image_similarity.py
# -*- coding: utf-8 -*-
"""
Installation of needed libraries
sudo apt-get install -y python-pip
sudo pip install PIL numpy
"""
import os, time, re, urllib
from PIL import Image
import logging
format= '%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s - %(funcName)s() - %(message)s'
format= '%(asctime)s - %(filename)s:%(lineno)s - %(message)s'
logging.basicConfig(level=logging.DEBUG, format=format)
logger = logging.getLogger(__name__)
def main():
download_base_directory = '/tmp/imagesimilarity/'
urls_image_pairs="""
http://www.linuxfestnorthwest.org/sites/default/files/sponsors/elephant.png
http://www.linuxfestnorthwest.org/sites/default/files/sponsors/elephant.png
#######
http://www.linuxfestnorthwest.org/sites/default/files/sponsors/elephant.png
http://terminaltwister.com/wp-content/uploads/2013/09/220px-Postgresql_elephant.svg_.png
#######
"""
begin_similarty_compare(urls_image_pairs, download_base_directory)
def begin_similarty_compare(url_texts, download_base_directory):
logger.debug("image file location: %s"%(download_base_directory ))
url_pairs = re.split('#+',url_texts)
urls = url_texts.strip().split()
idx=0 # counter for downloaded image names
for url_text in url_pairs:
pair = url_text.strip().split()
if not pair:
continue
filepath_url = []
for url in pair:
url = url.strip()
filename = url.split('/')[-1]
idx+=1
filename = "%02.f-%s"%(idx,filename) # creates unique enumerated filenames
#logger.debug("filename %s"%(filename))
filepath = os.path.join(download_base_directory, filename)
mkdir_p_filepath(filepath)
if not os.path.exists(filepath):
logger.debug("downloading image %s to %s ..."%(url, base_directory))
urllib.urlretrieve(url, filepath)
logger.debug("downloading done")
filepath_url.append( (filepath, url) )
logger.debug("*"*20)
logger.debug("compare images start")
image_filepath1, url1 = filepath_url[0][0], filepath_url[0][1]
logger.debug("image1: %s (%s)"%(get_filename(image_filepath1),url1))
image_filepath2, url2 = filepath_url[1][0], filepath_url[1][1]
logger.debug("image2: %s (%s)"%(get_filename(image_filepath2),url2))
t1=time.time()
similarity = image_similarity_bands_via_numpy(image_filepath1,image_filepath2)
duration = "%0.1f"%((time.time() - t1)*1000)
logger.debug("image_similarity_bands_via_numpy => %s took %s ms"%(similarity,duration ))
t1=time.time()
similarity = image_similarity_histogram_via_pil(image_filepath1,image_filepath2)
duration = "%0.1f"%((time.time() - t1)*1000)
logger.debug("image_similarity_histogram_via_pil => %s took %s ms"%(similarity,duration ))
t1=time.time()
similarity = image_similarity_vectors_via_numpy(image_filepath1,image_filepath2)
duration = "%0.1f"%((time.time() - t1)*1000)
logger.debug("image_similarity_vectors_via_numpy => %s took %s ms"%(similarity,duration ))
t1=time.time()
similarity = image_similarity_greyscale_hash_code(image_filepath1,image_filepath2)
duration = "%0.1f"%((time.time() - t1)*1000)
logger.debug("image_similarity_greyscale_hash_code => %s took %s ms"%(similarity,duration ))
logger.debug("compare images finished")
def image_similarity_bands_via_numpy(filepath1, filepath2):
import math
import operator
import numpy
image1 = Image.open(filepath1)
image2 = Image.open(filepath2)
# create thumbnails - resize em
image1 = get_thumbnail(image1)
image2 = get_thumbnail(image2)
# this eliminated unqual images - though not so smarts....
if image1.size != image2.size or image1.getbands() != image2.getbands():
return -1
s = 0
for band_index, band in enumerate(image1.getbands()):
m1 = numpy.array([p[band_index] for p in image1.getdata()]).reshape(*image1.size)
m2 = numpy.array([p[band_index] for p in image2.getdata()]).reshape(*image2.size)
s += numpy.sum(numpy.abs(m1-m2))
return s
def image_similarity_histogram_via_pil(filepath1, filepath2):
from PIL import Image
import math
import operator
image1 = Image.open(filepath1)
image2 = Image.open(filepath2)
image1 = get_thumbnail(image1)
image2 = get_thumbnail(image2)
h1 = image1.histogram()
h2 = image2.histogram()
rms = math.sqrt(reduce(operator.add, list(map(lambda a,b: (a-b)**2, h1, h2)))/len(h1) )
return rms
def image_similarity_vectors_via_numpy(filepath1, filepath2):
# source: http://www.syntacticbayleaves.com/2008/12/03/determining-image-similarity/
# may throw: Value Error: matrices are not aligned .
import Image
from numpy import average, linalg, dot
import sys
image1 = Image.open(filepath1)
image2 = Image.open(filepath2)
image1 = get_thumbnail(image1, stretch_to_fit=True)
image2 = get_thumbnail(image2, stretch_to_fit=True)
images = [image1, image2]
vectors = []
norms = []
for image in images:
vector = []
for pixel_tuple in image.getdata():
vector.append(average(pixel_tuple))
vectors.append(vector)
norms.append(linalg.norm(vector, 2))
a, b = vectors
a_norm, b_norm = norms
# ValueError: matrices are not aligned !
res = dot(a / a_norm, b / b_norm)
return res
def image_similarity_greyscale_hash_code(filepath1, filepath2):
# source: http://blog.safariflow.com/2013/11/26/image-hashing-with-python/
image1 = Image.open(filepath1)
image2 = Image.open(filepath2)
image1 = get_thumbnail(image1, greyscale=True)
image2 = get_thumbnail(image2, greyscale=True)
code1 = image_pixel_hash_code(image1)
code2 = image_pixel_hash_code(image2)
# use hamming distance to compare hashes
res = hamming_distance(code1,code2)
return res
def image_pixel_hash_code(image):
pixels = list(image.getdata())
avg = sum(pixels) / len(pixels)
bits = "".join(map(lambda pixel: '1' if pixel < avg else '0', pixels)) # '00010100...'
hexadecimal = int(bits, 2).__format__('016x').upper()
return hexadecimal
def hamming_distance(s1, s2):
len1, len2= len(s1),len(s2)
if len1!=len2:
"hamming distance works only for string of the same length, so i'll chop the longest sequence"
if len1>len2:
s1=s1[:-(len1-len2)]
else:
s2=s2[:-(len2-len1)]
assert len(s1) == len(s2)
return sum([ch1 != ch2 for ch1, ch2 in zip(s1, s2)])
def get_thumbnail(image, size=(128,128), stretch_to_fit=False, greyscale=False):
" get a smaller version of the image - makes comparison much faster/easier"
if not stretch_to_fit:
image.thumbnail(size, Image.ANTIALIAS)
else:
image = image.resize(size); # for faster computation
if greyscale:
image = image.convert("L") # Convert it to grayscale.
return image
def mkdir_p_filepath(path):
dirpath = os.path.dirname(os.path.abspath(path))
mkdir_p(dirpath)
def mkdir_p(path):
import errno
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else: raise
def get_filename(path):
# cross plattform filename from a given path
# source: http://stackoverflow.com/questions/8384737/python-extract-file-name-from-path-no-matter-what-the-os-path-format
import ntpath
head, tail = ntpath.split(path)
return tail or ntpath.basename(head)
if __name__ == "__main__":
main()
Testing
a@t400:~/lab/image-similarity$ python image_similarity.py 2013-12-22 20:28:19,507 - image_similarity.py:32 - image file location: /tmp/imagesimilarity/ 2013-12-22 20:28:19,507 - image_similarity.py:55 - ******************** 2013-12-22 20:28:19,507 - image_similarity.py:56 - compare images start 2013-12-22 20:28:19,509 - image_similarity.py:59 - image1: 01-elephant.png (http://www.linuxfestnorthwest.org/sites/default/files/sponsors/elephant.png) 2013-12-22 20:28:19,509 - image_similarity.py:62 - image2: 02-elephant.png (http://www.linuxfestnorthwest.org/sites/default/files/sponsors/elephant.png) 2013-12-22 20:28:19,958 - image_similarity.py:69 - image_similarity_bands_via_numpy => 0 took 448.1 ms 2013-12-22 20:28:20,242 - image_similarity.py:76 - image_similarity_histogram_via_pil => 0.0 took 284.0 ms 2013-12-22 20:28:21,379 - image_similarity.py:83 - image_similarity_vectors_via_numpy => 1.0 took 1136.5 ms 2013-12-22 20:28:21,676 - image_similarity.py:90 - image_similarity_greyscale_hash_code => 0 took 296.9 ms 2013-12-22 20:28:21,676 - image_similarity.py:93 - compare images finished 2013-12-22 20:28:21,676 - image_similarity.py:55 - ******************** 2013-12-22 20:28:21,676 - image_similarity.py:56 - compare images start 2013-12-22 20:28:21,677 - image_similarity.py:59 - image1: 03-elephant.png (http://www.linuxfestnorthwest.org/sites/default/files/sponsors/elephant.png) 2013-12-22 20:28:21,677 - image_similarity.py:62 - image2: 04-220px-Postgresql_elephant.svg_.png (http://terminaltwister.com/wp-content/uploads/2013/09/220px-Postgresql_elephant.svg_.png) 2013-12-22 20:28:21,831 - image_similarity.py:69 - image_similarity_bands_via_numpy => -1 took 153.8 ms 2013-12-22 20:28:21,985 - image_similarity.py:76 - image_similarity_histogram_via_pil => 509.38197848 took 154.5 ms 2013-12-22 20:28:23,094 - image_similarity.py:83 - image_similarity_vectors_via_numpy => 0.809369331383 took 1108.0 ms 2013-12-22 20:28:23,260 - image_similarity.py:90 - image_similarity_greyscale_hash_code => 3207 took 166.5 ms 2013-12-22 20:28:23,261 - image_similarity.py:93 - compare images finished
References
1. Chris Pickett, greyscale hash code: http://blog.safariflow.com/2013/11/26/image-hashing-with-python/
2. AutomatedTester, fortran: http://stackoverflow.com/questions/1927660/compare-two-images-the-python-linux-way/
3. Peter Hansen: http://stackoverflow.com/questions/1819124/image-comparison-algorithm
4. Carlo Cabanilla, http://www.syntacticbayleaves.com/2008/12/03/determining-image-similarity/
5. Attila Oláh, imgcmp.py: https://gist.github.com/attilaolah/1940208