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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:
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) |