs=np.sort(test_GTVs)
# test_Torso=np.sort(test_Torso)
# test_penalize=np.sort(test_penalize)
if test_vali == 1:
test_CTs = np.sort(validation_CTs)
test_GTVs = np.sort(validation_GTVs)
test_Torso = np.sort(validation_Torso)
test_penalize = np.sort(validation_penalize)
else:
test_CTs = np.sort(test_CTs)
test_GTVs = np.sort(test_GTVs)
test_Torso = np.sort(test_Torso)
test_penalize = np.sort(test_penalize)
lf = _loss_func()
learning_rate = 1E-4
# image = tf.placeholder(tf.float32, shape=[None, None, None, None, 1])
# label = tf.placeholder(tf.float32, shape=[None, None, None, None, 2])
image = tf.placeholder(
tf.float32, shape=[None, ct_cube_size, ct_cube_size, ct_cube_size, 1])
label = tf.placeholder(
tf.float32,
shape=[None, gtv_cube_size, gtv_cube_size, gtv_cube_size, 2])
ave_vali_acc = tf.placeholder(tf.float32)
ave_loss_vali = tf.placeholder(tf.float32)
dropout = tf.placeholder(tf.float32, name='dropout')
# dropout2=tf.placeholder(tf.float32,name='dropout2')
is_training = tf.placeholder(tf.bool, name='is_training')
is_training_bn = tf.placeholder(tf.bool, name='is_training_bn')
dense_net_dim = tf.placeholder(tf.int32, name='dense_net_dim')
pnalize = tf.placeholder(tf.float32, shape=[None, None, None, None, 2])
loss_coef = tf.placeholder(
tf.float32, shape=[None,
2]) # shape: batchno * 2 values for each class
_dn = _densenet_unet(densnet_unet_config, compression_coefficient,
growth_rate) # create object
dn_out = _dn.dens_net(image,
is_training,
dropout_rate1=0,
dropout_rate2=0,
dim=ct_cube_size,
is_training_bn=is_training_bn)
y = tf.nn.softmax(dn_out)
yyy = tf.nn.log_softmax(dn_out)
# y=_dn.vgg(image)
loss_instance = _loss_func()
accuracy = loss_instance.accuracy_fn(y, label)
[dice, edited_dice] = loss_instance.penalize_dice(logits=y,
labels=label,
penalize=pnalize)
# soft_dice_coef=self.loss_instance.soft_dice(logits=y, labels=label)
cost = tf.reduce_mean(1.0 - dice[1], name="cost")
# correct_prediction = tf.equal(tf.argmax(y, 4), tf.argmax(label, 4))
# accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# cost = tf.reduce_mean(lf.tversky(logits=y, labels=label, alpha=0.9, beta=0.1), name="cost")
# restore the model
sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(chckpnt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
_meas = _measure()
out_path = chckpnt_dir + 'output/'
copyfile(
'./test_densenet_unet.py',
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + 'test_densenet_unet.py')
jj = []
dd = []
dice_boxplot0 = []
dice_boxplot1 = []
dice_boxplot = []
jacc_boxplot = []
jacc_boxplot0 = []
jacc_boxplot1 = []
f1_boxplot0 = []
f1_boxplot1 = []
f1_boxplot_av = []
fpr_av = []
fnr_av = []
xtickNames = []
name_list = []
fpr0 = []
fpr1 = []
fnr0 = []
fnr1 = []
sp0 = []
sp1 = []
recall0 = []
recall1 = []
recall_av = []
presicion0 = []
presicion1 = []
presicion_av = []
img_class = image_class(test_CTs,
test_GTVs,
test_Torso,
test_penalize,
bunch_of_images_no=20,
is_training=1,
patch_window=ct_cube_size,
gtv_patch_window=gtv_cube_size)
for img_indx in range(1, len(test_CTs)):
print('img_indx:%s' % (img_indx))
ss = str(test_CTs[img_indx]).split("/")
name = ss[8] + '_' + ss[9]
name = (ss[8] + '_' + ss[9] + '_' +
ss[10].split('%')[0]).split('_CT')[0]
[
CT_image, GTV_image, Torso_image, volume_depth, voxel_size, origin,
direction
] = _rd.read_image_seg_volume(test_CTs, test_GTVs, test_Torso,
img_indx, ct_cube_size, gtv_cube_size)
_zz_img_gt = []
for _z in (range(
int(ct_cube_size / 2) + 1,
CT_image.shape[0] - int(ct_cube_size / 2) + 7,
int(ct_cube_size) - int(gap) + 1)):
_xx_img_gt = []
for _x in (range(
int(ct_cube_size / 2) + 1,
CT_image.shape[1] - int(ct_cube_size / 2) + 7,
int(ct_cube_size) - int(gap) + 1)):
_yy_img_gt = []
for _y in (range(
int(ct_cube_size / 2) + 1,
CT_image.shape[2] - int(ct_cube_size / 2) + 7,
int(ct_cube_size) - int(gap) + 1)):
ct = CT_image[_z - int(ct_cube_size / 2) - 1:_z +
int(ct_cube_size / 2),
_x - int(ct_cube_size / 2) - 1:_x +
int(ct_cube_size / 2),
_y - int(ct_cube_size / 2) - 1:_y +
int(ct_cube_size / 2)]
ct = ct[np.newaxis][..., np.newaxis]
gtv = GTV_image[_z - int(gtv_cube_size / 2) - 1:_z +
int(gtv_cube_size / 2),
_x - int(gtv_cube_size / 2) - 1:_x +
int(gtv_cube_size / 2),
_y - int(gtv_cube_size / 2) - 1:_y +
int(gtv_cube_size / 2)]
gtv = np.int32(gtv / np.max(GTV_image))
gtv = np.eye(2)[gtv]
gtv = gtv[np.newaxis]
if len(np.where(gtv[0, :, :, :, 1] != 0)[0]):
print('o')
[acc_vali, loss_vali, out, dn_out1] = sess.run(
[accuracy, cost, y, yyy],
feed_dict={
image: ct,
label: gtv,
# pnalize: pnlz,
# loss_coef:loss_coef_weights,
dropout: 1,
is_training: False,
ave_vali_acc: -1,
ave_loss_vali: -1,
dense_net_dim: ct_cube_size,
is_training_bn: False,
})
if len(_yy_img_gt) == 0:
_yy_img_gt = np.int32(gtv[0, :, :, :, 1])
_yy_img = np.int32(out[0, :, :, :, 1])
_yy_img_ct = CT_image[_z - int(gtv_cube_size / 2) -
1:_z + int(gtv_cube_size / 2),
_x - int(gtv_cube_size / 2) -
1:_x + int(gtv_cube_size / 2),
_y - int(gtv_cube_size / 2) -
1:_y + int(gtv_cube_size / 2)]
else:
_yy_img_gt = np.concatenate(
(_yy_img_gt, gtv[0, :, :, :, 1]), axis=2)
_yy_img = np.concatenate((_yy_img, out[0, :, :, :, 1]),
axis=2)
_yy_img_ct = np.concatenate(
(_yy_img_ct,
CT_image[_z - int(gtv_cube_size / 2) - 1:_z +
int(gtv_cube_size / 2),
_x - int(gtv_cube_size / 2) - 1:_x +
int(gtv_cube_size / 2),
_y - int(gtv_cube_size / 2) - 1:_y +
int(gtv_cube_size / 2)]),
axis=2)
if len(_xx_img_gt) == 0:
_xx_img_gt = _yy_img_gt
_xx_img = _yy_img
_xx_img_ct = _yy_img_ct
else:
_xx_img_gt = np.concatenate((_xx_img_gt, _yy_img_gt),
axis=1)
_xx_img = np.concatenate((_xx_img, _yy_img), axis=1)
_xx_img_ct = np.concatenate((_xx_img_ct, _yy_img_ct),
axis=1)
if len(_zz_img_gt) == 0:
_zz_img_gt = _xx_img_gt
_zz_img = _xx_img
_zz_img_ct = _xx_img_ct
else:
_zz_img_gt = np.concatenate((_zz_img_gt, _xx_img_gt), axis=0)
_zz_img = np.concatenate((_zz_img, _xx_img), axis=0)
_zz_img_ct = np.concatenate((_zz_img_ct, _xx_img_ct), axis=0)
name_list.append(name)
#
[TP, TN, FP, FN] = tp_tn_fp_fn(np.round(_zz_img), _zz_img_gt)
f1 = f1_measure(TP, TN, FP, FN)
print('%s: f1:%f,f1:%f' % (name, f1[0], f1[1]))
f1_boxplot0.append(f1[0])
f1_boxplot1.append(f1[1])
f1_boxplot_av.append((f1[0] + f1[1]) / 2)
fpr = FPR(TP, TN, FP, FN)
fpr0.append(fpr[0])
fpr1.append(fpr[1])
fpr_av.append((fpr[0] + fpr[1]) / 2)
fnr = FNR(TP, TN, FP, FN)
fnr0.append(fnr[0])
fnr1.append(fnr[1])
fnr_av.append((fnr[0] + fnr[1]) / 2)
precision = Precision(TP, TN, FP, FN)
presicion0.append(precision[0])
presicion1.append(precision[1])
presicion_av.append((precision[0] + precision[1]) / 2)
recall = Recall(TP, TN, FP, FN)
recall0.append(recall[0])
recall1.append(recall[1])
recall_av.append((recall[0] + recall[1]) / 2)
_zz_img1 = np.round(_zz_img)
segmentation = np.asarray(_zz_img1)
predicted_label = sitk.GetImageFromArray(segmentation.astype(np.uint8))
predicted_label.SetDirection(direction=direction)
predicted_label.SetOrigin(origin=origin)
predicted_label.SetSpacing(spacing=voxel_size)
sitk.WriteImage(
predicted_label,
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' +
Log + out_dir + name + '_result.mha')
segmentation = np.asarray(_zz_img)
predicted_label = sitk.GetImageFromArray(
segmentation.astype(np.float32))
predicted_label.SetDirection(direction=direction)
predicted_label.SetOrigin(origin=origin)
predicted_label.SetSpacing(spacing=voxel_size)
sitk.WriteImage(
predicted_label,
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' +
Log + out_dir + name + '_fuzzy.mha')
segmentation = np.asarray(_zz_img_gt)
predicted_label = sitk.GetImageFromArray(segmentation.astype(np.uint8))
predicted_label.SetDirection(direction=direction)
predicted_label.SetOrigin(origin=origin)
predicted_label.SetSpacing(spacing=voxel_size)
sitk.WriteImage(
predicted_label,
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' +
Log + out_dir + name + '_gtv.mha')
segmentation = np.asarray(_zz_img_ct)
predicted_label = sitk.GetImageFromArray(segmentation.astype(np.short))
predicted_label.SetDirection(direction=direction)
predicted_label.SetOrigin(origin=origin)
predicted_label.SetSpacing(spacing=voxel_size)
sitk.WriteImage(
predicted_label,
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' +
Log + out_dir + name + '_ct.mha')
# output(filename, sheet, list1, list2, x, y, z)
# output('/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/'+Log+out_dir +'file.xls','sheet 1',fnr,fpr,'a','b','c')
print('end')
f1_bp0 = []
f1_bp1 = []
f1_bp_av = []
f1_bp0.append((f1_boxplot0))
f1_bp1.append((f1_boxplot1))
f1_bp_av.append((f1_boxplot_av))
plt.figure()
plt.boxplot(f1_bp0, 0, '')
plt.title('Tumor Dice value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'f1_bp_tumor.png')
plt.figure()
plt.boxplot(f1_bp1, 0, '')
plt.title('Background Dice value for all the images ' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'f1_bp_background.png')
plt.figure()
plt.boxplot(f1_bp_av, 0, '')
plt.title('Average Dice value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'f1_bp_average.png')
#----------------------
fpr_bp0 = []
fpr_bp0.append((fpr0))
plt.figure()
plt.boxplot(fpr_bp0, 0, '')
plt.title('FPR Tumor value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fpr_bp_tumor.png')
fpr_bp1 = []
fpr_bp1.append((fpr1))
plt.figure()
plt.boxplot(fpr_bp1, 0, '')
plt.title('FPR Background value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fpr_bp_background.png')
fpr_bp = []
fpr_bp.append((fpr_av))
plt.figure()
plt.boxplot(fpr_bp, 0, '')
plt.title('FPR Average value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fpr_bp_average.png')
#----------------------
fnr_bp0 = []
fnr_bp0.append((fnr0))
plt.figure()
plt.boxplot(fnr_bp0, 0, '')
plt.title('FNR Tumor value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fnr_bp_tumor.png')
fnr_bp1 = []
fnr_bp1.append((fnr1))
plt.figure()
plt.boxplot(fnr_bp1, 0, '')
plt.title('FNR Background value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fnr_bp_background.png')
fnr_bp = []
fnr_bp.append((fnr_av))
plt.figure()
plt.boxplot(fnr_bp, 0, '')
plt.title('FNR Average value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fnr_bp_average.png')
#----------------------
pres_bp0 = []
pres_bp0.append((presicion0))
plt.figure()
plt.boxplot(pres_bp0, 0, '')
plt.title('Precision value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'precision_bp_tumor.png')
pres_bp1 = []
pres_bp1.append((presicion1))
plt.figure()
plt.boxplot(pres_bp1, 0, '')
plt.title('Precision Background value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'precision_bp_background.png')
pres_bp = []
pres_bp.append((presicion_av))
plt.figure()
plt.boxplot(pres_bp, 0, '')
plt.title('Precision Average value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'precision_bp_average.png')
#----------------------
recall_bp0 = []
recall_bp0.append((recall0))
plt.figure()
plt.boxplot(recall_bp0, 0, '')
plt.title('Recall value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'recall_bp_tumor.png')
recall_bp1 = []
recall_bp1.append((recall1))
plt.figure()
plt.boxplot(recall_bp1, 0, '')
plt.title('Recall Background value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'recall_bp_background.png')
recall_bp = []
recall_bp.append((recall_av))
plt.figure()
plt.boxplot(recall_bp, 0, '')
plt.title('Recall Average value for all the images' + plot_tag)
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'recall_bp_average.png')
#----------------------
plt.figure()
d_bp = []
d_bp.append((f1_boxplot0))
xs = [i for i, _ in enumerate(name_list)]
plt.bar(xs, f1_boxplot0, align='center')
plt.xticks(xs, name_list, rotation='vertical')
plt.margins(.05)
plt.subplots_adjust(bottom=0.45)
plt.title('Dice all images' + plot_tag)
plt.grid()
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'dice_bar.png')
#----------------------
plt.figure()
fnr_bar0 = []
fnr_bar0.append((fnr0))
xs = [i for i, _ in enumerate(name_list)]
plt.bar(xs, fnr0, align='center')
plt.xticks(xs, name_list, rotation='vertical')
plt.margins(.05)
plt.subplots_adjust(bottom=0.25)
plt.title('FNR Background all images' + plot_tag)
plt.grid()
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fnr_background_bar.png')
#----------------------
plt.figure()
fnr_bar1 = []
fnr_bar1.append((fnr1))
xs = [i for i, _ in enumerate(name_list)]
plt.bar(xs, fnr1, align='center')
plt.xticks(xs, name_list, rotation='vertical')
plt.margins(.05)
plt.subplots_adjust(bottom=0.25)
plt.title('FNR Tumor all images' + plot_tag)
plt.grid()
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fnr_tumor_bar.png')
#----------------------
plt.figure()
fpr_bar0 = []
fpr_bar0.append((fpr0))
xs = [i for i, _ in enumerate(name_list)]
plt.bar(xs, fpr0, align='center')
plt.xticks(xs, name_list, rotation='vertical')
plt.margins(.05)
plt.subplots_adjust(bottom=0.25)
plt.title('FPR Background all images' + plot_tag)
plt.grid()
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fpr_background_bar.png')
#----------------------
plt.figure()
fpr_bar1 = []
fpr_bar1.append((fpr1))
xs = [i for i, _ in enumerate(name_list)]
plt.bar(xs, fpr1, align='center')
plt.xticks(xs, name_list, rotation='vertical')
plt.margins(.05)
plt.subplots_adjust(bottom=0.25)
plt.title('FPR tumor all images' + plot_tag)
plt.grid()
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'fpr_tumor_bar.png')
#----------------------
plt.figure()
recall_bar0 = []
recall_bar0.append((recall0))
xs = [i for i, _ in enumerate(name_list)]
plt.bar(xs, recall0, align='center')
plt.xticks(xs, name_list, rotation='vertical')
plt.margins(.05)
plt.subplots_adjust(bottom=0.25)
plt.title('Recall Background all images' + plot_tag)
plt.grid()
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'recall_background_bar.png')
#----------------------
plt.figure()
recall_bar = []
recall_bar.append((recall1))
xs = [i for i, _ in enumerate(name_list)]
plt.bar(xs, recall1, align='center')
plt.xticks(xs, name_list, rotation='vertical')
plt.margins(.05)
plt.subplots_adjust(bottom=0.25)
plt.title('Recall tumor all images' + plot_tag)
plt.grid()
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'recall_tumor_bar.png')
#----------------------
plt.figure()
recall_bar = []
recall_bar.append((recall1))
xs = [i for i, _ in enumerate(name_list)]
plt.bar(xs, recall1, align='center')
plt.xticks(xs, name_list, rotation='vertical')
plt.margins(.05)
plt.subplots_adjust(bottom=0.25)
plt.title('Recall Average all images' + plot_tag)
plt.grid()
plt.savefig(
'/srv/2-lkeb-17-dl01/syousefi/TestCode/EsophagusProject/Code/' + Log +
out_dir + name + 'recall_average_bar.png')