Before Reporting

• I have pulled the latest code of main branch to run again and the bug still existed. 我已经拉取了主分支上最新的代码，重新运行之后，问题仍不能解决。

• I have read the README carefully and no error occured during the installation process. (Otherwise, we recommand that you can ask a question using the Question template) 我已经仔细阅读了README上的操作指引，并且在安装过程中没有错误发生。（否则，我们建议您使用Question模板向我们进行提问）

Search before reporting

• I have searched the DAMO-YOLO issues and found no similar bugs. 我已经在issue列表中搜索但是没有发现类似的bug报告。

OS

Windows

Device

Nvidia 4070Ti

CUDA version

11.8

TensorRT version

8.6.1.6

Python version

3.8

PyTorch version

2.2.2

torchvision version

0.17.2

Describe the bug

运行推理时，在damo/utils/visualize.py中报错，显示numpy数组是只读的不允许操作。
`
File "E:\proj\DAMO-YOLO\damo\utils\visualize.py", line 30, in vis
cv2.rectangle(img, (x0, y0), (x1, y1), color, 2)
│ │ │ │ │ │ │ └ [0, 113, 188]
│ │ │ │ │ │ └ 261
│ │ │ │ │ └ 322
│ │ │ │ └ 229
│ │ │ └ 309
│ │ └ array([[[255, 255, 255],
│ │ [255, 255, 255],
│ │ [255, 255, 255],
│ │ ...,
│ │ [255, 255, 255],
│ │ [255...
│ └
└ <module 'cv2' from 'C:\Users\chonpsk\.conda\envs\DAMO-YOLO\lib\site-packages\cv2\init.py'>

cv2.error: OpenCV(4.9.0) 👎 error: (-5:Bad argument) in function 'rectangle'

Overload resolution failed:

• img marked as output argument, but provided NumPy array marked as readonly
• Expected Ptrcv::UMat for argument 'img'
• img marked as output argument, but provided NumPy array marked as readonly
• Expected Ptrcv::UMat for argument 'img'
  `

To Reproduce

python demo.py image -f ./configs/damoyolo_tinynasL20_T.py --engine workdirs/damoyolo_tinynasL20_T/epoch_300_ckpt.pth --conf 0.1 --inf
er_size 640 640 --device cuda --path datasets/Custom_coco/images/val/66_crop_0.jpg

Hyper-parameters/Configs

使用默认超参数

Logs

2024-04-11 10:13:31.168 | ERROR | main::356 - An error has been caught in function '', process 'MainProcess' (980), thread 'MainThread' (23684):
Traceback (most recent call last):

File "demo.py", line 356, in
main()
└ <function main at 0x000001C91010F820>

File "demo.py", line 321, in main
vis_res = infer_engine.visualize(origin_img, bboxes, scores, cls_inds, conf=args.conf, save_name=os.path.basename(args.path), save_result=args.save_result)
│ │ │ │ │ │ │ │ │ │ │ │ │ │ └ True
│ │ │ │ │ │ │ │ │ │ │ │ │ └ Namespace(camid=0, conf=0.6, confi
g_file='./configs/damoyolo_tinynasL20_T.py', device='cuda', end2end=False, engine='workdirs...
│ │ │ │ │ │ │ │ │ │ │ │ └ 'datasets/Custom_coco/images/val/66_crop_0.jpg'
│ │ │ │ │ │ │ │ │ │ │ └ Namespace(camid=0, conf=0.6, config_file='./configs/damoyo
lo_tinynasL20_T.py', device='cuda', end2end=False, engine='workdirs...
│ │ │ │ │ │ │ │ │ │ └ <function basename at 0x000001C95AD39EE0>
│ │ │ │ │ │ │ │ │ └ <module 'ntpath' from 'C:\Users\chonpsk\.conda\envs\DAMO-YOLO\lib
\ntpath.py'>
│ │ │ │ │ │ │ │ └ <module 'os' from 'C:\Users\chonpsk\.conda\envs\DAMO-YOLO\lib\os.py'

          │            │         │           │       │       │              │    └ 0.6
          │            │         │           │       │       │              └ Namespace(camid=0, conf=0.6, config_file='./configs/damoyolo_tinynasL20_T.py', device='cuda', en

d2end=False, engine='workdirs...
│ │ │ │ │ └ tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
│ │ │ │ │ 0., 0., 0., 0...
│ │ │ │ └ tensor([0.6785, 0.6641, 0.6581, 0.6514, 0.6470, 0.6399, 0.5757, 0.5683, 0.5609,
│ │ │ │ 0.5585, 0.5564, 0.5532, 0.5524, 0.521...
│ │ │ └ tensor([[309.0302, 229.5414, 322.8981, 261.2090],
│ │ │ [267.1926, 28.5143, 280.6399, 46.0551],
│ │ │ [307.3100, 135.57...
│ │ └ array([[[255, 255, 255],
│ │ [255, 255, 255],
│ │ [255, 255, 255],
│ │ ...,
│ │ [255, 255, 255],
│ │ [255...
│ └ <function Infer.visualize at 0x000001C91010F700>
└ <main.Infer object at 0x000001C95AE46550>

File "demo.py", line 249, in visualize
vis_img = vis(image, bboxes, scores, cls_inds, conf, self.class_names)
│ │ │ │ │ │ │ └ ['qx']
│ │ │ │ │ │ └ <main.Infer object at 0x000001C95AE46550>
│ │ │ │ │ └ 0.6
│ │ │ │ └ tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
│ │ │ │ 0., 0., 0., 0...
│ │ │ └ tensor([0.6785, 0.6641, 0.6581, 0.6514, 0.6470, 0.6399, 0.5757, 0.5683, 0.5609,
│ │ │ 0.5585, 0.5564, 0.5532, 0.5524, 0.521...
│ │ └ tensor([[309.0302, 229.5414, 322.8981, 261.2090],
│ │ [267.1926, 28.5143, 280.6399, 46.0551],
│ │ [307.3100, 135.57...
│ └ array([[[255, 255, 255],
│ [255, 255, 255],
│ [255, 255, 255],
│ ...,
│ [255, 255, 255],
│ [255...
└ <function vis at 0x000001C910064DC0>

File "E:\proj\DAMO-YOLO\damo\utils\visualize.py", line 30, in vis
cv2.rectangle(img, (x0, y0), (x1, y1), color, 2)
│ │ │ │ │ │ │ └ [0, 113, 188]
│ │ │ │ │ │ └ 261
│ │ │ │ │ └ 322
│ │ │ │ └ 229
│ │ │ └ 309
│ │ └ array([[[255, 255, 255],
│ │ [255, 255, 255],
│ │ [255, 255, 255],
│ │ ...,
│ │ [255, 255, 255],
│ │ [255...
│ └
└ <module 'cv2' from 'C:\Users\chonpsk\.conda\envs\DAMO-YOLO\lib\site-packages\cv2\init.py'>

cv2.error: OpenCV(4.9.0) 👎 error: (-5:Bad argument) in function 'rectangle'

Overload resolution failed:

• img marked as output argument, but provided NumPy array marked as readonly
• Expected Ptrcv::UMat for argument 'img'
• img marked as output argument, but provided NumPy array marked as readonly
• Expected Ptrcv::UMat for argument 'img'

Screenshots

No response

Additional

将 vis内对numpy数组拷贝后进行操作解决该问题：
`def vis(img, boxes, scores, cls_ids, conf=0.5, class_names=None):

for i in range(len(boxes)):
    box = boxes[i]
    cls_id = int(cls_ids[i])
    score = scores[i]
    if score < conf:
        continue
    x0 = int(box[0])
    y0 = int(box[1])
    x1 = int(box[2])
    y1 = int(box[3])

    color = (_COLORS[cls_id] * 255).astype(np.uint8).tolist()
    text = '{}:{:.1f}%'.format(class_names[cls_id], score * 100)
    txt_color = (0, 0, 0) if np.mean(_COLORS[cls_id]) > 0.5 else (255, 255,
                                                                  255)
    font = cv2.FONT_HERSHEY_SIMPLEX

    txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
    img_copy = np.copy(img)
    cv2.rectangle(img_copy, (x0, y0), (x1, y1), color, 2)
    # cv2.rectangle(img, (x0, y0), (x1, y1), color, 2)

    txt_bk_color = (_COLORS[cls_id] * 255 * 0.7).astype(np.uint8).tolist()
    # cv2.rectangle(img, (x0, y0 + 1),
    #               (x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])),
    #               txt_bk_color, -1)
    cv2.rectangle(img_copy, (x0, y0 + 1),
                  (x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])),
                  txt_bk_color, -1)
    # cv2.putText(img,
    #             text, (x0, y0 + txt_size[1]),
    #             font,
    #             0.4,
    #             txt_color,
    #             thickness=1)
    cv2.putText(img_copy,
                text, (x0, y0 + txt_size[1]),
                font,
                0.4,
                txt_color,
                thickness=1)

# return img
return img_copy`