core/runonnx/rtmdet.py

import numpy as np
import cv2
from typing import Tuple, List, Union
from .base_onnx import BaseONNX


class RTMDET_ONNX(BaseONNX):

    def __init__(self, model_path, input_size=(640, 640)):
        super().__init__(model_path, input_size)

    def preprocess_image(self, img_bgr: cv2.UMat):
        # 调整图片大小
        img_bgr = cv2.resize(img_bgr, self.input_size)

        # normalize mean and std
        img = (img_bgr - np.array([103.53, 116.28, 123.675])) / np.array([57.375, 57.12, 58.395])

        img = img.astype(np.float32)
        # 转换为浮点型并归一化
        # img = img.astype(np.float32) / 255.0
        
        # 调整维度顺序 (H,W,C) -> (C,H,W)
        img = np.transpose(img, (2, 0, 1))
        
        # 添加 batch 维度
        img = np.expand_dims(img, axis=0)

        return img
    

    def run_inference(self, image: np.ndarray):
        """
        Run inference on the image.

        Args:
            image (np.ndarray): The image to run inference on.

        Returns:
            tuple: A tuple containing the detection results and labels.
        """
        # 运行推理
        outputs = self.session.run(None, {self.input_name: image})

        """
        dets: 检测框 [batch, num_dets, [x1, y1, x2, y2, conf]] （[batch, num_dets, Reshape(dets_dim_2)]）
        labels: 标签 [batch,num_dets]
        """
        dets, labels = outputs

        return dets, labels

    def pred(self, image: List[Union[cv2.UMat, str]]) -> Tuple[List[int], float]:
        """
        Predict the detection results of the image.

        Args:
            image (cv2.UMat, str): The image to predict.

        Returns:
           xyxy (list[int, int, int, int]): The detection results.
           conf (float): The confidence of the detection results.
        """
        if isinstance(image, str):
            img_bgr = cv2.imread(image)
        else:
            img_bgr = image.copy()

        original_w, original_h = img_bgr.shape[1], img_bgr.shape[0]

        image = self.preprocess_image(img_bgr)
        dets, labels = self.run_inference(image)

        # 获取置信度最高的检测框
        # dets = dets[0][0]
        # labels = labels[0][0]

        x1, y1, x2, y2, conf = dets[0][0]

        xyxy = [x1, y1, x2, y2]

        xyxy = self.transform_xyxy_to_original(xyxy, original_w, original_h)

        return xyxy, conf
    
    def transform_xyxy_to_original(self, xyxy, original_w, original_h) -> List[int]:
        """
        将检测框从输入图像的尺寸转换为原始图像的尺寸
        """
        x1, y1, x2, y2 = xyxy

        input_w, input_h = self.input_size
        ratio_w, ratio_h = original_w / input_w, original_h / input_h

        x1, y1, x2, y2 = x1 * ratio_w, y1 * ratio_h, x2 * ratio_w, y2 * ratio_h
        # 转换为整数
        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)

        return [x1, y1, x2, y2]
    
    def draw_pred(self, img: cv2.UMat, xyxy: List[int], conf: float, is_rgb: bool = True) -> cv2.UMat:
        """
        Draw the detection results on the image.
        """

        if not is_rgb:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        x1, y1, x2, y2 = xyxy

        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
        cv2.putText(img, f"{conf:.2f}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

        return img