支持yolov5和yolov8双模型

其中ChtDeploy中代码如下：

import onnxruntime
import numpy as np
import cv2

class ChtDeploy():
    def __init__(self, img_path, onnx_path, iou_threshold=0.45, conf_threshold=0.3, detect_w=640, detect_h= 640):
        self.img = cv2.imread(img_path)#h,w,c
        self.img_h = self.img.shape[0]
        self.img_w = self.img.shape[1]
        self.iou_threshold = iou_threshold
        self.conf_threshold = conf_threshold
        self.detect_w = detect_w
        self.detect_h = detect_h
        self.onnx = onnx_path
        self.max_wh = max(self.detect_h, self.detect_w)

    def letterbox(self):
        if(self.img_h == self.detect_h and self.img_w == self.detect_w):
            return self.img

        scale = min(self.detect_w / self.img_w, self.detect_h / self.img_h)  # 缩放比例
        nw, nh = int(self.img_w * scale), int(self.img_h * scale)
        image = cv2.resize(self.img, (nw, nh), interpolation=cv2.INTER_LINEAR)
        image_back = np.ones((self.detect_h, self.detect_w, 3), dtype=np.uint8) * 128
        # 将image放在画布中心区域-letterbox
        image_back[(self.detect_h - nh) // 2: (self.detect_h - nh) // 2 + nh, (self.detect_w - nw) // 2:(self.detect_w - nw) // 2 + nw, :] = image
        return image_back

    def img2input(self, img):
        img = np.transpose(img, (2, 0, 1))
        img = img / 255
        return np.expand_dims(img, axis=0).astype(np.float32)  # (1,3,640,640)

    def infer(self, onnx, img):
        session = onnxruntime.InferenceSession(onnx)
        input_name = session.get_inputs()[0].name
        label_name = session.get_outputs()[0].name
        pred = session.run([label_name], {input_name: img})[0]
        return pred #yolov8 1 * 84 * 8400 yolov5 1 * 25200 * 85

    def xywh_to_x1y1x2y2(self, boxes):
        # 提取中心点坐标和宽高
        x_center, y_center, width, height = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3]

        # 计算左上角和右下角坐标
        x1 = x_center - width / 2
        y1 = y_center - height / 2
        x2 = x_center + width / 2
        y2 = y_center + height / 2

        # 将计算结果组合成新的数组
        xyxy_boxes = np.stack((x1, y1, x2, y2), axis=1)

        return xyxy_boxes

    def normalpred(self, pred):#the style of v8 to v5
        if pred.shape[1] < pred.shape[2]: #v8
            pred = np.squeeze(pred).T #1 * 84 * 8400 -> 8400 * 84
            scores = np.max(pred[:, 4:], axis=1)
            classes = np.argmax(pred[:, 4:], axis=1)
            mask = scores > self.conf_threshold #置信度过滤
            boxes = self.xywh_to_x1y1x2y2(pred[mask])
            scores = scores[mask]
            classes = classes[mask]
            return boxes, scores, classes

        pred = np.squeeze(pred)
        scores = pred[:, 4]
        classes = np.argmax(pred[:, 5:], axis=1)
        mask = scores > self.conf_threshold  # 置信度过滤
        boxes = self.xywh_to_x1y1x2y2(pred[mask])
        scores = scores[mask]
        classes = classes[mask]
        return boxes, scores, classes

    def box_area(self, boxes):
        return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])

    def box_iou(self, box1, box2):
        area1 = self.box_area(box1)  # N
        area2 = self.box_area(box2)  # M
        # broadcasting, 两个数组各维度大小 从后往前对比一致， 或者 有一维度值为1；
        lt = np.maximum(box1[:, np.newaxis, :2], box2[:, :2])
        rb = np.minimum(box1[:, np.newaxis, 2:], box2[:, 2:])
        wh = rb - lt
        wh = np.maximum(0, wh)  # [N, M, 2]
        inter = wh[:, :, 0] * wh[:, :, 1]
        iou = inter / (area1[:, np.newaxis] + area2 - inter)
        return iou

    def numpy_nms(self, boxes, scores, iou_threshold):

        idxs = scores.argsort()  # 按分数 降序排列的索引 [N]
        keep = []
        while idxs.size > 0:  # 统计数组中元素的个数
            max_score_index = idxs[-1]
            max_score_box = boxes[max_score_index][None, :]
            keep.append(max_score_index)
            if idxs.size == 1:
                break
            idxs = idxs[:-1]  # 将得分最大框 从索引中删除； 剩余索引对应的框 和 得分最大框 计算IoU；
            other_boxes = boxes[idxs]  # [?, 4]
            ious = self.box_iou(max_score_box, other_boxes)  # 一个框和其余框比较 1XM
            idxs = idxs[ious[0] <= iou_threshold]

        return keep

    def draw_res(self, boxes, img, color=(255, 255, 0), thickness=2):
        for box in boxes:
            x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
            cv2.rectangle(img, (x1, y1), (x2, y2), color, thickness)
        cv2.imshow('detect', img)
        cv2.waitKey(0)
        cv2.destroyAllWindows()

    def deploy(self):
        img = self.letterbox()
        img1 = self.img2input(img)
        pred = self.infer(self.onnx, img1)
        boxes, scores, classes = self.normalpred(pred)
        c = classes * self.max_wh
        nb = boxes + c[:, np.newaxis]
        id = self.numpy_nms(nb, scores, self.iou_threshold)
        self.draw_res(boxes[id], img)


d = ChtDeploy(img_path="gg.webp", onnx_path="yolov8n.onnx")
d.deploy()