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#!python
import os
import sys
import json
import time
import argparse
import cv2 as cv
import numpy as np
from datetime import datetime
from flask import Flask, request, render_template, jsonify
from PIL import Image # todo: try to remove PIL dependency
import re
sanitize_re = re.compile('[\W]+')
valid_exts = ['.gif', '.jpg', '.jpeg', '.png']
from dotenv import load_dotenv
load_dotenv()
from feature_extractor import FeatureExtractor
DEFAULT_LIMIT = 50
app = Flask(__name__, static_url_path="/search/static", static_folder="static")
# static api routes - this routing is actually handled in the JS
@app.route('/', methods=['GET'])
def index():
return app.send_static_file('metadata.html')
# search using an uploaded file
@app.route('/search/api/upload', methods=['POST'])
def upload():
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' })
uploaded_fn = datetime.now().isoformat() + "_" + basename
uploaded_fn = sanitize_re.sub('', uploaded_fn)
uploaded_img_path = "static/uploaded/" + uploaded_fn + ext
uploaded_img_path = uploaded_img_path.lower()
print('query: {}'.format(uploaded_img_path))
img = Image.open(file.stream).convert('RGB')
# img.save(uploaded_img_path)
# vec = db.load_feature_vector_from_file(uploaded_img_path)
vec = fe.extract(img)
# print(vec.shape)
results = db.search(vec, limit=limit)
query = {
'timing': time.time() - start,
}
print(results)
return jsonify({
'results': results,
})
if __name__=="__main__":
app.run("0.0.0.0", debug=False)
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