pyexifwrangle 0.2.1

A Python package for wrangling EXIF data extracted from images using Phil Harvey's EXIFTool.

Wrangle EXIF Data in Python

A Python package for wrangling EXIF data extracted from images using Phil Harvey's EXIFTool.

Set-up

Install pyexifwrangle with pip

$ pip install pyexifwrangle

Install Phil Harvey's EXIFTool

Install Phil Harvey's EXIFTool from https://exiftool.org/. This site has installation instructions if you need them.

Usage

Get EXIF data

After installing EXIFTool, you can extract EXIF data from every image in a folder, including subdirectories, save the results in a csv file, and return the results in a Pandas DataFrame.

import pyexifwrangle.wrangle as wr

df = wr.get_exif(input_dir='path/to/images', output_csv='path/to/output.csv')

Load the EXIF data

If you already used get_exif() to save the EXIF to a csv file, you can use read_exif() to load the csv file into a Pandas data frame. In this case, the output of get_exif() is the same as the output from read_exif().

import pyexifwrangle.wrangle as wr

df = wr.read_exif('path/to/output.csv', filename_col='SourceFile')

The function wrangle.read_exif uses the Pandas package to load the csv into a data frame. The parameter filename_col is the name of the column that contains the filenames of the images. The absolute file paths are included with the filenames in the filename_col. After reading the EXIF data into a Pandas data frame, this function removes any images whose filename starts with '.'.

Make columns from folder names

I often organize my images into folders and sub-folders. For example one of my projects has the following folder tree:

├── Samsung_phones  # main directory
│   ├── s21  # model
│   │   ├── s21_1  # phone name
│   │   │ 	├── blank  # scene type
│   │   │	│	├── front  # camera
│   │   │	│	│	├──image1.jpg
│   │   │	│	│	├──image2.jpg
│   │   │	│	│	├──...
│   │   │	│	├── telephoto
│   │   │	│	│	├──image1.jpg
│   │   │	│	│	├──image2.jpg
│   │   │	│	│	├──...
│   │   │	│	├── ultra
│   │   │	│	│	├──image1.jpg
│   │   │	│	│	├──image2.jpg
│   │   │	│	│	├──...
│   │   │	│	├── wide
│   │   │	│	│	├──image1.jpg
│   │   │	│	│	├──image2.jpg
│   │   │	│	│	├──...
│   │   │ 	├── natural
│   │   │	│	├── front  
│   │   │	│	├── telephoto
│   │   │	│	├── ultra
│   │   │	│	├── wide
│   │   ├── s21_2 
│   │   │ 	├── blank
│   │   │	│	├── front
│   │   │	│	├── telephoto
│   │   │	│	├── ultra
│   │   │	│	├── wide
│   │   │ 	├── natural
│   │   │	│	├── front  
│   │   │	│	├── telephoto
│   │   │	│	├── ultra
│   │   │	│	├── wide

Extract the folder names from the images' absolute filepaths and make a new column for each folder.

df = wr.filename2columns(df=df, filename_col='SourceFile', columns=['model', 'phone', 'scene_type', 'camera', 'image'])

Search for missing EXIF data

Find images missing EXIF data. For example, search the data frame for images that don't have an Aperture.

missing = wr.check_missing_exif(df=df, column='Aperture')

Count images per group(s)

Group images by column(s) and count the number of images per group.

counts = wr.count_images_by_columns(df=df, columns=['model', 'phone', 'scene_type', 'camera'])

Optionally, you can sort the output of count_images_by_columns

counts_sorted = wr.count_images_by_columns(df=df, columns=['model', 'phone', 'scene_type', 'camera'], sorted=
['phone', 'camera', 'Aperture'])