a d@s6dZddlmZddlmZddlmZd ddZdS) zImage summaries and TensorFlow operations to create them, V2 versions. An image summary stores the width, height, and PNG-encoded data for zero or more images in a rank-1 string array: `[w, h, png0, png1, ...]`. )tf2)metadata)lazy_tensor_creatorNc stjd|d}ttjjddp$tjj}||d|gd<\}}tjfdd} tjj || ||dWdS1s|0YdS) a Write an image summary. See also `tf.summary.scalar`, `tf.summary.SummaryWriter`. Writes a collection of images to the current default summary writer. Data appears in TensorBoard's 'Images' dashboard. Like `tf.summary.scalar` points, each collection of images is associated with a `step` and a `name`. All the image collections with the same `name` constitute a time series of image collections. This example writes 2 random grayscale images: ```python w = tf.summary.create_file_writer('test/logs') with w.as_default(): image1 = tf.random.uniform(shape=[8, 8, 1]) image2 = tf.random.uniform(shape=[8, 8, 1]) tf.summary.image("grayscale_noise", [image1, image2], step=0) ``` To avoid clipping, data should be converted to one of the following: - floating point values in the range [0,1], or - uint8 values in the range [0,255] ```python # Convert the original dtype=int32 `Tensor` into `dtype=float64`. rgb_image_float = tf.constant([ [[1000, 0, 0], [0, 500, 1000]], ]) / 1000 tf.summary.image("picture", [rgb_image_float], step=0) # Convert original dtype=uint8 `Tensor` into proper range. rgb_image_uint8 = tf.constant([ [[1, 1, 0], [0, 0, 1]], ], dtype=tf.uint8) * 255 tf.summary.image("picture", [rgb_image_uint8], step=1) ``` Arguments: name: A name for this summary. The summary tag used for TensorBoard will be this name prefixed by any active name scopes. data: A `Tensor` representing pixel data with shape `[k, h, w, c]`, where `k` is the number of images, `h` and `w` are the height and width of the images, and `c` is the number of channels, which should be 1, 2, 3, or 4 (grayscale, grayscale with alpha, RGB, RGBA). Any of the dimensions may be statically unknown (i.e., `None`). Floating point data will be clipped to the range [0,1]. Other data types will be clipped into an allowed range for safe casting to uint8, using `tf.image.convert_image_dtype`. step: Explicit `int64`-castable monotonic step value for this summary. If omitted, this defaults to `tf.summary.experimental.get_step()`, which must not be None. max_outputs: Optional `int` or rank-0 integer `Tensor`. At most this many images will be emitted at each step. When more than `max_outputs` many images are provided, the first `max_outputs` many images will be used and the rest silently discarded. description: Optional long-form description for this summary, as a constant `str`. Markdown is supported. Defaults to empty. Returns: True on success, or false if no summary was emitted because no default summary writer was available. Raises: ValueError: if a default writer exists, but no step was provided and `tf.summary.experimental.get_step()` is None. N)Z display_name description summary_scopeZ image_summary)valuescstjdtjtjjtjdd}|d}tjdddrXtj |nBtj tjj |tj ddt tj d d d kfd d d d tj |d }tjtj|dddtj|dddgdd}tj|gd dS)NT)ZsaturateiZencode_each_image)Zdtypename)inputrcsSNrZencoded_imagesrm/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/tensorboard/plugins/image/summary_v2.pyz,image..lazy_tensor..cSstgtjSr)tfZconstantstringrrrrrrwidth)r height dimensions)Zaxis)r debuggingZ assert_rankZassert_non_negativeimageZconvert_image_dtypeZuint8compatZforward_compatibleZ encode_pngZmap_fnrZcondshapestack as_stringconcat)ZimagesZlimited_imagesZ image_shaperdata max_outputsrr lazy_tensorns2    zimage..lazy_tensor)tagZtensorstepr) rZcreate_summary_metadatagetattrrsummaryZ experimentalrrZLazyTensorCreatorwrite) r r"r&r#rZsummary_metadatarr%_r$rr!rrsE #r)NrN) __doc__Ztensorboard.compatrrZtensorboard.plugins.imagerZtensorboard.utilrrrrrrs