import base64
from flask import Flask, request, jsonify
from flask_cors import CORS
from Algorithms.TargetFollower import TargetFollower
from Interfaces.DataProcessor import DataProcessor
import numpy as np
import cv2

app = Flask(__name__)
CORS(app)

data_processor = DataProcessor()

def decode_image_from_base64(image_base64: str, color: bool = True) -> np.ndarray:
    image_data = base64.b64decode(image_base64)
    np_arr = np.frombuffer(image_data, np.uint8)
    image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR if color else cv2.IMREAD_GRAYSCALE)
    return image

@app.route('/follow-vector', methods=['POST'])
def follow_vector():
    try:
        data = request.json
        # data['rgb'] = decode_image_from_base64(data['rgb'], color=True)
        # data['depth'] = decode_image_from_base64(data['depth'], color=False)
        detections_info, annotated_image = data_processor.inline_detection(data)

        if not detections_info and annotated_image is None:
            return jsonify({'error': 'No detections found.'}), 400

        # Указываем параметры камеры
        image_height, image_width, _ = annotated_image.shape
        camera_position = np.array([image_width / 2, image_height, 0])

        # todo: add detections_info to the kafka topic

        # Инициализируем TargetFollower и рассчитываем target вектор
        follower = TargetFollower(detections_info, annotated_image, camera_position=camera_position)
        selected_target = follower.select_target('person', nearest=True)
        follow_vector = follower.calculate_follow_vector()

        return jsonify({
            'detections_info': detections_info,
            'follow_vector': follow_vector.tolist(),
            'target': follower.get_target()
        })

    except Exception as e:
        print(e)
        return jsonify({'error': str(e)}), 500


if __name__ == '__main__':
    app.run(host="0.0.0.0", port=5000, debug=True)