Merge remote-tracking branch 'origin/refactor' into refactoring-2024-08
kba
4 months ago
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8ec9fc6da2
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__import__("pkg_resources").declare_namespace(__name__)
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# Pixelwise Segmentation
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> Pixelwise segmentation for document images
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## Introduction
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This repository contains the source code for training an encoder model for document image segmentation.
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## Installation
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Either clone the repository via `git clone https://github.com/qurator-spk/sbb_pixelwise_segmentation.git` or download and unpack the [ZIP](https://github.com/qurator-spk/sbb_pixelwise_segmentation/archive/master.zip).
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### Pretrained encoder
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Download our pretrained weights and add them to a ``pretrained_model`` folder:
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https://qurator-data.de/sbb_pixelwise_segmentation/pretrained_encoder/
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## Usage
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### Train
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To train a model, run: ``python train.py with config_params.json``
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### Ground truth format
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Lables for each pixel are identified by a number. So if you have a
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binary case, ``n_classes`` should be set to ``2`` and labels should
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be ``0`` and ``1`` for each class and pixel.
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In the case of multiclass, just set ``n_classes`` to the number of classes
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you have and the try to produce the labels by pixels set from ``0 , 1 ,2 .., n_classes-1``.
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The labels format should be png.
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Our lables are 3 channel png images but only information of first channel is used.
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If you have an image label with height and width of 10, for a binary case the first channel should look like this:
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Label: [ [1, 0, 0, 1, 1, 0, 0, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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...,
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ]
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This means that you have an image by `10*10*3` and `pixel[0,0]` belongs
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to class `1` and `pixel[0,1]` belongs to class `0`.
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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.
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### Training , evaluation and output
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The train and evaluation folders should contain subfolders of images and labels.
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The output folder should be an empty folder where the output model will be written to.
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### Parameter configuration
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* patches: If you want to break input images into smaller patches (input size of the model) you need to set this parameter to ``true``. In the case that the model should see the image once, like page extraction, patches should be set to ``false``.
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* n_batch: Number of batches at each iteration.
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* n_classes: Number of classes. In the case of binary classification this should be 2.
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* n_epochs: Number of epochs.
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* input_height: This indicates the height of model's input.
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* input_width: This indicates the width of model's input.
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* weight_decay: Weight decay of l2 regularization of model layers.
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* augmentation: If you want to apply any kind of augmentation this parameter should first set to ``true``.
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* 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.
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* 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.
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* scaling: If ``true``, scaling will be applied on image. Scale of scaling is given with "scales" in train.py file.
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* rotation_not_90: If ``true``, rotation (not 90 degree) will be applied on image. Rothation angles are given with "thetha" in train.py file.
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* rotation: If ``true``, 90 degree rotation will be applied on image.
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* binarization: If ``true``,Otsu thresholding will be applied to augment the input data with binarized images.
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* scaling_bluring: If ``true``, combination of scaling and blurring will be applied on image.
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* scaling_binarization: If ``true``, combination of scaling and binarization will be applied on image.
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* scaling_flip: If ``true``, combination of scaling and flip will be applied on image.
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* continue_training: If ``true``, it means that you have already trained a model and you would like to continue the training. So it is needed to provide the dir of trained model with "dir_of_start_model" and index for naming the models. For example if you have already trained for 3 epochs then your last index is 2 and if you want to continue from model_1.h5, you can set "index_start" to 3 to start naming model with index 3.
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* 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``
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* 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".
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* dir_train: This is the directory of "images" and "labels" (dir_train should include two subdirectories with names of images and labels ) for raw images and labels. Namely they are not prepared (not resized and not augmented) yet for training the model. When we run this tool these raw data will be transformed to suitable size needed for the model and they will be written in "dir_output" in train and eval directories. Each of train and eval include "images" and "labels" sub-directories.
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import os
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import sys
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import tensorflow as tf
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import keras, warnings
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from keras.optimizers import *
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from sacred import Experiment
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from models import *
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from utils import *
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from metrics import *
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def configuration():
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gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
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session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
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if __name__ == '__main__':
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n_classes = 2
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input_height = 224
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input_width = 448
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weight_decay = 1e-6
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pretraining = False
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dir_of_weights = 'model_bin_sbb_ens.h5'
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# configuration()
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model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining)
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model.load_weights(dir_of_weights)
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model.save('./name_in_another_python_version.h5')
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{
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"n_classes" : 3,
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"n_epochs" : 2,
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"input_height" : 448,
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"input_width" : 672,
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"weight_decay" : 1e-6,
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"n_batch" : 2,
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"learning_rate": 1e-4,
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"patches" : true,
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"pretraining" : true,
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"augmentation" : false,
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"flip_aug" : false,
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"blur_aug" : false,
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"scaling" : true,
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"binarization" : false,
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"scaling_bluring" : false,
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"scaling_binarization" : false,
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"scaling_flip" : false,
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"rotation": false,
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"rotation_not_90": false,
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"continue_training": false,
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"index_start": 0,
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"dir_of_start_model": " ",
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"weighted_loss": false,
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"is_loss_soft_dice": false,
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"data_is_provided": false,
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"dir_train": "/path/to/training/files/train",
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"dir_eval": "/path/to/training/files/eval",
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"dir_output": "/path/to/training/files/output"
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}
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from keras import backend as K
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import tensorflow as tf
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import numpy as np
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def focal_loss(gamma=2., alpha=4.):
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gamma = float(gamma)
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alpha = float(alpha)
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def focal_loss_fixed(y_true, y_pred):
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"""Focal loss for multi-classification
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FL(p_t)=-alpha(1-p_t)^{gamma}ln(p_t)
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Notice: y_pred is probability after softmax
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gradient is d(Fl)/d(p_t) not d(Fl)/d(x) as described in paper
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d(Fl)/d(p_t) * [p_t(1-p_t)] = d(Fl)/d(x)
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Focal Loss for Dense Object Detection
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https://arxiv.org/abs/1708.02002
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Arguments:
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y_true {tensor} -- ground truth labels, shape of [batch_size, num_cls]
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y_pred {tensor} -- model's output, shape of [batch_size, num_cls]
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Keyword Arguments:
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gamma {float} -- (default: {2.0})
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alpha {float} -- (default: {4.0})
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Returns:
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[tensor] -- loss.
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"""
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epsilon = 1.e-9
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y_true = tf.convert_to_tensor(y_true, tf.float32)
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y_pred = tf.convert_to_tensor(y_pred, tf.float32)
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model_out = tf.add(y_pred, epsilon)
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ce = tf.multiply(y_true, -tf.log(model_out))
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weight = tf.multiply(y_true, tf.pow(tf.subtract(1., model_out), gamma))
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fl = tf.multiply(alpha, tf.multiply(weight, ce))
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reduced_fl = tf.reduce_max(fl, axis=1)
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return tf.reduce_mean(reduced_fl)
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return focal_loss_fixed
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def weighted_categorical_crossentropy(weights=None):
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""" weighted_categorical_crossentropy
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Args:
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* weights<ktensor|nparray|list>: crossentropy weights
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Returns:
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* weighted categorical crossentropy function
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"""
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def loss(y_true, y_pred):
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labels_floats = tf.cast(y_true, tf.float32)
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per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred)
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if weights is not None:
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weight_mask = tf.maximum(tf.reduce_max(tf.constant(
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np.array(weights, dtype=np.float32)[None, None, None])
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* labels_floats, axis=-1), 1.0)
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per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
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return tf.reduce_mean(per_pixel_loss)
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return loss
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def image_categorical_cross_entropy(y_true, y_pred, weights=None):
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"""
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:param y_true: tensor of shape (batch_size, height, width) representing the ground truth.
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:param y_pred: tensor of shape (batch_size, height, width) representing the prediction.
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:return: The mean cross-entropy on softmaxed tensors.
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"""
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labels_floats = tf.cast(y_true, tf.float32)
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per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred)
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if weights is not None:
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weight_mask = tf.maximum(
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tf.reduce_max(tf.constant(
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np.array(weights, dtype=np.float32)[None, None, None])
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* labels_floats, axis=-1), 1.0)
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per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
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return tf.reduce_mean(per_pixel_loss)
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def class_tversky(y_true, y_pred):
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smooth = 1.0 # 1.00
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y_true = K.permute_dimensions(y_true, (3, 1, 2, 0))
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y_pred = K.permute_dimensions(y_pred, (3, 1, 2, 0))
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y_true_pos = K.batch_flatten(y_true)
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y_pred_pos = K.batch_flatten(y_pred)
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true_pos = K.sum(y_true_pos * y_pred_pos, 1)
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false_neg = K.sum(y_true_pos * (1 - y_pred_pos), 1)
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false_pos = K.sum((1 - y_true_pos) * y_pred_pos, 1)
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alpha = 0.2 # 0.5
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beta = 0.8
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return (true_pos + smooth) / (true_pos + alpha * false_neg + (beta) * false_pos + smooth)
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def focal_tversky_loss(y_true, y_pred):
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pt_1 = class_tversky(y_true, y_pred)
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gamma = 1.3 # 4./3.0#1.3#4.0/3.00# 0.75
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return K.sum(K.pow((1 - pt_1), gamma))
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def generalized_dice_coeff2(y_true, y_pred):
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n_el = 1
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for dim in y_true.shape:
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n_el *= int(dim)
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n_cl = y_true.shape[-1]
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w = K.zeros(shape=(n_cl,))
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w = (K.sum(y_true, axis=(0, 1, 2))) / (n_el)
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w = 1 / (w ** 2 + 0.000001)
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numerator = y_true * y_pred
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numerator = w * K.sum(numerator, (0, 1, 2))
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numerator = K.sum(numerator)
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denominator = y_true + y_pred
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denominator = w * K.sum(denominator, (0, 1, 2))
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denominator = K.sum(denominator)
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return 2 * numerator / denominator
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def generalized_dice_coeff(y_true, y_pred):
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axes = tuple(range(1, len(y_pred.shape) - 1))
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Ncl = y_pred.shape[-1]
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w = K.zeros(shape=(Ncl,))
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w = K.sum(y_true, axis=axes)
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w = 1 / (w ** 2 + 0.000001)
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# Compute gen dice coef:
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numerator = y_true * y_pred
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numerator = w * K.sum(numerator, axes)
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numerator = K.sum(numerator)
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denominator = y_true + y_pred
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denominator = w * K.sum(denominator, axes)
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denominator = K.sum(denominator)
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gen_dice_coef = 2 * numerator / denominator
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return gen_dice_coef
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def generalized_dice_loss(y_true, y_pred):
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return 1 - generalized_dice_coeff2(y_true, y_pred)
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def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
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'''
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Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.
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Assumes the `channels_last` format.
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# Arguments
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y_true: b x X x Y( x Z...) x c One hot encoding of ground truth
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y_pred: b x X x Y( x Z...) x c Network output, must sum to 1 over c channel (such as after softmax)
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epsilon: Used for numerical stability to avoid divide by zero errors
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# References
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V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
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https://arxiv.org/abs/1606.04797
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More details on Dice loss formulation
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https://mediatum.ub.tum.de/doc/1395260/1395260.pdf (page 72)
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Adapted from https://github.com/Lasagne/Recipes/issues/99#issuecomment-347775022
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'''
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# skip the batch and class axis for calculating Dice score
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axes = tuple(range(1, len(y_pred.shape) - 1))
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numerator = 2. * K.sum(y_pred * y_true, axes)
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denominator = K.sum(K.square(y_pred) + K.square(y_true), axes)
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return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
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def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last=True, mean_per_class=False,
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verbose=False):
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|
"""
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|
Compute mean metrics of two segmentation masks, via Keras.
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|
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|
IoU(A,B) = |A & B| / (| A U B|)
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|
Dice(A,B) = 2*|A & B| / (|A| + |B|)
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Args:
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|
y_true: true masks, one-hot encoded.
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y_pred: predicted masks, either softmax outputs, or one-hot encoded.
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metric_name: metric to be computed, either 'iou' or 'dice'.
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|
metric_type: one of 'standard' (default), 'soft', 'naive'.
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|
In the standard version, y_pred is one-hot encoded and the mean
|
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|
is taken only over classes that are present (in y_true or y_pred).
|
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The 'soft' version of the metrics are computed without one-hot
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|
encoding y_pred.
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|
The 'naive' version return mean metrics where absent classes contribute
|
||||||
|
to the class mean as 1.0 (instead of being dropped from the mean).
|
||||||
|
drop_last = True: boolean flag to drop last class (usually reserved
|
||||||
|
for background class in semantic segmentation)
|
||||||
|
mean_per_class = False: return mean along batch axis for each class.
|
||||||
|
verbose = False: print intermediate results such as intersection, union
|
||||||
|
(as number of pixels).
|
||||||
|
Returns:
|
||||||
|
IoU/Dice of y_true and y_pred, as a float, unless mean_per_class == True
|
||||||
|
in which case it returns the per-class metric, averaged over the batch.
|
||||||
|
|
||||||
|
Inputs are B*W*H*N tensors, with
|
||||||
|
B = batch size,
|
||||||
|
W = width,
|
||||||
|
H = height,
|
||||||
|
N = number of classes
|
||||||
|
"""
|
||||||
|
|
||||||
|
flag_soft = (metric_type == 'soft')
|
||||||
|
flag_naive_mean = (metric_type == 'naive')
|
||||||
|
|
||||||
|
# always assume one or more classes
|
||||||
|
num_classes = K.shape(y_true)[-1]
|
||||||
|
|
||||||
|
if not flag_soft:
|
||||||
|
# get one-hot encoded masks from y_pred (true masks should already be one-hot)
|
||||||
|
y_pred = K.one_hot(K.argmax(y_pred), num_classes)
|
||||||
|
y_true = K.one_hot(K.argmax(y_true), num_classes)
|
||||||
|
|
||||||
|
# if already one-hot, could have skipped above command
|
||||||
|
# keras uses float32 instead of float64, would give error down (but numpy arrays or keras.to_categorical gives float64)
|
||||||
|
y_true = K.cast(y_true, 'float32')
|
||||||
|
y_pred = K.cast(y_pred, 'float32')
|
||||||
|
|
||||||
|
# intersection and union shapes are batch_size * n_classes (values = area in pixels)
|
||||||
|
axes = (1, 2) # W,H axes of each image
|
||||||
|
intersection = K.sum(K.abs(y_true * y_pred), axis=axes)
|
||||||
|
mask_sum = K.sum(K.abs(y_true), axis=axes) + K.sum(K.abs(y_pred), axis=axes)
|
||||||
|
union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
|
||||||
|
|
||||||
|
smooth = .001
|
||||||
|
iou = (intersection + smooth) / (union + smooth)
|
||||||
|
dice = 2 * (intersection + smooth) / (mask_sum + smooth)
|
||||||
|
|
||||||
|
metric = {'iou': iou, 'dice': dice}[metric_name]
|
||||||
|
|
||||||
|
# define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise
|
||||||
|
mask = K.cast(K.not_equal(union, 0), 'float32')
|
||||||
|
|
||||||
|
if drop_last:
|
||||||
|
metric = metric[:, :-1]
|
||||||
|
mask = mask[:, :-1]
|
||||||
|
|
||||||
|
if verbose:
|
||||||
|
print('intersection, union')
|
||||||
|
print(K.eval(intersection), K.eval(union))
|
||||||
|
print(K.eval(intersection / union))
|
||||||
|
|
||||||
|
# return mean metrics: remaining axes are (batch, classes)
|
||||||
|
if flag_naive_mean:
|
||||||
|
return K.mean(metric)
|
||||||
|
|
||||||
|
# take mean only over non-absent classes
|
||||||
|
class_count = K.sum(mask, axis=0)
|
||||||
|
non_zero = tf.greater(class_count, 0)
|
||||||
|
non_zero_sum = tf.boolean_mask(K.sum(metric * mask, axis=0), non_zero)
|
||||||
|
non_zero_count = tf.boolean_mask(class_count, non_zero)
|
||||||
|
|
||||||
|
if verbose:
|
||||||
|
print('Counts of inputs with class present, metrics for non-absent classes')
|
||||||
|
print(K.eval(class_count), K.eval(non_zero_sum / non_zero_count))
|
||||||
|
|
||||||
|
return K.mean(non_zero_sum / non_zero_count)
|
||||||
|
|
||||||
|
|
||||||
|
def mean_iou(y_true, y_pred, **kwargs):
|
||||||
|
"""
|
||||||
|
Compute mean Intersection over Union of two segmentation masks, via Keras.
|
||||||
|
|
||||||
|
Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.
|
||||||
|
"""
|
||||||
|
return seg_metrics(y_true, y_pred, metric_name='iou', **kwargs)
|
||||||
|
|
||||||
|
|
||||||
|
def Mean_IOU(y_true, y_pred):
|
||||||
|
nb_classes = K.int_shape(y_pred)[-1]
|
||||||
|
iou = []
|
||||||
|
true_pixels = K.argmax(y_true, axis=-1)
|
||||||
|
pred_pixels = K.argmax(y_pred, axis=-1)
|
||||||
|
void_labels = K.equal(K.sum(y_true, axis=-1), 0)
|
||||||
|
for i in range(0, nb_classes): # exclude first label (background) and last label (void)
|
||||||
|
true_labels = K.equal(true_pixels, i) # & ~void_labels
|
||||||
|
pred_labels = K.equal(pred_pixels, i) # & ~void_labels
|
||||||
|
inter = tf.to_int32(true_labels & pred_labels)
|
||||||
|
union = tf.to_int32(true_labels | pred_labels)
|
||||||
|
legal_batches = K.sum(tf.to_int32(true_labels), axis=1) > 0
|
||||||
|
ious = K.sum(inter, axis=1) / K.sum(union, axis=1)
|
||||||
|
iou.append(K.mean(tf.gather(ious, indices=tf.where(legal_batches)))) # returns average IoU of the same objects
|
||||||
|
iou = tf.stack(iou)
|
||||||
|
legal_labels = ~tf.debugging.is_nan(iou)
|
||||||
|
iou = tf.gather(iou, indices=tf.where(legal_labels))
|
||||||
|
return K.mean(iou)
|
||||||
|
|
||||||
|
|
||||||
|
def iou_vahid(y_true, y_pred):
|
||||||
|
nb_classes = tf.shape(y_true)[-1] + tf.to_int32(1)
|
||||||
|
true_pixels = K.argmax(y_true, axis=-1)
|
||||||
|
pred_pixels = K.argmax(y_pred, axis=-1)
|
||||||
|
iou = []
|
||||||
|
|
||||||
|
for i in tf.range(nb_classes):
|
||||||
|
tp = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.equal(pred_pixels, i)))
|
||||||
|
fp = K.sum(tf.to_int32(K.not_equal(true_pixels, i) & K.equal(pred_pixels, i)))
|
||||||
|
fn = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.not_equal(pred_pixels, i)))
|
||||||
|
iouh = tp / (tp + fp + fn)
|
||||||
|
iou.append(iouh)
|
||||||
|
return K.mean(iou)
|
||||||
|
|
||||||
|
|
||||||
|
def IoU_metric(Yi, y_predi):
|
||||||
|
# mean Intersection over Union
|
||||||
|
# Mean IoU = TP/(FN + TP + FP)
|
||||||
|
y_predi = np.argmax(y_predi, axis=3)
|
||||||
|
y_testi = np.argmax(Yi, axis=3)
|
||||||
|
IoUs = []
|
||||||
|
Nclass = int(np.max(Yi)) + 1
|
||||||
|
for c in range(Nclass):
|
||||||
|
TP = np.sum((Yi == c) & (y_predi == c))
|
||||||
|
FP = np.sum((Yi != c) & (y_predi == c))
|
||||||
|
FN = np.sum((Yi == c) & (y_predi != c))
|
||||||
|
IoU = TP / float(TP + FP + FN)
|
||||||
|
IoUs.append(IoU)
|
||||||
|
return K.cast(np.mean(IoUs), dtype='float32')
|
||||||
|
|
||||||
|
|
||||||
|
def IoU_metric_keras(y_true, y_pred):
|
||||||
|
# mean Intersection over Union
|
||||||
|
# Mean IoU = TP/(FN + TP + FP)
|
||||||
|
init = tf.global_variables_initializer()
|
||||||
|
sess = tf.Session()
|
||||||
|
sess.run(init)
|
||||||
|
|
||||||
|
return IoU_metric(y_true.eval(session=sess), y_pred.eval(session=sess))
|
||||||
|
|
||||||
|
|
||||||
|
def jaccard_distance_loss(y_true, y_pred, smooth=100):
|
||||||
|
"""
|
||||||
|
Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
|
||||||
|
= sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
|
||||||
|
|
||||||
|
The jaccard distance loss is usefull for unbalanced datasets. This has been
|
||||||
|
shifted so it converges on 0 and is smoothed to avoid exploding or disapearing
|
||||||
|
gradient.
|
||||||
|
|
||||||
|
Ref: https://en.wikipedia.org/wiki/Jaccard_index
|
||||||
|
|
||||||
|
@url: https://gist.github.com/wassname/f1452b748efcbeb4cb9b1d059dce6f96
|
||||||
|
@author: wassname
|
||||||
|
"""
|
||||||
|
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
|
||||||
|
sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
|
||||||
|
jac = (intersection + smooth) / (sum_ - intersection + smooth)
|
||||||
|
return (1 - jac) * smooth
|
||||||
@ -0,0 +1,294 @@
|
|||||||
|
from keras.models import *
|
||||||
|
from keras.layers import *
|
||||||
|
from keras import layers
|
||||||
|
from keras.regularizers import l2
|
||||||
|
|
||||||
|
resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
|
||||||
|
IMAGE_ORDERING = 'channels_last'
|
||||||
|
MERGE_AXIS = -1
|
||||||
|
|
||||||
|
|
||||||
|
def one_side_pad(x):
|
||||||
|
x = ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING)(x)
|
||||||
|
if IMAGE_ORDERING == 'channels_first':
|
||||||
|
x = Lambda(lambda x: x[:, :, :-1, :-1])(x)
|
||||||
|
elif IMAGE_ORDERING == 'channels_last':
|
||||||
|
x = Lambda(lambda x: x[:, :-1, :-1, :])(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def identity_block(input_tensor, kernel_size, filters, stage, block):
|
||||||
|
"""The identity block is the block that has no conv layer at shortcut.
|
||||||
|
# Arguments
|
||||||
|
input_tensor: input tensor
|
||||||
|
kernel_size: defualt 3, the kernel size of middle conv layer at main path
|
||||||
|
filters: list of integers, the filterss of 3 conv layer at main path
|
||||||
|
stage: integer, current stage label, used for generating layer names
|
||||||
|
block: 'a','b'..., current block label, used for generating layer names
|
||||||
|
# Returns
|
||||||
|
Output tensor for the block.
|
||||||
|
"""
|
||||||
|
filters1, filters2, filters3 = filters
|
||||||
|
|
||||||
|
if IMAGE_ORDERING == 'channels_last':
|
||||||
|
bn_axis = 3
|
||||||
|
else:
|
||||||
|
bn_axis = 1
|
||||||
|
|
||||||
|
conv_name_base = 'res' + str(stage) + block + '_branch'
|
||||||
|
bn_name_base = 'bn' + str(stage) + block + '_branch'
|
||||||
|
|
||||||
|
x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2a')(input_tensor)
|
||||||
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
|
||||||
|
x = Activation('relu')(x)
|
||||||
|
|
||||||
|
x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING,
|
||||||
|
padding='same', name=conv_name_base + '2b')(x)
|
||||||
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
|
||||||
|
x = Activation('relu')(x)
|
||||||
|
|
||||||
|
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
|
||||||
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
|
||||||
|
|
||||||
|
x = layers.add([x, input_tensor])
|
||||||
|
x = Activation('relu')(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
|
||||||
|
"""conv_block is the block that has a conv layer at shortcut
|
||||||
|
# Arguments
|
||||||
|
input_tensor: input tensor
|
||||||
|
kernel_size: defualt 3, the kernel size of middle conv layer at main path
|
||||||
|
filters: list of integers, the filterss of 3 conv layer at main path
|
||||||
|
stage: integer, current stage label, used for generating layer names
|
||||||
|
block: 'a','b'..., current block label, used for generating layer names
|
||||||
|
# Returns
|
||||||
|
Output tensor for the block.
|
||||||
|
Note that from stage 3, the first conv layer at main path is with strides=(2,2)
|
||||||
|
And the shortcut should have strides=(2,2) as well
|
||||||
|
"""
|
||||||
|
filters1, filters2, filters3 = filters
|
||||||
|
|
||||||
|
if IMAGE_ORDERING == 'channels_last':
|
||||||
|
bn_axis = 3
|
||||||
|
else:
|
||||||
|
bn_axis = 1
|
||||||
|
|
||||||
|
conv_name_base = 'res' + str(stage) + block + '_branch'
|
||||||
|
bn_name_base = 'bn' + str(stage) + block + '_branch'
|
||||||
|
|
||||||
|
x = Conv2D(filters1, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
|
||||||
|
name=conv_name_base + '2a')(input_tensor)
|
||||||
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
|
||||||
|
x = Activation('relu')(x)
|
||||||
|
|
||||||
|
x = Conv2D(filters2, kernel_size, data_format=IMAGE_ORDERING, padding='same',
|
||||||
|
name=conv_name_base + '2b')(x)
|
||||||
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
|
||||||
|
x = Activation('relu')(x)
|
||||||
|
|
||||||
|
x = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, name=conv_name_base + '2c')(x)
|
||||||
|
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
|
||||||
|
|
||||||
|
shortcut = Conv2D(filters3, (1, 1), data_format=IMAGE_ORDERING, strides=strides,
|
||||||
|
name=conv_name_base + '1')(input_tensor)
|
||||||
|
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
|
||||||
|
|
||||||
|
x = layers.add([x, shortcut])
|
||||||
|
x = Activation('relu')(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
def resnet50_unet_light(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
|
||||||
|
assert input_height % 32 == 0
|
||||||
|
assert input_width % 32 == 0
|
||||||
|
|
||||||
|
img_input = Input(shape=(input_height, input_width, 3))
|
||||||
|
|
||||||
|
if IMAGE_ORDERING == 'channels_last':
|
||||||
|
bn_axis = 3
|
||||||
|
else:
|
||||||
|
bn_axis = 1
|
||||||
|
|
||||||
|
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
|
||||||
|
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
|
||||||
|
name='conv1')(x)
|
||||||
|
f1 = x
|
||||||
|
|
||||||
|
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
|
||||||
|
x = Activation('relu')(x)
|
||||||
|
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
|
||||||
|
|
||||||
|
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
|
||||||
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
|
||||||
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
|
||||||
|
f2 = one_side_pad(x)
|
||||||
|
|
||||||
|
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
|
||||||
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
|
||||||
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
|
||||||
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
|
||||||
|
f3 = x
|
||||||
|
|
||||||
|
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
|
||||||
|
f4 = x
|
||||||
|
|
||||||
|
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
|
||||||
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
|
||||||
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
|
||||||
|
f5 = x
|
||||||
|
|
||||||
|
if pretraining:
|
||||||
|
model = Model(img_input, x).load_weights(resnet50_Weights_path)
|
||||||
|
|
||||||
|
v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5)
|
||||||
|
v512_2048 = (BatchNormalization(axis=bn_axis))(v512_2048)
|
||||||
|
v512_2048 = Activation('relu')(v512_2048)
|
||||||
|
|
||||||
|
v512_1024 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f4)
|
||||||
|
v512_1024 = (BatchNormalization(axis=bn_axis))(v512_1024)
|
||||||
|
v512_1024 = Activation('relu')(v512_1024)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v512_2048)
|
||||||
|
o = (concatenate([o, v512_1024], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (concatenate([o, f3], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (concatenate([o, f2], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (concatenate([o, f1], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (concatenate([o, img_input], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = (Activation('softmax'))(o)
|
||||||
|
|
||||||
|
model = Model(img_input, o)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def resnet50_unet(n_classes, input_height=224, input_width=224, weight_decay=1e-6, pretraining=False):
|
||||||
|
assert input_height % 32 == 0
|
||||||
|
assert input_width % 32 == 0
|
||||||
|
|
||||||
|
img_input = Input(shape=(input_height, input_width, 3))
|
||||||
|
|
||||||
|
if IMAGE_ORDERING == 'channels_last':
|
||||||
|
bn_axis = 3
|
||||||
|
else:
|
||||||
|
bn_axis = 1
|
||||||
|
|
||||||
|
x = ZeroPadding2D((3, 3), data_format=IMAGE_ORDERING)(img_input)
|
||||||
|
x = Conv2D(64, (7, 7), data_format=IMAGE_ORDERING, strides=(2, 2), kernel_regularizer=l2(weight_decay),
|
||||||
|
name='conv1')(x)
|
||||||
|
f1 = x
|
||||||
|
|
||||||
|
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
|
||||||
|
x = Activation('relu')(x)
|
||||||
|
x = MaxPooling2D((3, 3), data_format=IMAGE_ORDERING, strides=(2, 2))(x)
|
||||||
|
|
||||||
|
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
|
||||||
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
|
||||||
|
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
|
||||||
|
f2 = one_side_pad(x)
|
||||||
|
|
||||||
|
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
|
||||||
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
|
||||||
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
|
||||||
|
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
|
||||||
|
f3 = x
|
||||||
|
|
||||||
|
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
|
||||||
|
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
|
||||||
|
f4 = x
|
||||||
|
|
||||||
|
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
|
||||||
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
|
||||||
|
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
|
||||||
|
f5 = x
|
||||||
|
|
||||||
|
if pretraining:
|
||||||
|
Model(img_input, x).load_weights(resnet50_Weights_path)
|
||||||
|
|
||||||
|
v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(
|
||||||
|
f5)
|
||||||
|
v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
|
||||||
|
v1024_2048 = Activation('relu')(v1024_2048)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(v1024_2048)
|
||||||
|
o = (concatenate([o, f4], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(512, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (concatenate([o, f3], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(256, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (concatenate([o, f2], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(128, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (concatenate([o, f1], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(64, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (concatenate([o, img_input], axis=MERGE_AXIS))
|
||||||
|
o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)
|
||||||
|
o = (Conv2D(32, (3, 3), padding='valid', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay)))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = Activation('relu')(o)
|
||||||
|
|
||||||
|
o = Conv2D(n_classes, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(o)
|
||||||
|
o = (BatchNormalization(axis=bn_axis))(o)
|
||||||
|
o = (Activation('softmax'))(o)
|
||||||
|
|
||||||
|
model = Model(img_input, o)
|
||||||
|
|
||||||
|
return model
|
||||||
@ -0,0 +1,273 @@
|
|||||||
|
#! /usr/bin/env python3
|
||||||
|
|
||||||
|
__version__ = '1.0'
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import sys
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import warnings
|
||||||
|
import xml.etree.ElementTree as ET
|
||||||
|
from tqdm import tqdm
|
||||||
|
import cv2
|
||||||
|
|
||||||
|
with warnings.catch_warnings():
|
||||||
|
warnings.simplefilter("ignore")
|
||||||
|
|
||||||
|
__doc__ = \
|
||||||
|
"""
|
||||||
|
tool to extract 2d or 3d RGB images from page xml data. In former case output will be 1
|
||||||
|
2D image array which each class has filled with a pixel value. In the case of 3D RGB image
|
||||||
|
each class will be defined with a RGB value and beside images a text file of classes also will be produced.
|
||||||
|
This classes.txt file is required for dhsegment tool.
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
class pagexml2img:
|
||||||
|
def __init__(self, dir_in, out_dir, output_type):
|
||||||
|
self.dir = dir_in
|
||||||
|
self.output_dir = out_dir
|
||||||
|
self.output_type = output_type
|
||||||
|
|
||||||
|
def get_content_of_dir(self):
|
||||||
|
"""
|
||||||
|
Listing all ground truth page xml files. All files are needed to have xml format.
|
||||||
|
"""
|
||||||
|
|
||||||
|
gt_all = os.listdir(self.dir)
|
||||||
|
self.gt_list = [file for file in gt_all if file.split('.')[len(file.split('.')) - 1] == 'xml']
|
||||||
|
|
||||||
|
def get_images_of_ground_truth(self):
|
||||||
|
"""
|
||||||
|
Reading the page xml files and write the ground truth images into given output directory.
|
||||||
|
"""
|
||||||
|
|
||||||
|
if self.output_type == '3d' or self.output_type == '3D':
|
||||||
|
classes = np.array([[0, 0, 0, 1, 0, 0, 0, 0],
|
||||||
|
[255, 0, 0, 0, 1, 0, 0, 0],
|
||||||
|
[0, 255, 0, 0, 0, 1, 0, 0],
|
||||||
|
[0, 0, 255, 0, 0, 0, 1, 0],
|
||||||
|
[0, 255, 255, 0, 0, 0, 0, 1]])
|
||||||
|
|
||||||
|
for index in tqdm(range(len(self.gt_list))):
|
||||||
|
try:
|
||||||
|
tree1 = ET.parse(self.dir + '/' + self.gt_list[index])
|
||||||
|
root1 = tree1.getroot()
|
||||||
|
alltags = [elem.tag for elem in root1.iter()]
|
||||||
|
link = alltags[0].split('}')[0] + '}'
|
||||||
|
|
||||||
|
region_tags = np.unique([x for x in alltags if x.endswith('Region')])
|
||||||
|
|
||||||
|
for jj in root1.iter(link + 'Page'):
|
||||||
|
y_len = int(jj.attrib['imageHeight'])
|
||||||
|
x_len = int(jj.attrib['imageWidth'])
|
||||||
|
|
||||||
|
co_text = []
|
||||||
|
co_sep = []
|
||||||
|
co_img = []
|
||||||
|
co_table = []
|
||||||
|
|
||||||
|
for tag in region_tags:
|
||||||
|
if tag.endswith('}TextRegion') or tag.endswith('}Textregion') or tag.endswith(
|
||||||
|
'}textRegion') or tag.endswith('}textregion'):
|
||||||
|
|
||||||
|
for nn in root1.iter(tag):
|
||||||
|
for co_it in nn.iter(link + 'Coords'):
|
||||||
|
if bool(co_it.attrib) == False:
|
||||||
|
c_t_in = []
|
||||||
|
for ll in nn.iter(link + 'Point'):
|
||||||
|
c_t_in.append(
|
||||||
|
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
|
||||||
|
co_text.append(np.array(c_t_in))
|
||||||
|
print(co_text)
|
||||||
|
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
|
||||||
|
p_h = co_it.attrib['points'].split(' ')
|
||||||
|
co_text.append(
|
||||||
|
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||||
|
|
||||||
|
elif tag.endswith('}ImageRegion') or tag.endswith('}Imageregion') or tag.endswith(
|
||||||
|
'}imageRegion') or tag.endswith('}imageregion'):
|
||||||
|
for nn in root1.iter(tag):
|
||||||
|
for co_it in nn.iter(link + 'Coords'):
|
||||||
|
if bool(co_it.attrib) == False:
|
||||||
|
c_i_in = []
|
||||||
|
for ll in nn.iter(link + 'Point'):
|
||||||
|
c_i_in.append(
|
||||||
|
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
|
||||||
|
co_img.append(np.array(c_i_in))
|
||||||
|
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
|
||||||
|
p_h = co_it.attrib['points'].split(' ')
|
||||||
|
co_img.append(
|
||||||
|
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||||
|
|
||||||
|
elif tag.endswith('}SeparatorRegion') or tag.endswith('}Separatorregion') or tag.endswith(
|
||||||
|
'}separatorRegion') or tag.endswith('}separatorregion'):
|
||||||
|
for nn in root1.iter(tag):
|
||||||
|
for co_it in nn.iter(link + 'Coords'):
|
||||||
|
if bool(co_it.attrib) == False:
|
||||||
|
c_s_in = []
|
||||||
|
for ll in nn.iter(link + 'Point'):
|
||||||
|
c_s_in.append(
|
||||||
|
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
|
||||||
|
co_sep.append(np.array(c_s_in))
|
||||||
|
|
||||||
|
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
|
||||||
|
p_h = co_it.attrib['points'].split(' ')
|
||||||
|
co_sep.append(
|
||||||
|
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||||
|
|
||||||
|
elif tag.endswith('}TableRegion') or tag.endswith('}tableRegion') or tag.endswith(
|
||||||
|
'}Tableregion') or tag.endswith('}tableregion'):
|
||||||
|
for nn in root1.iter(tag):
|
||||||
|
for co_it in nn.iter(link + 'Coords'):
|
||||||
|
if bool(co_it.attrib) == False:
|
||||||
|
c_ta_in = []
|
||||||
|
for ll in nn.iter(link + 'Point'):
|
||||||
|
c_ta_in.append(
|
||||||
|
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
|
||||||
|
co_table.append(np.array(c_ta_in))
|
||||||
|
|
||||||
|
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
|
||||||
|
p_h = co_it.attrib['points'].split(' ')
|
||||||
|
co_table.append(
|
||||||
|
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
img = np.zeros((y_len, x_len, 3))
|
||||||
|
img_poly = cv2.fillPoly(img, pts=co_text, color=(255, 0, 0))
|
||||||
|
img_poly = cv2.fillPoly(img, pts=co_img, color=(0, 255, 0))
|
||||||
|
img_poly = cv2.fillPoly(img, pts=co_sep, color=(0, 0, 255))
|
||||||
|
img_poly = cv2.fillPoly(img, pts=co_table, color=(0, 255, 255))
|
||||||
|
|
||||||
|
try:
|
||||||
|
cv2.imwrite(self.output_dir + '/' + self.gt_list[index].split('-')[1].split('.')[0] + '.png',
|
||||||
|
img_poly)
|
||||||
|
except:
|
||||||
|
cv2.imwrite(self.output_dir + '/' + self.gt_list[index].split('.')[0] + '.png', img_poly)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
np.savetxt(self.output_dir + '/../classes.txt', classes)
|
||||||
|
|
||||||
|
if self.output_type == '2d' or self.output_type == '2D':
|
||||||
|
for index in tqdm(range(len(self.gt_list))):
|
||||||
|
try:
|
||||||
|
tree1 = ET.parse(self.dir + '/' + self.gt_list[index])
|
||||||
|
root1 = tree1.getroot()
|
||||||
|
alltags = [elem.tag for elem in root1.iter()]
|
||||||
|
link = alltags[0].split('}')[0] + '}'
|
||||||
|
|
||||||
|
region_tags = np.unique([x for x in alltags if x.endswith('Region')])
|
||||||
|
|
||||||
|
for jj in root1.iter(link + 'Page'):
|
||||||
|
y_len = int(jj.attrib['imageHeight'])
|
||||||
|
x_len = int(jj.attrib['imageWidth'])
|
||||||
|
|
||||||
|
co_text = []
|
||||||
|
co_sep = []
|
||||||
|
co_img = []
|
||||||
|
co_table = []
|
||||||
|
|
||||||
|
for tag in region_tags:
|
||||||
|
if tag.endswith('}TextRegion') or tag.endswith('}Textregion') or tag.endswith(
|
||||||
|
'}textRegion') or tag.endswith('}textregion'):
|
||||||
|
|
||||||
|
for nn in root1.iter(tag):
|
||||||
|
for co_it in nn.iter(link + 'Coords'):
|
||||||
|
if bool(co_it.attrib) == False:
|
||||||
|
c_t_in = []
|
||||||
|
for ll in nn.iter(link + 'Point'):
|
||||||
|
c_t_in.append(
|
||||||
|
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
|
||||||
|
co_text.append(np.array(c_t_in))
|
||||||
|
print(co_text)
|
||||||
|
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
|
||||||
|
p_h = co_it.attrib['points'].split(' ')
|
||||||
|
co_text.append(
|
||||||
|
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||||
|
|
||||||
|
elif tag.endswith('}ImageRegion') or tag.endswith('}Imageregion') or tag.endswith(
|
||||||
|
'}imageRegion') or tag.endswith('}imageregion'):
|
||||||
|
for nn in root1.iter(tag):
|
||||||
|
for co_it in nn.iter(link + 'Coords'):
|
||||||
|
if bool(co_it.attrib) == False:
|
||||||
|
c_i_in = []
|
||||||
|
for ll in nn.iter(link + 'Point'):
|
||||||
|
c_i_in.append(
|
||||||
|
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
|
||||||
|
co_img.append(np.array(c_i_in))
|
||||||
|
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
|
||||||
|
p_h = co_it.attrib['points'].split(' ')
|
||||||
|
co_img.append(
|
||||||
|
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||||
|
|
||||||
|
elif tag.endswith('}SeparatorRegion') or tag.endswith('}Separatorregion') or tag.endswith(
|
||||||
|
'}separatorRegion') or tag.endswith('}separatorregion'):
|
||||||
|
for nn in root1.iter(tag):
|
||||||
|
for co_it in nn.iter(link + 'Coords'):
|
||||||
|
if bool(co_it.attrib) == False:
|
||||||
|
c_s_in = []
|
||||||
|
for ll in nn.iter(link + 'Point'):
|
||||||
|
c_s_in.append(
|
||||||
|
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
|
||||||
|
co_sep.append(np.array(c_s_in))
|
||||||
|
|
||||||
|
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
|
||||||
|
p_h = co_it.attrib['points'].split(' ')
|
||||||
|
co_sep.append(
|
||||||
|
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||||
|
|
||||||
|
elif tag.endswith('}TableRegion') or tag.endswith('}tableRegion') or tag.endswith(
|
||||||
|
'}Tableregion') or tag.endswith('}tableregion'):
|
||||||
|
for nn in root1.iter(tag):
|
||||||
|
for co_it in nn.iter(link + 'Coords'):
|
||||||
|
if bool(co_it.attrib) == False:
|
||||||
|
c_ta_in = []
|
||||||
|
for ll in nn.iter(link + 'Point'):
|
||||||
|
c_ta_in.append(
|
||||||
|
[int(np.float(ll.attrib['x'])), int(np.float(ll.attrib['y']))])
|
||||||
|
co_table.append(np.array(c_ta_in))
|
||||||
|
|
||||||
|
elif bool(co_it.attrib) == True and 'points' in co_it.attrib.keys():
|
||||||
|
p_h = co_it.attrib['points'].split(' ')
|
||||||
|
co_table.append(
|
||||||
|
np.array([[int(x.split(',')[0]), int(x.split(',')[1])] for x in p_h]))
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
img = np.zeros((y_len, x_len))
|
||||||
|
img_poly = cv2.fillPoly(img, pts=co_text, color=(1, 1, 1))
|
||||||
|
img_poly = cv2.fillPoly(img, pts=co_img, color=(2, 2, 2))
|
||||||
|
img_poly = cv2.fillPoly(img, pts=co_sep, color=(3, 3, 3))
|
||||||
|
img_poly = cv2.fillPoly(img, pts=co_table, color=(4, 4, 4))
|
||||||
|
try:
|
||||||
|
cv2.imwrite(self.output_dir + '/' + self.gt_list[index].split('-')[1].split('.')[0] + '.png',
|
||||||
|
img_poly)
|
||||||
|
except:
|
||||||
|
cv2.imwrite(self.output_dir + '/' + self.gt_list[index].split('.')[0] + '.png', img_poly)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
|
||||||
|
def run(self):
|
||||||
|
self.get_content_of_dir()
|
||||||
|
self.get_images_of_ground_truth()
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
|
||||||
|
parser.add_argument('-dir_in', '--dir_in', dest='inp1', default=None, help='directory of page-xml files')
|
||||||
|
parser.add_argument('-dir_out', '--dir_out', dest='inp2', default=None,
|
||||||
|
help='directory where ground truth images would be written')
|
||||||
|
parser.add_argument('-type', '--type', dest='inp3', default=None,
|
||||||
|
help='this defines how output should be. A 2d image array or a 3d image array encoded with RGB color. Just pass 2d or 3d. The file will be saved one directory up. 2D image array is 3d but only information of one channel would be enough since all channels have the same values.')
|
||||||
|
options = parser.parse_args()
|
||||||
|
|
||||||
|
possibles = globals()
|
||||||
|
possibles.update(locals())
|
||||||
|
x = pagexml2img(options.inp1, options.inp2, options.inp3)
|
||||||
|
x.run()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
@ -0,0 +1,221 @@
|
|||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import tensorflow as tf
|
||||||
|
from tensorflow.compat.v1.keras.backend import set_session
|
||||||
|
import keras, warnings
|
||||||
|
from keras.optimizers import *
|
||||||
|
from sacred import Experiment
|
||||||
|
from models import *
|
||||||
|
from utils import *
|
||||||
|
from metrics import *
|
||||||
|
from keras.models import load_model
|
||||||
|
from tqdm import tqdm
|
||||||
|
|
||||||
|
|
||||||
|
def configuration():
|
||||||
|
keras.backend.clear_session()
|
||||||
|
tf.reset_default_graph()
|
||||||
|
warnings.filterwarnings('ignore')
|
||||||
|
|
||||||
|
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
|
||||||
|
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
|
||||||
|
|
||||||
|
config.gpu_options.allow_growth = True
|
||||||
|
config.gpu_options.per_process_gpu_memory_fraction = 0.95 # 0.95
|
||||||
|
config.gpu_options.visible_device_list = "0"
|
||||||
|
set_session(tf.Session(config=config))
|
||||||
|
|
||||||
|
|
||||||
|
def get_dirs_or_files(input_data):
|
||||||
|
if os.path.isdir(input_data):
|
||||||
|
image_input, labels_input = os.path.join(input_data, 'images/'), os.path.join(input_data, 'labels/')
|
||||||
|
# Check if training dir exists
|
||||||
|
assert os.path.isdir(image_input), "{} is not a directory".format(image_input)
|
||||||
|
assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input)
|
||||||
|
return image_input, labels_input
|
||||||
|
|
||||||
|
|
||||||
|
ex = Experiment()
|
||||||
|
|
||||||
|
|
||||||
|
@ex.config
|
||||||
|
def config_params():
|
||||||
|
n_classes = None # Number of classes. If your case study is binary case the set it to 2 and otherwise give your number of cases.
|
||||||
|
n_epochs = 1
|
||||||
|
input_height = 224 * 1
|
||||||
|
input_width = 224 * 1
|
||||||
|
weight_decay = 1e-6 # Weight decay of l2 regularization of model layers.
|
||||||
|
n_batch = 1 # Number of batches at each iteration.
|
||||||
|
learning_rate = 1e-4
|
||||||
|
patches = False # Make patches of image in order to use all information of image. In the case of page
|
||||||
|
# extraction this should be set to false since model should see all image.
|
||||||
|
augmentation = False
|
||||||
|
flip_aug = False # Flip image (augmentation).
|
||||||
|
blur_aug = False # Blur patches of image (augmentation).
|
||||||
|
scaling = False # Scaling of patches (augmentation) will be imposed if this set to true.
|
||||||
|
binarization = False # Otsu thresholding. Used for augmentation in the case of binary case like textline prediction. For multicases should not be applied.
|
||||||
|
dir_train = None # Directory of training dataset (sub-folders should be named images and labels).
|
||||||
|
dir_eval = None # Directory of validation dataset (sub-folders should be named images and labels).
|
||||||
|
dir_output = None # Directory of output where the model should be saved.
|
||||||
|
pretraining = False # Set true to load pretrained weights of resnet50 encoder.
|
||||||
|
scaling_bluring = False
|
||||||
|
scaling_binarization = False
|
||||||
|
scaling_flip = False
|
||||||
|
thetha = [10, -10]
|
||||||
|
blur_k = ['blur', 'guass', 'median'] # Used in order to blur image. Used for augmentation.
|
||||||
|
scales = [0.5, 2] # Scale patches with these scales. Used for augmentation.
|
||||||
|
flip_index = [0, 1, -1] # Flip image. Used for augmentation.
|
||||||
|
continue_training = False # If
|
||||||
|
index_start = 0
|
||||||
|
dir_of_start_model = ''
|
||||||
|
is_loss_soft_dice = False
|
||||||
|
weighted_loss = False
|
||||||
|
data_is_provided = False
|
||||||
|
|
||||||
|
|
||||||
|
@ex.automain
|
||||||
|
def run(n_classes, n_epochs, input_height,
|
||||||
|
input_width, weight_decay, weighted_loss,
|
||||||
|
index_start, dir_of_start_model, is_loss_soft_dice,
|
||||||
|
n_batch, patches, augmentation, flip_aug,
|
||||||
|
blur_aug, scaling, binarization,
|
||||||
|
blur_k, scales, dir_train, data_is_provided,
|
||||||
|
scaling_bluring, scaling_binarization, rotation,
|
||||||
|
rotation_not_90, thetha, scaling_flip, continue_training,
|
||||||
|
flip_index, dir_eval, dir_output, pretraining, learning_rate):
|
||||||
|
if data_is_provided:
|
||||||
|
dir_train_flowing = os.path.join(dir_output, 'train')
|
||||||
|
dir_eval_flowing = os.path.join(dir_output, 'eval')
|
||||||
|
|
||||||
|
dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images')
|
||||||
|
dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels')
|
||||||
|
|
||||||
|
dir_flow_eval_imgs = os.path.join(dir_eval_flowing, 'images')
|
||||||
|
dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels')
|
||||||
|
|
||||||
|
configuration()
|
||||||
|
|
||||||
|
else:
|
||||||
|
dir_img, dir_seg = get_dirs_or_files(dir_train)
|
||||||
|
dir_img_val, dir_seg_val = get_dirs_or_files(dir_eval)
|
||||||
|
|
||||||
|
# make first a directory in output for both training and evaluations in order to flow data from these directories.
|
||||||
|
dir_train_flowing = os.path.join(dir_output, 'train')
|
||||||
|
dir_eval_flowing = os.path.join(dir_output, 'eval')
|
||||||
|
|
||||||
|
dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images/')
|
||||||
|
dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels/')
|
||||||
|
|
||||||
|
dir_flow_eval_imgs = os.path.join(dir_eval_flowing, 'images/')
|
||||||
|
dir_flow_eval_labels = os.path.join(dir_eval_flowing, 'labels/')
|
||||||
|
|
||||||
|
if os.path.isdir(dir_train_flowing):
|
||||||
|
os.system('rm -rf ' + dir_train_flowing)
|
||||||
|
os.makedirs(dir_train_flowing)
|
||||||
|
else:
|
||||||
|
os.makedirs(dir_train_flowing)
|
||||||
|
|
||||||
|
if os.path.isdir(dir_eval_flowing):
|
||||||
|
os.system('rm -rf ' + dir_eval_flowing)
|
||||||
|
os.makedirs(dir_eval_flowing)
|
||||||
|
else:
|
||||||
|
os.makedirs(dir_eval_flowing)
|
||||||
|
|
||||||
|
os.mkdir(dir_flow_train_imgs)
|
||||||
|
os.mkdir(dir_flow_train_labels)
|
||||||
|
|
||||||
|
os.mkdir(dir_flow_eval_imgs)
|
||||||
|
os.mkdir(dir_flow_eval_labels)
|
||||||
|
|
||||||
|
# set the gpu configuration
|
||||||
|
configuration()
|
||||||
|
|
||||||
|
# writing patches into a sub-folder in order to be flowed from directory.
|
||||||
|
provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
|
||||||
|
dir_flow_train_labels,
|
||||||
|
input_height, input_width, blur_k, blur_aug,
|
||||||
|
flip_aug, binarization, scaling, scales, flip_index,
|
||||||
|
scaling_bluring, scaling_binarization, rotation,
|
||||||
|
rotation_not_90, thetha, scaling_flip,
|
||||||
|
augmentation=augmentation, patches=patches)
|
||||||
|
|
||||||
|
provide_patches(dir_img_val, dir_seg_val, dir_flow_eval_imgs,
|
||||||
|
dir_flow_eval_labels,
|
||||||
|
input_height, input_width, blur_k, blur_aug,
|
||||||
|
flip_aug, binarization, scaling, scales, flip_index,
|
||||||
|
scaling_bluring, scaling_binarization, rotation,
|
||||||
|
rotation_not_90, thetha, scaling_flip,
|
||||||
|
augmentation=False, patches=patches)
|
||||||
|
|
||||||
|
if weighted_loss:
|
||||||
|
weights = np.zeros(n_classes)
|
||||||
|
if data_is_provided:
|
||||||
|
for obj in os.listdir(dir_flow_train_labels):
|
||||||
|
try:
|
||||||
|
label_obj = cv2.imread(dir_flow_train_labels + '/' + obj)
|
||||||
|
label_obj_one_hot = get_one_hot(label_obj, label_obj.shape[0], label_obj.shape[1], n_classes)
|
||||||
|
weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
else:
|
||||||
|
|
||||||
|
for obj in os.listdir(dir_seg):
|
||||||
|
try:
|
||||||
|
label_obj = cv2.imread(dir_seg + '/' + obj)
|
||||||
|
label_obj_one_hot = get_one_hot(label_obj, label_obj.shape[0], label_obj.shape[1], n_classes)
|
||||||
|
weights += (label_obj_one_hot.sum(axis=0)).sum(axis=0)
|
||||||
|
except:
|
||||||
|
pass
|
||||||
|
|
||||||
|
weights = 1.00 / weights
|
||||||
|
|
||||||
|
weights = weights / float(np.sum(weights))
|
||||||
|
weights = weights / float(np.min(weights))
|
||||||
|
weights = weights / float(np.sum(weights))
|
||||||
|
|
||||||
|
if continue_training:
|
||||||
|
if is_loss_soft_dice:
|
||||||
|
model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss})
|
||||||
|
if weighted_loss:
|
||||||
|
model = load_model(dir_of_start_model, compile=True,
|
||||||
|
custom_objects={'loss': weighted_categorical_crossentropy(weights)})
|
||||||
|
if not is_loss_soft_dice and not weighted_loss:
|
||||||
|
model = load_model(dir_of_start_model, compile=True)
|
||||||
|
else:
|
||||||
|
# get our model.
|
||||||
|
index_start = 0
|
||||||
|
model = resnet50_unet(n_classes, input_height, input_width, weight_decay, pretraining)
|
||||||
|
|
||||||
|
# if you want to see the model structure just uncomment model summary.
|
||||||
|
# model.summary()
|
||||||
|
|
||||||
|
if not is_loss_soft_dice and not weighted_loss:
|
||||||
|
model.compile(loss='categorical_crossentropy',
|
||||||
|
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
|
||||||
|
if is_loss_soft_dice:
|
||||||
|
model.compile(loss=soft_dice_loss,
|
||||||
|
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
|
||||||
|
|
||||||
|
if weighted_loss:
|
||||||
|
model.compile(loss=weighted_categorical_crossentropy(weights),
|
||||||
|
optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
|
||||||
|
|
||||||
|
# generating train and evaluation data
|
||||||
|
train_gen = data_gen(dir_flow_train_imgs, dir_flow_train_labels, batch_size=n_batch,
|
||||||
|
input_height=input_height, input_width=input_width, n_classes=n_classes)
|
||||||
|
val_gen = data_gen(dir_flow_eval_imgs, dir_flow_eval_labels, batch_size=n_batch,
|
||||||
|
input_height=input_height, input_width=input_width, n_classes=n_classes)
|
||||||
|
|
||||||
|
for i in tqdm(range(index_start, n_epochs + index_start)):
|
||||||
|
model.fit_generator(
|
||||||
|
train_gen,
|
||||||
|
steps_per_epoch=int(len(os.listdir(dir_flow_train_imgs)) / n_batch) - 1,
|
||||||
|
validation_data=val_gen,
|
||||||
|
validation_steps=1,
|
||||||
|
epochs=1)
|
||||||
|
model.save(dir_output + '/' + 'model_' + str(i) + '.h5')
|
||||||
|
|
||||||
|
# os.system('rm -rf '+dir_train_flowing)
|
||||||
|
# os.system('rm -rf '+dir_eval_flowing)
|
||||||
|
|
||||||
|
# model.save(dir_output+'/'+'model'+'.h5')
|
||||||
@ -0,0 +1,494 @@
|
|||||||
|
import os
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
import seaborn as sns
|
||||||
|
from scipy.ndimage.interpolation import map_coordinates
|
||||||
|
from scipy.ndimage.filters import gaussian_filter
|
||||||
|
import random
|
||||||
|
from tqdm import tqdm
|
||||||
|
import imutils
|
||||||
|
import math
|
||||||
|
|
||||||
|
|
||||||
|
def bluring(img_in, kind):
|
||||||
|
if kind == 'guass':
|
||||||
|
img_blur = cv2.GaussianBlur(img_in, (5, 5), 0)
|
||||||
|
elif kind == "median":
|
||||||
|
img_blur = cv2.medianBlur(img_in, 5)
|
||||||
|
elif kind == 'blur':
|
||||||
|
img_blur = cv2.blur(img_in, (5, 5))
|
||||||
|
return img_blur
|
||||||
|
|
||||||
|
|
||||||
|
def elastic_transform(image, alpha, sigma, seedj, random_state=None):
|
||||||
|
"""Elastic deformation of images as described in [Simard2003]_.
|
||||||
|
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
|
||||||
|
Convolutional Neural Networks applied to Visual Document Analysis", in
|
||||||
|
Proc. of the International Conference on Document Analysis and
|
||||||
|
Recognition, 2003.
|
||||||
|
"""
|
||||||
|
if random_state is None:
|
||||||
|
random_state = np.random.RandomState(seedj)
|
||||||
|
|
||||||
|
shape = image.shape
|
||||||
|
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
|
||||||
|
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
|
||||||
|
dz = np.zeros_like(dx)
|
||||||
|
|
||||||
|
x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
|
||||||
|
indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1))
|
||||||
|
|
||||||
|
distored_image = map_coordinates(image, indices, order=1, mode='reflect')
|
||||||
|
return distored_image.reshape(image.shape)
|
||||||
|
|
||||||
|
|
||||||
|
def rotation_90(img):
|
||||||
|
img_rot = np.zeros((img.shape[1], img.shape[0], img.shape[2]))
|
||||||
|
img_rot[:, :, 0] = img[:, :, 0].T
|
||||||
|
img_rot[:, :, 1] = img[:, :, 1].T
|
||||||
|
img_rot[:, :, 2] = img[:, :, 2].T
|
||||||
|
return img_rot
|
||||||
|
|
||||||
|
|
||||||
|
def rotatedRectWithMaxArea(w, h, angle):
|
||||||
|
"""
|
||||||
|
Given a rectangle of size wxh that has been rotated by 'angle' (in
|
||||||
|
radians), computes the width and height of the largest possible
|
||||||
|
axis-aligned rectangle (maximal area) within the rotated rectangle.
|
||||||
|
"""
|
||||||
|
if w <= 0 or h <= 0:
|
||||||
|
return 0, 0
|
||||||
|
|
||||||
|
width_is_longer = w >= h
|
||||||
|
side_long, side_short = (w, h) if width_is_longer else (h, w)
|
||||||
|
|
||||||
|
# since the solutions for angle, -angle and 180-angle are all the same,
|
||||||
|
# if suffices to look at the first quadrant and the absolute values of sin,cos:
|
||||||
|
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
|
||||||
|
if side_short <= 2. * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
|
||||||
|
# half constrained case: two crop corners touch the longer side,
|
||||||
|
# the other two corners are on the mid-line parallel to the longer line
|
||||||
|
x = 0.5 * side_short
|
||||||
|
wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
|
||||||
|
else:
|
||||||
|
# fully constrained case: crop touches all 4 sides
|
||||||
|
cos_2a = cos_a * cos_a - sin_a * sin_a
|
||||||
|
wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
|
||||||
|
|
||||||
|
return wr, hr
|
||||||
|
|
||||||
|
|
||||||
|
def rotate_max_area(image, rotated, rotated_label, angle):
|
||||||
|
""" image: cv2 image matrix object
|
||||||
|
angle: in degree
|
||||||
|
"""
|
||||||
|
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0],
|
||||||
|
math.radians(angle))
|
||||||
|
h, w, _ = rotated.shape
|
||||||
|
y1 = h // 2 - int(hr / 2)
|
||||||
|
y2 = y1 + int(hr)
|
||||||
|
x1 = w // 2 - int(wr / 2)
|
||||||
|
x2 = x1 + int(wr)
|
||||||
|
return rotated[y1:y2, x1:x2], rotated_label[y1:y2, x1:x2]
|
||||||
|
|
||||||
|
|
||||||
|
def rotation_not_90_func(img, label, thetha):
|
||||||
|
rotated = imutils.rotate(img, thetha)
|
||||||
|
rotated_label = imutils.rotate(label, thetha)
|
||||||
|
return rotate_max_area(img, rotated, rotated_label, thetha)
|
||||||
|
|
||||||
|
|
||||||
|
def color_images(seg, n_classes):
|
||||||
|
ann_u = range(n_classes)
|
||||||
|
if len(np.shape(seg)) == 3:
|
||||||
|
seg = seg[:, :, 0]
|
||||||
|
|
||||||
|
seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
|
||||||
|
colors = sns.color_palette("hls", n_classes)
|
||||||
|
|
||||||
|
for c in ann_u:
|
||||||
|
c = int(c)
|
||||||
|
segl = (seg == c)
|
||||||
|
seg_img[:, :, 0] += segl * (colors[c][0])
|
||||||
|
seg_img[:, :, 1] += segl * (colors[c][1])
|
||||||
|
seg_img[:, :, 2] += segl * (colors[c][2])
|
||||||
|
return seg_img
|
||||||
|
|
||||||
|
|
||||||
|
def resize_image(seg_in, input_height, input_width):
|
||||||
|
return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
|
||||||
|
|
||||||
|
|
||||||
|
def get_one_hot(seg, input_height, input_width, n_classes):
|
||||||
|
seg = seg[:, :, 0]
|
||||||
|
seg_f = np.zeros((input_height, input_width, n_classes))
|
||||||
|
for j in range(n_classes):
|
||||||
|
seg_f[:, :, j] = (seg == j).astype(int)
|
||||||
|
return seg_f
|
||||||
|
|
||||||
|
|
||||||
|
def IoU(Yi, y_predi):
|
||||||
|
# mean Intersection over Union
|
||||||
|
# Mean IoU = TP/(FN + TP + FP)
|
||||||
|
|
||||||
|
IoUs = []
|
||||||
|
classes_true = np.unique(Yi)
|
||||||
|
for c in classes_true:
|
||||||
|
TP = np.sum((Yi == c) & (y_predi == c))
|
||||||
|
FP = np.sum((Yi != c) & (y_predi == c))
|
||||||
|
FN = np.sum((Yi == c) & (y_predi != c))
|
||||||
|
IoU = TP / float(TP + FP + FN)
|
||||||
|
print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c, TP, FP, FN, IoU))
|
||||||
|
IoUs.append(IoU)
|
||||||
|
mIoU = np.mean(IoUs)
|
||||||
|
print("_________________")
|
||||||
|
print("Mean IoU: {:4.3f}".format(mIoU))
|
||||||
|
return mIoU
|
||||||
|
|
||||||
|
|
||||||
|
def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_classes):
|
||||||
|
c = 0
|
||||||
|
n = [f for f in os.listdir(img_folder) if not f.startswith('.')] # os.listdir(img_folder) #List of training images
|
||||||
|
random.shuffle(n)
|
||||||
|
while True:
|
||||||
|
img = np.zeros((batch_size, input_height, input_width, 3)).astype('float')
|
||||||
|
mask = np.zeros((batch_size, input_height, input_width, n_classes)).astype('float')
|
||||||
|
|
||||||
|
for i in range(c, c + batch_size): # initially from 0 to 16, c = 0.
|
||||||
|
# print(img_folder+'/'+n[i])
|
||||||
|
|
||||||
|
try:
|
||||||
|
filename = n[i].split('.')[0]
|
||||||
|
|
||||||
|
train_img = cv2.imread(img_folder + '/' + n[i]) / 255.
|
||||||
|
train_img = cv2.resize(train_img, (input_width, input_height),
|
||||||
|
interpolation=cv2.INTER_NEAREST) # Read an image from folder and resize
|
||||||
|
|
||||||
|
img[i - c] = train_img # add to array - img[0], img[1], and so on.
|
||||||
|
train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
|
||||||
|
# print(mask_folder+'/'+filename+'.png')
|
||||||
|
# print(train_mask.shape)
|
||||||
|
train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
|
||||||
|
n_classes)
|
||||||
|
# train_mask = train_mask.reshape(224, 224, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]
|
||||||
|
|
||||||
|
mask[i - c] = train_mask
|
||||||
|
except:
|
||||||
|
img[i - c] = np.ones((input_height, input_width, 3)).astype('float')
|
||||||
|
mask[i - c] = np.zeros((input_height, input_width, n_classes)).astype('float')
|
||||||
|
|
||||||
|
c += batch_size
|
||||||
|
if c + batch_size >= len(os.listdir(img_folder)):
|
||||||
|
c = 0
|
||||||
|
random.shuffle(n)
|
||||||
|
yield img, mask
|
||||||
|
|
||||||
|
|
||||||
|
def otsu_copy(img):
|
||||||
|
img_r = np.zeros(img.shape)
|
||||||
|
img1 = img[:, :, 0]
|
||||||
|
img2 = img[:, :, 1]
|
||||||
|
img3 = img[:, :, 2]
|
||||||
|
_, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||||
|
_, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||||
|
_, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
|
||||||
|
img_r[:, :, 0] = threshold1
|
||||||
|
img_r[:, :, 1] = threshold1
|
||||||
|
img_r[:, :, 2] = threshold1
|
||||||
|
return img_r
|
||||||
|
|
||||||
|
|
||||||
|
def get_patches(dir_img_f, dir_seg_f, img, label, height, width, indexer):
|
||||||
|
if img.shape[0] < height or img.shape[1] < width:
|
||||||
|
img, label = do_padding(img, label, height, width)
|
||||||
|
|
||||||
|
img_h = img.shape[0]
|
||||||
|
img_w = img.shape[1]
|
||||||
|
|
||||||
|
nxf = img_w / float(width)
|
||||||
|
nyf = img_h / float(height)
|
||||||
|
|
||||||
|
if nxf > int(nxf):
|
||||||
|
nxf = int(nxf) + 1
|
||||||
|
if nyf > int(nyf):
|
||||||
|
nyf = int(nyf) + 1
|
||||||
|
|
||||||
|
nxf = int(nxf)
|
||||||
|
nyf = int(nyf)
|
||||||
|
|
||||||
|
for i in range(nxf):
|
||||||
|
for j in range(nyf):
|
||||||
|
index_x_d = i * width
|
||||||
|
index_x_u = (i + 1) * width
|
||||||
|
|
||||||
|
index_y_d = j * height
|
||||||
|
index_y_u = (j + 1) * height
|
||||||
|
|
||||||
|
if index_x_u > img_w:
|
||||||
|
index_x_u = img_w
|
||||||
|
index_x_d = img_w - width
|
||||||
|
if index_y_u > img_h:
|
||||||
|
index_y_u = img_h
|
||||||
|
index_y_d = img_h - height
|
||||||
|
|
||||||
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
||||||
|
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
||||||
|
|
||||||
|
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
|
||||||
|
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
|
||||||
|
indexer += 1
|
||||||
|
|
||||||
|
return indexer
|
||||||
|
|
||||||
|
|
||||||
|
def do_padding(img, label, height, width):
|
||||||
|
height_new = img.shape[0]
|
||||||
|
width_new = img.shape[1]
|
||||||
|
|
||||||
|
h_start = 0
|
||||||
|
w_start = 0
|
||||||
|
|
||||||
|
if img.shape[0] < height:
|
||||||
|
h_start = int(abs(height - img.shape[0]) / 2.)
|
||||||
|
height_new = height
|
||||||
|
|
||||||
|
if img.shape[1] < width:
|
||||||
|
w_start = int(abs(width - img.shape[1]) / 2.)
|
||||||
|
width_new = width
|
||||||
|
|
||||||
|
img_new = np.ones((height_new, width_new, img.shape[2])).astype(float) * 255
|
||||||
|
label_new = np.zeros((height_new, width_new, label.shape[2])).astype(float)
|
||||||
|
|
||||||
|
img_new[h_start:h_start + img.shape[0], w_start:w_start + img.shape[1], :] = np.copy(img[:, :, :])
|
||||||
|
label_new[h_start:h_start + label.shape[0], w_start:w_start + label.shape[1], :] = np.copy(label[:, :, :])
|
||||||
|
|
||||||
|
return img_new, label_new
|
||||||
|
|
||||||
|
|
||||||
|
def get_patches_num_scale(dir_img_f, dir_seg_f, img, label, height, width, indexer, n_patches, scaler):
|
||||||
|
if img.shape[0] < height or img.shape[1] < width:
|
||||||
|
img, label = do_padding(img, label, height, width)
|
||||||
|
|
||||||
|
img_h = img.shape[0]
|
||||||
|
img_w = img.shape[1]
|
||||||
|
|
||||||
|
height_scale = int(height * scaler)
|
||||||
|
width_scale = int(width * scaler)
|
||||||
|
|
||||||
|
nxf = img_w / float(width_scale)
|
||||||
|
nyf = img_h / float(height_scale)
|
||||||
|
|
||||||
|
if nxf > int(nxf):
|
||||||
|
nxf = int(nxf) + 1
|
||||||
|
if nyf > int(nyf):
|
||||||
|
nyf = int(nyf) + 1
|
||||||
|
|
||||||
|
nxf = int(nxf)
|
||||||
|
nyf = int(nyf)
|
||||||
|
|
||||||
|
for i in range(nxf):
|
||||||
|
for j in range(nyf):
|
||||||
|
index_x_d = i * width_scale
|
||||||
|
index_x_u = (i + 1) * width_scale
|
||||||
|
|
||||||
|
index_y_d = j * height_scale
|
||||||
|
index_y_u = (j + 1) * height_scale
|
||||||
|
|
||||||
|
if index_x_u > img_w:
|
||||||
|
index_x_u = img_w
|
||||||
|
index_x_d = img_w - width_scale
|
||||||
|
if index_y_u > img_h:
|
||||||
|
index_y_u = img_h
|
||||||
|
index_y_d = img_h - height_scale
|
||||||
|
|
||||||
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
||||||
|
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
||||||
|
|
||||||
|
img_patch = resize_image(img_patch, height, width)
|
||||||
|
label_patch = resize_image(label_patch, height, width)
|
||||||
|
|
||||||
|
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
|
||||||
|
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
|
||||||
|
indexer += 1
|
||||||
|
|
||||||
|
return indexer
|
||||||
|
|
||||||
|
|
||||||
|
def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, indexer, scaler):
|
||||||
|
img = resize_image(img, int(img.shape[0] * scaler), int(img.shape[1] * scaler))
|
||||||
|
label = resize_image(label, int(label.shape[0] * scaler), int(label.shape[1] * scaler))
|
||||||
|
|
||||||
|
if img.shape[0] < height or img.shape[1] < width:
|
||||||
|
img, label = do_padding(img, label, height, width)
|
||||||
|
|
||||||
|
img_h = img.shape[0]
|
||||||
|
img_w = img.shape[1]
|
||||||
|
|
||||||
|
height_scale = int(height * 1)
|
||||||
|
width_scale = int(width * 1)
|
||||||
|
|
||||||
|
nxf = img_w / float(width_scale)
|
||||||
|
nyf = img_h / float(height_scale)
|
||||||
|
|
||||||
|
if nxf > int(nxf):
|
||||||
|
nxf = int(nxf) + 1
|
||||||
|
if nyf > int(nyf):
|
||||||
|
nyf = int(nyf) + 1
|
||||||
|
|
||||||
|
nxf = int(nxf)
|
||||||
|
nyf = int(nyf)
|
||||||
|
|
||||||
|
for i in range(nxf):
|
||||||
|
for j in range(nyf):
|
||||||
|
index_x_d = i * width_scale
|
||||||
|
index_x_u = (i + 1) * width_scale
|
||||||
|
|
||||||
|
index_y_d = j * height_scale
|
||||||
|
index_y_u = (j + 1) * height_scale
|
||||||
|
|
||||||
|
if index_x_u > img_w:
|
||||||
|
index_x_u = img_w
|
||||||
|
index_x_d = img_w - width_scale
|
||||||
|
if index_y_u > img_h:
|
||||||
|
index_y_u = img_h
|
||||||
|
index_y_d = img_h - height_scale
|
||||||
|
|
||||||
|
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
||||||
|
label_patch = label[index_y_d:index_y_u, index_x_d:index_x_u, :]
|
||||||
|
|
||||||
|
# img_patch=resize_image(img_patch,height,width)
|
||||||
|
# label_patch=resize_image(label_patch,height,width)
|
||||||
|
|
||||||
|
cv2.imwrite(dir_img_f + '/img_' + str(indexer) + '.png', img_patch)
|
||||||
|
cv2.imwrite(dir_seg_f + '/img_' + str(indexer) + '.png', label_patch)
|
||||||
|
indexer += 1
|
||||||
|
|
||||||
|
return indexer
|
||||||
|
|
||||||
|
|
||||||
|
def provide_patches(dir_img, dir_seg, dir_flow_train_imgs,
|
||||||
|
dir_flow_train_labels,
|
||||||
|
input_height, input_width, blur_k, blur_aug,
|
||||||
|
flip_aug, binarization, scaling, scales, flip_index,
|
||||||
|
scaling_bluring, scaling_binarization, rotation,
|
||||||
|
rotation_not_90, thetha, scaling_flip,
|
||||||
|
augmentation=False, patches=False):
|
||||||
|
imgs_cv_train = np.array(os.listdir(dir_img))
|
||||||
|
segs_cv_train = np.array(os.listdir(dir_seg))
|
||||||
|
|
||||||
|
indexer = 0
|
||||||
|
for im, seg_i in tqdm(zip(imgs_cv_train, segs_cv_train)):
|
||||||
|
img_name = im.split('.')[0]
|
||||||
|
if not patches:
|
||||||
|
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
|
||||||
|
resize_image(cv2.imread(dir_img + '/' + im), input_height, input_width))
|
||||||
|
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
|
||||||
|
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
|
||||||
|
indexer += 1
|
||||||
|
|
||||||
|
if augmentation:
|
||||||
|
if flip_aug:
|
||||||
|
for f_i in flip_index:
|
||||||
|
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
|
||||||
|
resize_image(cv2.flip(cv2.imread(dir_img + '/' + im), f_i), input_height,
|
||||||
|
input_width))
|
||||||
|
|
||||||
|
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
|
||||||
|
resize_image(cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
|
||||||
|
input_height, input_width))
|
||||||
|
indexer += 1
|
||||||
|
|
||||||
|
if blur_aug:
|
||||||
|
for blur_i in blur_k:
|
||||||
|
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
|
||||||
|
(resize_image(bluring(cv2.imread(dir_img + '/' + im), blur_i), input_height,
|
||||||
|
input_width)))
|
||||||
|
|
||||||
|
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
|
||||||
|
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height,
|
||||||
|
input_width))
|
||||||
|
indexer += 1
|
||||||
|
|
||||||
|
if binarization:
|
||||||
|
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
|
||||||
|
resize_image(otsu_copy(cv2.imread(dir_img + '/' + im)), input_height, input_width))
|
||||||
|
|
||||||
|
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
|
||||||
|
resize_image(cv2.imread(dir_seg + '/' + img_name + '.png'), input_height, input_width))
|
||||||
|
indexer += 1
|
||||||
|
|
||||||
|
if patches:
|
||||||
|
|
||||||
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
cv2.imread(dir_img + '/' + im), cv2.imread(dir_seg + '/' + img_name + '.png'),
|
||||||
|
input_height, input_width, indexer=indexer)
|
||||||
|
|
||||||
|
if augmentation:
|
||||||
|
|
||||||
|
if rotation:
|
||||||
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
rotation_90(cv2.imread(dir_img + '/' + im)),
|
||||||
|
rotation_90(cv2.imread(dir_seg + '/' + img_name + '.png')),
|
||||||
|
input_height, input_width, indexer=indexer)
|
||||||
|
|
||||||
|
if rotation_not_90:
|
||||||
|
|
||||||
|
for thetha_i in thetha:
|
||||||
|
img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/' + im),
|
||||||
|
cv2.imread(
|
||||||
|
dir_seg + '/' + img_name + '.png'),
|
||||||
|
thetha_i)
|
||||||
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
img_max_rotated,
|
||||||
|
label_max_rotated,
|
||||||
|
input_height, input_width, indexer=indexer)
|
||||||
|
if flip_aug:
|
||||||
|
for f_i in flip_index:
|
||||||
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
cv2.flip(cv2.imread(dir_img + '/' + im), f_i),
|
||||||
|
cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'), f_i),
|
||||||
|
input_height, input_width, indexer=indexer)
|
||||||
|
if blur_aug:
|
||||||
|
for blur_i in blur_k:
|
||||||
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
bluring(cv2.imread(dir_img + '/' + im), blur_i),
|
||||||
|
cv2.imread(dir_seg + '/' + img_name + '.png'),
|
||||||
|
input_height, input_width, indexer=indexer)
|
||||||
|
|
||||||
|
if scaling:
|
||||||
|
for sc_ind in scales:
|
||||||
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
cv2.imread(dir_img + '/' + im),
|
||||||
|
cv2.imread(dir_seg + '/' + img_name + '.png'),
|
||||||
|
input_height, input_width, indexer=indexer, scaler=sc_ind)
|
||||||
|
if binarization:
|
||||||
|
indexer = get_patches(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
otsu_copy(cv2.imread(dir_img + '/' + im)),
|
||||||
|
cv2.imread(dir_seg + '/' + img_name + '.png'),
|
||||||
|
input_height, input_width, indexer=indexer)
|
||||||
|
|
||||||
|
if scaling_bluring:
|
||||||
|
for sc_ind in scales:
|
||||||
|
for blur_i in blur_k:
|
||||||
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
bluring(cv2.imread(dir_img + '/' + im), blur_i),
|
||||||
|
cv2.imread(dir_seg + '/' + img_name + '.png'),
|
||||||
|
input_height, input_width, indexer=indexer,
|
||||||
|
scaler=sc_ind)
|
||||||
|
|
||||||
|
if scaling_binarization:
|
||||||
|
for sc_ind in scales:
|
||||||
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
otsu_copy(cv2.imread(dir_img + '/' + im)),
|
||||||
|
cv2.imread(dir_seg + '/' + img_name + '.png'),
|
||||||
|
input_height, input_width, indexer=indexer, scaler=sc_ind)
|
||||||
|
|
||||||
|
if scaling_flip:
|
||||||
|
for sc_ind in scales:
|
||||||
|
for f_i in flip_index:
|
||||||
|
indexer = get_patches_num_scale_new(dir_flow_train_imgs, dir_flow_train_labels,
|
||||||
|
cv2.flip(cv2.imread(dir_img + '/' + im), f_i),
|
||||||
|
cv2.flip(cv2.imread(dir_seg + '/' + img_name + '.png'),
|
||||||
|
f_i),
|
||||||
|
input_height, input_width, indexer=indexer,
|
||||||
|
scaler=sc_ind)
|
||||||
@ -0,0 +1,228 @@
|
|||||||
|
import numpy as np
|
||||||
|
import cv2
|
||||||
|
from scipy.signal import find_peaks
|
||||||
|
from scipy.ndimage import gaussian_filter1d
|
||||||
|
|
||||||
|
from .contour import find_new_features_of_contours, return_contours_of_interested_region
|
||||||
|
from .resize import resize_image
|
||||||
|
from .rotate import rotate_image
|
||||||
|
|
||||||
|
|
||||||
|
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None):
|
||||||
|
mask_marginals = np.zeros((text_with_lines.shape[0], text_with_lines.shape[1]))
|
||||||
|
mask_marginals = mask_marginals.astype(np.uint8)
|
||||||
|
|
||||||
|
text_with_lines = text_with_lines.astype(np.uint8)
|
||||||
|
# text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
|
||||||
|
|
||||||
|
text_with_lines_eroded = cv2.erode(text_with_lines, kernel, iterations=5)
|
||||||
|
|
||||||
|
if text_with_lines.shape[0] <= 1500:
|
||||||
|
pass
|
||||||
|
elif text_with_lines.shape[0] > 1500 and text_with_lines.shape[0] <= 1800:
|
||||||
|
text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.5), text_with_lines.shape[1])
|
||||||
|
text_with_lines = cv2.erode(text_with_lines, kernel, iterations=5)
|
||||||
|
text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0],
|
||||||
|
text_with_lines_eroded.shape[1])
|
||||||
|
else:
|
||||||
|
text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.8), text_with_lines.shape[1])
|
||||||
|
text_with_lines = cv2.erode(text_with_lines, kernel, iterations=7)
|
||||||
|
text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0],
|
||||||
|
text_with_lines_eroded.shape[1])
|
||||||
|
|
||||||
|
text_with_lines_y = text_with_lines.sum(axis=0)
|
||||||
|
text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0)
|
||||||
|
|
||||||
|
thickness_along_y_percent = text_with_lines_y_eroded.max() / (float(text_with_lines.shape[0])) * 100
|
||||||
|
|
||||||
|
# print(thickness_along_y_percent,'thickness_along_y_percent')
|
||||||
|
|
||||||
|
if thickness_along_y_percent < 30:
|
||||||
|
min_textline_thickness = 8
|
||||||
|
elif thickness_along_y_percent >= 30 and thickness_along_y_percent < 50:
|
||||||
|
min_textline_thickness = 20
|
||||||
|
else:
|
||||||
|
min_textline_thickness = 40
|
||||||
|
|
||||||
|
if thickness_along_y_percent >= 14:
|
||||||
|
|
||||||
|
text_with_lines_y_rev = -1 * text_with_lines_y[:]
|
||||||
|
# print(text_with_lines_y)
|
||||||
|
# print(text_with_lines_y_rev)
|
||||||
|
|
||||||
|
# plt.plot(text_with_lines_y)
|
||||||
|
# plt.show()
|
||||||
|
|
||||||
|
text_with_lines_y_rev = text_with_lines_y_rev - np.min(text_with_lines_y_rev)
|
||||||
|
|
||||||
|
# plt.plot(text_with_lines_y_rev)
|
||||||
|
# plt.show()
|
||||||
|
sigma_gaus = 1
|
||||||
|
region_sum_0 = gaussian_filter1d(text_with_lines_y, sigma_gaus)
|
||||||
|
|
||||||
|
region_sum_0_rev = gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
|
||||||
|
|
||||||
|
# plt.plot(region_sum_0_rev)
|
||||||
|
# plt.show()
|
||||||
|
region_sum_0_updown = region_sum_0[len(region_sum_0)::-1]
|
||||||
|
|
||||||
|
first_nonzero = (next((i for i, x in enumerate(region_sum_0) if x), None))
|
||||||
|
last_nonzero = (next((i for i, x in enumerate(region_sum_0_updown) if x), None))
|
||||||
|
|
||||||
|
last_nonzero = len(region_sum_0) - last_nonzero
|
||||||
|
|
||||||
|
# img_sum_0_smooth_rev=-region_sum_0
|
||||||
|
|
||||||
|
mid_point = (last_nonzero + first_nonzero) / 2.
|
||||||
|
|
||||||
|
one_third_right = (last_nonzero - mid_point) / 3.0
|
||||||
|
one_third_left = (mid_point - first_nonzero) / 3.0
|
||||||
|
|
||||||
|
# img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
|
||||||
|
|
||||||
|
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
|
||||||
|
|
||||||
|
peaks = np.array(peaks)
|
||||||
|
|
||||||
|
# print(region_sum_0[peaks])
|
||||||
|
# #plt.plot(region_sum_0)
|
||||||
|
# #plt.plot(peaks,region_sum_0[peaks],'*')
|
||||||
|
# #plt.show()
|
||||||
|
# print(first_nonzero,last_nonzero,peaks)
|
||||||
|
peaks = peaks[(peaks > first_nonzero) & (peaks < last_nonzero)]
|
||||||
|
|
||||||
|
# print(first_nonzero,last_nonzero,peaks)
|
||||||
|
|
||||||
|
# print(region_sum_0[peaks]<10)
|
||||||
|
# ###peaks=peaks[region_sum_0[peaks]<25 ]
|
||||||
|
|
||||||
|
# print(region_sum_0[peaks])
|
||||||
|
peaks = peaks[region_sum_0[peaks] < min_textline_thickness]
|
||||||
|
# print(peaks)
|
||||||
|
# print(first_nonzero,last_nonzero,one_third_right,one_third_left)
|
||||||
|
|
||||||
|
if num_col == 1:
|
||||||
|
peaks_right = peaks[peaks > mid_point]
|
||||||
|
peaks_left = peaks[peaks < mid_point]
|
||||||
|
if num_col == 2:
|
||||||
|
peaks_right = peaks[peaks > (mid_point + one_third_right)]
|
||||||
|
peaks_left = peaks[peaks < (mid_point - one_third_left)]
|
||||||
|
|
||||||
|
try:
|
||||||
|
point_right = np.min(peaks_right)
|
||||||
|
except:
|
||||||
|
point_right = last_nonzero
|
||||||
|
|
||||||
|
try:
|
||||||
|
point_left = np.max(peaks_left)
|
||||||
|
except:
|
||||||
|
point_left = first_nonzero
|
||||||
|
|
||||||
|
# print(point_left,point_right)
|
||||||
|
# print(text_regions.shape)
|
||||||
|
if point_right >= mask_marginals.shape[1]:
|
||||||
|
point_right = mask_marginals.shape[1] - 1
|
||||||
|
|
||||||
|
try:
|
||||||
|
mask_marginals[:, point_left:point_right] = 1
|
||||||
|
except:
|
||||||
|
mask_marginals[:, :] = 1
|
||||||
|
|
||||||
|
# print(mask_marginals.shape,point_left,point_right,'nadosh')
|
||||||
|
mask_marginals_rotated = rotate_image(mask_marginals, -slope_deskew)
|
||||||
|
|
||||||
|
# print(mask_marginals_rotated.shape,'nadosh')
|
||||||
|
mask_marginals_rotated_sum = mask_marginals_rotated.sum(axis=0)
|
||||||
|
|
||||||
|
mask_marginals_rotated_sum[mask_marginals_rotated_sum != 0] = 1
|
||||||
|
index_x = np.array(range(len(mask_marginals_rotated_sum))) + 1
|
||||||
|
|
||||||
|
index_x_interest = index_x[mask_marginals_rotated_sum == 1]
|
||||||
|
|
||||||
|
min_point_of_left_marginal = np.min(index_x_interest) - 16
|
||||||
|
max_point_of_right_marginal = np.max(index_x_interest) + 16
|
||||||
|
|
||||||
|
if min_point_of_left_marginal < 0:
|
||||||
|
min_point_of_left_marginal = 0
|
||||||
|
if max_point_of_right_marginal >= text_regions.shape[1]:
|
||||||
|
max_point_of_right_marginal = text_regions.shape[1] - 1
|
||||||
|
|
||||||
|
# print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
|
||||||
|
# print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
|
||||||
|
# plt.imshow(mask_marginals)
|
||||||
|
# plt.show()
|
||||||
|
|
||||||
|
# plt.imshow(mask_marginals_rotated)
|
||||||
|
# plt.show()
|
||||||
|
|
||||||
|
text_regions[(mask_marginals_rotated[:, :] != 1) & (text_regions[:, :] == 1)] = 4
|
||||||
|
|
||||||
|
# plt.imshow(text_regions)
|
||||||
|
# plt.show()
|
||||||
|
|
||||||
|
pixel_img = 4
|
||||||
|
min_area_text = 0.00001
|
||||||
|
polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
|
||||||
|
|
||||||
|
cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = find_new_features_of_contours(
|
||||||
|
polygons_of_marginals)
|
||||||
|
|
||||||
|
text_regions[(text_regions[:, :] == 4)] = 1
|
||||||
|
|
||||||
|
marginlas_should_be_main_text = []
|
||||||
|
|
||||||
|
x_min_marginals_left = []
|
||||||
|
x_min_marginals_right = []
|
||||||
|
|
||||||
|
for i in range(len(cx_text_only)):
|
||||||
|
|
||||||
|
x_width_mar = abs(x_min_text_only[i] - x_max_text_only[i])
|
||||||
|
y_height_mar = abs(y_min_text_only[i] - y_max_text_only[i])
|
||||||
|
# print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
|
||||||
|
if x_width_mar > 16 and y_height_mar / x_width_mar < 18:
|
||||||
|
marginlas_should_be_main_text.append(polygons_of_marginals[i])
|
||||||
|
if x_min_text_only[i] < (mid_point - one_third_left):
|
||||||
|
x_min_marginals_left_new = x_min_text_only[i]
|
||||||
|
if len(x_min_marginals_left) == 0:
|
||||||
|
x_min_marginals_left.append(x_min_marginals_left_new)
|
||||||
|
else:
|
||||||
|
x_min_marginals_left[0] = min(x_min_marginals_left[0], x_min_marginals_left_new)
|
||||||
|
else:
|
||||||
|
x_min_marginals_right_new = x_min_text_only[i]
|
||||||
|
if len(x_min_marginals_right) == 0:
|
||||||
|
x_min_marginals_right.append(x_min_marginals_right_new)
|
||||||
|
else:
|
||||||
|
x_min_marginals_right[0] = min(x_min_marginals_right[0], x_min_marginals_right_new)
|
||||||
|
|
||||||
|
if len(x_min_marginals_left) == 0:
|
||||||
|
x_min_marginals_left = [0]
|
||||||
|
if len(x_min_marginals_right) == 0:
|
||||||
|
x_min_marginals_right = [text_regions.shape[1] - 1]
|
||||||
|
|
||||||
|
# print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
|
||||||
|
|
||||||
|
# print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
|
||||||
|
text_regions = cv2.fillPoly(text_regions, pts=marginlas_should_be_main_text, color=(4, 4))
|
||||||
|
|
||||||
|
# print(np.unique(text_regions))
|
||||||
|
|
||||||
|
# text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
|
||||||
|
# text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
|
||||||
|
|
||||||
|
text_regions[:, :int(min_point_of_left_marginal)][text_regions[:, :int(min_point_of_left_marginal)] == 1] = 0
|
||||||
|
text_regions[:, int(max_point_of_right_marginal):][text_regions[:, int(max_point_of_right_marginal):] == 1] = 0
|
||||||
|
|
||||||
|
# ##text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
|
||||||
|
|
||||||
|
# ##text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
|
||||||
|
# plt.plot(region_sum_0)
|
||||||
|
# plt.plot(peaks,region_sum_0[peaks],'*')
|
||||||
|
# plt.show()
|
||||||
|
|
||||||
|
# plt.imshow(text_regions)
|
||||||
|
# plt.show()
|
||||||
|
|
||||||
|
# sys.exit()
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
return text_regions
|
||||||
@ -1,4 +1,5 @@
|
|||||||
import cv2
|
import cv2
|
||||||
|
|
||||||
|
|
||||||
def resize_image(img_in, input_height, input_width):
|
def resize_image(img_in, input_height, input_width):
|
||||||
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
|
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
|
||||||
@ -1 +1 @@
|
|||||||
qurator/eynollah/ocrd-tool.json
|
eynollah/eynollah/ocrd-tool.json
|
||||||
@ -0,0 +1,42 @@
|
|||||||
|
[build-system]
|
||||||
|
requires = ["setuptools>=61.0"]
|
||||||
|
build-backend = "setuptools.build_meta"
|
||||||
|
|
||||||
|
[project]
|
||||||
|
name = "eynollah"
|
||||||
|
version = "0.3.0"
|
||||||
|
authors = [
|
||||||
|
{name = "Vahid Rezanezhad"}
|
||||||
|
]
|
||||||
|
description = "Document Layout Analysis"
|
||||||
|
readme = "README.md"
|
||||||
|
requires-python = ">=3.8"
|
||||||
|
keywords = ["document layout analysis", "semantic segmentation"]
|
||||||
|
classifiers = [
|
||||||
|
"Development Status :: 4 - Beta",
|
||||||
|
"Environment :: Console",
|
||||||
|
"Intended Audience :: Science/Research",
|
||||||
|
"License :: OSI Approved :: Apache Software License",
|
||||||
|
"Programming Language :: Python :: 3",
|
||||||
|
"Programming Language :: Python :: 3 :: Only",
|
||||||
|
"Topic :: Scientific/Engineering :: Image Processing",
|
||||||
|
]
|
||||||
|
dependencies = [
|
||||||
|
"ocrd >= 2.63.3",
|
||||||
|
"numpy <= 1.24.4",
|
||||||
|
"scikit-learn <= 1.3.2",
|
||||||
|
"tensorflow <= 2.13.1",
|
||||||
|
"imutils >= 0.5.4",
|
||||||
|
"matplotlib <= 3.7.5",
|
||||||
|
"setuptools >= 61",
|
||||||
|
]
|
||||||
|
|
||||||
|
# TODO: test dependencies
|
||||||
|
|
||||||
|
[project.scripts]
|
||||||
|
eynollah = "eynollah.eynollah.cli:main"
|
||||||
|
ocrd-eynollah-segment = "eynollah.eynollah.ocrd_cli:main"
|
||||||
|
|
||||||
|
[project.urls]
|
||||||
|
Homepage = "https://github.com/qurator-spk/eynollah"
|
||||||
|
Repository = "https://github.com/qurator-spk/eynollah.git"
|
||||||
@ -1,252 +0,0 @@
|
|||||||
import numpy as np
|
|
||||||
import cv2
|
|
||||||
from scipy.signal import find_peaks
|
|
||||||
from scipy.ndimage import gaussian_filter1d
|
|
||||||
|
|
||||||
|
|
||||||
from .contour import find_new_features_of_contours, return_contours_of_interested_region
|
|
||||||
from .resize import resize_image
|
|
||||||
from .rotate import rotate_image
|
|
||||||
|
|
||||||
def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, kernel=None):
|
|
||||||
mask_marginals=np.zeros((text_with_lines.shape[0],text_with_lines.shape[1]))
|
|
||||||
mask_marginals=mask_marginals.astype(np.uint8)
|
|
||||||
|
|
||||||
|
|
||||||
text_with_lines=text_with_lines.astype(np.uint8)
|
|
||||||
##text_with_lines=cv2.erode(text_with_lines,self.kernel,iterations=3)
|
|
||||||
|
|
||||||
text_with_lines_eroded=cv2.erode(text_with_lines,kernel,iterations=5)
|
|
||||||
|
|
||||||
if text_with_lines.shape[0]<=1500:
|
|
||||||
pass
|
|
||||||
elif text_with_lines.shape[0]>1500 and text_with_lines.shape[0]<=1800:
|
|
||||||
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.5),text_with_lines.shape[1])
|
|
||||||
text_with_lines=cv2.erode(text_with_lines,kernel,iterations=5)
|
|
||||||
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
|
|
||||||
else:
|
|
||||||
text_with_lines=resize_image(text_with_lines,int(text_with_lines.shape[0]*1.8),text_with_lines.shape[1])
|
|
||||||
text_with_lines=cv2.erode(text_with_lines,kernel,iterations=7)
|
|
||||||
text_with_lines=resize_image(text_with_lines,text_with_lines_eroded.shape[0],text_with_lines_eroded.shape[1])
|
|
||||||
|
|
||||||
|
|
||||||
text_with_lines_y=text_with_lines.sum(axis=0)
|
|
||||||
text_with_lines_y_eroded=text_with_lines_eroded.sum(axis=0)
|
|
||||||
|
|
||||||
thickness_along_y_percent=text_with_lines_y_eroded.max()/(float(text_with_lines.shape[0]))*100
|
|
||||||
|
|
||||||
#print(thickness_along_y_percent,'thickness_along_y_percent')
|
|
||||||
|
|
||||||
if thickness_along_y_percent<30:
|
|
||||||
min_textline_thickness=8
|
|
||||||
elif thickness_along_y_percent>=30 and thickness_along_y_percent<50:
|
|
||||||
min_textline_thickness=20
|
|
||||||
else:
|
|
||||||
min_textline_thickness=40
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
if thickness_along_y_percent>=14:
|
|
||||||
|
|
||||||
text_with_lines_y_rev=-1*text_with_lines_y[:]
|
|
||||||
#print(text_with_lines_y)
|
|
||||||
#print(text_with_lines_y_rev)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#plt.plot(text_with_lines_y)
|
|
||||||
#plt.show()
|
|
||||||
|
|
||||||
|
|
||||||
text_with_lines_y_rev=text_with_lines_y_rev-np.min(text_with_lines_y_rev)
|
|
||||||
|
|
||||||
#plt.plot(text_with_lines_y_rev)
|
|
||||||
#plt.show()
|
|
||||||
sigma_gaus=1
|
|
||||||
region_sum_0= gaussian_filter1d(text_with_lines_y, sigma_gaus)
|
|
||||||
|
|
||||||
region_sum_0_rev=gaussian_filter1d(text_with_lines_y_rev, sigma_gaus)
|
|
||||||
|
|
||||||
#plt.plot(region_sum_0_rev)
|
|
||||||
#plt.show()
|
|
||||||
region_sum_0_updown=region_sum_0[len(region_sum_0)::-1]
|
|
||||||
|
|
||||||
first_nonzero=(next((i for i, x in enumerate(region_sum_0) if x), None))
|
|
||||||
last_nonzero=(next((i for i, x in enumerate(region_sum_0_updown) if x), None))
|
|
||||||
|
|
||||||
|
|
||||||
last_nonzero=len(region_sum_0)-last_nonzero
|
|
||||||
|
|
||||||
##img_sum_0_smooth_rev=-region_sum_0
|
|
||||||
|
|
||||||
|
|
||||||
mid_point=(last_nonzero+first_nonzero)/2.
|
|
||||||
|
|
||||||
|
|
||||||
one_third_right=(last_nonzero-mid_point)/3.0
|
|
||||||
one_third_left=(mid_point-first_nonzero)/3.0
|
|
||||||
|
|
||||||
#img_sum_0_smooth_rev=img_sum_0_smooth_rev-np.min(img_sum_0_smooth_rev)
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
peaks, _ = find_peaks(text_with_lines_y_rev, height=0)
|
|
||||||
|
|
||||||
|
|
||||||
peaks=np.array(peaks)
|
|
||||||
|
|
||||||
|
|
||||||
#print(region_sum_0[peaks])
|
|
||||||
##plt.plot(region_sum_0)
|
|
||||||
##plt.plot(peaks,region_sum_0[peaks],'*')
|
|
||||||
##plt.show()
|
|
||||||
#print(first_nonzero,last_nonzero,peaks)
|
|
||||||
peaks=peaks[(peaks>first_nonzero) & ((peaks<last_nonzero))]
|
|
||||||
|
|
||||||
#print(first_nonzero,last_nonzero,peaks)
|
|
||||||
|
|
||||||
|
|
||||||
#print(region_sum_0[peaks]<10)
|
|
||||||
####peaks=peaks[region_sum_0[peaks]<25 ]
|
|
||||||
|
|
||||||
#print(region_sum_0[peaks])
|
|
||||||
peaks=peaks[region_sum_0[peaks]<min_textline_thickness ]
|
|
||||||
#print(peaks)
|
|
||||||
#print(first_nonzero,last_nonzero,one_third_right,one_third_left)
|
|
||||||
|
|
||||||
if num_col==1:
|
|
||||||
peaks_right=peaks[peaks>mid_point]
|
|
||||||
peaks_left=peaks[peaks<mid_point]
|
|
||||||
if num_col==2:
|
|
||||||
peaks_right=peaks[peaks>(mid_point+one_third_right)]
|
|
||||||
peaks_left=peaks[peaks<(mid_point-one_third_left)]
|
|
||||||
|
|
||||||
|
|
||||||
try:
|
|
||||||
point_right=np.min(peaks_right)
|
|
||||||
except:
|
|
||||||
point_right=last_nonzero
|
|
||||||
|
|
||||||
|
|
||||||
try:
|
|
||||||
point_left=np.max(peaks_left)
|
|
||||||
except:
|
|
||||||
point_left=first_nonzero
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#print(point_left,point_right)
|
|
||||||
#print(text_regions.shape)
|
|
||||||
if point_right>=mask_marginals.shape[1]:
|
|
||||||
point_right=mask_marginals.shape[1]-1
|
|
||||||
|
|
||||||
try:
|
|
||||||
mask_marginals[:,point_left:point_right]=1
|
|
||||||
except:
|
|
||||||
mask_marginals[:,:]=1
|
|
||||||
|
|
||||||
#print(mask_marginals.shape,point_left,point_right,'nadosh')
|
|
||||||
mask_marginals_rotated=rotate_image(mask_marginals,-slope_deskew)
|
|
||||||
|
|
||||||
#print(mask_marginals_rotated.shape,'nadosh')
|
|
||||||
mask_marginals_rotated_sum=mask_marginals_rotated.sum(axis=0)
|
|
||||||
|
|
||||||
mask_marginals_rotated_sum[mask_marginals_rotated_sum!=0]=1
|
|
||||||
index_x=np.array(range(len(mask_marginals_rotated_sum)))+1
|
|
||||||
|
|
||||||
index_x_interest=index_x[mask_marginals_rotated_sum==1]
|
|
||||||
|
|
||||||
min_point_of_left_marginal=np.min(index_x_interest)-16
|
|
||||||
max_point_of_right_marginal=np.max(index_x_interest)+16
|
|
||||||
|
|
||||||
if min_point_of_left_marginal<0:
|
|
||||||
min_point_of_left_marginal=0
|
|
||||||
if max_point_of_right_marginal>=text_regions.shape[1]:
|
|
||||||
max_point_of_right_marginal=text_regions.shape[1]-1
|
|
||||||
|
|
||||||
|
|
||||||
#print(np.min(index_x_interest) ,np.max(index_x_interest),'minmaxnew')
|
|
||||||
#print(mask_marginals_rotated.shape,text_regions.shape,'mask_marginals_rotated')
|
|
||||||
#plt.imshow(mask_marginals)
|
|
||||||
#plt.show()
|
|
||||||
|
|
||||||
#plt.imshow(mask_marginals_rotated)
|
|
||||||
#plt.show()
|
|
||||||
|
|
||||||
text_regions[(mask_marginals_rotated[:,:]!=1) & (text_regions[:,:]==1)]=4
|
|
||||||
|
|
||||||
#plt.imshow(text_regions)
|
|
||||||
#plt.show()
|
|
||||||
|
|
||||||
pixel_img=4
|
|
||||||
min_area_text=0.00001
|
|
||||||
polygons_of_marginals=return_contours_of_interested_region(text_regions,pixel_img,min_area_text)
|
|
||||||
|
|
||||||
cx_text_only,cy_text_only ,x_min_text_only,x_max_text_only, y_min_text_only ,y_max_text_only,y_cor_x_min_main=find_new_features_of_contours(polygons_of_marginals)
|
|
||||||
|
|
||||||
text_regions[(text_regions[:,:]==4)]=1
|
|
||||||
|
|
||||||
marginlas_should_be_main_text=[]
|
|
||||||
|
|
||||||
x_min_marginals_left=[]
|
|
||||||
x_min_marginals_right=[]
|
|
||||||
|
|
||||||
for i in range(len(cx_text_only)):
|
|
||||||
|
|
||||||
x_width_mar=abs(x_min_text_only[i]-x_max_text_only[i])
|
|
||||||
y_height_mar=abs(y_min_text_only[i]-y_max_text_only[i])
|
|
||||||
#print(x_width_mar,y_height_mar,y_height_mar/x_width_mar,'y_height_mar')
|
|
||||||
if x_width_mar>16 and y_height_mar/x_width_mar<18:
|
|
||||||
marginlas_should_be_main_text.append(polygons_of_marginals[i])
|
|
||||||
if x_min_text_only[i]<(mid_point-one_third_left):
|
|
||||||
x_min_marginals_left_new=x_min_text_only[i]
|
|
||||||
if len(x_min_marginals_left)==0:
|
|
||||||
x_min_marginals_left.append(x_min_marginals_left_new)
|
|
||||||
else:
|
|
||||||
x_min_marginals_left[0]=min(x_min_marginals_left[0],x_min_marginals_left_new)
|
|
||||||
else:
|
|
||||||
x_min_marginals_right_new=x_min_text_only[i]
|
|
||||||
if len(x_min_marginals_right)==0:
|
|
||||||
x_min_marginals_right.append(x_min_marginals_right_new)
|
|
||||||
else:
|
|
||||||
x_min_marginals_right[0]=min(x_min_marginals_right[0],x_min_marginals_right_new)
|
|
||||||
|
|
||||||
if len(x_min_marginals_left)==0:
|
|
||||||
x_min_marginals_left=[0]
|
|
||||||
if len(x_min_marginals_right)==0:
|
|
||||||
x_min_marginals_right=[text_regions.shape[1]-1]
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
#print(x_min_marginals_left[0],x_min_marginals_right[0],'margo')
|
|
||||||
|
|
||||||
#print(marginlas_should_be_main_text,'marginlas_should_be_main_text')
|
|
||||||
text_regions=cv2.fillPoly(text_regions, pts =marginlas_should_be_main_text, color=(4,4))
|
|
||||||
|
|
||||||
#print(np.unique(text_regions))
|
|
||||||
|
|
||||||
#text_regions[:,:int(x_min_marginals_left[0])][text_regions[:,:int(x_min_marginals_left[0])]==1]=0
|
|
||||||
#text_regions[:,int(x_min_marginals_right[0]):][text_regions[:,int(x_min_marginals_right[0]):]==1]=0
|
|
||||||
|
|
||||||
text_regions[:,:int(min_point_of_left_marginal)][text_regions[:,:int(min_point_of_left_marginal)]==1]=0
|
|
||||||
text_regions[:,int(max_point_of_right_marginal):][text_regions[:,int(max_point_of_right_marginal):]==1]=0
|
|
||||||
|
|
||||||
###text_regions[:,0:point_left][text_regions[:,0:point_left]==1]=4
|
|
||||||
|
|
||||||
###text_regions[:,point_right:][ text_regions[:,point_right:]==1]=4
|
|
||||||
#plt.plot(region_sum_0)
|
|
||||||
#plt.plot(peaks,region_sum_0[peaks],'*')
|
|
||||||
#plt.show()
|
|
||||||
|
|
||||||
|
|
||||||
#plt.imshow(text_regions)
|
|
||||||
#plt.show()
|
|
||||||
|
|
||||||
#sys.exit()
|
|
||||||
else:
|
|
||||||
pass
|
|
||||||
return text_regions
|
|
||||||
@ -1,8 +1,8 @@
|
|||||||
# ocrd includes opencv, numpy, shapely, click
|
# ocrd includes opencv, numpy, shapely, click
|
||||||
ocrd >= 3.0.0a2
|
ocrd >= 3.0.0a2
|
||||||
numpy <1.24.0
|
numpy <= 1.24.4
|
||||||
scikit-learn >= 0.23.2
|
scikit-learn <= 1.3.2
|
||||||
tensorflow == 2.12.1
|
tensorflow <= 2.13.1
|
||||||
imutils >= 0.5.3
|
imutils >= 0.5.4
|
||||||
matplotlib
|
matplotlib <= 3.7.5
|
||||||
setuptools >= 50
|
setuptools >= 61
|
||||||
|
|||||||
@ -1,11 +1,13 @@
|
|||||||
import cv2
|
import cv2
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from qurator.eynollah.utils.pil_cv2 import check_dpi
|
from eynollah.eynollah.utils.pil_cv2 import check_dpi
|
||||||
from tests.base import main
|
from tests.base import main
|
||||||
|
|
||||||
|
|
||||||
def test_dpi():
|
def test_dpi():
|
||||||
fpath = str(Path(__file__).parent.joinpath('resources', 'kant_aufklaerung_1784_0020.tif'))
|
fpath = str(Path(__file__).parent.joinpath('resources', 'kant_aufklaerung_1784_0020.tif'))
|
||||||
assert 230 == check_dpi(cv2.imread(fpath))
|
assert 230 == check_dpi(cv2.imread(fpath))
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
main(__file__)
|
main(__file__)
|
||||||
|
|||||||
@ -1,7 +1,7 @@
|
|||||||
def test_utils_import():
|
def test_utils_import():
|
||||||
import qurator.eynollah.utils
|
import eynollah.eynollah.utils
|
||||||
import qurator.eynollah.utils.contour
|
import eynollah.eynollah.utils.contour
|
||||||
import qurator.eynollah.utils.drop_capitals
|
import eynollah.eynollah.utils.drop_capitals
|
||||||
import qurator.eynollah.utils.drop_capitals
|
import eynollah.eynollah.utils.drop_capitals
|
||||||
import qurator.eynollah.utils.is_nan
|
import eynollah.eynollah.utils.is_nan
|
||||||
import qurator.eynollah.utils.rotate
|
import eynollah.eynollah.utils.rotate
|
||||||
|
|||||||
@ -1,14 +1,16 @@
|
|||||||
from pytest import main
|
from pytest import main
|
||||||
from qurator.eynollah.utils.xml import create_page_xml
|
from eynollah.eynollah.utils.xml import create_page_xml
|
||||||
from ocrd_models.ocrd_page import to_xml
|
from ocrd_models.ocrd_page import to_xml
|
||||||
|
|
||||||
PAGE_2019 = 'http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15'
|
PAGE_2019 = 'http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15'
|
||||||
|
|
||||||
|
|
||||||
def test_create_xml():
|
def test_create_xml():
|
||||||
pcgts = create_page_xml('/path/to/img.tif', 100, 100)
|
pcgts = create_page_xml('/path/to/img.tif', 100, 100)
|
||||||
xmlstr = to_xml(pcgts)
|
xmlstr = to_xml(pcgts)
|
||||||
assert 'xmlns:pc="%s"' % PAGE_2019 in xmlstr
|
assert 'xmlns:pc="%s"' % PAGE_2019 in xmlstr
|
||||||
assert 'Metadata' in xmlstr
|
assert 'Metadata' in xmlstr
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
main([__file__])
|
main([__file__])
|
||||||
|
|||||||
Loading…
Reference in New Issue