/
test.py
238 lines (180 loc) · 7.72 KB
/
test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
# -*- 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()