&Vfz6dZddlmZddlmZddlmZddZdS) 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_creatorNcftjd|}ttjjddptjj}||d|g5\}}tjfd} tj || ||cdddS#1swxYwYdS)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) display_name description summary_scope image_summary)valuesctjdtjtjtjd}|d}tj|}tj|}tj tj |ddtj |dd gd }tj ||gd S) NT)saturate)inputwidth)nameheight dimensionsr)axis) tf debugging assert_rankassert_non_negativeimageconvert_image_dtypeuint8 encode_pngshapestack as_stringconcat)imageslimited_imagesencoded_images image_shaperdata max_outputss a/var/www/html/software/conda/lib/python3.11/site-packages/tensorboard/plugins/image/summary_v2.py lazy_tensorzimage..lazy_tensorns L $ $T1 - - - L , ,[ 9 9 9X11$41PPF#L[L1NX00@@N(000KLQg>>>LQh???" J9j.9BBB B)tagtensorstepr) rcreate_summary_metadatagetattrrsummary experimentalr rLazyTensorCreatorwrite) rr(r/r)r summary_metadatar r-_r+s ` ` r*rrs3J 7{  '$?? $ : #  ot[$&?    #q  . C C C C C / . C$zKd=M   3                  s>B&&B*-B*)NrN) __doc__tensorboard.compatrrtensorboard.plugins.imagertensorboard.utilrrr,r*r=sk)(((((......000000h h h h h h r,