rumadra / resize_dataset_pascalvoc

Package to resize ALL your dataset images (into local folder) and change the pascal_VOC coodinates in your .xml file

Resize-pascal-voc

It's a package for resizing ALL your dataset images (into local folder) and changing the pascal_VOC coodinates in your .xml files.

Index

• How it works?
• Warnings
• Install requirements
• Usage
• Example
• Folder structure example
• Results
  • Original Image
  • Resized image
  • Boundies box image
• Parameters

How it works?

• Walk by paths and searching by .jpg and .xml files
• Resize the image and change the XY coordinates of each object in xml file.
• Save the new files into output path. Folder structure example for more details
• If save_box_images = 1, draw the boundies box in the resized image and save it in an other file(output_path/boxes_images/))

Warnings

1º: Don't worry if you have a big folder structure with many nested folders, the package will walk recursively in your dataset folder and recreate the same structure into output_path.

2º: The .jpg and .xml files must be in the same folder

Install requirements

pip install -r requirements.txt

Usage

python3 main.py -p <IMAGES_&_XML_PATH> --output <IMAGES_&_XML> --new_x <NEW_X_SIZE> --new_y <NEW_X_SIZE> --save_box_images <FLAG>"

Example

python3 main.py -p /home/italojs/Pictures/dataset --output ./output --new_x 150 --new_y 150 --save_box_images 1

Folder structure example

Look this randon folder structure with ALL my dataset

dataset
├── IMG_20181109_165212.jpg
├── IMG_20181109_165212.xml
├── IMG_20181109_165213.jpg
├── IMG_20181109_165213.xml
├── test
│   ├── IMG_20181109_163524.jpg
│   ├── IMG_20181109_163524.xml
│   ├── IMG_20181109_163525.jpg
│   └── IMG_20181109_163525.xml
├── train
│   ├── class1
│   │   ├── IMG_20181109_162519.jpg
│   │   ├── IMG_20181109_162519.xml
│   │   ├── IMG_20181109_162523.jpg
│   │   └── IMG_20181109_162523.xml
│   ├── class2
│   │   ├── IMG_20181109_162814.jpg
│   │   ├── IMG_20181109_162814.xml
│   │   ├── IMG_20181109_162818.jpg
│   │   └── IMG_20181109_162818.xml
│   ├── IMG_20181109_163315.jpg
│   ├── IMG_20181109_163315.xml
│   ├── IMG_20181109_163316.jpg
│   └── IMG_20181109_163316.xml
└── validation
    ├── IMG_20181109_164824.jpg
    ├── IMG_20181109_164824.xml
    ├── IMG_20181109_164825.jpg
    └── IMG_20181109_164825.xml

After executed the package with:

python3 main.py -p /home/italojs/Pictures/dataset --output ./output --new_x 200 --new_y 200 --save_box_images 1

The package will resize the images, rewrite the xml files and create the same folder structure into output path with new images and xml files.

output
├── boxes_images
│   ├── boxed_IMG_20181109_165212.jpg
│   └── boxed_IMG_20181109_165213.jpg
├── IMG_20181109_165212_new.jpg
├── IMG_20181109_165212_new.xml
├── IMG_20181109_165213_new.jpg
├── IMG_20181109_165213_new.xml
├── test
│   ├── IMG_20181109_163524_new.jpg
│   ├── IMG_20181109_163524_new.xml
│   ├── IMG_20181109_163525_new.jpg
│   └── IMG_20181109_163525_new.xml
├── train
│   ├── class1
│   │   ├── IMG_20181109_162519_new.jpg
│   │   ├── IMG_20181109_162519_new.xml
│   │   ├── IMG_20181109_162523_new.jpg
│   │   └── IMG_20181109_162523_new.xml
│   ├── class2
│   │   ├── IMG_20181109_162814_new.jpg
│   │   ├── IMG_20181109_162814_new.xml
│   │   ├── IMG_20181109_162818_new.jpg
│   │   └── IMG_20181109_162818_new.xml
│   ├── IMG_20181109_163315_new.jpg
│   ├── IMG_20181109_163315_new.xml
│   ├── IMG_20181109_163316_new.jpg
│   └── IMG_20181109_163316_new.xml
└── validation
    ├── IMG_20181109_164824_new.jpg
    ├── IMG_20181109_164824_new.xml
    ├── IMG_20181109_164825_new.jpg
    └── IMG_20181109_164825_new.xml

Results

Original Image

Here wee have the original image (650x590) an your xml:

<annotation>
  <folder>imagens</folder>
  <filename>IMG_20181109_165212</filename>
  <path>[IMAGE_PATH]\IMG_20181109_165212.jpg</path>
  <source>
    <database>Unknown</database>
  </source>
  <size>
    <width>650</width>
    <height>590</height>
    <depth>3</depth>
  </size>
  <segmented>0</segmented>
  <object>
    <name>CNH</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>74</xmin>
      <ymin>192</ymin>
      <xmax>267</xmax>
      <ymax>499</ymax>
    </bndbox>
  </object>
  <object>
    <name>CNH</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>297</xmin>
      <ymin>168</ymin>
      <xmax>483</xmax>
      <ymax>478</ymax>
    </bndbox>
  </object>
  <object>
    <name>CNH</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>310</xmin>
      <ymin>1</ymin>
      <xmax>499</xmax>
      <ymax>103</ymax>
    </bndbox>
  </object>
</annotation>

Resized image

Here is the resized image (200x200) and new xml:

<annotation>
  <folder>imagens</folder>
  <filename>IMG_20181109_165212</filename>
  <path>C:\Users\pc-casa\Music\imagens\IMG_20181109_165212.jpg</path>
  <source>
    <database>Unknown</database>
  </source>
  <size>
    <width>650</width>
    <height>590</height>
    <depth>3</depth>
  </size>
  <segmented>0</segmented>
  <object>
    <name>CNH</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>17</xmin>
      <ymin>49</ymin>
      <xmax>62</xmax>
      <ymax>127</ymax>
    </bndbox>
  </object>
  <object>
    <name>CNH</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>69</xmin>
      <ymin>43</ymin>
      <xmax>111</xmax>
      <ymax>122</ymax>
    </bndbox>
  </object>
  <object>
    <name>CNH</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>72</xmin>
      <ymin>0</ymin>
      <xmax>115</xmax>
      <ymax>26</ymax>
    </bndbox>
  </object>
</annotation>

Boundies box image

Here is a resized image (200x200), but with the boundies box drawed:

Parameters

To know more about parameters use python main.py -h:

usage: main.py [-h] -p DATASET_PATH -o OUTPUT_PATH -x X -y Y
               [-s SAVE_BOX_IMAGES]

optional arguments:
  -h, --help            show this help message and exit
  -p DATASET_PATH, --path DATASET_PATH
                        Path to dataset data ?(image and annotations).
  -o OUTPUT_PATH, --output OUTPUT_PATH
                        Path that will be saved the resized dataset
  -x X, --new_x X       The new x images size
  -y Y, --new_y Y       The new y images size
  -s SAVE_BOX_IMAGES, --save_box_images SAVE_BOX_IMAGES
                        If True, it will save the resized image and a drawed
                        image with the boxes in the images