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eynollah/eynollah/train/README.md
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# Pixelwise Segmentation
> Pixelwise segmentation for document images
## Introduction
This repository contains the source code for training an encoder model for document image segmentation.
## Installation
Either clone the repository via `git clone https://github.com/qurator-spk/sbb_pixelwise_segmentation.git` or download and unpack the [ZIP](https://github.com/qurator-spk/sbb_pixelwise_segmentation/archive/master.zip).
### Pretrained encoder
Download our pretrained weights and add them to a ``pretrained_model`` folder:
https://qurator-data.de/sbb_pixelwise_segmentation/pretrained_encoder/
## Usage
### Train
To train a model, run: ``python train.py with config_params.json``
### Ground truth format
Lables for each pixel are identified by a number. So if you have a
binary case, ``n_classes`` should be set to ``2`` and labels should
be ``0`` and ``1`` for each class and pixel.
In the case of multiclass, just set ``n_classes`` to the number of classes
you have and the try to produce the labels by pixels set from ``0 , 1 ,2 .., n_classes-1``.
The labels format should be png.
Our lables are 3 channel png images but only information of first channel is used.
If you have an image label with height and width of 10, for a binary case the first channel should look like this:
Label: [ [1, 0, 0, 1, 1, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...,
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ]
This means that you have an image by `10*10*3` and `pixel[0,0]` belongs
to class `1` and `pixel[0,1]` belongs to class `0`.
A small sample of training data for binarization experiment can be found here, [Training data sample](https://qurator-data.de/~vahid.rezanezhad/binarization_training_data_sample/), which contains images and lables folders.
### Training , evaluation and output
The train and evaluation folders should contain subfolders of images and labels.
The output folder should be an empty folder where the output model will be written to.
### Parameter configuration
* patches: If you want to break input images into smaller patches (input size of the model) you need to set this parameter to ``true``. In the case that the model should see the image once, like page extraction, patches should be set to ``false``.
* n_batch: Number of batches at each iteration.
* n_classes: Number of classes. In the case of binary classification this should be 2.
* n_epochs: Number of epochs.
* input_height: This indicates the height of model's input.
* input_width: This indicates the width of model's input.
* weight_decay: Weight decay of l2 regularization of model layers.
* augmentation: If you want to apply any kind of augmentation this parameter should first set to ``true``.
* 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: 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.
* scaling_binarization: If ``true``, combination of scaling and binarization will be applied on image.
* scaling_flip: If ``true``, combination of scaling and flip will be applied on image.
* continue_training: If ``true``, it means that you have already trained a model and you would like to continue the training. So it is needed to provide the dir of trained model with "dir_of_start_model" and index for naming the models. For example if you have already trained for 3 epochs then your last index is 2 and if you want to continue from model_1.h5, you can set "index_start" to 3 to start naming model with index 3.
* 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.

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eynollah/eynollah/train/build_model_load_pretrained_weights_and_save.py
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import os
import sys
import tensorflow as tf
import keras , warnings
from keras.optimizers import *
from sacred import Experiment
from models import *
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))
if __name__=='__main__':
n_classes = 2
input_height = 224
input_width = 448
weight_decay = 1e-6
pretraining = False
dir_of_weights = 'model_bin_sbb_ens.h5'
#configuration()
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')

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eynollah/eynollah/train/config_params.json
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{
"n_classes" : 3,
"n_epochs" : 2,
"input_height" : 448,
"input_width" : 672,
"weight_decay" : 1e-6,
"n_batch" : 2,
"learning_rate": 1e-4,
"patches" : true,
"pretraining" : true,
"augmentation" : false,
"flip_aug" : false,
"blur_aug" : false,
"scaling" : true,
"binarization" : false,
"scaling_bluring" : false,
"scaling_binarization" : false,
"scaling_flip" : false,
"rotation": false,
"rotation_not_90": false,
"continue_training": false,
"index_start": 0,
"dir_of_start_model": " ",
"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"
}

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eynollah/eynollah/train/metrics.py
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from keras import backend as K
import tensorflow as tf
import numpy as np
def focal_loss(gamma=2., alpha=4.):
gamma = float(gamma)
alpha = float(alpha)
def focal_loss_fixed(y_true, y_pred):
"""Focal loss for multi-classification
FL(p_t)=-alpha(1-p_t)^{gamma}ln(p_t)
Notice: y_pred is probability after softmax
gradient is d(Fl)/d(p_t) not d(Fl)/d(x) as described in paper
d(Fl)/d(p_t) * [p_t(1-p_t)] = d(Fl)/d(x)
Focal Loss for Dense Object Detection
https://arxiv.org/abs/1708.02002
Arguments:
y_true {tensor} -- ground truth labels, shape of [batch_size, num_cls]
y_pred {tensor} -- model's output, shape of [batch_size, num_cls]
Keyword Arguments:
gamma {float} -- (default: {2.0})
alpha {float} -- (default: {4.0})
Returns:
[tensor] -- loss.
"""
epsilon = 1.e-9
y_true = tf.convert_to_tensor(y_true, tf.float32)
y_pred = tf.convert_to_tensor(y_pred, tf.float32)
model_out = tf.add(y_pred, epsilon)
ce = tf.multiply(y_true, -tf.log(model_out))
weight = tf.multiply(y_true, tf.pow(tf.subtract(1., model_out), gamma))
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
Args:
* weights<ktensor|nparray|list>: crossentropy weights
Returns:
* weighted categorical crossentropy function
"""
def loss(y_true, y_pred):
labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
if weights is not None:
weight_mask = tf.maximum(tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, 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.
:param y_pred: tensor of shape (batch_size, height, width) representing the prediction.
:return: The mean cross-entropy on softmaxed tensors.
"""
labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
if weights is not None:
weight_mask = tf.maximum(
tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0)
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
y_true = K.permute_dimensions(y_true, (3,1,2,0))
y_pred = K.permute_dimensions(y_pred, (3,1,2,0))
y_true_pos = K.batch_flatten(y_true)
y_pred_pos = K.batch_flatten(y_pred)
true_pos = K.sum(y_true_pos * y_pred_pos, 1)
false_neg = K.sum(y_true_pos * (1-y_pred_pos), 1)
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)
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 = 1/(w**2+0.000001)
numerator = y_true*y_pred
numerator = w*K.sum(numerator,(0,1,2))
numerator = K.sum(numerator)
denominator = 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]
w = K.zeros(shape=(Ncl,))
w = K.sum(y_true, axis=axes)
w = 1/(w**2+0.000001)
# Compute gen dice coef:
numerator = y_true*y_pred
numerator = w*K.sum(numerator,axes)
numerator = K.sum(numerator)
denominator = y_true+y_pred
denominator = w*K.sum(denominator,axes)
denominator = K.sum(denominator)
gen_dice_coef = 2*numerator/denominator
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.
# Arguments
y_true: b x X x Y( x Z...) x c One hot encoding of ground truth
y_pred: b x X x Y( x Z...) x c Network output, must sum to 1 over c channel (such as after softmax)
epsilon: Used for numerical stability to avoid divide by zero errors
# References
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://arxiv.org/abs/1606.04797
More details on Dice loss formulation
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))
numerator = 2. * K.sum(y_pred * y_true, axes)
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):
"""
Compute mean metrics of two segmentation masks, via Keras.
IoU(A,B) = |A & B| / (| A U B|)
Dice(A,B) = 2*|A & B| / (|A| + |B|)
Args:
y_true: true masks, one-hot encoded.
y_pred: predicted masks, either softmax outputs, or one-hot encoded.
metric_name: metric to be computed, either 'iou' or 'dice'.
metric_type: one of 'standard' (default), 'soft', 'naive'.
In the standard version, y_pred is one-hot encoded and the mean
is taken only over classes that are present (in y_true or y_pred).
The 'soft' version of the metrics are computed without one-hot
encoding y_pred.
The 'naive' version return mean metrics where absent classes contribute
to the class mean as 1.0 (instead of being dropped from the mean).
drop_last = True: boolean flag to drop last class (usually reserved
for background class in semantic segmentation)
mean_per_class = False: return mean along batch axis for each class.
verbose = False: print intermediate results such as intersection, union
(as number of pixels).
Returns:
IoU/Dice of y_true and y_pred, as a float, unless mean_per_class == True
in which case it returns the per-class metric, averaged over the batch.
Inputs are B*W*H*N tensors, with
B = batch size,
W = width,
H = height,
N = number of classes
"""
flag_soft = (metric_type == 'soft')
flag_naive_mean = (metric_type == 'naive')
# always assume one or more classes
num_classes = K.shape(y_true)[-1]
if not flag_soft:
# get one-hot encoded masks from y_pred (true masks should already be one-hot)
y_pred = K.one_hot(K.argmax(y_pred), num_classes)
y_true = K.one_hot(K.argmax(y_true), num_classes)
# if already one-hot, could have skipped above command
# keras uses float32 instead of float64, would give error down (but numpy arrays or keras.to_categorical gives float64)
y_true = K.cast(y_true, 'float32')
y_pred = K.cast(y_pred, 'float32')
# intersection and union shapes are batch_size * n_classes (values = area in pixels)
axes = (1,2) # W,H axes of each image
intersection = K.sum(K.abs(y_true * y_pred), axis=axes)
mask_sum = K.sum(K.abs(y_true), axis=axes) + K.sum(K.abs(y_pred), axis=axes)
union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
smooth = .001
iou = (intersection + smooth) / (union + smooth)
dice = 2 * (intersection + smooth)/(mask_sum + smooth)
metric = {'iou': iou, 'dice': dice}[metric_name]
# define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise
mask = K.cast(K.not_equal(union, 0), 'float32')
if drop_last:
metric = metric[:,:-1]
mask = mask[:,:-1]
if verbose:
print('intersection, union')
print(K.eval(intersection), K.eval(union))
print(K.eval(intersection/union))
# return mean metrics: remaining axes are (batch, classes)
if flag_naive_mean:
return K.mean(metric)
# take mean only over non-absent classes
class_count = K.sum(mask, axis=0)
non_zero = tf.greater(class_count, 0)
non_zero_sum = tf.boolean_mask(K.sum(metric * mask, axis=0), non_zero)
non_zero_count = tf.boolean_mask(class_count, non_zero)
if verbose:
print('Counts of inputs with class present, metrics for non-absent classes')
print(K.eval(class_count), K.eval(non_zero_sum / non_zero_count))
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.
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 = []
true_pixels = K.argmax(y_true, axis=-1)
pred_pixels = K.argmax(y_pred, axis=-1)
void_labels = K.equal(K.sum(y_true, axis=-1), 0)
for i in range(0, nb_classes): # exclude first label (background) and last label (void)
true_labels = K.equal(true_pixels, i)# & ~void_labels
pred_labels = K.equal(pred_pixels, i)# & ~void_labels
inter = tf.to_int32(true_labels & pred_labels)
union = tf.to_int32(true_labels | pred_labels)
legal_batches = K.sum(tf.to_int32(true_labels), axis=1)>0
ious = K.sum(inter, axis=1)/K.sum(union, axis=1)
iou.append(K.mean(tf.gather(ious, indices=tf.where(legal_batches)))) # returns average IoU of the same objects
iou = tf.stack(iou)
legal_labels = ~tf.debugging.is_nan(iou)
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)
pred_pixels = K.argmax(y_pred, axis=-1)
iou = []
for i in tf.range(nb_classes):
tp=K.sum( tf.to_int32( K.equal(true_pixels, i) & K.equal(pred_pixels, i) ) )
fp=K.sum( tf.to_int32( K.not_equal(true_pixels, i) & K.equal(pred_pixels, i) ) )
fn=K.sum( tf.to_int32( K.equal(true_pixels, i) & K.not_equal(pred_pixels, i) ) )
iouh=tp/(tp+fp+fn)
iou.append(iouh)
return K.mean(iou)
def IoU_metric(Yi,y_predi):
## 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 = []
Nclass = int(np.max(Yi)) + 1
for c in range(Nclass):
TP = np.sum( (Yi == c)&(y_predi==c) )
FP = np.sum( (Yi != c)&(y_predi==c) )
FN = np.sum( (Yi == c)&(y_predi != c))
IoU = TP/float(TP + FP + FN)
IoUs.append(IoU)
return K.cast( np.mean(IoUs) ,dtype='float32' )
def IoU_metric_keras(y_true, y_pred):
## 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|)
= sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
The jaccard distance loss is usefull for unbalanced datasets. This has been
shifted so it converges on 0 and is smoothed to avoid exploding or disapearing
gradient.
Ref: https://en.wikipedia.org/wiki/Jaccard_index
@url: https://gist.github.com/wassname/f1452b748efcbeb4cb9b1d059dce6f96
@author: wassname
"""
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return (1 - jac) * smooth

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eynollah/eynollah/train/models.py
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from keras.models import *
from keras.layers import *
from keras import layers
from keras.regularizers import l2
resnet50_Weights_path='./pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
IMAGE_ORDERING ='channels_last'
MERGE_AXIS=-1
def one_side_pad( x ):
x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
if IMAGE_ORDERING == 'channels_first':
x = Lambda(lambda x : x[: , : , :-1 , :-1 ] )(x)
elif IMAGE_ORDERING == 'channels_last':
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
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the filterss of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
"""
filters1, filters2, filters3 = filters
if IMAGE_ORDERING == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING ,
padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3 , (1, 1), data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
x = layers.add([x, input_tensor])
x = Activation('relu')(x)
return x
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
"""conv_block is the block that has a conv layer at shortcut
# Arguments
input_tensor: input tensor
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the filterss of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
# Returns
Output tensor for the block.
Note that from stage 3, the first conv layer at main path is with strides=(2,2)
And the shortcut should have strides=(2,2) as well
"""
filters1, filters2, filters3 = filters
if IMAGE_ORDERING == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , strides=strides,
name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING , padding='same',
name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
shortcut = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , strides=strides,
name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
x = layers.add([x, shortcut])
x = Activation('relu')(x)
return x
def resnet50_unet_light(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':
bn_axis = 3
else:
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)
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')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
f3 = x
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
f4 = x
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
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)
o = ( Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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)
o = ( Conv2D( 256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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)
o = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay) ) )(o)
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)
o = ( Conv2D( 64 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
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)
o = ( Conv2D( 32 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
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':
bn_axis = 3
else:
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)
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')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
f3 = x
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
f4 = x
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
f5 = x
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 = ( 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)
o = ( Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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)
o = ( Conv2D( 256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o)
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)
o = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay) ) )(o)
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)
o = ( Conv2D( 64 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
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)
o = ( Conv2D( 32 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o)
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

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eynollah/eynollah/train/train.py
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import os
import sys
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
import keras , warnings
from keras.optimizers import *
from sacred import Experiment
from models import *
from utils import *
from metrics import *
from keras.models import load_model
from tqdm import tqdm
def configuration():
keras.backend.clear_session()
tf.reset_default_graph()
warnings.filterwarnings('ignore')
os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction=0.95#0.95
config.gpu_options.visible_device_list="0"
set_session(tf.Session(config=config))
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/')
# Check if training dir exists
assert os.path.isdir(image_input), "{} is not a directory".format(image_input)
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. If your case study is binary case the set it to 2 and otherwise give your number of cases.
n_epochs=1
input_height=224*1
input_width=224*1
weight_decay=1e-6 # Weight decay of l2 regularization of model layers.
n_batch=1 # Number of batches at each iteration.
learning_rate=1e-4
patches=False # Make patches of image in order to use all information of image. In the case of page
# extraction this should be set to false since model should see all image.
augmentation=False
flip_aug=False # Flip image (augmentation).
blur_aug=False # Blur patches of image (augmentation).
scaling=False # Scaling of patches (augmentation) will be imposed if this set to true.
binarization=False # Otsu thresholding. Used for augmentation in the case of binary case like textline prediction. For multicases should not be applied.
dir_train=None # Directory of training dataset (sub-folders should be named images and labels).
dir_eval=None # Directory of validation dataset (sub-folders should be named images and labels).
dir_output=None # Directory of output where the model should be saved.
pretraining=False # Set true to load pretrained weights of resnet50 encoder.
scaling_bluring=False
scaling_binarization=False
scaling_flip=False
thetha=[10,-10]
blur_k=['blur','guass','median'] # Used in order to blur image. Used for augmentation.
scales= [ 0.5, 2 ] # Scale patches with these scales. Used for augmentation.
flip_index=[0,1,-1] # Flip image. Used for augmentation.
continue_training = False # If
index_start = 0
dir_of_start_model = ''
is_loss_soft_dice = False
weighted_loss = False
data_is_provided = False
@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,
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')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels')
dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels')
configuration()
else:
dir_img,dir_seg=get_dirs_or_files(dir_train)
dir_img_val,dir_seg_val=get_dirs_or_files(dir_eval)
# make first a directory in output for both training and evaluations in order to flow data from these directories.
dir_train_flowing=os.path.join(dir_output,'train')
dir_eval_flowing=os.path.join(dir_output,'eval')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images/')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels/')
dir_flow_eval_imgs=os.path.join(dir_eval_flowing,'images/')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels/')
if os.path.isdir(dir_train_flowing):
os.system('rm -rf '+dir_train_flowing)
os.makedirs(dir_train_flowing)
else:
os.makedirs(dir_train_flowing)
if os.path.isdir(dir_eval_flowing):
os.system('rm -rf '+dir_eval_flowing)
os.makedirs(dir_eval_flowing)
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,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=augmentation,patches=patches)
provide_patches(dir_img_val,dir_seg_val,dir_flow_eval_imgs,
dir_flow_eval_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
scaling_bluring,scaling_binarization,rotation,
rotation_not_90,thetha,scaling_flip,
augmentation=False,patches=patches)
if weighted_loss:
weights=np.zeros(n_classes)
if data_is_provided:
for obj in os.listdir(dir_flow_train_labels):
try:
label_obj=cv2.imread(dir_flow_train_labels+'/'+obj)
label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
except:
pass
else:
for obj in os.listdir(dir_seg):
try:
label_obj=cv2.imread(dir_seg+'/'+obj)
label_obj_one_hot=get_one_hot( label_obj,label_obj.shape[0],label_obj.shape[1],n_classes)
weights+=(label_obj_one_hot.sum(axis=0)).sum(axis=0)
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)})
if not is_loss_soft_dice and not weighted_loss:
model = load_model (dir_of_start_model, compile = True)
else:
#get our model.
index_start = 0
model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining)
#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'])
if is_loss_soft_dice:
model.compile(loss=soft_dice_loss,
optimizer = Adam(lr=learning_rate),metrics=['accuracy'])
if weighted_loss:
model.compile(loss=weighted_categorical_crossentropy(weights),
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 )
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 )
for i in tqdm(range(index_start, n_epochs+index_start)):
model.fit_generator(
train_gen,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch)-1,
validation_data=val_gen,
validation_steps=1,
epochs=1)
model.save(dir_output+'/'+'model_'+str(i)+'.h5')
#os.system('rm -rf '+dir_train_flowing)
#os.system('rm -rf '+dir_eval_flowing)
#model.save(dir_output+'/'+'model'+'.h5')

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import os
import cv2
import numpy as np
import seaborn as sns
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
import random
from tqdm import tqdm
import imutils
import math
def bluring(img_in,kind):
if kind=='guass':
img_blur = cv2.GaussianBlur(img_in,(5,5),0)
elif kind=="median":
img_blur = cv2.medianBlur(img_in,5)
elif kind=='blur':
img_blur=cv2.blur(img_in,(5,5))
return img_blur
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
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
"""
if random_state is None:
random_state = np.random.RandomState(seedj)
shape = image.shape
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
dz = np.zeros_like(dx)
x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))
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
img_rot[:,:,1]=img[:,:,1].T
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
radians), computes the width and height of the largest possible
axis-aligned rectangle (maximal area) within the rotated rectangle.
"""
if w <= 0 or h <= 0:
return 0,0
width_is_longer = w >= h
side_long, side_short = (w,h) if width_is_longer else (h,w)
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2.*sin_a*cos_a*side_long or abs(sin_a-cos_a) < 1e-10:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5*side_short
wr,hr = (x/sin_a,x/cos_a) if width_is_longer else (x/cos_a,x/sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a*cos_a - sin_a*sin_a
wr,hr = (w*cos_a - h*sin_a)/cos_2a, (h*cos_a - w*sin_a)/cos_2a
return wr,hr
def rotate_max_area(image,rotated, rotated_label,angle):
""" image: cv2 image matrix object
angle: in degree
"""
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0],
math.radians(angle))
h, w, _ = rotated.shape
y1 = h//2 - int(hr/2)
y2 = y1 + int(hr)
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:
seg=seg[:,:,0]
seg_img=np.zeros((np.shape(seg)[0],np.shape(seg)[1],3)).astype(float)
colors=sns.color_palette("hls", n_classes)
for c in ann_u:
c=int(c)
segl=(seg==c)
seg_img[:,:,0]+=segl*(colors[c][0])
seg_img[:,:,1]+=segl*(colors[c][1])
seg_img[:,:,2]+=segl*(colors[c][2])
return seg_img
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))
for j in range(n_classes):
seg_f[:,:,j]=(seg==j).astype(int)
return seg_f
def IoU(Yi,y_predi):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
IoUs = []
classes_true=np.unique(Yi)
for c in classes_true:
TP = np.sum( (Yi == c)&(y_predi==c) )
FP = np.sum( (Yi != c)&(y_predi==c) )
FN = np.sum( (Yi == c)&(y_predi != c))
IoU = TP/float(TP + FP + FN)
print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c,TP,FP,FN,IoU))
IoUs.append(IoU)
mIoU = np.mean(IoUs)
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
random.shuffle(n)
while True:
img = np.zeros((batch_size, input_height, input_width, 3)).astype('float')
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]
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
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 = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
mask[i-c] = train_mask
except:
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))):
c=0
random.shuffle(n)
yield img, mask
def otsu_copy(img):
img_r=np.zeros(img.shape)
img1=img[:,:,0]
img2=img[:,:,1]
img3=img[:,:,2]
_, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
_, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
img_r[:,:,0]=threshold1
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):
if img.shape[0]<height or img.shape[1]<width:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
nxf=img_w/float(width)
nyf=img_h/float(height)
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width
index_x_u=(i+1)*width
index_y_d=j*height
index_y_u=(j+1)*height
if index_x_u>img_w:
index_x_u=img_w
index_x_d=img_w-width
if index_y_u>img_h:
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,:]
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
return indexer
def do_padding(img,label,height,width):
height_new=img.shape[0]
width_new=img.shape[1]
h_start=0
w_start=0
if img.shape[0]<height:
h_start=int( abs(height-img.shape[0])/2. )
height_new=height
if img.shape[1]<width:
w_start=int( abs(width-img.shape[1])/2. )
width_new=width
img_new=np.ones((height_new,width_new,img.shape[2])).astype(float)*255
label_new=np.zeros((height_new,width_new,label.shape[2])).astype(float)
img_new[h_start:h_start+img.shape[0],w_start:w_start+img.shape[1],:]=np.copy(img[:,:,:])
label_new[h_start:h_start+label.shape[0],w_start:w_start+label.shape[1],:]=np.copy(label[:,:,:])
return img_new,label_new
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)
img_h=img.shape[0]
img_w=img.shape[1]
height_scale=int(height*scaler)
width_scale=int(width*scaler)
nxf=img_w/float(width_scale)
nyf=img_h/float(height_scale)
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width_scale
index_x_u=(i+1)*width_scale
index_y_d=j*height_scale
index_y_u=(j+1)*height_scale
if index_x_u>img_w:
index_x_u=img_w
index_x_d=img_w-width_scale
if index_y_u>img_h:
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,:]
img_patch=resize_image(img_patch,height,width)
label_patch=resize_image(label_patch,height,width)
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
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))
if img.shape[0]<height or img.shape[1]<width:
img,label=do_padding(img,label,height,width)
img_h=img.shape[0]
img_w=img.shape[1]
height_scale=int(height*1)
width_scale=int(width*1)
nxf=img_w/float(width_scale)
nyf=img_h/float(height_scale)
if nxf>int(nxf):
nxf=int(nxf)+1
if nyf>int(nyf):
nyf=int(nyf)+1
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf):
for j in range(nyf):
index_x_d=i*width_scale
index_x_u=(i+1)*width_scale
index_y_d=j*height_scale
index_y_u=(j+1)*height_scale
if index_x_u>img_w:
index_x_u=img_w
index_x_d=img_w-width_scale
if index_y_u>img_h:
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,:]
#img_patch=resize_image(img_patch,height,width)
#label_patch=resize_image(label_patch,height,width)
cv2.imwrite(dir_img_f+'/img_'+str(indexer)+'.png', img_patch )
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch )
indexer+=1
return indexer
def provide_patches(dir_img,dir_seg,dir_flow_train_imgs,
dir_flow_train_labels,
input_height,input_width,blur_k,blur_aug,
flip_aug,binarization,scaling,scales,flip_index,
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))
indexer=0
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 ) )
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) )
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) )
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) ) )
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 binarization:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png',
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 ))
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'),
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') ),
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)
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels,
img_max_rotated,
label_max_rotated,
input_height,input_width,indexer=indexer)
if flip_aug:
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),
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,
cv2.imread(dir_img+'/'+im) ,
cv2.imread(dir_seg+'/'+img_name+'.png'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
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'),
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)
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'),
input_height,input_width,indexer=indexer,scaler=sc_ind)
if scaling_flip:
for sc_ind in scales:
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)
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