U Ph# @slddlmZddlmZmZddlZddlZddlm Z ddl m Z ddl m Z mZed\ZZedd d \ZZerdd lmZdd lmZdd lmZdd lmZd Zn@eddd \ZZeddd \ZZeddd \ZZeddd \ZZdddgZd0ddddddddd Zd1dddddddd#d$dZd2d%dddddd&d'd(d)dZd3d-dd%dddddd'd. d/dZdS)4) annotations) TYPE_CHECKINGAnyN) NdarrayTensor rescale_array)convert_data_typeoptional_importPILzPIL.GifImagePluginImage)name)Summary) SummaryWriterTz$tensorboard.compat.proto.summary_pb2r ztensorboardX.proto.summary_pb2ztorch.utils.tensorboardr tensorboardXmake_animated_gif_summaryadd_animated_gifplot_2d_or_3d_image?strznp.ndarray | torch.Tensorz%SummaryWriter | SummaryWriterX | Noneintfloatr)tagimagewriter frame_dim scale_factorreturncst|jdkrtdt|tjd^}}fddt||dD}dd|D}d}tj |ddD] } || 7}qj|d 7}|D]} tj | D] } || 7}qq|d 7}t rt |t rtnt} | jd d d |d } | j|| d} | | gdS)aFunction to actually create the animated gif. Args: tag: Data identifier image: 3D image tensors expected to be in `HWD` format writer: the tensorboard writer to plot image frame_dim: the dimension used as frames for GIF image, expect data shape as `HWD`, default to `0`. scale_factor: amount to multiply values by. if the image data is between 0 and 1, using 255 for this value will scale it to displayable range zF3D image tensors expected to be in `HWD` format, len(image.shape) != 3) output_typecs g|]}|jtjddqS)F)copy)astypenpuint8.0irT/home/dell461/cl/sdc2/HISourceFinder-master-l/src/monai/visualize/img2tensorboard.py ?sz(_image3_animated_gif..rcSsg|]}t|qSr')GifImage fromarray)r$imr'r'r(r)@ss! NETSCAPE2.0; )heightwidth colorspaceencoded_image_string)rr)value)lenshapeAssertionErrorrr!ndarraymoveaxisr GifImagePlugin getheadergetdatahas_tensorboardx isinstanceSummaryWriterXSummaryXr r Value)rrrrrimage_np_imsZimg_strb_datar%summaryZsummary_image_str image_summaryr'r&r(_image3_animated_gif*s"  rIr)rrrmax_outrrrc Cs|dkr dnd}|dkr |dn|}g}tt|t|jdD]p}t|tjrt||ddddddfjddn||ddddddf} |t || || |||q@|S)amCreates an animated gif out of an image tensor in 'CHWD' format and returns Summary. Args: tag: Data identifier image: The image, expected to be in `CHWD` format writer: the tensorboard writer to plot image max_out: maximum number of image channels to animate through frame_dim: the dimension used as frames for GIF image, expect input data shape as `CHWD`, default to `-3` (the first spatial dim) scale_factor: amount to multiply values by. if the image data is between 0 and 1, using 255 for this value will scale it to displayable range r0z/imagez /image/{}rN)dim) rangeminlistr7r?torchTensorsqueezeappendrIformat) rrrrKrrsuffixZ summary_opZit_iZone_channel_imgr'r'r(rPsJzSummaryWriter | SummaryWriterXz int | NoneNone)rr image_tensorrKrr global_steprc Cs2t||||||d}|D]}|||qdS)aCreates an animated gif out of an image tensor in 'CHWD' format and writes it with SummaryWriter. Args: writer: Tensorboard SummaryWriter to write to tag: Data identifier image_tensor: tensor for the image to add, expected to be in `CHWD` format max_out: maximum number of image channels to animate through frame_dim: the dimension used as frames for GIF image, expect input data shape as `CHWD`, default to `-3` (the first spatial dim) scale_factor: amount to multiply values by. If the image data is between 0 and 1, using 255 for this value will scale it to displayable range global_step: Global step value to record )rrrrKrrN)r_get_file_writer add_summary) rrrWrKrrrXrGsr'r'r(rtsr0outputz#NdarrayTensor | list[NdarrayTensor]) datasteprindex max_channelsr max_framesrrcCs||}|dkr|dn|}t|tjr8|n|} | jdkrvt| dd} d} |j|d| | || ddS| jdkr | j ddkr|dkrd } |j|d| | || ddSd} t | d|D]6\} } t| dd} |j|d| d| | || dqdS| jd kr| j d d} | d gt | } | j ddkr|dkrt rt|trt| |d } |j|| d||d ddSt|| j d}tjdd| d|Ddd} t||d| |||ddSdS)aXPlot 2D or 3D image on the TensorBoard, 3D image will be converted to GIF image. Note: Plot 3D or 2D image(with more than 3 channels) as separate images. And if writer is from TensorBoardX, data has 3 channels and `max_channels=3`, will plot as RGB video. Args: data: target data to be plotted as image on the TensorBoard. The data is expected to have 'NCHW[D]' dimensions or a list of data with `CHW[D]` dimensions, and only plot the first in the batch. step: current step to plot in a chart. writer: specify TensorBoard or TensorBoardX SummaryWriter to plot the image. index: plot which element in the input data batch, default is the first element. max_channels: number of channels to plot. frame_dim: if plotting 3D image as GIF, specify the dimension used as frames, expect input data shape as `NCHWD`, default to `-3` (the first spatial dim) max_frames: if plot 3D RGB image as video in TensorBoardX, set the FPS to `max_frames`. tag: tag of the plotted image on TensorBoard. rr0HWrD) dataformatsNrCHWrJZNCHWT)fpsrecSsg|]}t|ddqS)rrr#r'r'r(r)sz'plot_2d_or_3d_image..)axisZ_HWD)rKrrX)r?rPrQdetachcpunumpyndimr add_imager7 enumeratereshaperOr>r@r!r: add_videorNstackr)r^r_rr`rarrbr data_indexdrejd2spatialr'r'r(rs:     $ , )rr)NrrJr)rrJrN)rr0rJr\r]) __future__rtypingrrrnr!rP monai.configrmonai.transformsr monai.utilsrr r rDr*Z$tensorboard.compat.proto.summary_pb2r rrr@ZtensorboardX.proto.summary_pb2rAZtorch.utils.tensorboardr>__all__rIrrrr'r'r'r( sL         )("