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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']
LIMIT = 9
THRESHOLD = 0.3
api = Blueprint('api', __name__)
faiss_datasets = load_faiss_databases()
@api.route('/')
def index():
"""List the datasets and their fields"""
return jsonify({ 'datasets': list_datasets() })
@api.route('/dataset/<name>')
def show(name):
"""Show the data that a dataset will return"""
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):
"""Query an image against FAISS and return the matching identities"""
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)
if not bboxes or not len(bboxes):
return jsonify({
'error': 'bbox'
})
bbox = bboxes[0]
if not bbox:
return jsonify({
'error': 'bbox'
})
dim = im_np.shape[:2][::-1]
bbox = bbox.to_dim(dim) # convert back to real dimensions
print("got bbox")
if not bbox:
return jsonify({
'error': 'bbox'
})
# extract 128-D vector
recognition = face_recognition.RecognitionDLIB(gpu=-1)
vec = recognition.vec(im_np, bbox)
query = np.array([ vec ]).astype('float32')
# query FAISS
distances, indexes = faiss_dataset.search(query, LIMIT)
if len(indexes) == 0 or len(indexes[0]) == 0:
return jsonify({
'error': 'nomatch'
})
# get the results for this single query...
distances = distances[0]
indexes = indexes[0]
dists = []
ids = []
for _d, _i in zip(distances, indexes):
if _d <= THRESHOLD:
dists.append(round(float(_d), 2))
ids.append(_i+1)
results = [ dataset.get_identity(_i) for _i in ids ]
print(distances)
print(ids)
query = {
'timing': round(time.time() - start, 3),
}
print(results)
return jsonify({
'query': query,
'results': results,
'distances': dists,
})
@api.route('/dataset/<name>/name', methods=['GET'])
def name_lookup(dataset):
"""Find a name in the 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,
})
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