blurry photos detection

blur_detector

#! /bin/bash

#
# Author: Pasquale Ceres ([email protected])
# License: GPL ver. 3
#
# Script originally developed for the 
# "San Lorenzo Martire Caposele Church Records Digitalization Project"
# 
# http://www.facebook.com/group.php?gid=188973755117&v=photos
# http://ars.altervista.org/PhpGedView/index.php
#

#
# parameters
#
image_ext=JPG                    # image extension
logfile=report.txt               # logfile with the list of all photos sorted by blur level
logfile_full=report_unsorted.txt # logfile with the list of all photos not sorted
temp_folder=/tmp                 # temporary folder
output_folder=blurry_folder      # folder into which the most blurred photos are copied
perc_output_images=20            # [%] percentage of most blurred images to be copied into output folder



#
# show usage if no params in input
#
if [ ! $# == 1 ]; then
  echo "Usage:"
  echo "$0 <filename> : analyse the single image, determining its blur factor"
  echo "$0 <folder>   : analyse the images inside the folder"
  exit
fi


#
# calculations
#
input=$1

if [ -d $input ]; then
  # report on the files in the folder
  #
  # usage:
  # blur_detector <folder_with_images>
  #

  echo "batch blur estimator..."
  
  touch $logfile_full
  rm $logfile_full
  find $input -name "*.$image_ext" -exec blur_detector {} >> $logfile_full \;
  
  
  # sort by blur index (from worst to best)
  cat $logfile_full | sort -d -k 8.2 > $logfile
  
 
  # copy blurred images to output folder
  num_files=`cat $logfile | wc -l` # number of image files found
  num_output_images=`echo $num_files*$perc_output_images/100.0 | bc | gawk '{printf("%d\n",int($1)+1)}'` # take a percentage

  echo "copying $num_output_images (out of $num_files) blurriest photos into output folder..."

  mkdir $output_folder
  head $logfile -n $num_output_images | gawk '{n=split($1,a,"/");filename=sprintf("%03d_%s", FNR, a[n]);print filename; system("cp " $1 " ./'$output_folder'/" filename)}'  
  
else 
  # report on single file
  #
  # usage:
  # blur_detector <image_filename>
  #

  filename=$input

  # original size
  size0=`identify $filename | gawk '{print $3}'`

  # reduce size, just take upper part of image
  convert $filename -gravity north -crop 100x20% +repage $temp_folder/check0.jpg

  size=`identify $temp_folder/check0.jpg | gawk '{print $3}'`

  # create black image
  convert -size $size xc:black $temp_folder/black.jpg

  # create check image
  convert -noise 2 -sigmoidal-contrast 6x25% -edge 2 $temp_folder/check0.jpg $temp_folder/check.jpg

  # calculate blur factor
  blur_value=`compare -metric RMSE $temp_folder/check.jpg $temp_folder/black.jpg $temp_folder/out.jpg 2>&1`

  echo "$filename : $size0 -> $size : $blur_value"
  
  # remove work files
  rm $temp_folder/check0.jpg $temp_folder/check.jpg $temp_folder/black.jpg $temp_folder/out.jpg
fi

It's been almost 10 years since @KnutssonDevelopment commented and I wonder if since then you've found an alternative? You would think that if camera software could utilize auto-focus to determine when a photo was sharp, it could simply change the rules and apply it to interpreting the data presented on a screen, but it's odd how difficult it can be to find something so trivial. Or, perhaps that's just the lazy of us. Personally, I sometimes have to sort through 20,000 photos and anything to automate this would save me at least an hour.

As a top Google search result, if anyone would like to try an alternative, here's a Python script version:

#
# Sorts pictures in current directory into two subdirs, blurred and ok
#

import os
import shutil
import cv2

FOCUS_THRESHOLD = 80
BLURRED_DIR = 'blurred'
OK_DIR = 'ok'

blur_count = 0
files = [f for f in os.listdir('.') if f.endswith('.jpg')]

try:
   os.makedirs(BLURRED_DIR)
   os.makedirs(OK_DIR)
except:
   pass

for infile in files:

   print('Processing file %s ...' % (infile))
   cv_image = cv2.imread(infile)

   # Covert to grayscale
   gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)

   # Compute the Laplacian of the image and then the focus
   #     measure is simply the variance of the Laplacian
   variance_of_laplacian = cv2.Laplacian(gray, cv2.CV_64F).var()

   # If below threshold, it's blurry
   if variance_of_laplacian < FOCUS_THRESHOLD:
      shutil.move(infile, BLURRED_DIR)
      blur_count += 1
   else:
      shutil.move(infile, OK_DIR)

print('Done.  Processed %d files into %d blurred, and %d ok.' % (len(files), blur_count, len(files)-blur_count))

Courtesy of https://www.pyimagesearch.com/2015/09/07/blur-detection-with-opencv/