app.py

import os
import pathlib
import numpy as np
from PIL import Image
from feature_extractor import FeatureExtractor
from datetime import datetime
from flask import Flask, request, render_template, jsonify, make_response
from pathlib import Path
from image_util import download_images_parallel_starting_point as images_downloader
from offline import extract_features_in_path
import constants
from DeepImageSearch import Index,LoadData,SearchImage
import shutil

app = Flask(__name__)

# Read image features
fe = FeatureExtractor()
features = []
img_paths = []
for feature_path in Path(constants.FEATURED).glob("*.npy"):
    features.append(np.load(feature_path))
    # img_paths.append(Path("./static/img") / (feature_path.stem))
    img_paths.append(feature_path.stem) #TODO change to product ID 
features = np.array(features)


@app.route('/', methods=['GET', 'POST'])
def index():
    if request.method == 'POST':
        file = request.files['query_img']

        # Save query image
        img = Image.open(file.stream)  # PIL image
        uploaded_img_path = "static/uploaded/" + datetime.now().isoformat().replace(":", ".") + "_" + file.filename
        img.save(uploaded_img_path)

        # Run search
        query = fe.extract(img)
        # L2 distances to features
        dists = np.linalg.norm(features-query, axis=1)
        ids = np.argsort(dists)[:30]  # Top 30 results
        scores = [(dists[id], img_paths[id]) for id in ids]

        return render_template('index.html',
                               query_path=uploaded_img_path,
                               scores=scores)
    else:
        return render_template('index.html')

@app.route('/api/v1/detect', methods=['POST'])
def detect():
    print('just got started')
    if (request.files['image']):
        print('I am in')
        file = request.files['image']

        # Save query image
        img = Image.open(file.stream)  # PIL image
        # uploaded_img_path = "static/uploaded/" + datetime.now().isoformat().replace(":", ".") + "_" + img
        # img.save(uploaded_img_path)
        scores = SearchImage().get_similar_images(image_path = file.stream,number_of_images=5)
        print(scores)

        # Store results in a dictionary
        results = []

        for key in scores:
            results.append({
                "filename": str(scores[key]).split('\\')[-1]
            })

        print(jsonify(results))
        response = make_response(
            jsonify(results),
            200,
        )
        response.headers["Content-Type"] = "application/json"
        return response
    else:
        print('there is no file')
        response = make_response(
            jsonify({"error": "Please use the image param in the body"}),
            400,
        )
        response.headers["Content-Type"] = "application/json"
        return response

@app.route('/api/v1/inference', methods=['POST'])
def train():
    request_data = request.get_json()
    if request_data:
        if 'images' in request_data:
            #1 - download_images
            images_downloader(request_data['images'])
            print(constants.INFERENCE_QUEUE_DIR)
            # cloth segmentation goes here
            os.chdir(pathlib.Path(__file__).parent.resolve())
            if (os.path.exists('meta-data-files')):
                shutil.rmtree('meta-data-files', ignore_errors=True)
                # os.rmdir('meta-data-files')
            image_list = LoadData().from_folder(folder_list = [constants.INFERENCE_QUEUE_DIR])
            Index(image_list).Start()

            response = make_response(
                jsonify({"training_status":"done"}),
                200,
            )
            response.headers["Content-Type"] = "application/json"
            return response

        else:
                response = make_response(
                    jsonify({"error": "Please use the images paramter in the body"}),
                    400,
                )
                response.headers["Content-Type"] = "application/json"
                return response

    else:
            response = make_response(
                    jsonify({"error" : "This endpoint requires request paramter named: images only, and dosen't accept empty body requests"}),
                    400,
                )
            response.headers["Content-Type"] = "application/json"
            return response

@app.errorhandler(404)
def not_found(e):
    """Page not found."""
    # return make_response(render_template("error.html"), 404)
    return render_template("error.html")


if __name__ == "__main__":
    app.run(debug=False, host='127.0.0.1')