I have trained a yolov9 model on custom dataset for instance segmentation, now I want to get segmentation area after segmentation.

An output like the given below image but for each and every object segmented in the image.

from pathlib import Path
import numpy as np
import torch
import cv2


model = torch.hub.load('.', 'custom', path='yolov9-inst/runs/train-seg/gelan-c-seg15/weights/best.pt', source='local') 
# Image
img = 'WALL-INSTANCEE-2/test/images/5a243513a69b150001f56c31_emptyroom6_jpeg_jpg.rf.7aa8f6a9aefbb1c76adc60a7b392dcd6.jpg'
# Inference
res = model(img)


#  Iterate detection results (helpful for multiple images)
for r in res:
    img = np.copy(r.orig_img)
    img_name = Path(r.path).stem  # source image base-name

    # Iterate each object contour (multiple detections)
    for ci, c in enumerate(r):
        #  Get detection class name
        label = c.names[c.boxes.cls.tolist().pop()]

# Create binary mask
b_mask = np.zeros(img.shape[:2], np.uint8)

#  Extract contour result
contour = c.masks.xy.pop()
#  Changing the type
contour = contour.astype(np.int32)
#  Reshaping
contour = contour.reshape(-1, 1, 2)


# Draw contour onto mask
_ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)

But I am getting this error while finding res only.

YOLO ???? v0.1-104-g5b1ea9a Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (NVIDIA RTX A5000, 24248MiB)

Fusing layers... 
gelan-c-seg-custom summary: 414 layers, 27364441 parameters, 0 gradients, 144.2 GFLOPs
WARNING ⚠️ YOLO SegmentationModel is not yet AutoShape compatible. You will not be able to run inference with this model.
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[84], line 6
      4 img = 'WALL-INSTANCEE-2/test/images/5a243513a69b150001f56c31_emptyroom6_jpeg_jpg.rf.7aa8f6a9aefbb1c76adc60a7b392dcd6.jpg'
      5 # Inference
----> 6 results = model(img)

File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1518, in Module._wrapped_call_impl(self, *args, **kwargs)
   1516     return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1517 else:
-> 1518     return self._call_impl(*args, **kwargs)

File /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py:1527, in Module._call_impl(self, *args, **kwargs)
   1522 # If we don't have any hooks, we want to skip the rest of the logic in
   1523 # this function, and just call forward.
   1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
   1525         or _global_backward_pre_hooks or _global_backward_hooks
   1526         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1527     return forward_call(*args, **kwargs)
   1529 try:
   1530     result = None

File /workspace/yolov9-inst/./models/common.py:868, in DetectMultiBackend.forward(self, im, augment, visualize)
    866 def forward(self, im, augment=False, visualize=False):
    867     # YOLO MultiBackend inference
--> 868     b, ch, h, w = im.shape  # batch, channel, height, width
    869     if self.fp16 and im.dtype != torch.float16:
    870         im = im.half()  # to FP16

AttributeError: 'str' object has no attribute 'shape'

Please can anyone help me to resolve this issue