model.py

from __future__ import annotations

import os

import huggingface_hub
import numpy as np
import torch
import torch.nn as nn
import yaml  # type: ignore
from mmdet.apis import inference_detector, init_detector





class Model:

    def __init__(self, model_name: str):
        self.device = torch.device(
            'cuda:0' if torch.cuda.is_available() else 'cpu')
        self.model_name = model_name
        self.model = self._load_model(model_name)


    def _load_model(self, name: str) -> nn.Module:
        return init_detector('configs/_base_/faster-rcnn_r50_fpn_1x_coco.py', 'models/orgaquant_pretrained.pth' , device=self.device)

    def set_model(self, name: str) -> None:
        if name == self.model_name:
            return
        self.model_name = name
        self.model = self._load_model(name)

    def detect_and_visualize(
        self, image: np.ndarray, score_threshold: float
    ) -> tuple[list[np.ndarray] | tuple[list[np.ndarray],
                                        list[list[np.ndarray]]]
               | dict[str, np.ndarray], np.ndarray]:
        out = self.detect(image)
        vis = self.visualize_detection_results(image, out, score_threshold)
        return out, vis

    def detect(
        self, image: np.ndarray
    ) -> list[np.ndarray] | tuple[
            list[np.ndarray], list[list[np.ndarray]]] | dict[str, np.ndarray]:
        out = inference_detector(self.model, image)
        return out

    def visualize_detection_results(
            self,
            image: np.ndarray,
            detection_results: list[np.ndarray]
        | tuple[list[np.ndarray], list[list[np.ndarray]]]
        | dict[str, np.ndarray],
            score_threshold: float = 0.3) -> np.ndarray:
        print('Detection results',detection_results)
        vis = self.model.show_result(image,
                                     detection_results,
                                     score_thr=score_threshold,
                                     bbox_color=None,
                                     text_color=(200, 200, 200),
                                     mask_color=None)
        return vis


class AppModel(Model):
    def run(
        self, model_name: str, image: np.ndarray, score_threshold: float
    ) -> tuple[list[np.ndarray] | tuple[list[np.ndarray],
                                        list[list[np.ndarray]]]
               | dict[str, np.ndarray], np.ndarray]:
        self.set_model(model_name)
        return self.detect_and_visualize(image, score_threshold)