pyMultipleImgAnnot

Multiple images binary annotator.

Annotation binary tool for quicly annotate good/bad images. Tested in Ubuntu, Windows and MacOS. Suppose you have the following folder structure:

dataset/

	2019-30-12/ 

		12/
	
			02-00-02-693_rgb.png
		
			...
		
		13/
			09-21-03-274_rgb.png
		
			...
		
		14/

	2020-01-01/

		...

One subfolder per row is loaded, with images resized and square-cropped to 80x80 pixel. Only subfolders satisfying the requirements are loaded (see parameters for details). Subfolders images are loaded in batches (parameter n).

You want to select only good (it's up yo you define "good", in this case is the cat) images from subfolders (12, 13, 14 ..). In my case main folder is a date, subfolders are unique IDs. In each subfolder there are many images with timestamp as name. In this way you can filter images by timestamp. In the first column the subfolder name is loaded in the textbox. You can change it (for example if you want join different subfolders). Using the checkbox you can flag that subfolder (it's just a binary flag, it's up to you to give it a meaning). By default every image is selected (not greyed):

• Left click: deselect (greyed out)
• Right click: deselect the entire row
• Central click: open the image with the OS image viewer.

Result File

Clicking on the button "confirm" produce a csv file with the following structure: original_subfolderID;current_subfolderID;flag;imgX.png;imgY.png;...

Only folders with at least one selected image are present in the result file. First row of the csv must be discarded, is used to temporary mark the last processed folder, to resume from there.

Requirements:

Python 3, with the following packages (you can install them using pip):

• pillow

Usage

python3 main.py [-h] -i IMAGES_FOLDER [-n MAX_FOLDERS] [-r RESULT_FILE] [-k NUMBER_FILTER] [-t TIME_FILTER]

arguments:

  -h, --help           show this help message and exit

  -i, IMAGES_FOLDER    path to the first level image folder (Required)
  
  -n, MAX_FOLDERS      number of second level folder to load, default 200. Keep this number not too high, or you will go out of memory.
  
  -r, RESULT_FILE      result csv file, default "result_{IMAGES_FOLDER}.csv"
  
  -k, NUMBER_FILTER    Minimum number of files for a second level folder to be loaded, default 0
  
  -t, TIME_FILTER      Starting hour, only second level folders whose images name start from t as hour will be loaded, default 0

Move files:

After the annotation, can be useful to move the selected files. You can use move_files.py for this! Put all result files you have in a specific folder if needed. Selected images from subfolders are copied to the new destination, preserving the folder structure and joining folder with the same current_subfolderID in the result file. At the end the script also create a 7z archive with the same content of the destination folder.

python3 move_files.py [-h] -result RESULT_FILE_FOLDER -dst DESTINATION_FOLDER -src SOURCE_FOLDER [--filter]

arguments:

  -h, --help                    show this help message and exit

  -result, RESULT_FILE_FOLDER   folder containing result csv files, generated by annotation (result/ in this example, which contain result_2019-30-12.csv, result_2020-01-01.csv ...)
  
  -dst, DESTINATION_FOLDER      destination folder 
  
  -src, SOURCE_FOLDER           source folder for images (dataset/ in this example)
  
  --filter                      if specified exclude from copy flagged folders