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import os
import re
import time
import dlib
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.models.sql_factory import list_datasets, get_dataset, get_table
sanitize_re = re.compile('[\W]+')
valid_exts = ['.gif', '.jpg', '.jpeg', '.png']
api = Blueprint('api', __name__)
@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)
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' })
img = Image.open(file.stream).convert('RGB')
# Face detection
detector = face_detector.DetectorDLIBHOG()
# get detection as BBox object
bboxes = detector.detect(im, largest=True)
bbox = bboxes[0]
dim = im.shape[:2][::-1]
bbox = bbox.to_dim(dim) # convert back to real dimensions
# face recognition/vector
recognition = face_recognition.RecognitionDLIB(gpu=-1)
# print(vec.shape)
# results = db.search(vec, limit=limit)
# with the result we have an ID
# query the sql dataset for the UUID etc here
query = {
'timing': time.time() - start,
}
results = []
print(results)
return jsonify({
'query': query,
'results': results,
})
@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,
})
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