YOLOv8水果检测示例代码换成640输入图像出现目标框绘制错误，什么原因？

不能只改这里，模型的输入也要改，你要换一个模型

from libs.YOLO import YOLOv8
import os,sys,gc
import ulab.numpy as np
import image

# 如果是 COCO-80 模型，请使用完整列表：
labels = [
    "person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light",
    "fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow",
    "elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee",
    "skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard",
    "tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich",
    "orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed",
    "dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven",
    "toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"
]

# —— 确保最前面就定义好 split_path ——
def split_path(path):
    parts = path.rsplit('/', 1)
    if len(parts) == 1:
        dir_path, filename = '', parts[0]
    else:
        dir_path, filename = parts
    name_parts = filename.rsplit('.', 1)
    if len(name_parts) == 1:
        name, ext = name_parts[0], ''
    else:
        name, ext = name_parts
        ext = '.' + ext
    return dir_path, name, ext

# 从本地读入图片，并实现HWC转CHW
def read_img(img_path):
    img_data = image.Image(img_path)
    img_data_rgb888=img_data.to_rgb888()
    img_hwc=img_data_rgb888.to_numpy_ref()
    shape=img_hwc.shape
    img_tmp = img_hwc.reshape((shape[0] * shape[1], shape[2]))
    img_tmp_trans = img_tmp.transpose()
    img_res=img_tmp_trans.copy()
    img_return=img_res.reshape((shape[2],shape[0],shape[1]))
    print(img_return.shape[2],"," ,img_return.shape[1])
    print(img_data_rgb888.height())
    return img_return,img_data_rgb888

if __name__=="__main__":
    # 可以根据您的模型自行修改路径参数
    img_path="/data/test_apple.jpg"
    kmodel_path="/sdcard/examples/kmodel/yolov8n_320.kmodel"
    # labels = ["apple","banana","orange"]
    # print("hello")
    confidence_threshold = 0.5
    nms_threshold = 0.45
    model_input_size=[320,320]
    img,img_ori=read_img(img_path)
    # print(img.shape[2],img.shape[1])
    rgb888p_size=[img.shape[2],img.shape[1]] #img.shape[2]=1024,img.shape[1]=1024
    # 初始化YOLOv8实例
    yolo=YOLOv8(task_type="detect",
                mode="image",
                kmodel_path=kmodel_path,
                labels=labels,
                rgb888p_size=rgb888p_size,
                model_input_size=model_input_size,
                conf_thresh=confidence_threshold,
                nms_thresh=nms_threshold,
                debug_mode=0)
    yolo.config_preprocess()


    try:
        res=yolo.run(img)

        valid_res = []
        if res is None or len(res) == 0:
            print("⚠️ 未检测到任何目标！")
        else:
            print(f"✅ 共检测到 {len(res)} 条原始结果：")
            for i, row in enumerate(res):
                cid = int(row[5])
                name = labels[cid] if 0 <= cid < len(labels) else "unknown"
                print(f"  {i}: class_id={cid} ({name}), conf={row[4]:.2f}, box={[int(x) for x in row[:4]]}")

        yolo.draw_result(res, img_ori)

        # 生成功能：拼接保存路径
        dir_path, name, ext = split_path(img_path)
        save_name = "{}_result{}".format(name, ext)
        save_path = dir_path + '/' + save_name if dir_path else save_name

        img_to_save = img_ori.to_jpeg(quality=95)

        # 保存结果图像
        img_to_save.save(save_path)
        print("检测结果已保存到:", save_path)


        gc.collect()
    except Exception as e:
        sys.print_exception(e)
    finally:
        yolo.deinit()