qurator-spk
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sbb_pixelwise_segmentation
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code formatting with black; typos

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cneud 1 month ago
parent 5f84938839
commit 02b1436f39
8 changed files with 737 additions and 764 deletions
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4
README.md
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@ -63,7 +63,7 @@ The output folder should be an empty folder where the output model will be writt
* flip_aug: If ``true``, different types of filp will be applied on image. Type of flips is given with "flip_index" in train.py file.
* blur_aug: If ``true``, different types of blurring will be applied on image. Type of blurrings is given with "blur_k" in train.py file.
* scaling: If ``true``, scaling will be applied on image. Scale of scaling is given with "scales" in train.py file.
* rotation_not_90: If ``true``, rotation (not 90 degree) will be applied on image. Rothation angles are given with "thetha" in train.py file.
* rotation_not_90: If ``true``, rotation (not 90 degree) will be applied on image. Rotation angles are given with "thetha" in train.py file.
* rotation: If ``true``, 90 degree rotation will be applied on image.
* binarization: If ``true``,Otsu thresholding will be applied to augment the input data with binarized images.
* scaling_bluring: If ``true``, combination of scaling and blurring will be applied on image.
@ -73,5 +73,3 @@ The output folder should be an empty folder where the output model will be writt
* weighted_loss: If ``true``, this means that you want to apply weighted categorical_crossentropy as loss fucntion. Be carefull if you set to ``true``the parameter "is_loss_soft_dice" should be ``false``
* data_is_provided: If you have already provided the input data you can set this to ``true``. Be sure that the train and eval data are in "dir_output". Since when once we provide training data we resize and augment them and then we write them in sub-directories train and eval in "dir_output".
* dir_train: This is the directory of "images" and "labels" (dir_train should include two subdirectories with names of images and labels ) for raw images and labels. Namely they are not prepared (not resized and not augmented) yet for training the model. When we run this tool these raw data will be transformed to suitable size needed for the model and they will be written in "dir_output" in train and eval directories. Each of train and eval include "images" and "labels" sub-directories.

4
build_model_load_pretrained_weights_and_save.py
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@ -9,8 +9,6 @@ from utils import *
from metrics import *
def configuration():
gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
@ -29,5 +27,3 @@ if __name__=='__main__':
model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining)
model.load_weights(dir_of_weights)
model.save('./name_in_another_python_version.h5')

6
config_params.json
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@ -24,7 +24,7 @@
"weighted_loss": false,
"is_loss_soft_dice": false,
"data_is_provided": false,
"dir_train": "/home/vahid/Documents/handwrittens_train/train",
"dir_eval": "/home/vahid/Documents/handwrittens_train/eval",
"dir_output": "/home/vahid/Documents/handwrittens_train/output"
"dir_train": "/train",
"dir_eval": "/eval",
"dir_output": "/output"
}

43
metrics.py
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@ -2,8 +2,8 @@ from tensorflow.keras import backend as K
import tensorflow as tf
import numpy as np
def focal_loss(gamma=2., alpha=4.):
def focal_loss(gamma=2., alpha=4.):
gamma = float(gamma)
alpha = float(alpha)
@ -37,8 +37,10 @@ def focal_loss(gamma=2., alpha=4.):
fl = tf.multiply(alpha, tf.multiply(weight, ce))
reduced_fl = tf.reduce_max(fl, axis=1)
return tf.reduce_mean(reduced_fl)
return focal_loss_fixed
def weighted_categorical_crossentropy(weights=None):
""" weighted_categorical_crossentropy
@ -58,7 +60,10 @@ def weighted_categorical_crossentropy(weights=None):
* labels_floats, axis=-1), 1.0)
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss)
return loss
def image_categorical_cross_entropy(y_true, y_pred, weights=None):
"""
:param y_true: tensor of shape (batch_size, height, width) representing the ground truth.
@ -77,6 +82,8 @@ def image_categorical_cross_entropy(y_true, y_pred, weights=None):
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss)
def class_tversky(y_true, y_pred):
smooth = 1.0 # 1.00
@ -90,20 +97,22 @@ def class_tversky(y_true, y_pred):
false_pos = K.sum((1 - y_true_pos) * y_pred_pos, 1)
alpha = 0.2 # 0.5
beta = 0.8
return (true_pos + smooth)/(true_pos + alpha*false_neg + (beta)*false_pos + smooth)
return (true_pos + smooth) / (true_pos + alpha * false_neg + beta * false_pos + smooth)
def focal_tversky_loss(y_true, y_pred):
pt_1 = class_tversky(y_true, y_pred)
gamma = 1.3 # 4./3.0#1.3#4.0/3.00# 0.75
return K.sum(K.pow((1 - pt_1), gamma))
def generalized_dice_coeff2(y_true, y_pred):
n_el = 1
for dim in y_true.shape:
n_el *= int(dim)
n_cl = y_true.shape[-1]
w = K.zeros(shape=(n_cl,))
w = (K.sum(y_true, axis=(0,1,2)))/(n_el)
w = (K.sum(y_true, axis=(0, 1, 2))) / n_el
w = 1 / (w ** 2 + 0.000001)
numerator = y_true * y_pred
numerator = w * K.sum(numerator, (0, 1, 2))
@ -112,6 +121,8 @@ def generalized_dice_coeff2(y_true, y_pred):
denominator = w * K.sum(denominator, (0, 1, 2))
denominator = K.sum(denominator)
return 2 * numerator / denominator
def generalized_dice_coeff(y_true, y_pred):
axes = tuple(range(1, len(y_pred.shape) - 1))
Ncl = y_pred.shape[-1]
@ -131,10 +142,13 @@ def generalized_dice_coeff(y_true, y_pred):
return gen_dice_coef
def generalized_dice_loss(y_true, y_pred):
return 1 - generalized_dice_coeff2(y_true, y_pred)
def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
'''
"""
Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.
Assumes the `channels_last` format.
@ -150,7 +164,7 @@ def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
https://mediatum.ub.tum.de/doc/1395260/1395260.pdf (page 72)
Adapted from https://github.com/Lasagne/Recipes/issues/99#issuecomment-347775022
'''
"""
# skip the batch and class axis for calculating Dice score
axes = tuple(range(1, len(y_pred.shape) - 1))
@ -160,7 +174,9 @@ def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
denominator = K.sum(K.square(y_pred) + K.square(y_true), axes)
return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last = True, mean_per_class=False, verbose=False):
def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last=True, mean_per_class=False,
verbose=False):
"""
Compute mean metrics of two segmentation masks, via Keras.
@ -250,6 +266,7 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
return K.mean(non_zero_sum / non_zero_count)
def mean_iou(y_true, y_pred, **kwargs):
"""
Compute mean Intersection over Union of two segmentation masks, via Keras.
@ -257,6 +274,8 @@ def mean_iou(y_true, y_pred, **kwargs):
Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.
"""
return seg_metrics(y_true, y_pred, metric_name='iou', **kwargs)
def Mean_IOU(y_true, y_pred):
nb_classes = K.int_shape(y_pred)[-1]
iou = []
@ -276,6 +295,7 @@ def Mean_IOU(y_true, y_pred):
iou = tf.gather(iou, indices=tf.where(legal_labels))
return K.mean(iou)
def iou_vahid(y_true, y_pred):
nb_classes = tf.shape(y_true)[-1] + tf.to_int32(1)
true_pixels = K.argmax(y_true, axis=-1)
@ -292,8 +312,8 @@ def iou_vahid(y_true, y_pred):
def IoU_metric(Yi, y_predi):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
# mean Intersection over Union
# Mean IoU = TP/(FN + TP + FP)
y_predi = np.argmax(y_predi, axis=3)
y_testi = np.argmax(Yi, axis=3)
IoUs = []
@ -308,14 +328,15 @@ def IoU_metric(Yi,y_predi):
def IoU_metric_keras(y_true, y_pred):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
# mean Intersection over Union
# Mean IoU = TP/(FN + TP + FP)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
return IoU_metric(y_true.eval(session=sess), y_pred.eval(session=sess))
def jaccard_distance_loss(y_true, y_pred, smooth=100):
"""
Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
@ -334,5 +355,3 @@ def jaccard_distance_loss(y_true, y_pred, smooth=100):
sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return (1 - jac) * smooth

39
models.py
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@ -16,6 +16,7 @@ def one_side_pad( x ):
x = Lambda(lambda x: x[:, :-1, :-1, :])(x)
return x
def identity_block(input_tensor, kernel_size, filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut.
# Arguments
@ -103,7 +104,6 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
assert input_height % 32 == 0
assert input_width % 32 == 0
img_input = Input(shape=(input_height, input_width, 3))
if IMAGE_ORDERING == 'channels_last':
@ -112,20 +112,19 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
bn_axis = 1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
name='conv1')(x)
f1 = x
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
f2 = one_side_pad(x)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
@ -145,22 +144,17 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
f5 = x
if pretraining:
model = Model(img_input, x).load_weights(resnet50_Weights_path)
v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5)
v512_2048 = (BatchNormalization(axis=bn_axis))(v512_2048)
v512_2048 = Activation('relu')(v512_2048)
v512_1024 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f4)
v512_1024 = (BatchNormalization(axis=bn_axis))(v512_1024)
v512_1024 = Activation('relu')(v512_1024)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v512_2048)
o = (concatenate([o, v512_1024], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -168,7 +162,6 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f3], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -176,7 +169,6 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f2], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -184,8 +176,6 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f1], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -193,7 +183,6 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, img_input], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -201,21 +190,18 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(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):
assert input_height % 32 == 0
assert input_width % 32 == 0
img_input = Input(shape=(input_height, input_width, 3))
if IMAGE_ORDERING == 'channels_last':
@ -224,20 +210,19 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
bn_axis = 1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2),kernel_regularizer=l2(weight_decay), name='conv1')(x)
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
name='conv1')(x)
f1 = x
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
f2 = one_side_pad(x)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
@ -260,11 +245,11 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
if pretraining:
Model(img_input, x).load_weights(resnet50_Weights_path)
v1024_2048 = Conv2D( 1024 , (1, 1) , padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) )( f5 )
v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(
f5)
v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
v1024_2048 = Activation('relu')(v1024_2048)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
o = (concatenate([o, f4], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -272,7 +257,6 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f3], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -280,7 +264,6 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f2], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -288,7 +271,6 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f1], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -296,7 +278,6 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, img_input], axis=MERGE_AXIS))
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
@ -304,14 +285,10 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o)
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
o = (BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(o)
model = Model(img_input, o)
return model

2
requirements.txt
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@ -4,3 +4,5 @@ opencv-python-headless
seaborn
tqdm
imutils
numpy
scipy

34
train.py
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@ -11,12 +11,14 @@ from metrics import *
from tensorflow.keras.models import load_model
from tqdm import tqdm
def configuration():
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)
set_session(session)
def get_dirs_or_files(input_data):
if os.path.isdir(input_data):
image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/')
@ -25,8 +27,10 @@ def get_dirs_or_files(input_data):
assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input)
return image_input, labels_input
ex = Experiment()
@ex.config
def config_params():
n_classes = None # Number of classes. In the case of binary classification this should be 2.
@ -60,18 +64,17 @@ def config_params():
weighted_loss = False # Use weighted categorical cross entropy as loss fucntion. When set to true, "is_loss_soft_dice" must be false.
data_is_provided = False # Only set this to true when you have already provided the input data and the train and eval data are in "dir_output".
@ex.automain
def run(n_classes, n_epochs, input_height,
input_width, weight_decay, weighted_loss,
index_start, dir_of_start_model, is_loss_soft_dice,
n_batch,patches,augmentation,flip_aug
,blur_aug,scaling, binarization,
n_batch, patches, augmentation, flip_aug,
blur_aug, scaling, binarization,
blur_k, scales, dir_train, data_is_provided,
scaling_bluring, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip, continue_training,
flip_index, dir_eval, dir_output, pretraining, learning_rate):
if data_is_provided:
dir_train_flowing = os.path.join(dir_output, 'train')
dir_eval_flowing = os.path.join(dir_output, 'eval')
@ -110,18 +113,15 @@ def run(n_classes,n_epochs,input_height,
else:
os.makedirs(dir_eval_flowing)
os.mkdir(dir_flow_train_imgs)
os.mkdir(dir_flow_train_labels)
os.mkdir(dir_flow_eval_imgs)
os.mkdir(dir_flow_eval_labels)
# set the gpu configuration
configuration()
# writing patches into a sub-folder in order to be flowed from directory.
provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels,
@ -139,8 +139,6 @@ def run(n_classes,n_epochs,input_height,
rotation_not_90, thetha, scaling_flip,
augmentation=False, patches=patches)
if weighted_loss:
weights = np.zeros(n_classes)
if data_is_provided:
@ -161,20 +159,18 @@ def run(n_classes,n_epochs,input_height,
except:
pass
weights = 1.00 / weights
weights = weights / float(np.sum(weights))
weights = weights / float(np.min(weights))
weights = weights / float(np.sum(weights))
if continue_training:
if is_loss_soft_dice:
model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss})
if weighted_loss:
model = load_model (dir_of_start_model, compile = True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
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)
else:
@ -185,7 +181,6 @@ def run(n_classes,n_epochs,input_height,
# if you want to see the model structure just uncomment model summary.
# model.summary()
if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
@ -212,18 +207,7 @@ def run(n_classes,n_epochs,input_height,
epochs=1)
model.save(dir_output + '/' + 'model_' + str(i))
# os.system('rm -rf '+dir_train_flowing)
# os.system('rm -rf '+dir_eval_flowing)
# model.save(dir_output+'/'+'model'+'.h5')

91
utils.py
Unescape Escape View File

@ -10,9 +10,8 @@ import imutils
import math
def bluring(img_in, kind):
if kind=='guass':
if kind == 'gauss':
img_blur = cv2.GaussianBlur(img_in, (5, 5), 0)
elif kind == "median":
img_blur = cv2.medianBlur(img_in, 5)
@ -20,8 +19,8 @@ def bluring(img_in,kind):
img_blur = cv2.blur(img_in, (5, 5))
return img_blur
def elastic_transform(image, alpha, sigma,seedj, random_state=None):
def elastic_transform(image, alpha, sigma, seedj, random_state=None):
"""Elastic deformation of images as described in [Simard2003]_.
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
@ -42,6 +41,7 @@ def elastic_transform(image, alpha, sigma,seedj, random_state=None):
distored_image = map_coordinates(image, indices, order=1, mode='reflect')
return distored_image.reshape(image.shape)
def rotation_90(img):
img_rot = np.zeros((img.shape[1], img.shape[0], img.shape[2]))
img_rot[:, :, 0] = img[:, :, 0].T
@ -49,6 +49,7 @@ def rotation_90(img):
img_rot[:, :, 2] = img[:, :, 2].T
return img_rot
def rotatedRectWithMaxArea(w, h, angle):
"""
Given a rectangle of size wxh that has been rotated by 'angle' (in
@ -76,6 +77,7 @@ def rotatedRectWithMaxArea(w, h, angle):
return wr, hr
def rotate_max_area(image, rotated, rotated_label, angle):
""" image: cv2 image matrix object
angle: in degree
@ -88,11 +90,14 @@ def rotate_max_area(image,rotated, rotated_label,angle):
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2], rotated_label[y1:y2, x1:x2]
def rotation_not_90_func(img, label, thetha):
rotated = imutils.rotate(img, thetha)
rotated_label = imutils.rotate(label, thetha)
return rotate_max_area(img, rotated, rotated_label, thetha)
def color_images(seg, n_classes):
ann_u = range(n_classes)
if len(np.shape(seg)) == 3:
@ -112,6 +117,8 @@ def color_images(seg, n_classes):
def resize_image(seg_in, input_height, input_width):
return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def get_one_hot(seg, input_height, input_width, n_classes):
seg = seg[:, :, 0]
seg_f = np.zeros((input_height, input_width, n_classes))
@ -137,6 +144,8 @@ def IoU(Yi,y_predi):
print("_________________")
print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU
def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes):
c = 0
n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images
@ -152,13 +161,15 @@ def data_gen(img_folder, mask_folder, batch_size,input_height, input_width,n_cla
filename = n[i].split('.')[0]
train_img = cv2.imread(img_folder + '/' + n[i]) / 255.
train_img = cv2.resize(train_img, (input_width, input_height),interpolation=cv2.INTER_NEAREST)# Read an image from folder and resize
train_img = cv2.resize(train_img, (input_width, input_height),
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.
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)
train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
n_classes)
# 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
@ -166,14 +177,13 @@ def data_gen(img_folder, mask_folder, batch_size,input_height, input_width,n_cla
img[i - c] = np.ones((input_height, input_width, 3)).astype('float')
mask[i - c] = np.zeros((input_height, input_width, n_classes)).astype('float')
c += batch_size
if(c+batch_size>=len(os.listdir(img_folder))):
if c + batch_size >= len(os.listdir(img_folder)):
c = 0
random.shuffle(n)
yield img, mask
def otsu_copy(img):
img_r = np.zeros(img.shape)
img1 = img[:, :, 0]
@ -186,8 +196,9 @@ def otsu_copy(img):
img_r[:, :, 1] = threshold1
img_r[:, :, 2] = threshold1
return img_r
def get_patches(dir_img_f,dir_seg_f,img,label,height,width,indexer):
def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer):
if img.shape[0] < height or img.shape[1] < width:
img, label = do_padding(img, label, height, width)
@ -220,7 +231,6 @@ def get_patches(dir_img_f,dir_seg_f,img,label,height,width,indexer):
index_y_u = img_h
index_y_d = img_h - height
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
@ -230,8 +240,8 @@ def get_patches(dir_img_f,dir_seg_f,img,label,height,width,indexer):
return indexer
def do_padding(img,label,height,width):
def do_padding(img, label, height, width):
height_new = img.shape[0]
width_new = img.shape[1]
@ -256,8 +266,6 @@ def do_padding(img,label,height,width):
def get_patches_num_scale(dir_img_f, dir_seg_f, img, label, height, width, indexer, n_patches, scaler):
if img.shape[0] < height or img.shape[1] < width:
img, label = do_padding(img, label, height, width)
@ -267,7 +275,6 @@ def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,n_p
height_scale = int(height * scaler)
width_scale = int(width * scaler)
nxf = img_w / float(width_scale)
nyf = img_h / float(height_scale)
@ -294,7 +301,6 @@ def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,n_p
index_y_u = img_h
index_y_d = img_h - height_scale
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
@ -307,6 +313,7 @@ def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,n_p
return indexer
def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, indexer, scaler):
img = resize_image(img, int(img.shape[0] * scaler), int(img.shape[1] * scaler))
label = resize_image(label, int(label.shape[0] * scaler), int(label.shape[1] * scaler))
@ -320,7 +327,6 @@ def get_patches_num_scale_new(dir_img_f,dir_seg_f,img,label,height,width,indexer
height_scale = int(height * 1)
width_scale = int(width * 1)
nxf = img_w / float(width_scale)
nyf = img_h / float(height_scale)
@ -347,7 +353,6 @@ def get_patches_num_scale_new(dir_img_f,dir_seg_f,img,label,height,width,indexer
index_y_u = img_h
index_y_d = img_h - height_scale
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
@ -368,7 +373,6 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
scaling_bluring, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip,
augmentation=False, patches=False):
imgs_cv_train = np.array(os.listdir(dir_img))
segs_cv_train = np.array(os.listdir(dir_seg))
@ -376,30 +380,35 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
for im, seg_i in tqdm(zip(imgs_cv_train, segs_cv_train)):
img_name = im.split('.')[0]
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_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))
indexer += 1
if augmentation:
if flip_aug:
for f_i in flip_index:
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
resize_image(cv2.flip(cv2.imread(dir_img+'/'+im),f_i),input_height,input_width) )
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_seg + '/' + img_name + '.png'), f_i),
input_height, input_width))
indexer += 1
if blur_aug:
for blur_i in blur_k:
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
(resize_image(bluring(cv2.imread(dir_img+'/'+im),blur_i),input_height,input_width) ) )
(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_seg + '/' + img_name + '.png'), input_height,
input_width))
indexer += 1
if binarization:
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width))
@ -408,11 +417,6 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
indexer += 1
if patches:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
@ -422,8 +426,6 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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')),
@ -432,7 +434,10 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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)
img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/' + im),
cv2.imread(
dir_seg + '/' + img_name + '.png'),
thetha_i)
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
img_max_rotated,
label_max_rotated,
@ -445,13 +450,11 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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'),
input_height, input_width, indexer=indexer)
if scaling:
for sc_ind in scales:
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
@ -464,15 +467,14 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height, input_width, indexer=indexer)
if scaling_bluring:
for sc_ind in scales:
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'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
input_height, input_width, indexer=indexer,
scaler=sc_ind)
if scaling_binarization:
for sc_ind in scales:
@ -486,12 +488,7 @@ def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
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) ,
input_height,input_width,indexer=indexer,scaler=sc_ind)
cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'),
f_i),
input_height, input_width, indexer=indexer,
scaler=sc_ind)

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