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.
https://github.com/ianare/exif-py
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
Installation of the required package is recommended using the PyPI package manager.
pip install exifread
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))
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,
.
.
.
}
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(''
)
print(f'Settings ISO {iso} {focallength} ' Camera {brand} {model} Lens {lens} Date {date} 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)
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.
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}
The code for this tutorial can be found on Github!
This tutorial was written by 