Conversation

Fuuuck... that python face_recognition library was able to match the image on the left to the image on the right 😲

Try it yourself (where /tmp/foo.jpg is a picture with a face):
```
#!/usr/bin/env python3
import face_recognition, io, re, requests

searx_server = 'search.gibberfish.org'
search_term = 'bernie'
url = f'https://{searx_server}/?q={search_term}&categories=images&language=en-US&format=json'
hits =[]
misses = []
with open('/tmp/foo.jpg', 'rb') as sourcefile:
original = face_recognition.load_image_file(io.BytesIO(sourcefile.read()))
original_encoding = face_recognition.face_encodings(original)[0]
r = requests.get(url)
results = r.json()
count = 0
for item in results['results']:
img_url = item['img_src']
if re.match(r'https?://', img_url):
try:
r = requests.get(img_url, stream=True, timeout=3.0)
if r.status_code == requests.codes.ok:
r.raw.decode_content = True
unknown = face_recognition.load_image_file(io.BytesIO(r.raw.read()))
unknown_encoding = face_recognition.face_encodings(unknown)[0]
results = face_recognition.compare_faces([original_encoding], unknown_encoding)
if results[0]:
hits.append(img_url)
else:
misses.append(img_url)
count = count + 1
print(count)
if count > 25:
break
except:
pass
print("Hits:")
[print(h) for h in hits]
print("Misses")
[print(m) for m in misses]
```

#python #face_recognition #facialrecognition #bernie