image-match ist kurz gesagt eine Bildsuchmaschine. Sie können jedes Bild registrieren und das Bild anhand des Bildes als Schlüssel finden. Durch die Kombination mit Elasticsearch ist es auch möglich, eine Bildsuche mit extrem hoher Geschwindigkeit durchzuführen.
| Artikel | Erläuterung |
|---|---|
| Überprüfungsdatum | 2016.04.08 |
| OS | Mac OS X 10.10.5 |
| Python | 2.7.11 |
| Elasticsearch | 2.2.1 |
$ brew install py2cairo cairo elasticsearch
$ pip install numpy scipy image_match
Versuchen wir ein Beispiel für README mit Bildübereinstimmung.
first.py
from image_match.goldberg import ImageSignature
gis = ImageSignature()
# a = gis.generate_signature('https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg/687px-Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg')
a = gis.generate_signature('687px-Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg')
#b = gis.generate_signature('https://pixabay.com/static/uploads/photo/2012/11/28/08/56/mona-lisa-67506_960_720.jpg')
b = gis.generate_signature('mona-lisa-67506_960_720.jpg')
print(gis.normalized_distance(a, b))
c = gis.generate_signature('https://upload.wikimedia.org/wikipedia/commons/e/e0/Caravaggio_-_Cena_in_Emmaus.jpg')
print(gis.normalized_distance(a, c))
d = gis.generate_signature('https://c2.staticflickr.com/8/7158/6814444991_08d82de57e_z.jpg')
print(gis.normalized_distance(a, d))
Ich werde es versuchen.
$ python first.py
0.220951701409
0.684462753815
0.422527138625
Wenn Sie die URL für das Zielbild angeben, können Sie das Bild abrufen und suchen. Es wird einige Zeit dauern, daher ist es eine gute Idee, es im Voraus mit "curl" usw. zu löschen und den Dateipfad direkt anzugeben.
with Elasticsearch Verwenden wir es mit Elasticsearch.
$ elasticsearch
[2016-04-08 15:18:47,952][INFO ][node ] [Alexander Lexington] version[2.2.1], pid[59417], build[d045fc2/2016-03-09T09:38:54Z]
[2016-04-08 15:18:47,952][INFO ][node ] [Alexander Lexington] initializing ...
[2016-04-08 15:18:48,945][INFO ][plugins ] [Alexander Lexington] modules [lang-expression, lang-groovy], plugins [], sites []
[2016-04-08 15:18:48,998][INFO ][env ] [Alexander Lexington] using [1] data paths, mounts [[/ (/dev/disk1)]], net usable_space [69.9gb], net total_space [232.6gb], spins? [unknown], types [hfs]
[2016-04-08 15:18:48,998][INFO ][env ] [Alexander Lexington] heap size [990.7mb], compressed ordinary object pointers [true]
[2016-04-08 15:18:48,999][WARN ][env ] [Alexander Lexington] max file descriptors [10240] for elasticsearch process likely too low, consider increasing to at least [65536]
[2016-04-08 15:18:52,884][INFO ][node ] [Alexander Lexington] initialized
[2016-04-08 15:18:52,884][INFO ][node ] [Alexander Lexington] starting ...
[2016-04-08 15:18:53,095][INFO ][transport ] [Alexander Lexington] publish_address {127.0.0.1:9300}, bound_addresses {[fe80::1]:9300}, {[::1]:9300}, {127.0.0.1:9300}
[2016-04-08 15:18:53,109][INFO ][discovery ] [Alexander Lexington] elasticsearch_hattori-h/FnX_ySN8RP2my8GcBTZsvw
[2016-04-08 15:18:56,148][INFO ][cluster.service ] [Alexander Lexington] new_master {Alexander Lexington}{FnX_ySN8RP2my8GcBTZsvw}{127.0.0.1}{127.0.0.1:9300}, reason: zen-disco-join(elected_as_master, [0] joins received)
[2016-04-08 15:18:56,182][INFO ][http ] [Alexander Lexington] publish_address {127.0.0.1:9200}, bound_addresses {[fe80::1]:9200}, {[::1]:9200}, {127.0.0.1:9200}
[2016-04-08 15:18:56,182][INFO ][node ] [Alexander Lexington] started
[2016-04-08 15:18:56,230][INFO ][gateway ] [Alexander Lexington] recovered [0] indices into cluster_state
[2016-04-08 15:34:22,441][INFO ][cluster.metadata ] [Alexander Lexington] [images] creating index, cause [auto(index api)], templates [], shards [5]/[1], mappings [image]
[2016-04-08 15:34:22,889][INFO ][cluster.routing.allocation] [Alexander Lexington] Cluster health status changed from [RED] to [YELLOW](reason: [shards started [[images][4]] ...]).
[2016-04-08 15:34:22,998][INFO ][cluster.metadata ] [Alexander Lexington] [images] update_mapping [image]
Führen Sie nach dem Starten von Elasticsearch das folgende Skript aus.
first_with_es.py
import json
from elasticsearch import Elasticsearch
from image_match.elasticsearch_driver import SignatureES
es = Elasticsearch()
ses = SignatureES(es)
image_set = (
'https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg/687px-Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg',
'https://pixabay.com/static/uploads/photo/2012/11/28/08/56/mona-lisa-67506_960_720.jpg',
'https://upload.wikimedia.org/wikipedia/commons/e/e0/Caravaggio_-_Cena_in_Emmaus.jpg',
'https://c2.staticflickr.com/8/7158/6814444991_08d82de57e_z.jpg',
)
for img in image_set:
print("add image to Elasticsearch. img=%s" % img)
ses.add_image(img)
search_image_set = (
'https://pixabay.com/static/uploads/photo/2012/11/28/08/56/mona-lisa-67506_960_720.jpg',
'http://i.imgur.com/CVYBCCy.jpg',
'http://i.imgur.com/T5AusYd.jpg',
)
for img in search_image_set:
print("=== search %s ===" % img)
print(json.dumps(ses.search_image(img, all_orientations=True), indent=2))
$ first_with_es.py
add image to Elasticsearch. img=https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg/687px-Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg
add image to Elasticsearch. img=https://pixabay.com/static/uploads/photo/2012/11/28/08/56/mona-lisa-67506_960_720.jpg
add image to Elasticsearch. img=https://upload.wikimedia.org/wikipedia/commons/e/e0/Caravaggio_-_Cena_in_Emmaus.jpg
add image to Elasticsearch. img=https://c2.staticflickr.com/8/7158/6814444991_08d82de57e_z.jpg
=== search https://pixabay.com/static/uploads/photo/2012/11/28/08/56/mona-lisa-67506_960_720.jpg ===
[
{
"path": "https://pixabay.com/static/uploads/photo/2012/11/28/08/56/mona-lisa-67506_960_720.jpg ",
"score": 2.435569,
"dist": 0.0,
"id": "AVP0lC4XSbcGjA3_XZUG"
},
{
"path": "https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg/687px-Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg ",
"score": 0.029808408,
"dist": 0.22095170140933634,
"id": "AVP0lCRBSbcGjA3_XZUF"
},
{
"path": "https://c2.staticflickr.com/8/7158/6814444991_08d82de57e_z.jpg ",
"score": 0.004886414,
"dist": 0.42325822368808808,
"id": "AVP0lDflSbcGjA3_XZUI"
}
]
=== search http://i.imgur.com/CVYBCCy.jpg ===
[
{
"path": "https://pixabay.com/static/uploads/photo/2012/11/28/08/56/mona-lisa-67506_960_720.jpg ",
"score": 0.20739666,
"dist": 0.15454905655638429,
"id": "AVP0lC4XSbcGjA3_XZUG"
},
{
"path": "https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg/687px-Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg ",
"score": 0.016346568,
"dist": 0.24980626832071956,
"id": "AVP0lCRBSbcGjA3_XZUF"
},
{
"path": "https://c2.staticflickr.com/8/7158/6814444991_08d82de57e_z.jpg ",
"score": 0.0031033582,
"dist": 0.43156216266051023,
"id": "AVP0lDflSbcGjA3_XZUI"
}
]
=== search http://i.imgur.com/T5AusYd.jpg ===
[
{
"path": "https://pixabay.com/static/uploads/photo/2012/11/28/08/56/mona-lisa-67506_960_720.jpg ",
"score": 1.5544797,
"dist": 0.069116439263706961,
"id": "AVP0lC4XSbcGjA3_XZUG"
},
{
"path": "https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg/687px-Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.jpg ",
"score": 0.029808408,
"dist": 0.22484320805049718,
"id": "AVP0lCRBSbcGjA3_XZUF"
},
{
"path": "https://c2.staticflickr.com/8/7158/6814444991_08d82de57e_z.jpg ",
"score": 0.004886414,
"dist": 0.42394015619010844,
"id": "AVP0lDflSbcGjA3_XZUI"
}
]
Je größer die Anzahl der "Punkte" (kleiner, näher an "dist") ist, desto ähnlicher sind die Bilder.
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