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
|
import os
import re
import time
import dlib
import numpy as np
from flask import Blueprint, request, jsonify
from PIL import Image # todo: try to remove PIL dependency
from app.processors import face_recognition
from app.processors import face_detector
from app.processors.faiss import load_faiss_databases
from app.models.sql_factory import load_sql_datasets, list_datasets, get_dataset, get_table
from app.utils.im_utils import pil2np
sanitize_re = re.compile('[\W]+')
valid_exts = ['.gif', '.jpg', '.jpeg', '.png']
api = Blueprint('api', __name__)
faiss_datasets = load_faiss_databases()
@api.route('/')
def index():
return jsonify({ 'datasets': list_datasets() })
@api.route('/dataset/<name>')
def show(name):
dataset = get_dataset(name)
if dataset:
return jsonify(dataset.describe())
else:
return jsonify({ 'status': 404 })
@api.route('/dataset/<name>/face/', methods=['POST'])
def upload(name):
start = time.time()
dataset = get_dataset(name)
if name not in faiss_datasets:
return jsonify({
'error': 'invalid dataset'
})
faiss_dataset = faiss_datasets[name]
file = request.files['query_img']
fn = file.filename
if fn.endswith('blob'):
fn = 'filename.jpg'
basename, ext = os.path.splitext(fn)
print("got {}, type {}".format(basename, ext))
if ext.lower() not in valid_exts:
return jsonify({ 'error': 'not an image' })
im = Image.open(file.stream).convert('RGB')
im_np = pil2np(im)
# Face detection
detector = face_detector.DetectorDLIBHOG()
# get detection as BBox object
bboxes = detector.detect(im_np, largest=True)
bbox = bboxes[0]
dim = im_np.shape[:2][::-1]
bbox = bbox.to_dim(dim) # convert back to real dimensions
# face recognition/vector
recognition = face_recognition.RecognitionDLIB(gpu=-1)
vec = recognition.vec(im_np, bbox)
# print(vec)
query = np.array([ vec ]).astype('float32')
# query FAISS!
distances, indexes = faiss_dataset.search(query, 10)
if len(indexes) == 0:
print("weird, no results!")
return []
# get the results for this single query...
distances = distances[0]
indexes = indexes[0]
if len(indexes) == 0:
print("no results!")
return []
lookup = {}
ids = [i+1 for i in indexes]
for _d, _i in zip(distances, indexes):
lookup[_i+1] = _d
print(distances)
print(indexes)
# with the result we have an ID
# query the sql dataset for the UUID etc here
query = {
'timing': time.time() - start,
}
results = [ dataset.get_identity(id) for id in ids ]
print(results)
return jsonify({
'results': results,
'distances': distances.tolist(),
'indexes': indexes.tolist(),
})
@api.route('/dataset/<name>/name', methods=['GET'])
def name_lookup(dataset):
start = time.time()
dataset = get_dataset(name)
# we have a query from the request query string...
# use this to do a like* query on the identities_meta table
query = {
'timing': time.time() - start,
}
results = []
print(results)
return jsonify({
'query': query,
'results': results,
})
|
