Extracting EXIF (Meta)Data From Images With Python

This tutorial is the first part of a series where different aspects of programming with Python are explained, using Python and public libraries to make useful tools.

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Repository

https://github.com/ianare/exif-py

What will I learn

• Install ExifRead
• What is EXIF data
• Extract EXIF data from images
• Process EXIF data
• Analyse images

Requirements

• Python3.6
• ExifRead

Difficulty

• basic

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Tutorial

Setup

Download the files from Github. There are 6 files. The code is contained in exif.py the other 5 files are images which can be used to test the code on. exif.py takes 1 argument which is the filename for the image. In addition, the argument analysis is used to perform a model analysis on all the images.

Run scripts as following:
> python exif.py 1.jpg

Install ExifRead

Installation of the required package is recommended using the PyPI package manager.

pip install exifread

What is EXIF data

Also referred to as metadata. When taking a photo the device stores a lot of additional information inside the image file. Information like the datetime and location or more specific data like theISO, aperture and focal length. This information can be used by photographers when comparing images to see which settings worked best, learning from other photographers by looking at which settings they used or data analysis.

(Right click->get info (for Mac))

Extract EXIF data from images

Processing the image with exifread.process_image() returns a dictionary containing all the EXIF data.

def process_image(filename):
    # Open image file for reading (binary mode)
    f = open(filename, 'rb')

    # Return Exif tags
    tags = exifread.process_file(f)
    return tags

Quite a lot of information can be stored which also adds to the image file size. Photographer can choice to omitEXIF data to to save on bandwidth or to keep information about their ways of working private.

{
    'Image Make': (0x010F) ASCII = SONY @ 110,
    'Image Model': (0x0110) ASCII = ILCE - 7 M3 @ 116,
    'Image XResolution': (0x011A) Ratio = 240 @ 126,
    'Image YResolution': (0x011B) Ratio = 240 @ 134,
    'Image ResolutionUnit': (0x0128) Short = Pixels / Inch @ 66,
  .
  .
  .
}

Pastebin to the entire EXIF

Process EXIF data

Photographers often share the main settings used for capturing a photo and what gear was used. For this example the data will be extracted from the the image and put into a html table for easy sharing. The same works for applying the code on a downloaded image from the web, learning more how the image was taken. The code can easily be adjusted depending on which data is needed.

def create_table(tags):
    # extract required data
    brand = tags['Image Make']
    model = tags['Image Model']
    date = tags['Image DateTime']
    shutterspeed = tags['EXIF ExposureTime']
    focallength = tags['EXIF FocalLength']
    aperture = tags['EXIF FNumber']
    iso = tags['EXIF ISOSpeedRatings']
    lens = tags['EXIF LensModel']

    # generate a htlm table
    print('<table>')
    print(f'<tr><td>Settings</td><td><b>ISO {iso} {focallength} ' +
          f' mm f/{aperture} {shutterspeed} sec </b></td></tr>')
    print(f'<tr><td>Camera</td><td><b>{brand} {model}</b></td></tr>')
    print(f'<tr><td>Lens</td><td><b>{lens}</b></td></tr>')
    print(f'<tr><td>Date</td><td><b>{date}</b></td></tr>')
    print('</table>')

Analyse images

Imagine having a batch of images and you want to know which camera was used the most often. By looping through all the images, creating keys for unique camera models with a value indicating their frequency this can be easily answered.

def perform_analysis():
    # Empty dict
    camera_models = {}

    # Loop through images 1-5.jpg
    for image_num in range(1, 6):
        filename = f'{image_num}.jpg'
        # Extract str from the IfdTag
        model = str(process_image(filename)['Image Model'])

        # Fill dict with unique models and increase frequency
        if model in camera_models:
            camera_models[model] += 1
        else:
            camera_models[model] = 1

    print(camera_models)

Running the code

Five photos come with the code, running exif.py on the first three of them shows how useful EXIF data is when working with different cameras and different lenses.

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python exif.py 1.jpg

Settings	ISO 100 192 mm f/11 3/5 sec
Camera	SONY ILCE-7M3
Lens	FE 70-200mm F4 G OSS
Date	2018:07:04 20:56:11

python exif.py 2.jpg

Settings	ISO 3200 50 mm f/16/5 1/60 sec
Camera	NIKON CORPORATION NIKON D7500
Lens	50.0 mm f/1.8
Date	2017:12:17 17:24:47

python exif.py 3.jpg

Settings	ISO 100 32 mm f/25 13 sec
Camera	NIKON CORPORATION NIKON D3400
Lens	18.0-140.0 mm f/3.5-5.6
Date	2018:05:11 01:46:48

Using the argument analysis will perform the analysis, looping over all the images and counting which modelcamera was used with what frequency.

python exif.py analysis

{'ILCE-7M3': 3, 'NIKON D7500': 1, 'NIKON D3400': 1}

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The code for this tutorial can be found on Github!

This tutorial was written by @juliank.