import cv2
import numpy as np
import json

from flask import Flask, url_for
app = Flask("Kotely")

@app.route('/')
def api_root():
    return 'Welcome'

@app.route('/image/<path:imagepath>')
def info(imagepath):
    return recognition("/"+imagepath)

def get_output_layers(net):
    layer_names = net.getLayerNames()
    output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    return output_layers

def recognition(photo):
    image = cv2.imread(photo)

    Width = image.shape[1]
    Height = image.shape[0]
    scale = 0.00392

    with open("yolov3.txt", 'r') as f:
        classes = [line.strip() for line in f.readlines()]

    COLORS = np.random.uniform(0, 255, size=(len(classes), 3))

    net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

    blob = cv2.dnn.blobFromImage(image, scale, (416, 416), (0, 0, 0), True, crop=False)

    net.setInput(blob)

    outs = net.forward(get_output_layers(net))

    class_ids = []
    confidences = []
    boxes = []
    conf_threshold = 0.5
    nms_threshold = 0.4

    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                class_ids.append(class_id)
    data = '{"path":"'+photo+'"}'
    js = json.loads(data)

    if 15 in class_ids:
        js.update({"cat":1})
        return js

    else:
        js.update({"cat": 0})
        return js

if __name__ == '__main__':
    app.run(port="8809")