qurator-spk
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sbb_pixelwise_segmentation
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adding enhancement training

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pull/18/head
vahidrezanezhad 8 months ago
parent dbb84507ed
commit 38db3e9289
5 changed files with 119 additions and 68 deletions
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20
config_params.json
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@ -1,15 +1,15 @@
{
"model_name" : "resnet50_unet",
"task": "classification",
"n_classes" : 2,
"n_epochs" : 7,
"input_height" : 224,
"input_width" : 224,
"task": "enhancement",
"n_classes" : 3,
"n_epochs" : 3,
"input_height" : 448,
"input_width" : 448,
"weight_decay" : 1e-6,
"n_batch" : 6,
"n_batch" : 3,
"learning_rate": 1e-4,
"f1_threshold_classification": 0.8,
"patches" : false,
"patches" : true,
"pretraining" : true,
"augmentation" : false,
"flip_aug" : false,
@ -35,7 +35,7 @@
"weighted_loss": false,
"is_loss_soft_dice": false,
"data_is_provided": false,
"dir_train": "/home/vahid/Downloads/image_classification_data/train",
"dir_eval": "/home/vahid/Downloads/image_classification_data/eval",
"dir_output": "/home/vahid/Downloads/image_classification_data/output"
"dir_train": "./training_data_sample_enhancement",
"dir_eval": "./eval",
"dir_output": "./out"
}

31
gt_for_enhancement_creator.py
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@ -0,0 +1,31 @@
import cv2
import os
def resize_image(seg_in, input_height, input_width):
return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
dir_imgs = './training_data_sample_enhancement/images'
dir_out_imgs = './training_data_sample_enhancement/images_gt'
dir_out_labs = './training_data_sample_enhancement/labels_gt'
ls_imgs = os.listdir(dir_imgs)
ls_scales = [ 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9]
for img in ls_imgs:
img_name = img.split('.')[0]
img_type = img.split('.')[1]
image = cv2.imread(os.path.join(dir_imgs, img))
for i, scale in enumerate(ls_scales):
height_sc = int(image.shape[0]*scale)
width_sc = int(image.shape[1]*scale)
image_down_scaled = resize_image(image, height_sc, width_sc)
image_back_to_org_scale = resize_image(image_down_scaled, image.shape[0], image.shape[1])
cv2.imwrite(os.path.join(dir_out_imgs, img_name+'_'+str(i)+'.'+img_type), image_back_to_org_scale)
cv2.imwrite(os.path.join(dir_out_labs, img_name+'_'+str(i)+'.'+img_type), image)

15
models.py
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@ -168,7 +168,7 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
return x
def resnet50_unet_light(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
def resnet50_unet_light(n_classes, input_height=224, input_width=224, taks="segmentation", weight_decay=1e-6, pretraining=False):
assert input_height % 32 == 0
assert input_width % 32 == 0
@ -259,14 +259,17 @@ def resnet50_unet_light(n_classes, input_height=224, input_width=224, weight_dec
o = Activation('relu')(o)
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
if task == "segmentation":
o = (BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(o)
else:
o = (Activation('sigmoid'))(o)
model = Model(img_input, o)
return model
def resnet50_unet(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
assert input_height % 32 == 0
assert input_width % 32 == 0
@ -354,15 +357,18 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, weight_decay=1e-
o = Activation('relu')(o)
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
if task == "segmentation":
o = (BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(o)
else:
o = (Activation('sigmoid'))(o)
model = Model(img_input, o)
return model
def vit_resnet50_unet(n_classes,patch_size, num_patches, input_height=224,input_width=224,weight_decay=1e-6,pretraining=False):
def vit_resnet50_unet(n_classes, patch_size, num_patches, input_height=224, input_width=224, task="segmentation", weight_decay=1e-6, pretraining=False):
inputs = layers.Input(shape=(input_height, input_width, 3))
IMAGE_ORDERING = 'channels_last'
bn_axis=3
@ -465,8 +471,11 @@ def vit_resnet50_unet(n_classes,patch_size, num_patches, input_height=224,input_
o = Activation('relu')(o)
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(o)
if task == "segmentation":
o = (BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(o)
else:
o = (Activation('sigmoid'))(o)
model = Model(inputs=inputs, outputs=o)

29
train.py
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@ -1,5 +1,6 @@
import os
import sys
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from tensorflow.compat.v1.keras.backend import set_session
import warnings
@ -91,7 +92,7 @@ def run(_config, n_classes, n_epochs, input_height,
num_patches_xy, model_name, flip_index, dir_eval, dir_output,
pretraining, learning_rate, task, f1_threshold_classification):
if task == "segmentation":
if task == "segmentation" or "enhancement":
num_patches = num_patches_xy[0]*num_patches_xy[1]
if data_is_provided:
@ -153,7 +154,7 @@ def run(_config, n_classes, n_epochs, input_height,
blur_aug, padding_white, padding_black, flip_aug, binarization,
scaling, degrading, brightening, scales, degrade_scales, brightness,
flip_index, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, augmentation=augmentation,
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation,
patches=patches)
provide_patches(imgs_list_test, segs_list_test, dir_img_val, dir_seg_val,
@ -161,7 +162,7 @@ def run(_config, n_classes, n_epochs, input_height,
blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization,
scaling, degrading, brightening, scales, degrade_scales, brightness,
flip_index, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, augmentation=False, patches=patches)
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=patches)
if weighted_loss:
weights = np.zeros(n_classes)
@ -191,30 +192,30 @@ def run(_config, n_classes, n_epochs, input_height,
if continue_training:
if model_name=='resnet50_unet':
if is_loss_soft_dice:
if is_loss_soft_dice and task == "segmentation":
model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss})
if weighted_loss:
if weighted_loss and task == "segmentation":
model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
if not is_loss_soft_dice and not weighted_loss:
model = load_model(dir_of_start_model , compile=True)
elif model_name=='hybrid_transformer_cnn':
if is_loss_soft_dice:
if is_loss_soft_dice and task == "segmentation":
model = load_model(dir_of_start_model, compile=True, custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches,'soft_dice_loss': soft_dice_loss})
if weighted_loss:
if weighted_loss and task == "segmentation":
model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
if not is_loss_soft_dice and not weighted_loss:
model = load_model(dir_of_start_model , compile=True,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
else:
index_start = 0
if model_name=='resnet50_unet':
model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining)
model = resnet50_unet(n_classes, input_height, input_width, task, weight_decay, pretraining)
elif model_name=='hybrid_transformer_cnn':
model = vit_resnet50_unet(n_classes, transformer_patchsize, num_patches, input_height, input_width,weight_decay,pretraining)
model = vit_resnet50_unet(n_classes, transformer_patchsize, num_patches, input_height, input_width, task, weight_decay, pretraining)
#if you want to see the model structure just uncomment model summary.
#model.summary()
if task == "segmentation":
if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
@ -224,12 +225,16 @@ def run(_config, n_classes, n_epochs, input_height,
if weighted_loss:
model.compile(loss=weighted_categorical_crossentropy(weights),
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
elif task == "enhancement":
model.compile(loss='mean_squared_error',
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
# generating train and evaluation data
train_gen = data_gen(dir_flow_train_imgs, dir_flow_train_labels, batch_size=n_batch,
input_height=input_height, input_width=input_width, n_classes=n_classes)
input_height=input_height, input_width=input_width, n_classes=n_classes, task=task)
val_gen = data_gen(dir_flow_eval_imgs, dir_flow_eval_labels, batch_size=n_batch,
input_height=input_height, input_width=input_width, n_classes=n_classes)
input_height=input_height, input_width=input_width, n_classes=n_classes, task=task)
##img_validation_patches = os.listdir(dir_flow_eval_imgs)
##score_best=[]

56
utils.py
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@ -268,7 +268,7 @@ def IoU(Yi, y_predi):
return mIoU
def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes):
def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes, task='segmentation'):
c = 0
n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images
random.shuffle(n)
@ -277,8 +277,6 @@ def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_c
mask = np.zeros((batch_size, input_height, input_width, n_classes)).astype('float')
for i in range(c, c + batch_size): # initially from 0 to 16, c = 0.
# print(img_folder+'/'+n[i])
try:
filename = n[i].split('.')[0]
@ -287,11 +285,14 @@ def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_c
interpolation=cv2.INTER_NEAREST) # Read an image from folder and resize
img[i - c] = train_img # add to array - img[0], img[1], and so on.
if task == "segmentation":
train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
# print(mask_folder+'/'+filename+'.png')
# print(train_mask.shape)
train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
n_classes)
elif task == "enhancement":
train_mask = cv2.imread(mask_folder + '/' + filename + '.png')/255.
train_mask = resize_image(train_mask, input_height, input_width)
# train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
mask[i - c] = train_mask
@ -539,14 +540,19 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
padding_white, padding_black, flip_aug, binarization, scaling, degrading,
brightening, scales, degrade_scales, brightness, flip_index,
scaling_bluring, scaling_brightness, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip, augmentation=False, patches=False):
rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=False):
indexer = 0
for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)):
img_name = im.split('.')[0]
if task == "segmentation":
dir_of_label_file = os.path.join(dir_seg, img_name + '.png')
elif task=="enhancement":
dir_of_label_file = os.path.join(dir_seg, im)
if not patches:
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(cv2.imread(dir_img + '/' + im), input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
indexer += 1
if augmentation:
@ -556,7 +562,7 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
resize_image(cv2.flip(cv2.imread(dir_img+'/'+im),f_i),input_height,input_width) )
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i), input_height, input_width))
resize_image(cv2.flip(cv2.imread(dir_of_label_file), f_i), input_height, input_width))
indexer += 1
if blur_aug:
@ -565,7 +571,7 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
(resize_image(bluring(cv2.imread(dir_img + '/' + im), blur_i), input_height, input_width)))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
indexer += 1
if binarization:
@ -573,26 +579,26 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
indexer += 1
if patches:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
cv2.imread(dir_img + '/' + im), cv2.imread(dir_seg + '/' + img_name + '.png'),
cv2.imread(dir_img + '/' + im), cv2.imread(dir_of_label_file),
input_height, input_width, indexer=indexer)
if augmentation:
if rotation:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
rotation_90(cv2.imread(dir_img + '/' + im)),
rotation_90(cv2.imread(dir_seg + '/' + img_name + '.png')),
rotation_90(cv2.imread(dir_of_label_file)),
input_height, input_width, indexer=indexer)
if rotation_not_90:
for thetha_i in thetha:
img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/'+im),
cv2.imread(dir_seg + '/'+img_name + '.png'), thetha_i)
cv2.imread(dir_of_label_file), thetha_i)
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
img_max_rotated,
label_max_rotated,
@ -601,24 +607,24 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
for f_i in flip_index:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
cv2.flip(cv2.imread(dir_img + '/' + im), f_i),
cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
cv2.flip(cv2.imread(dir_of_label_file), f_i),
input_height, input_width, indexer=indexer)
if blur_aug:
for blur_i in blur_k:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
bluring(cv2.imread(dir_img + '/' + im), blur_i),
cv2.imread(dir_seg + '/' + img_name + '.png'),
cv2.imread(dir_of_label_file),
input_height, input_width, indexer=indexer)
if padding_black:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
do_padding_black(cv2.imread(dir_img + '/' + im)),
do_padding_label(cv2.imread(dir_seg + '/' + img_name + '.png')),
do_padding_label(cv2.imread(dir_of_label_file)),
input_height, input_width, indexer=indexer)
if padding_white:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
do_padding_white(cv2.imread(dir_img + '/'+im)),
do_padding_label(cv2.imread(dir_seg + '/' + img_name + '.png')),
do_padding_label(cv2.imread(dir_of_label_file)),
input_height, input_width, indexer=indexer)
if brightening:
@ -626,7 +632,7 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
try:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
do_brightening(dir_img + '/' +im, factor),
cv2.imread(dir_seg + '/' + img_name + '.png'),
cv2.imread(dir_of_label_file),
input_height, input_width, indexer=indexer)
except:
pass
@ -634,20 +640,20 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
for sc_ind in scales:
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
cv2.imread(dir_img + '/' + im) ,
cv2.imread(dir_seg + '/' + img_name + '.png'),
cv2.imread(dir_of_label_file),
input_height, input_width, indexer=indexer, scaler=sc_ind)
if degrading:
for degrade_scale_ind in degrade_scales:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
do_degrading(cv2.imread(dir_img + '/' + im), degrade_scale_ind),
cv2.imread(dir_seg + '/' + img_name + '.png'),
cv2.imread(dir_of_label_file),
input_height, input_width, indexer=indexer)
if binarization:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
otsu_copy(cv2.imread(dir_img + '/' + im)),
cv2.imread(dir_seg + '/' + img_name + '.png'),
cv2.imread(dir_of_label_file),
input_height, input_width, indexer=indexer)
if scaling_brightness:
@ -657,7 +663,7 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
indexer = get_patches_num_scale_new(dir_flow_train_imgs,
dir_flow_train_labels,
do_brightening(dir_img + '/' + im, factor)
,cv2.imread(dir_seg + '/' + img_name + '.png')
,cv2.imread(dir_of_label_file)
,input_height, input_width, indexer=indexer, scaler=sc_ind)
except:
pass
@ -667,14 +673,14 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
for blur_i in blur_k:
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
bluring(cv2.imread(dir_img + '/' + im), blur_i),
cv2.imread(dir_seg + '/' + img_name + '.png'),
cv2.imread(dir_of_label_file),
input_height, input_width, indexer=indexer, scaler=sc_ind)
if scaling_binarization:
for sc_ind in scales:
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
otsu_copy(cv2.imread(dir_img + '/' + im)),
cv2.imread(dir_seg + '/' + img_name + '.png'),
cv2.imread(dir_of_label_file),
input_height, input_width, indexer=indexer, scaler=sc_ind)
if scaling_flip:
@ -682,5 +688,5 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
for f_i in flip_index:
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
cv2.flip( cv2.imread(dir_img + '/' + im), f_i),
cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
cv2.flip(cv2.imread(dir_of_label_file), f_i),
input_height, input_width, indexer=indexer, scaler=sc_ind)

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