德语中的可分动词是什么？

aufgeben, angeben, auftreten

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等比压缩部署ONNX

import cv2
import onnxruntime as ort
import numpy as np

# 加载 ONNX 模型
onnx_model_path = r"yolo.onnx"
session = ort.InferenceSession(onnx_model_path)

# 获取模型的输入和输出名称
input_name = session.get_inputs()[0].name
input_shape = session.get_inputs()[0].shape
output_name = session.get_outputs()[0].name
output_shape = session.get_outputs()[0].shape
print(f"input name {input_name}, input shape {input_shape}")
print(f"output name {output_name}, output shape {output_shape}")

detect_h = 640
detect_w = 640
max_wh = max(detect_h, detect_w)
iou_threshold = 0.45
conf_threshold = 0.5
namelist = ["red", "green", "yellow", "off"] # change

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

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

    scale = min(detect_w / img_w, detect_h / img_h)  # 缩放比例
    h_t, w_t = abs(detect_h - scale * img_h) / 2, abs(detect_w - scale * img_w) / 2
    A = np.array([[scale, 0, w_t], [0, scale, h_t]], dtype=np.float32)
    img_back = cv2.warpAffine(img, A, (detect_w, detect_h), borderValue=(128, 128, 128))
    return img_back, A

def infer(onnx_session, img):
    input_name = onnx_session.get_inputs()[0].name
    label_name = onnx_session.get_outputs()[0].name
    pred = onnx_session.run([label_name], {input_name: img})[0]
    return pred


# apply nms
def xywh_to_x1y1x2y2(boxes):
    """
    this function convert xywh to x1y1x2y2
    """
    # 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 normalize_pred(pred, conf_threshold):  # 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 > conf_threshold  # 置信度过滤
        boxes = 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 > conf_threshold  # 置信度过滤
    boxes = xywh_to_x1y1x2y2(pred[mask])
    scores = scores[mask]
    classes = classes[mask]
    return boxes, scores, classes

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

def box_iou(box1, box2):
    area1 = box_area(box1)  # N
    area2 = 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(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 = 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(img_h, img_w, scores, boxes, classes, img, A, namelist, color=(255, 255, 0), thickness=2):
    for _class, box, score in zip(classes, boxes, scores):
        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)
        # 在矩形内部添加文字
        # 设置字体和字体大小
        font = cv2.FONT_HERSHEY_SIMPLEX
        font_scale = 1
        thickness_text = 2

        # 设置文字内容和颜色
        text = '{0} {1:.2f}'.format(namelist[_class], score)
        print("===>",text)
        text_color = (255, 255, 255)  # 白色

        # 获取文字的宽度和高度
        (text_width, text_height), baseline = cv2.getTextSize(text, font, font_scale, thickness_text)
        # 计算文字的位置（矩形中心）
        text_x = x1
        text_y = y1

        # 在图片上添加文字
        cv2.putText(img, text, (text_x, text_y), font, font_scale, text_color, thickness_text)
    Q = cv2.invertAffineTransform(A)
    img = cv2.warpAffine(img, Q, (img_w, img_h), borderValue=(128, 128, 128))
    return img


cap = cv2.VideoCapture(r"F:\training\cv\test.mp4")
while cap.isOpened():
    # retval 为一个检测是否读取到图片帧的参数
    retval, img = cap.read()
    if not retval:
        print('读取失败')
        break
    # get img w
    img_h = img.shape[0]
    img_w = img.shape[1]
    img, A = letterbox(img, img_h, detect_h, img_w, detect_w)
    preprocessed_img = img2input(img)
    pred = infer(session, preprocessed_img)
    boxes, scores, classes = normalize_pred(pred, conf_threshold)
    c = classes * max_wh
    nb = boxes + c[:, np.newaxis]
    _id = numpy_nms(nb, scores, iou_threshold)
    img = draw_res(img_h, img_w, scores[_id], boxes[_id], classes[_id], img, A, namelist)
    cv2.imshow('detect', img)

    # 视频中每一帧数据显示的时间（毫秒），刷新率：90Hz（即90帧/秒）。
    key = cv2.waitKey(10)  # FPS：画面每秒传输帧数
    if key == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()