2VhKddlZddlZddlmZddlmZddlmZddlm Z  ddl m Z dZed d dZy#e $rdZ Y)wxYw) N)backend)ops) keras_export)BaseImagePreprocessingLayercttj|dk7r tdt ||}|d|df}|S)ayExtracts a batch of images for plotting. Args: images: The 4D tensor or NumPy array of images. num_images: The number of images to extract. batch_size: The original batch size of the images. Returns: A 4D tensor or NumPy array containing the extracted images. Raises: ValueError: If `images` is not a 4D tensor/array. zO`plot_images_gallery()` requires you to batch your `np.array` samples together.N.)lenrshape ValueErrormin)images num_images batch_size num_samplessamples Z/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/visualization/plot_image_gallery.py_extract_image_batchrsR 399V " 6  j*-K L[L#% &F Mz&keras.visualization.plot_image_galleryc jt td| | r td|| | td| | n|du} | xstj} t t j|dk(rt j|dnd}|xs1ttjtj|}|xs!ttj||z}||z}t|||}| dk(rt j|d }tj||||z||zfd d d d d\}}|jddt!|t"j$r7t |jdk(r|dk(rdnd}t#j&||}| |j)| dt+j-||d}t j.|}| dk(r|jdddd}|t j.|}|t j.|}t1|D]}t1|D] }||z|z}t!|t"j$r|||fn|}|j3||j5d|j7dd|j9dg}|5|t |kr'|j;d|j=||d|5|t |kr'|j;d|j=||d|s|j?djA|d  |/tjB|dd | | !tjDy| r)tjFtjDyy)"aDisplays a gallery of images with optional labels and predictions. Args: images: A 4D tensor or NumPy array of images. Shape should be `(batch_size, height, width, channels)`. y_true: A 1D tensor or NumPy array of true labels (class indices). Defaults to `None`. y_pred: A 1D tensor or NumPy array of predicted labels (class indices). Defaults to `None`. label_map: A dictionary mapping class indices to class names. Required if `y_true` or `y_pred` are provided. Defaults to `None`. value_range: A tuple specifying the value range of the images (e.g., `(0, 255)` or `(0, 1)`). Defaults to `(0, 255)`. rows: The number of rows in the gallery. If `None`, it's calculated based on the number of images and `cols`. Defaults to `None`. cols: The number of columns in the gallery. If `None`, it's calculated based on the number of images and `rows`. Defaults to `None`. scale: A float controlling the size of the displayed images. The images are scaled by this factor. Defaults to `2`. path: The path to save the generated gallery image. If `None`, the image is displayed using `plt.show()`. Defaults to `None`. show: Whether to display the image using `plt.show()`. If `True`, the image is displayed. If `False`, the image is not displayed. Ignored if `path` is not `None`. Defaults to `True` if `path` is `None`, `False` otherwise. transparent: A boolean, whether to save the figure with a transparent background. Defaults to `True`. dpi: The DPI (dots per inch) for saving the figure. Defaults to 60. legend_handles: A list of matplotlib `Patch` objects to use as legend handles. Defaults to `None`. data_format: The image data format `"channels_last"` or `"channels_first"`. Defaults to the Keras backend data format. Raises: ValueError: If both `path` and `show` are set to non-`None` values, if `images` is not a 4D tensor or array, or if `y_true` or `y_pred` are provided without a `label_map`. ImportError: if matplotlib is not installed. NzuThe `plot_image_gallery` function requires the `matplotlib` package. Please install it with `pip install matplotlib`.zEplot_gallery() expects either `path` to be set, or `show` to be true.zJIf `y_true` or `y_pred` are provided, a `label_map` must also be provided.rrchannels_first)rrFtightTrowcol)nrowsncolsfigsizeframeonlayoutsqueezesharexsharey)wspacehspacez lower center)handleslocr)r original_range target_rangerruint8)xyoffzLabel: UnknownzPred: z )fontsize)fname pad_inches bbox_inches transparentdpi)$plt ImportErrorr rimage_data_formatr rr intmathceilsqrtr transposesubplotssubplots_adjust isinstancenpndarray expand_dimslegendr_transform_value_rangeconvert_to_numpyrangeimshowastypemarginsaxisappendget set_titlejoinsavefigcloseshow)r y_truey_pred label_maprowscols value_rangescalepathrVr8r9legend_handles data_formatrrfigaxes expand_axisrrindex current_axis title_partss rplot_image_galleryrg,sr { H   D   f0i6G   #4$$,D<!:!:!)?1)D6"1%!J  83tyy:!678D  53tyyt!345DJ !&*j AF''v|4 te|, ICq+$ #DJJ1(<19a" ~~dK0! >~ > ( * A AkBF ! !& )F&&!!!Q1- %%f- %%f-T{K; KC$J$E",T2::">S#XD    u 4 4W = >  1 *   e $K!ec&k&9""immF5M9EFG!ec&k&9""Y]]6%=)DEF&&tyy'=&J' KK,  #       r) NNNNNr*rNNT<NN)r>numpyrE keras.srcrrkeras.src.api_exportrQkeras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layerrmatplotlib.pyplotpyplotr:r;rrgrrrps -# 467     [8[? CsAAA