o oi$@snddlmZddlmZmZmZddlZddlZddlm Z ddl m Z ddl mZdgZGdddeZdS) ) annotations)ClassVarOptionalUnionN)Tensor)PILImage) ModelBaseSemanticSegmentationc@seZdZUdZgdZded<gdZded<ed'd d Z d(d)ddZ d*ddZ   d+d,d d!Z    d-d.d%d&Z dS)/r zSemantic Segmentation is a module that wraps a semantic segmentation model. This module uses SegmentationModel library for semantic segmentation. )r r zClassVar[list[int]]ONNX_DEFAULT_INPUTSHAPE)r r r r ONNX_DEFAULT_OUTPUTSHAPEimagesUnion[Tensor, list[Tensor]]returncCsrt|ttfr(g}|D]}||d}||}||}||dq |S||}||}||}|S)aForward pass of the semantic segmentation model. Args: images: If list of RGB images. Each image is a Tensor with shape :math:`(3, H, W)`. If Tensor, a Tensor with shape :math:`(B, 3, H, W)`. Returns: output tensor. Nr) isinstancelisttuple pre_processormodelpost_processorappend)selfroutputsimageoutputrS/home/ubuntu/.local/lib/python3.10/site-packages/kornia/models/segmentation/base.pyforward)s"    zSemanticSegmentation.forwardrandomc num_classesintcolormapstr manual_seedrcCs<|dkrt}||tj|d|d}|Std|)anGet a color map of size num_classes. Args: num_classes: The number of colors in the color map. colormap: The colormap to use, can be "random" or a custom color map. manual_seed: The manual seed to use for the colormap. Returns: A tensor of shape (num_classes, 3) representing the color map. rr ) generatorzUnsupported colormap: )torch Generatorr%rand ValueError)rr!r#r%g_cpucolorsrrr get_colormapKs   z!SemanticSegmentation.get_colormap semantic_maskr,cCs|dkr d}n|dkrd}n td|jdt|j|dtjd|j|jdrc|j |d d }|| |}|dkrJ| d dd}|S|dkrZ| dddd }|Std|jdtd ) aVisualize the output of the segmentation model. Args: semantic_mask: The output of the segmentation model. Shape should be (C, H, W) or (B, C, H, W). colors: The color map to use for visualizing the output of the segmentation model. Shape should be (num_classes, 3). Returns: A tensor of shape (3, H, W) or (B, 3, H, W) representing the visualized output of the segmentation model. Raises: ValueError: If the shape of the semantic mask is not of shape (C, H, W) or (B, C, H, W). ValueError: If the shape of the colors is not of shape (num_classes, 3). ValueError: If only muliclass segmentation is supported. Please ensure a softmax is used, or submit a PR. r rz>Semantic mask must be of shape (C, H, W) or (B, C, H, W), got .)dim)dtypedeviceT)r2keepdimzZOnly muliclass segmentation is supported. Please ensure a softmax is used, or submit a PR.) r2r*shaper'allclosesumtensorr3r4argmaxsqueezepermute)rr.r, channel_dimrrrrvisualize_outputas,      z%SemanticSegmentation.visualize_outputNr'semantic_masks%Optional[Union[Tensor, list[Tensor]]] output_type.Union[Tensor, list[Tensor], list[Image.Image]]c Cs|dur ||}t|ttfr:g}|D]$}|jdkr$td|jd|j|d||d}| | ||qn|j|d||d}| ||}|j ||t|t rXdd Sd d S) amVisualize the segmentation masks. Args: images: If list of RGB images. Each image is a Tensor with shape :math:`(3, H, W)`. If Tensor, a Tensor with shape :math:`(B, 3, H, W)`. semantic_masks: If list of segmentation masks. Each mask is a Tensor with shape :math:`(C, H, W)`. If Tensor, a Tensor with shape :math:`(B, C, H, W)`. output_type: The type of output, can be "torch" or "PIL". colormap: The colormap to use, can be "random" or a custom color map. manual_seed: The manual seed to use for the colormap. Nr z.Semantic mask must be of shape (C, H, W), got r1r)r%r0TF)is_batch) rrrrndimr*r7r-sizerr?_tensor_to_typer) rrr@rBr#r%rr.r,rrr visualizes$   "zSemanticSegmentation.visualize directory Optional[str]Nonec Cs|j|||||d}t|trt|trtj|d|dd}n:t|ttfrPt|ttfrPg}tt |D]} | tj|| dd|| ddddq5n t dt ||j ||dd|j ||d d|j ||d ddS) aSave the segmentation results. Args: images: If list of RGB images. Each image is a Tensor with shape :math:`(3, H, W)`. If Tensor, a Tensor with shape :math:`(B, 3, H, W)`. semantic_masks: If list of segmentation masks. Each mask is a Tensor with shape :math:`(C, H, W)`. If Tensor, a Tensor with shape :math:`(B, C, H, W)`. directory: The directory to save the results. output_type: The type of output, can be "torch" or "PIL". colormap: The colormap to use, can be "random" or a custom color map. manual_seed: The manual seed to use for the colormap. )r#r%g?g?Nrz6`images` should be a Tensor or a list of Tensors. Got _src)suffix_mask_overlay)rHrrkorniaenhance add_weightedrrrangelenrr*type _save_outputs) rrr@rIrBr#r% colored_masksoverlaidirrrsaves0 0zSemanticSegmentation.save)rrrr)rr )r!r"r#r$r%r"rr)r.rr,rrr)Nr'rr ) rrr@rArBr$r#r$r%r"rrC)NNr'rr )rrr@rArIrJrBr$r#r$r%r"rrK)__name__ __module__ __qualname____doc__r __annotations__r r'inference_moderr-r?rHrZrrrrr s&   ! 1 1) __future__rtypingrrrr'rP kornia.corerkornia.core.externalrImagekornia.models.baser__all__r rrrrs