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sbb_pixelwise_segmentation
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cneud 1 month ago
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6
README.md
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@ -48,7 +48,7 @@ If you have an image label with height and width of 10, for a binary case the fi
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. 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 ### Training , evaluation and output
The train and evaluation folders should contain subfolders of images and labels. 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. The output folder should be an empty folder where the output model will be written to.
### Parameter configuration ### Parameter configuration
@ -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. * 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. * 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. * 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. * 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. * 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_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`` * 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". * 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. * 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.

14
build_model_load_pretrained_weights_and_save.py
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@ -9,25 +9,21 @@ from utils import *
from metrics import * from metrics import *
def configuration(): def configuration():
gpu_options = tf.compat.v1.GPUOptions(allow_growth=True) gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options)) session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
if __name__=='__main__': if __name__ == '__main__':
n_classes = 2 n_classes = 2
input_height = 224 input_height = 224
input_width = 448 input_width = 448
weight_decay = 1e-6 weight_decay = 1e-6
pretraining = False pretraining = False
dir_of_weights = 'model_bin_sbb_ens.h5' dir_of_weights = 'model_bin_sbb_ens.h5'
#configuration() # configuration()
model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining) model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining)
model.load_weights(dir_of_weights) model.load_weights(dir_of_weights)
model.save('./name_in_another_python_version.h5') model.save('./name_in_another_python_version.h5')

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

205
metrics.py
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@ -2,8 +2,8 @@ from tensorflow.keras import backend as K
import tensorflow as tf import tensorflow as tf
import numpy as np import numpy as np
def focal_loss(gamma=2., alpha=4.):
def focal_loss(gamma=2., alpha=4.):
gamma = float(gamma) gamma = float(gamma)
alpha = float(alpha) alpha = float(alpha)
@ -37,8 +37,10 @@ def focal_loss(gamma=2., alpha=4.):
fl = tf.multiply(alpha, tf.multiply(weight, ce)) fl = tf.multiply(alpha, tf.multiply(weight, ce))
reduced_fl = tf.reduce_max(fl, axis=1) reduced_fl = tf.reduce_max(fl, axis=1)
return tf.reduce_mean(reduced_fl) return tf.reduce_mean(reduced_fl)
return focal_loss_fixed return focal_loss_fixed
def weighted_categorical_crossentropy(weights=None): def weighted_categorical_crossentropy(weights=None):
""" weighted_categorical_crossentropy """ weighted_categorical_crossentropy
@ -50,117 +52,131 @@ def weighted_categorical_crossentropy(weights=None):
def loss(y_true, y_pred): def loss(y_true, y_pred):
labels_floats = tf.cast(y_true, tf.float32) labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred) per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred)
if weights is not None: if weights is not None:
weight_mask = tf.maximum(tf.reduce_max(tf.constant( weight_mask = tf.maximum(tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None]) np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0) * labels_floats, axis=-1), 1.0)
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None] per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss) return tf.reduce_mean(per_pixel_loss)
return loss return loss
def image_categorical_cross_entropy(y_true, y_pred, weights=None): 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_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. :param y_pred: tensor of shape (batch_size, height, width) representing the prediction.
:return: The mean cross-entropy on softmaxed tensors. :return: The mean cross-entropy on softmaxed tensors.
""" """
labels_floats = tf.cast(y_true, tf.float32) labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred) per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred)
if weights is not None: if weights is not None:
weight_mask = tf.maximum( weight_mask = tf.maximum(
tf.reduce_max(tf.constant( tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None]) np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0) * labels_floats, axis=-1), 1.0)
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None] per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss) return tf.reduce_mean(per_pixel_loss)
def class_tversky(y_true, y_pred): def class_tversky(y_true, y_pred):
smooth = 1.0#1.00 smooth = 1.0 # 1.00
y_true = K.permute_dimensions(y_true, (3,1,2,0)) y_true = K.permute_dimensions(y_true, (3, 1, 2, 0))
y_pred = K.permute_dimensions(y_pred, (3,1,2,0)) y_pred = K.permute_dimensions(y_pred, (3, 1, 2, 0))
y_true_pos = K.batch_flatten(y_true) y_true_pos = K.batch_flatten(y_true)
y_pred_pos = K.batch_flatten(y_pred) y_pred_pos = K.batch_flatten(y_pred)
true_pos = K.sum(y_true_pos * y_pred_pos, 1) true_pos = K.sum(y_true_pos * y_pred_pos, 1)
false_neg = K.sum(y_true_pos * (1-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) false_pos = K.sum((1 - y_true_pos) * y_pred_pos, 1)
alpha = 0.2#0.5 alpha = 0.2 # 0.5
beta=0.8 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): def focal_tversky_loss(y_true, y_pred):
pt_1 = class_tversky(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 gamma = 1.3 # 4./3.0#1.3#4.0/3.00# 0.75
return K.sum(K.pow((1-pt_1), gamma)) return K.sum(K.pow((1 - pt_1), gamma))
def generalized_dice_coeff2(y_true, y_pred): def generalized_dice_coeff2(y_true, y_pred):
n_el = 1 n_el = 1
for dim in y_true.shape: for dim in y_true.shape:
n_el *= int(dim) n_el *= int(dim)
n_cl = y_true.shape[-1] n_cl = y_true.shape[-1]
w = K.zeros(shape=(n_cl,)) 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) w = 1 / (w ** 2 + 0.000001)
numerator = y_true*y_pred numerator = y_true * y_pred
numerator = w*K.sum(numerator,(0,1,2)) numerator = w * K.sum(numerator, (0, 1, 2))
numerator = K.sum(numerator) numerator = K.sum(numerator)
denominator = y_true+y_pred denominator = y_true + y_pred
denominator = w*K.sum(denominator,(0,1,2)) denominator = w * K.sum(denominator, (0, 1, 2))
denominator = K.sum(denominator) denominator = K.sum(denominator)
return 2*numerator/denominator return 2 * numerator / denominator
def generalized_dice_coeff(y_true, y_pred): def generalized_dice_coeff(y_true, y_pred):
axes = tuple(range(1, len(y_pred.shape)-1)) axes = tuple(range(1, len(y_pred.shape) - 1))
Ncl = y_pred.shape[-1] Ncl = y_pred.shape[-1]
w = K.zeros(shape=(Ncl,)) w = K.zeros(shape=(Ncl,))
w = K.sum(y_true, axis=axes) w = K.sum(y_true, axis=axes)
w = 1/(w**2+0.000001) w = 1 / (w ** 2 + 0.000001)
# Compute gen dice coef: # Compute gen dice coef:
numerator = y_true*y_pred numerator = y_true * y_pred
numerator = w*K.sum(numerator,axes) numerator = w * K.sum(numerator, axes)
numerator = K.sum(numerator) numerator = K.sum(numerator)
denominator = y_true+y_pred denominator = y_true + y_pred
denominator = w*K.sum(denominator,axes) denominator = w * K.sum(denominator, axes)
denominator = K.sum(denominator) denominator = K.sum(denominator)
gen_dice_coef = 2*numerator/denominator gen_dice_coef = 2 * numerator / denominator
return gen_dice_coef return gen_dice_coef
def generalized_dice_loss(y_true, y_pred): def generalized_dice_loss(y_true, y_pred):
return 1 - generalized_dice_coeff2(y_true, y_pred) return 1 - generalized_dice_coeff2(y_true, y_pred)
def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
'''
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. Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.
Assumes the `channels_last` format. Assumes the `channels_last` format.
# Arguments # Arguments
y_true: b x X x Y( x Z...) x c One hot encoding of ground truth 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) 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 epsilon: Used for numerical stability to avoid divide by zero errors
# References # References
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://arxiv.org/abs/1606.04797 https://arxiv.org/abs/1606.04797
More details on Dice loss formulation More details on Dice loss formulation
https://mediatum.ub.tum.de/doc/1395260/1395260.pdf (page 72) https://mediatum.ub.tum.de/doc/1395260/1395260.pdf (page 72)
Adapted from https://github.com/Lasagne/Recipes/issues/99#issuecomment-347775022 Adapted from https://github.com/Lasagne/Recipes/issues/99#issuecomment-347775022
''' """
# skip the batch and class axis for calculating Dice score # skip the batch and class axis for calculating Dice score
axes = tuple(range(1, len(y_pred.shape)-1)) axes = tuple(range(1, len(y_pred.shape) - 1))
numerator = 2. * K.sum(y_pred * y_true, axes) numerator = 2. * K.sum(y_pred * y_true, axes)
denominator = K.sum(K.square(y_pred) + K.square(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 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. Compute mean metrics of two segmentation masks, via Keras.
@ -193,13 +209,13 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
H = height, H = height,
N = number of classes N = number of classes
""" """
flag_soft = (metric_type == 'soft') flag_soft = (metric_type == 'soft')
flag_naive_mean = (metric_type == 'naive') flag_naive_mean = (metric_type == 'naive')
# always assume one or more classes # always assume one or more classes
num_classes = K.shape(y_true)[-1] num_classes = K.shape(y_true)[-1]
if not flag_soft: if not flag_soft:
# get one-hot encoded masks from y_pred (true masks should already be one-hot) # 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_pred = K.one_hot(K.argmax(y_pred), num_classes)
@ -211,29 +227,29 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
y_pred = K.cast(y_pred, 'float32') y_pred = K.cast(y_pred, 'float32')
# intersection and union shapes are batch_size * n_classes (values = area in pixels) # intersection and union shapes are batch_size * n_classes (values = area in pixels)
axes = (1,2) # W,H axes of each image axes = (1, 2) # W,H axes of each image
intersection = K.sum(K.abs(y_true * y_pred), axis=axes) 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) 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 union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
smooth = .001 smooth = .001
iou = (intersection + smooth) / (union + smooth) iou = (intersection + smooth) / (union + smooth)
dice = 2 * (intersection + smooth)/(mask_sum + smooth) dice = 2 * (intersection + smooth) / (mask_sum + smooth)
metric = {'iou': iou, 'dice': dice}[metric_name] 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 # 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') mask = K.cast(K.not_equal(union, 0), 'float32')
if drop_last: if drop_last:
metric = metric[:,:-1] metric = metric[:, :-1]
mask = mask[:,:-1] mask = mask[:, :-1]
if verbose: if verbose:
print('intersection, union') print('intersection, union')
print(K.eval(intersection), K.eval(union)) print(K.eval(intersection), K.eval(union))
print(K.eval(intersection/union)) print(K.eval(intersection / union))
# return mean metrics: remaining axes are (batch, classes) # return mean metrics: remaining axes are (batch, classes)
if flag_naive_mean: if flag_naive_mean:
return K.mean(metric) return K.mean(metric)
@ -243,13 +259,14 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
non_zero = tf.greater(class_count, 0) non_zero = tf.greater(class_count, 0)
non_zero_sum = tf.boolean_mask(K.sum(metric * mask, axis=0), non_zero) non_zero_sum = tf.boolean_mask(K.sum(metric * mask, axis=0), non_zero)
non_zero_count = tf.boolean_mask(class_count, non_zero) non_zero_count = tf.boolean_mask(class_count, non_zero)
if verbose: if verbose:
print('Counts of inputs with class present, metrics for non-absent classes') 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)) print(K.eval(class_count), K.eval(non_zero_sum / non_zero_count))
return K.mean(non_zero_sum / non_zero_count) return K.mean(non_zero_sum / non_zero_count)
def mean_iou(y_true, y_pred, **kwargs): def mean_iou(y_true, y_pred, **kwargs):
""" """
Compute mean Intersection over Union of two segmentation masks, via Keras. Compute mean Intersection over Union of two segmentation masks, via Keras.
@ -257,65 +274,69 @@ def mean_iou(y_true, y_pred, **kwargs):
Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed 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) return seg_metrics(y_true, y_pred, metric_name='iou', **kwargs)
def Mean_IOU(y_true, y_pred): def Mean_IOU(y_true, y_pred):
nb_classes = K.int_shape(y_pred)[-1] nb_classes = K.int_shape(y_pred)[-1]
iou = [] iou = []
true_pixels = K.argmax(y_true, axis=-1) true_pixels = K.argmax(y_true, axis=-1)
pred_pixels = K.argmax(y_pred, axis=-1) pred_pixels = K.argmax(y_pred, axis=-1)
void_labels = K.equal(K.sum(y_true, axis=-1), 0) 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) for i in range(0, nb_classes): # exclude first label (background) and last label (void)
true_labels = K.equal(true_pixels, i)# & ~void_labels true_labels = K.equal(true_pixels, i) # & ~void_labels
pred_labels = K.equal(pred_pixels, i)# & ~void_labels pred_labels = K.equal(pred_pixels, i) # & ~void_labels
inter = tf.to_int32(true_labels & pred_labels) inter = tf.to_int32(true_labels & pred_labels)
union = 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 legal_batches = K.sum(tf.to_int32(true_labels), axis=1) > 0
ious = K.sum(inter, axis=1)/K.sum(union, axis=1) 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.append(K.mean(tf.gather(ious, indices=tf.where(legal_batches)))) # returns average IoU of the same objects
iou = tf.stack(iou) iou = tf.stack(iou)
legal_labels = ~tf.debugging.is_nan(iou) legal_labels = ~tf.debugging.is_nan(iou)
iou = tf.gather(iou, indices=tf.where(legal_labels)) iou = tf.gather(iou, indices=tf.where(legal_labels))
return K.mean(iou) return K.mean(iou)
def iou_vahid(y_true, y_pred): def iou_vahid(y_true, y_pred):
nb_classes = tf.shape(y_true)[-1]+tf.to_int32(1) nb_classes = tf.shape(y_true)[-1] + tf.to_int32(1)
true_pixels = K.argmax(y_true, axis=-1) true_pixels = K.argmax(y_true, axis=-1)
pred_pixels = K.argmax(y_pred, axis=-1) pred_pixels = K.argmax(y_pred, axis=-1)
iou = [] iou = []
for i in tf.range(nb_classes): for i in tf.range(nb_classes):
tp=K.sum( tf.to_int32( K.equal(true_pixels, i) & K.equal(pred_pixels, i) ) ) 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) ) ) 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) ) ) fn = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.not_equal(pred_pixels, i)))
iouh=tp/(tp+fp+fn) iouh = tp / (tp + fp + fn)
iou.append(iouh) iou.append(iouh)
return K.mean(iou) return K.mean(iou)
def IoU_metric(Yi,y_predi): def IoU_metric(Yi, y_predi):
## mean Intersection over Union # mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP) # Mean IoU = TP/(FN + TP + FP)
y_predi = np.argmax(y_predi, axis=3) y_predi = np.argmax(y_predi, axis=3)
y_testi = np.argmax(Yi, axis=3) y_testi = np.argmax(Yi, axis=3)
IoUs = [] IoUs = []
Nclass = int(np.max(Yi)) + 1 Nclass = int(np.max(Yi)) + 1
for c in range(Nclass): for c in range(Nclass):
TP = np.sum( (Yi == c)&(y_predi==c) ) TP = np.sum((Yi == c) & (y_predi == c))
FP = np.sum( (Yi != c)&(y_predi==c) ) FP = np.sum((Yi != c) & (y_predi == c))
FN = np.sum( (Yi == c)&(y_predi != c)) FN = np.sum((Yi == c) & (y_predi != c))
IoU = TP/float(TP + FP + FN) IoU = TP / float(TP + FP + FN)
IoUs.append(IoU) IoUs.append(IoU)
return K.cast( np.mean(IoUs) ,dtype='float32' ) return K.cast(np.mean(IoUs), dtype='float32')
def IoU_metric_keras(y_true, y_pred): def IoU_metric_keras(y_true, y_pred):
## mean Intersection over Union # mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP) # Mean IoU = TP/(FN + TP + FP)
init = tf.global_variables_initializer() init = tf.global_variables_initializer()
sess = tf.Session() sess = tf.Session()
sess.run(init) sess.run(init)
return IoU_metric(y_true.eval(session=sess), y_pred.eval(session=sess)) return IoU_metric(y_true.eval(session=sess), y_pred.eval(session=sess))
def jaccard_distance_loss(y_true, y_pred, smooth=100): def jaccard_distance_loss(y_true, y_pred, smooth=100):
""" """
Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|) 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) sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
jac = (intersection + smooth) / (sum_ - intersection + smooth) jac = (intersection + smooth) / (sum_ - intersection + smooth)
return (1 - jac) * smooth return (1 - jac) * smooth

237
models.py
Unescape Escape View File

@ -3,19 +3,20 @@ from tensorflow.keras.layers import *
from tensorflow.keras import layers from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2 from tensorflow.keras.regularizers import l2
resnet50_Weights_path='./pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5' resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
IMAGE_ORDERING ='channels_last' IMAGE_ORDERING = 'channels_last'
MERGE_AXIS=-1 MERGE_AXIS = -1
def one_side_pad( x ): def one_side_pad(x):
x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x) x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
if IMAGE_ORDERING == 'channels_first': if IMAGE_ORDERING == 'channels_first':
x = Lambda(lambda x : x[: , : , :-1 , :-1 ] )(x) x = Lambda(lambda x: x[:, :, :-1, :-1])(x)
elif IMAGE_ORDERING == 'channels_last': elif IMAGE_ORDERING == 'channels_last':
x = Lambda(lambda x : x[: , :-1 , :-1 , : ] )(x) x = Lambda(lambda x: x[:, :-1, :-1, :])(x)
return x return x
def identity_block(input_tensor, kernel_size, filters, stage, block): def identity_block(input_tensor, kernel_size, filters, stage, block):
"""The identity block is the block that has no conv layer at shortcut. """The identity block is the block that has no conv layer at shortcut.
# Arguments # Arguments
@ -28,7 +29,7 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
Output tensor for the block. Output tensor for the block.
""" """
filters1, filters2, filters3 = filters filters1, filters2, filters3 = filters
if IMAGE_ORDERING == 'channels_last': if IMAGE_ORDERING == 'channels_last':
bn_axis = 3 bn_axis = 3
else: else:
@ -37,16 +38,16 @@ def identity_block(input_tensor, kernel_size, filters, stage, block):
conv_name_base = 'res' + str(stage) + block + '_branch' conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + 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 = 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 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x) x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING , x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING,
padding='same', name=conv_name_base + '2b')(x) padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x) x = Activation('relu')(x)
x = Conv2D(filters3 , (1, 1), data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(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 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
x = layers.add([x, input_tensor]) x = layers.add([x, input_tensor])
@ -68,7 +69,7 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
And the shortcut should have strides=(2,2) as well And the shortcut should have strides=(2,2) as well
""" """
filters1, filters2, filters3 = filters filters1, filters2, filters3 = filters
if IMAGE_ORDERING == 'channels_last': if IMAGE_ORDERING == 'channels_last':
bn_axis = 3 bn_axis = 3
else: else:
@ -77,20 +78,20 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
conv_name_base = 'res' + str(stage) + block + '_branch' conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch'
x = Conv2D(filters1, (1, 1) , data_format=IMAGE_ORDERING , strides=strides, x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
name=conv_name_base + '2a')(input_tensor) name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x) x = Activation('relu')(x)
x = Conv2D(filters2, kernel_size , data_format=IMAGE_ORDERING , padding='same', x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING, padding='same',
name=conv_name_base + '2b')(x) name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x) x = Activation('relu')(x)
x = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , name=conv_name_base + '2c')(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 = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
shortcut = Conv2D(filters3, (1, 1) , data_format=IMAGE_ORDERING , strides=strides, shortcut = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
name=conv_name_base + '1')(input_tensor) name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
@ -99,12 +100,11 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
return x 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, weight_decay=1e-6, pretraining=False):
assert input_height%32 == 0 assert input_height % 32 == 0
assert input_width%32 == 0 assert input_width % 32 == 0
img_input = Input(shape=(input_height, input_width, 3))
img_input = Input(shape=(input_height,input_width , 3 ))
if IMAGE_ORDERING == 'channels_last': if IMAGE_ORDERING == 'channels_last':
bn_axis = 3 bn_axis = 3
@ -112,25 +112,24 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
bn_axis = 1 bn_axis = 1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input) 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 f1 = x
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x) x = Activation('relu')(x)
x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(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 = 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='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
f2 = one_side_pad(x ) f2 = one_side_pad(x)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') 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='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
f3 = x f3 = x
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') 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='b')
@ -138,85 +137,72 @@ def resnet50_unet_light(n_classes,input_height=224,input_width=224,weight_decay=
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') 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='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
f4 = x f4 = x
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') 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='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
f5 = x f5 = x
if pretraining: if pretraining:
model=Model( img_input , x ).load_weights(resnet50_Weights_path) 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 = 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 = ( BatchNormalization(axis=bn_axis))(v512_2048)
v512_2048 = Activation('relu')(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 = 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 = (BatchNormalization(axis=bn_axis))(v512_1024)
v512_1024 = Activation('relu')(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 = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v512_2048)
o = ( concatenate([ o ,f3],axis=MERGE_AXIS ) ) o = (concatenate([o, v512_1024], axis=MERGE_AXIS))
o = ( ZeroPadding2D( (1,1), data_format=IMAGE_ORDERING))(o) 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 = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = ( BatchNormalization(axis=bn_axis))(o) o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o) o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o) o = (concatenate([o, f3], axis=MERGE_AXIS))
o = ( concatenate([o,f2],axis=MERGE_AXIS ) ) o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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 = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay) ) )(o) o = (BatchNormalization(axis=bn_axis))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o) o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = (concatenate([o, f2], axis=MERGE_AXIS))
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o) o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,f1],axis=MERGE_AXIS ) ) o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o) o = (BatchNormalization(axis=bn_axis))(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 = 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 = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,img_input],axis=MERGE_AXIS ) ) o = (concatenate([o, img_input], axis=MERGE_AXIS))
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o) 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 = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = ( BatchNormalization(axis=bn_axis))(o) o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(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 = 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) o = (Activation('softmax'))(o)
model = Model(img_input, o)
model = Model( img_input , o )
return model 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
def resnet50_unet(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
img_input = Input(shape=(input_height,input_width , 3 )) assert input_height % 32 == 0
assert input_width % 32 == 0
img_input = Input(shape=(input_height, input_width, 3))
if IMAGE_ORDERING == 'channels_last': if IMAGE_ORDERING == 'channels_last':
bn_axis = 3 bn_axis = 3
@ -224,25 +210,24 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
bn_axis = 1 bn_axis = 1
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input) 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 f1 = x
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x) x = Activation('relu')(x)
x = MaxPooling2D((3, 3) , data_format=IMAGE_ORDERING , strides=(2, 2))(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 = 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='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
f2 = one_side_pad(x ) f2 = one_side_pad(x)
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a') 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='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
f3 = x f3 = x
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a') 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='b')
@ -250,68 +235,60 @@ def resnet50_unet(n_classes,input_height=224,input_width=224,weight_decay=1e-6,p
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d') 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='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f') x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
f4 = x f4 = x
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a') 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='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c') x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
f5 = x f5 = x
if pretraining: if pretraining:
Model( img_input , x ).load_weights(resnet50_Weights_path) 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))(
v1024_2048 = ( BatchNormalization(axis=bn_axis))(v1024_2048) f5)
v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
v1024_2048 = Activation('relu')(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))(v1024_2048)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o) o = (concatenate([o, f4], axis=MERGE_AXIS))
o = ( concatenate([ o ,f3],axis=MERGE_AXIS ) ) o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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 = ( Conv2D( 256, (3, 3), padding='valid', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay)))(o) o = (BatchNormalization(axis=bn_axis))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o) o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o) o = (concatenate([o, f3], axis=MERGE_AXIS))
o = ( concatenate([o,f2],axis=MERGE_AXIS ) ) o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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 = ( Conv2D( 128 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay) ) )(o) o = (BatchNormalization(axis=bn_axis))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o) o = Activation('relu')(o)
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = ( UpSampling2D( (2,2), data_format=IMAGE_ORDERING))(o) o = (concatenate([o, f2], axis=MERGE_AXIS))
o = ( concatenate([o,f1],axis=MERGE_AXIS ) ) o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
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 = ( Conv2D( 64 , (3, 3), padding='valid' , data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay) ))(o) o = (BatchNormalization(axis=bn_axis))(o)
o = ( BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(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 = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
o = ( concatenate([o,img_input],axis=MERGE_AXIS ) ) o = (concatenate([o, img_input], axis=MERGE_AXIS))
o = ( ZeroPadding2D((1,1) , data_format=IMAGE_ORDERING ))(o) 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 = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
o = ( BatchNormalization(axis=bn_axis))(o) o = (BatchNormalization(axis=bn_axis))(o)
o = Activation('relu')(o) o = Activation('relu')(o)
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(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 = ( BatchNormalization(axis=bn_axis))(o)
o = (Activation('softmax'))(o) o = (Activation('softmax'))(o)
model = Model( img_input , o )
model = Model(img_input, o)
return model return model

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

274
train.py
Unescape Escape View File

@ -11,12 +11,14 @@ from metrics import *
from tensorflow.keras.models import load_model from tensorflow.keras.models import load_model
from tqdm import tqdm from tqdm import tqdm
def configuration(): def configuration():
config = tf.compat.v1.ConfigProto() config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config) session = tf.compat.v1.Session(config=config)
set_session(session) set_session(session)
def get_dirs_or_files(input_data): def get_dirs_or_files(input_data):
if os.path.isdir(input_data): if os.path.isdir(input_data):
image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/') image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/')
@ -25,205 +27,187 @@ def get_dirs_or_files(input_data):
assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input) assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input)
return image_input, labels_input return image_input, labels_input
ex = Experiment() ex = Experiment()
@ex.config @ex.config
def config_params(): def config_params():
n_classes=None # Number of classes. In the case of binary classification this should be 2. n_classes = None # Number of classes. In the case of binary classification this should be 2.
n_epochs=1 # Number of epochs. n_epochs = 1 # Number of epochs.
input_height=224*1 # Height of model's input in pixels. input_height = 224 * 1 # Height of model's input in pixels.
input_width=224*1 # Width of model's input in pixels. input_width = 224 * 1 # Width of model's input in pixels.
weight_decay=1e-6 # Weight decay of l2 regularization of model layers. weight_decay = 1e-6 # Weight decay of l2 regularization of model layers.
n_batch=1 # Number of batches at each iteration. n_batch = 1 # Number of batches at each iteration.
learning_rate=1e-4 # Set the learning rate. learning_rate = 1e-4 # Set the learning rate.
patches=False # Divides input image into smaller patches (input size of the model) when set to true. For the model to see the full image, like page extraction, set this to false. patches = False # Divides input image into smaller patches (input size of the model) when set to true. For the model to see the full image, like page extraction, set this to false.
augmentation=False # To apply any kind of augmentation, this parameter must be set to true. augmentation = False # To apply any kind of augmentation, this parameter must be set to true.
flip_aug=False # If true, different types of flipping will be applied to the image. Types of flips are defined with "flip_index" in train.py. flip_aug = False # If true, different types of flipping will be applied to the image. Types of flips are defined with "flip_index" in train.py.
blur_aug=False # If true, different types of blurring will be applied to the image. Types of blur are defined with "blur_k" in train.py. blur_aug = False # If true, different types of blurring will be applied to the image. Types of blur are defined with "blur_k" in train.py.
scaling=False # If true, scaling will be applied to the image. The amount of scaling is defined with "scales" in train.py. scaling = False # If true, scaling will be applied to the image. The amount of scaling is defined with "scales" in train.py.
binarization=False # If true, Otsu thresholding will be applied to augment the input with binarized images. binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
dir_train=None # Directory of training dataset with subdirectories having the names "images" and "labels". dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels".
dir_eval=None # Directory of validation dataset with subdirectories having the names "images" and "labels". dir_eval = None # Directory of validation dataset with subdirectories having the names "images" and "labels".
dir_output=None # Directory where the output model will be saved. dir_output = None # Directory where the output model will be saved.
pretraining=False # Set to true to load pretrained weights of ResNet50 encoder. pretraining = False # Set to true to load pretrained weights of ResNet50 encoder.
scaling_bluring=False # If true, a combination of scaling and blurring will be applied to the image. scaling_bluring = False # If true, a combination of scaling and blurring will be applied to the image.
scaling_binarization=False # If true, a combination of scaling and binarization will be applied to the image. scaling_binarization = False # If true, a combination of scaling and binarization will be applied to the image.
scaling_flip=False # If true, a combination of scaling and flipping will be applied to the image. scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image.
thetha=[10,-10] # Rotate image by these angles for augmentation. thetha = [10, -10] # Rotate image by these angles for augmentation.
blur_k=['blur','gauss','median'] # Blur image for augmentation. blur_k = ['blur', 'gauss', 'median'] # Blur image for augmentation.
scales=[0.5,2] # Scale patches for augmentation. scales = [0.5, 2] # Scale patches for augmentation.
flip_index=[0,1,-1] # Flip image for augmentation. flip_index = [0, 1, -1] # Flip image for augmentation.
continue_training = False # Set to true if you would like to continue training an already trained a model. continue_training = False # Set to true if you would like to continue training an already trained a model.
index_start = 0 # Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3. index_start = 0 # Index of model to continue training from. E.g. if you trained for 3 epochs and last index is 2, to continue from model_1.h5, set "index_start" to 3 to start naming model with index 3.
dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model. dir_of_start_model = '' # Directory containing pretrained encoder to continue training the model.
is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false. is_loss_soft_dice = False # Use soft dice as loss function. When set to true, "weighted_loss" must be false.
weighted_loss = False # Use weighted categorical cross entropy as loss fucntion. When set to true, "is_loss_soft_dice" must be false. 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". 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 @ex.automain
def run(n_classes,n_epochs,input_height, def run(n_classes, n_epochs, input_height,
input_width,weight_decay,weighted_loss, input_width, weight_decay, weighted_loss,
index_start,dir_of_start_model,is_loss_soft_dice, index_start, dir_of_start_model, is_loss_soft_dice,
n_batch,patches,augmentation,flip_aug n_batch, patches, augmentation, flip_aug,
,blur_aug,scaling, binarization, blur_aug, scaling, binarization,
blur_k,scales,dir_train,data_is_provided, blur_k, scales, dir_train, data_is_provided,
scaling_bluring,scaling_binarization,rotation, scaling_bluring, scaling_binarization, rotation,
rotation_not_90,thetha,scaling_flip,continue_training, rotation_not_90, thetha, scaling_flip, continue_training,
flip_index,dir_eval ,dir_output,pretraining,learning_rate): flip_index, dir_eval, dir_output, pretraining, learning_rate):
if data_is_provided: if data_is_provided:
dir_train_flowing=os.path.join(dir_output,'train') dir_train_flowing = os.path.join(dir_output, 'train')
dir_eval_flowing=os.path.join(dir_output,'eval') dir_eval_flowing = os.path.join(dir_output, 'eval')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images') dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels') 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_imgs = os.path.join(dir_eval_flowing, 'images')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels') dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels')
configuration() configuration()
else: else:
dir_img,dir_seg=get_dirs_or_files(dir_train) dir_img, dir_seg = get_dirs_or_files(dir_train)
dir_img_val,dir_seg_val=get_dirs_or_files(dir_eval) 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. # 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_train_flowing = os.path.join(dir_output, 'train')
dir_eval_flowing=os.path.join(dir_output,'eval') dir_eval_flowing = os.path.join(dir_output, 'eval')
dir_flow_train_imgs=os.path.join(dir_train_flowing,'images/') dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images/')
dir_flow_train_labels=os.path.join(dir_train_flowing,'labels/') 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_imgs = os.path.join(dir_eval_flowing, 'images/')
dir_flow_eval_labels=os.path.join(dir_eval_flowing,'labels/') dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels/')
if os.path.isdir(dir_train_flowing): if os.path.isdir(dir_train_flowing):
os.system('rm -rf '+dir_train_flowing) os.system('rm -rf ' + dir_train_flowing)
os.makedirs(dir_train_flowing) os.makedirs(dir_train_flowing)
else: else:
os.makedirs(dir_train_flowing) os.makedirs(dir_train_flowing)
if os.path.isdir(dir_eval_flowing): if os.path.isdir(dir_eval_flowing):
os.system('rm -rf '+dir_eval_flowing) os.system('rm -rf ' + dir_eval_flowing)
os.makedirs(dir_eval_flowing) os.makedirs(dir_eval_flowing)
else: else:
os.makedirs(dir_eval_flowing) os.makedirs(dir_eval_flowing)
os.mkdir(dir_flow_train_imgs) os.mkdir(dir_flow_train_imgs)
os.mkdir(dir_flow_train_labels) os.mkdir(dir_flow_train_labels)
os.mkdir(dir_flow_eval_imgs) os.mkdir(dir_flow_eval_imgs)
os.mkdir(dir_flow_eval_labels) os.mkdir(dir_flow_eval_labels)
#set the gpu configuration
configuration()
# set the gpu configuration
configuration()
#writing patches into a sub-folder in order to be flowed from directory. # writing patches into a sub-folder in order to be flowed from directory.
provide_patches(dir_img,dir_seg,dir_flow_train_imgs, provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels, dir_flow_train_labels,
input_height,input_width,blur_k,blur_aug, input_height, input_width, blur_k, blur_aug,
flip_aug,binarization,scaling,scales,flip_index, flip_aug, binarization, scaling, scales, flip_index,
scaling_bluring,scaling_binarization,rotation, scaling_bluring, scaling_binarization, rotation,
rotation_not_90,thetha,scaling_flip, rotation_not_90, thetha, scaling_flip,
augmentation=augmentation,patches=patches) augmentation=augmentation, patches=patches)
provide_patches(dir_img_val,dir_seg_val,dir_flow_eval_imgs, provide_patches(dir_img_val, dir_seg_val, dir_flow_eval_imgs,
dir_flow_eval_labels, dir_flow_eval_labels,
input_height,input_width,blur_k,blur_aug, input_height, input_width, blur_k, blur_aug,
flip_aug,binarization,scaling,scales,flip_index, flip_aug, binarization, scaling, scales, flip_index,
scaling_bluring,scaling_binarization,rotation, scaling_bluring, scaling_binarization, rotation,
rotation_not_90,thetha,scaling_flip, rotation_not_90, thetha, scaling_flip,
augmentation=False,patches=patches) augmentation=False, patches=patches)
if weighted_loss: if weighted_loss:
weights=np.zeros(n_classes) weights = np.zeros(n_classes)
if data_is_provided: if data_is_provided:
for obj in os.listdir(dir_flow_train_labels): for obj in os.listdir(dir_flow_train_labels):
try: try:
label_obj=cv2.imread(dir_flow_train_labels+'/'+obj) 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) 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) weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0)
except: except:
pass pass
else: else:
for obj in os.listdir(dir_seg): for obj in os.listdir(dir_seg):
try: try:
label_obj=cv2.imread(dir_seg+'/'+obj) 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) 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) weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0)
except: except:
pass pass
weights = 1.00 / weights
weights=1.00/weights
weights = weights / float(np.sum(weights))
weights=weights/float(np.sum(weights)) weights = weights / float(np.min(weights))
weights=weights/float(np.min(weights)) weights = weights / float(np.sum(weights))
weights=weights/float(np.sum(weights))
if continue_training: if continue_training:
if is_loss_soft_dice: if is_loss_soft_dice:
model = load_model (dir_of_start_model, compile = True, custom_objects={'soft_dice_loss': soft_dice_loss}) model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss})
if weighted_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: if not is_loss_soft_dice and not weighted_loss:
model = load_model (dir_of_start_model, compile = True) model = load_model(dir_of_start_model, compile=True)
else: else:
#get our model. # get our model.
index_start = 0 index_start = 0
model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining) model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining)
#if you want to see the model structure just uncomment model summary. # if you want to see the model structure just uncomment model summary.
#model.summary() # model.summary()
if not is_loss_soft_dice and not weighted_loss: if not is_loss_soft_dice and not weighted_loss:
model.compile(loss='categorical_crossentropy', model.compile(loss='categorical_crossentropy',
optimizer = Adam(lr=learning_rate),metrics=['accuracy']) optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
if is_loss_soft_dice: if is_loss_soft_dice:
model.compile(loss=soft_dice_loss, model.compile(loss=soft_dice_loss,
optimizer = Adam(lr=learning_rate),metrics=['accuracy']) optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
if weighted_loss: if weighted_loss:
model.compile(loss=weighted_categorical_crossentropy(weights), model.compile(loss=weighted_categorical_crossentropy(weights),
optimizer = Adam(lr=learning_rate),metrics=['accuracy']) optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
#generating train and evaluation data # generating train and evaluation data
train_gen = data_gen(dir_flow_train_imgs,dir_flow_train_labels, batch_size = n_batch, 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)
val_gen = data_gen(dir_flow_eval_imgs,dir_flow_eval_labels, batch_size = n_batch, 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)
for i in tqdm(range(index_start, n_epochs+index_start)): for i in tqdm(range(index_start, n_epochs + index_start)):
model.fit_generator( model.fit_generator(
train_gen, train_gen,
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs))/n_batch)-1, steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
validation_data=val_gen, validation_data=val_gen,
validation_steps=1, validation_steps=1,
epochs=1) epochs=1)
model.save(dir_output+'/'+'model_'+str(i)) 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')
# os.system('rm -rf '+dir_train_flowing)
# os.system('rm -rf '+dir_eval_flowing)
# model.save(dir_output+'/'+'model'+'.h5')

757
utils.py
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@ -10,18 +10,17 @@ import imutils
import math import math
def bluring(img_in, kind):
def bluring(img_in,kind): if kind == 'gauss':
if kind=='guass': img_blur = cv2.GaussianBlur(img_in, (5, 5), 0)
img_blur = cv2.GaussianBlur(img_in,(5,5),0) elif kind == "median":
elif kind=="median": img_blur = cv2.medianBlur(img_in, 5)
img_blur = cv2.medianBlur(img_in,5) elif kind == 'blur':
elif kind=='blur': img_blur = cv2.blur(img_in, (5, 5))
img_blur=cv2.blur(img_in,(5,5))
return img_blur 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]_. """Elastic deformation of images as described in [Simard2003]_.
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in Convolutional Neural Networks applied to Visual Document Analysis", in
@ -37,461 +36,459 @@ def elastic_transform(image, alpha, sigma,seedj, random_state=None):
dz = np.zeros_like(dx) dz = np.zeros_like(dx)
x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2])) 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)) 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') distored_image = map_coordinates(image, indices, order=1, mode='reflect')
return distored_image.reshape(image.shape) return distored_image.reshape(image.shape)
def rotation_90(img): def rotation_90(img):
img_rot=np.zeros((img.shape[1],img.shape[0],img.shape[2])) img_rot = np.zeros((img.shape[1], img.shape[0], img.shape[2]))
img_rot[:,:,0]=img[:,:,0].T img_rot[:, :, 0] = img[:, :, 0].T
img_rot[:,:,1]=img[:,:,1].T img_rot[:, :, 1] = img[:, :, 1].T
img_rot[:,:,2]=img[:,:,2].T img_rot[:, :, 2] = img[:, :, 2].T
return img_rot return img_rot
def rotatedRectWithMaxArea(w, h, angle): def rotatedRectWithMaxArea(w, h, angle):
""" """
Given a rectangle of size wxh that has been rotated by 'angle' (in Given a rectangle of size wxh that has been rotated by 'angle' (in
radians), computes the width and height of the largest possible radians), computes the width and height of the largest possible
axis-aligned rectangle (maximal area) within the rotated rectangle. axis-aligned rectangle (maximal area) within the rotated rectangle.
""" """
if w <= 0 or h <= 0: if w <= 0 or h <= 0:
return 0,0 return 0, 0
width_is_longer = w >= h width_is_longer = w >= h
side_long, side_short = (w,h) if width_is_longer else (h,w) 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, # 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: # 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)) 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: 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, # half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line # the other two corners are on the mid-line parallel to the longer line
x = 0.5*side_short x = 0.5 * side_short
wr,hr = (x/sin_a,x/cos_a) if width_is_longer else (x/cos_a,x/sin_a) wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
else: else:
# fully constrained case: crop touches all 4 sides # fully constrained case: crop touches all 4 sides
cos_2a = cos_a*cos_a - sin_a*sin_a 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 wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
return wr,hr return wr, hr
def rotate_max_area(image,rotated, rotated_label,angle):
def rotate_max_area(image, rotated, rotated_label, angle):
""" image: cv2 image matrix object """ image: cv2 image matrix object
angle: in degree angle: in degree
""" """
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0],
math.radians(angle)) math.radians(angle))
h, w, _ = rotated.shape h, w, _ = rotated.shape
y1 = h//2 - int(hr/2) y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr) y2 = y1 + int(hr)
x1 = w//2 - int(wr/2) x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr) x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2],rotated_label[y1:y2, x1:x2] 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) def rotation_not_90_func(img, label, thetha):
return rotate_max_area(img, rotated,rotated_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): def color_images(seg, n_classes):
ann_u=range(n_classes) ann_u = range(n_classes)
if len(np.shape(seg))==3: if len(np.shape(seg)) == 3:
seg=seg[:,:,0] seg = seg[:, :, 0]
seg_img=np.zeros((np.shape(seg)[0],np.shape(seg)[1],3)).astype(float) seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
colors=sns.color_palette("hls", n_classes) colors = sns.color_palette("hls", n_classes)
for c in ann_u: for c in ann_u:
c=int(c) c = int(c)
segl=(seg==c) segl = (seg == c)
seg_img[:,:,0]+=segl*(colors[c][0]) seg_img[:, :, 0] += segl * (colors[c][0])
seg_img[:,:,1]+=segl*(colors[c][1]) seg_img[:, :, 1] += segl * (colors[c][1])
seg_img[:,:,2]+=segl*(colors[c][2]) seg_img[:, :, 2] += segl * (colors[c][2])
return seg_img return seg_img
def resize_image(seg_in,input_height,input_width): def resize_image(seg_in, input_height, input_width):
return cv2.resize(seg_in,(input_width,input_height),interpolation=cv2.INTER_NEAREST) 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)) 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): for j in range(n_classes):
seg_f[:,:,j]=(seg==j).astype(int) seg_f[:, :, j] = (seg == j).astype(int)
return seg_f return seg_f
def IoU(Yi,y_predi): def IoU(Yi, y_predi):
## mean Intersection over Union ## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP) ## Mean IoU = TP/(FN + TP + FP)
IoUs = [] IoUs = []
classes_true=np.unique(Yi) classes_true = np.unique(Yi)
for c in classes_true: for c in classes_true:
TP = np.sum( (Yi == c)&(y_predi==c) ) TP = np.sum((Yi == c) & (y_predi == c))
FP = np.sum( (Yi != c)&(y_predi==c) ) FP = np.sum((Yi != c) & (y_predi == c))
FN = np.sum( (Yi == c)&(y_predi != c)) FN = np.sum((Yi == c) & (y_predi != c))
IoU = TP/float(TP + FP + FN) 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)) 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) IoUs.append(IoU)
mIoU = np.mean(IoUs) mIoU = np.mean(IoUs)
print("_________________") print("_________________")
print("Mean IoU: {:4.3f}".format(mIoU)) print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU 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):
c = 0 c = 0
n = [f for f in os.listdir(img_folder) if not f.startswith('.')]# os.listdir(img_folder) #List of training images n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images
random.shuffle(n) random.shuffle(n)
while True: while True:
img = np.zeros((batch_size, input_height, input_width, 3)).astype('float') 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') 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. for i in range(c, c + batch_size): # initially from 0 to 16, c = 0.
#print(img_folder+'/'+n[i]) # print(img_folder+'/'+n[i])
try: try:
filename=n[i].split('.')[0] filename = n[i].split('.')[0]
train_img = cv2.imread(img_folder+'/'+n[i])/255. 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') img[i - c] = train_img # add to array - img[0], img[1], and so on.
#print(mask_folder+'/'+filename+'.png') train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
#print(train_mask.shape) # print(mask_folder+'/'+filename+'.png')
train_mask = get_one_hot( resize_image(train_mask,input_height,input_width),input_height,input_width,n_classes) # print(train_mask.shape)
#train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3] train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
n_classes)
mask[i-c] = train_mask # 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: except:
img[i-c] = np.ones((input_height, input_width, 3)).astype('float') 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') 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+=batch_size c = 0
if(c+batch_size>=len(os.listdir(img_folder))):
c=0
random.shuffle(n) random.shuffle(n)
yield img, mask yield img, mask
def otsu_copy(img): def otsu_copy(img):
img_r=np.zeros(img.shape) img_r = np.zeros(img.shape)
img1=img[:,:,0] img1 = img[:, :, 0]
img2=img[:,:,1] img2 = img[:, :, 1]
img3=img[:,:,2] img3 = img[:, :, 2]
_, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) _, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
_, threshold2 = cv2.threshold(img2, 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) _, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_r[:,:,0]=threshold1 img_r[:, :, 0] = threshold1
img_r[:,:,1]=threshold1 img_r[:, :, 1] = threshold1
img_r[:,:,2]=threshold1 img_r[:, :, 2] = threshold1
return img_r 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: def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer):
img,label=do_padding(img,label,height,width) 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] img_h = img.shape[0]
img_w = img.shape[1]
nxf=img_w/float(width)
nyf=img_h/float(height) nxf = img_w / float(width)
nyf = img_h / float(height)
if nxf>int(nxf):
nxf=int(nxf)+1 if nxf > int(nxf):
if nyf>int(nyf): nxf = int(nxf) + 1
nyf=int(nyf)+1 if nyf > int(nyf):
nyf = int(nyf) + 1
nxf=int(nxf)
nyf=int(nyf) nxf = int(nxf)
nyf = int(nyf)
for i in range(nxf): for i in range(nxf):
for j in range(nyf): for j in range(nyf):
index_x_d=i*width index_x_d = i * width
index_x_u=(i+1)*width index_x_u = (i + 1) * width
index_y_d=j*height index_y_d = j * height
index_y_u=(j+1)*height index_y_u = (j + 1) * height
if index_x_u>img_w: if index_x_u > img_w:
index_x_u=img_w index_x_u = img_w
index_x_d=img_w-width index_x_d = img_w - width
if index_y_u>img_h: if index_y_u > img_h:
index_y_u=img_h index_y_u = img_h
index_y_d=img_h-height index_y_d = img_h - height
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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, :]
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_img_f+'/img_'+str(indexer)+'.png', img_patch ) cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch ) indexer += 1
indexer+=1
return indexer return indexer
def do_padding(img,label,height,width):
def do_padding(img, label, height, width):
height_new=img.shape[0] height_new = img.shape[0]
width_new=img.shape[1] width_new = img.shape[1]
h_start=0 h_start = 0
w_start=0 w_start = 0
if img.shape[0]<height: if img.shape[0] < height:
h_start=int( abs(height-img.shape[0])/2. ) h_start = int(abs(height - img.shape[0]) / 2.)
height_new=height height_new = height
if img.shape[1]<width: if img.shape[1] < width:
w_start=int( abs(width-img.shape[1])/2. ) w_start = int(abs(width - img.shape[1]) / 2.)
width_new=width width_new = width
img_new=np.ones((height_new,width_new,img.shape[2])).astype(float)*255 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) 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[:,:,:]) 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[:,:,:]) label_new[h_start:h_start + label.shape[0], w_start:w_start + label.shape[1], :] = np.copy(label[:, :, :])
return img_new,label_new return img_new, label_new
def get_patches_num_scale(dir_img_f,dir_seg_f,img,label,height,width,indexer,n_patches,scaler): 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)
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]
img_h=img.shape[0]
img_w=img.shape[1] height_scale = int(height * scaler)
width_scale = int(width * scaler)
height_scale=int(height*scaler)
width_scale=int(width*scaler) nxf = img_w / float(width_scale)
nyf = img_h / float(height_scale)
nxf=img_w/float(width_scale) if nxf > int(nxf):
nyf=img_h/float(height_scale) nxf = int(nxf) + 1
if nyf > int(nyf):
if nxf>int(nxf): nyf = int(nyf) + 1
nxf=int(nxf)+1
if nyf>int(nyf): nxf = int(nxf)
nyf=int(nyf)+1 nyf = int(nyf)
nxf=int(nxf)
nyf=int(nyf)
for i in range(nxf): for i in range(nxf):
for j in range(nyf): for j in range(nyf):
index_x_d=i*width_scale index_x_d = i * width_scale
index_x_u=(i+1)*width_scale index_x_u = (i + 1) * width_scale
index_y_d=j*height_scale index_y_d = j * height_scale
index_y_u=(j+1)*height_scale index_y_u = (j + 1) * height_scale
if index_x_u>img_w: if index_x_u > img_w:
index_x_u=img_w index_x_u = img_w
index_x_d=img_w-width_scale index_x_d = img_w - width_scale
if index_y_u>img_h: if index_y_u > img_h:
index_y_u=img_h index_y_u = img_h
index_y_d=img_h-height_scale index_y_d = img_h - height_scale
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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, :]
label_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
img_patch = resize_image(img_patch, height, width)
img_patch=resize_image(img_patch,height,width) label_patch = resize_image(label_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_img_f+'/img_'+str(indexer)+'.png', img_patch ) cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch ) indexer += 1
indexer+=1
return indexer 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)) def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, indexer, scaler):
label=resize_image(label,int(label.shape[0]*scaler),int(label.shape[1]*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) 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] img_h = img.shape[0]
img_w = img.shape[1]
height_scale=int(height*1)
width_scale=int(width*1) height_scale = int(height * 1)
width_scale = int(width * 1)
nxf=img_w/float(width_scale) nxf = img_w / float(width_scale)
nyf=img_h/float(height_scale) nyf = img_h / float(height_scale)
if nxf>int(nxf): if nxf > int(nxf):
nxf=int(nxf)+1 nxf = int(nxf) + 1
if nyf>int(nyf): if nyf > int(nyf):
nyf=int(nyf)+1 nyf = int(nyf) + 1
nxf=int(nxf) nxf = int(nxf)
nyf=int(nyf) nyf = int(nyf)
for i in range(nxf): for i in range(nxf):
for j in range(nyf): for j in range(nyf):
index_x_d=i*width_scale index_x_d = i * width_scale
index_x_u=(i+1)*width_scale index_x_u = (i + 1) * width_scale
index_y_d=j*height_scale index_y_d = j * height_scale
index_y_u=(j+1)*height_scale index_y_u = (j + 1) * height_scale
if index_x_u>img_w: if index_x_u > img_w:
index_x_u=img_w index_x_u = img_w
index_x_d=img_w-width_scale index_x_d = img_w - width_scale
if index_y_u>img_h: if index_y_u > img_h:
index_y_u=img_h index_y_u = img_h
index_y_d=img_h-height_scale index_y_d = img_h - height_scale
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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, :]
label_patch=label[index_y_d:index_y_u,index_x_d:index_x_u,:]
# img_patch=resize_image(img_patch,height,width)
#img_patch=resize_image(img_patch,height,width) # label_patch=resize_image(label_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_img_f+'/img_'+str(indexer)+'.png', img_patch ) cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
cv2.imwrite(dir_seg_f+'/img_'+str(indexer)+'.png' , label_patch ) indexer += 1
indexer+=1
return indexer return indexer
def provide_patches(dir_img,dir_seg,dir_flow_train_imgs, def provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels, dir_flow_train_labels,
input_height,input_width,blur_k,blur_aug, input_height, input_width, blur_k, blur_aug,
flip_aug,binarization,scaling,scales,flip_index, flip_aug, binarization, scaling, scales, flip_index,
scaling_bluring,scaling_binarization,rotation, scaling_bluring, scaling_binarization, rotation,
rotation_not_90,thetha,scaling_flip, rotation_not_90, thetha, scaling_flip,
augmentation=False,patches=False): augmentation=False, patches=False):
imgs_cv_train = np.array(os.listdir(dir_img))
imgs_cv_train=np.array(os.listdir(dir_img)) segs_cv_train = np.array(os.listdir(dir_seg))
segs_cv_train=np.array(os.listdir(dir_seg))
indexer = 0
indexer=0 for im, seg_i in tqdm(zip(imgs_cv_train, segs_cv_train)):
for im, seg_i in tqdm(zip(imgs_cv_train,segs_cv_train)): img_name = im.split('.')[0]
img_name=im.split('.')[0]
if not patches: 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_imgs + '/img_' + str(indexer) + '.png',
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_img + '/' + im), input_height, input_width))
indexer+=1 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 augmentation:
if flip_aug: if flip_aug:
for f_i in flip_index: for f_i in flip_index:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png', 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) ) cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
indexer+=1 resize_image(cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
input_height, input_width))
if blur_aug: indexer += 1
if blur_aug:
for blur_i in blur_k: for blur_i in blur_k:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png', 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) ) cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
indexer+=1 resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height,
input_width))
indexer += 1
if binarization: if binarization:
cv2.imwrite(dir_flow_train_imgs+'/img_'+str(indexer)+'.png', cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
resize_image(otsu_copy( cv2.imread(dir_img+'/'+im)),input_height,input_width )) resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width))
cv2.imwrite(dir_flow_train_labels+'/img_'+str(indexer)+'.png', 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 indexer += 1
if patches: if patches:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels, 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_seg + '/' + img_name + '.png'),
input_height,input_width,indexer=indexer) input_height, input_width, indexer=indexer)
if augmentation: if augmentation:
if rotation: if rotation:
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
rotation_90(cv2.imread(dir_img + '/' + im)),
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels, rotation_90(cv2.imread(dir_seg + '/' + img_name + '.png')),
rotation_90( cv2.imread(dir_img+'/'+im) ), input_height, input_width, indexer=indexer)
rotation_90( cv2.imread(dir_seg+'/'+img_name+'.png') ),
input_height,input_width,indexer=indexer)
if rotation_not_90: if rotation_not_90:
for thetha_i in thetha: 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),
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels, cv2.imread(
img_max_rotated, dir_seg + '/' + img_name + '.png'),
label_max_rotated, thetha_i)
input_height,input_width,indexer=indexer) 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: if flip_aug:
for f_i in flip_index: for f_i in flip_index:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels, 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_img + '/' + im), f_i),
cv2.flip( cv2.imread(dir_seg+'/'+img_name+'.png') ,f_i), cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
input_height,input_width,indexer=indexer) input_height, input_width, indexer=indexer)
if blur_aug: if blur_aug:
for blur_i in blur_k: 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)
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels, if scaling:
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: for sc_ind in scales:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels, indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
cv2.imread(dir_img+'/'+im) , cv2.imread(dir_img + '/' + im),
cv2.imread(dir_seg+'/'+img_name+'.png'), 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 binarization: if binarization:
indexer=get_patches(dir_flow_train_imgs,dir_flow_train_labels, indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)), otsu_copy(cv2.imread(dir_img + '/' + im)),
cv2.imread(dir_seg+'/'+img_name+'.png'), cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height,input_width,indexer=indexer) input_height, input_width, indexer=indexer)
if scaling_bluring:
if scaling_bluring:
for sc_ind in scales: for sc_ind in scales:
for blur_i in blur_k: for blur_i in blur_k:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels, indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
bluring( cv2.imread(dir_img+'/'+im) , blur_i) , bluring(cv2.imread(dir_img + '/' + im), blur_i),
cv2.imread(dir_seg+'/'+img_name+'.png') , 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: if scaling_binarization:
for sc_ind in scales: for sc_ind in scales:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels, indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
otsu_copy( cv2.imread(dir_img+'/'+im)) , otsu_copy(cv2.imread(dir_img + '/' + im)),
cv2.imread(dir_seg+'/'+img_name+'.png'), 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_flip: if scaling_flip:
for sc_ind in scales: for sc_ind in scales:
for f_i in flip_index: for f_i in flip_index:
indexer=get_patches_num_scale_new(dir_flow_train_imgs,dir_flow_train_labels, 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_img + '/' + im), f_i),
cv2.flip(cv2.imread(dir_seg+'/'+img_name+'.png') ,f_i) , cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'),
input_height,input_width,indexer=indexer,scaler=sc_ind) f_i),
input_height, input_width, indexer=indexer,
scaler=sc_ind)

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