o  i3$@sDddlmZddlmZmZddlZddlZddlm Z ddl m Z ddl m Z mZed\ZZedd d \ZZerUdd lmZdd lmZdd lmZdd lmZd Zn eddd \ZZeddd \ZZeddd \ZZeddd \ZZgdZ d;dd'd(Z $ %  d?d@d.d/Z 0 % 1 2dAdBd9d:ZdS)C) annotations) TYPE_CHECKINGAnyN) NdarrayTensor rescale_array)convert_data_typeoptional_importPILzPIL.GifImagePluginImage)name)Summary) SummaryWriterTz$tensorboard.compat.proto.summary_pb2r ztensorboardX.proto.summary_pb2ztorch.utils.tensorboardr tensorboardX)make_animated_gif_summaryadd_animated_gifplot_2d_or_3d_image?tagstrimagenp.ndarray | torch.Tensorwriter%SummaryWriter | SummaryWriterX | None frame_dimint scale_factorfloatreturnrcst|jdkr tdt|tjd^}}fddt||dD}dd|D}d}tj |ddD]} || 7}q5|d 7}|D]} tj | D]} || 7}qJqB|d 7}t r_t |t r_tnt} | jd d d |d } | j|| d} | | gdS)aFunction to actually create the animated gif. Args: tag: Data identifier image: 3D image tensors expected to be in `HWD` format writer: the tensorboard writer to plot image frame_dim: the dimension used as frames for GIF image, expect data shape as `HWD`, default to `0`. scale_factor: amount to multiply values by. if the image data is between 0 and 1, using 255 for this value will scale it to displayable range zF3D image tensors expected to be in `HWD` format, len(image.shape) != 3) output_typecs g|] }|jtjddqS)F)copy)astypenpuint8.0ira/home/dell461/cl/sdc2/last_ska_mid/HISourceFinder-master-l/src/monai/visualize/img2tensorboard.py ?s z(_image3_animated_gif..rcSsg|]}t|qSr))GifImage fromarray)r&imr)r)r*r+@ss! NETSCAPE2.0; )heightwidth colorspaceencoded_image_string)rr)value)lenshapeAssertionErrorrr#ndarraymoveaxisr GifImagePlugin getheadergetdatahas_tensorboardx isinstanceSummaryWriterXSummaryXr r Value)rrrrrimage_np_imsZimg_strb_datar'summaryZsummary_image_str image_summaryr)r(r*_image3_animated_gif*s$   rKrmax_outc Cs|dkrdnd}|dkr|dn|}g}tt|t|jdD]8}t|tjr:||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 r2z/imagez /image/{}rN)dim) rangeminlistr9rAtorchTensorsqueezeappendrKformat) rrrrMrrsuffixZ summary_opZit_iZone_channel_imgr)r)r*rPsJrSummaryWriter | SummaryWriterX image_tensor global_step int | NoneNonec Cs2t||||||d}|D] }|||q dS)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 )rrrrMrrN)r_get_file_writer add_summary) rrrYrMrrrZrIsr)r)r*rts  rr2outputdata#NdarrayTensor | list[NdarrayTensor]stepindex max_channels max_framescCs||}|dkr |dn|}t|tjr|nt|} | jdkr>t | dd} d} |j |d| | || ddS| jdkr| j ddkr`|dkr`d } |j |d| | || ddSd} t | d|D]\} } t | dd} |j |d| d| | || dqjdS| jd kr| j d d} | d gt| } | j d}|dkr|dkrtrt|trt| |d } |j|| d||d ddSt||}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. rr2HWrF) dataformatsNrCHWrLZNCHWT)fpsrjcSsg|]}t|ddqS)rrr%r)r)r*r+sz'plot_2d_or_3d_image..)axisZ_HWD)rMrrZ)rArRrSdetachcpunumpyr#asarrayndimr add_imager9 enumeratereshaperQr@rBr< add_videorPstackr)rbrdrrerfrrgr data_indexdrjjd2spatialZd_chansr)r)r*rs@$    $    r)rr) rrrrrrrrrrrr)NrrLr)rrrrrrrMrrrrrrr )rrLrN)rrXrrrYrrMrrrrrrZr[rr\)rr2rLr`ra)rbrcrdrrrXrerrfrrrrgrrrrr\) __future__rtypingrrrsr#rR monai.configrmonai.transformsr monai.utilsrr r rFr,Z$tensorboard.compat.proto.summary_pb2r rrrBZtensorboardX.proto.summary_pb2rCZtorch.utils.tensorboardr@__all__rKrrrr)r)r)r*sN         ) ( "