o bi@sddlZddlZddlmZddlmZddlmZddlm Z ddl m Z zddl m Z Wn ey9dZ Ynwed           dddZdS)N)backend)ops) keras_export)draw_bounding_boxes)plot_image_gallery)patchesz-keras.visualization.plot_bounding_box_galleryrrr ;?FcKs tdurtd| p | } | p| } |pt}t|}t|dkr(td||dkr2t|d}t |}t j t ||| |d}|durM||||| d}|durY||||| d}| ry|ratd tj t|d d d tj t|d d d g}t|f||||d|S)a Plots a gallery of images with bounding boxes. This function can display both ground truth and predicted bounding boxes on a set of images. It supports various bounding box formats and can include class labels and a legend. Args: images: A 4D tensor or NumPy array of images. Shape should be `(batch_size, height, width, channels)`. bounding_box_format: The format of the bounding boxes. Refer [keras-io](TODO) y_true: A dictionary containing the ground truth bounding boxes and labels. Should have the same structure as the `bounding_boxes` argument in `keras.visualization.draw_bounding_boxes`. Defaults to `None`. y_pred: A dictionary containing the predicted bounding boxes and labels. Should have the same structure as `y_true`. 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)`. true_color: A tuple of three integers representing the RGB color for the ground truth bounding boxes. Defaults to `(0, 188, 212)`. pred_color: A tuple of three integers representing the RGB color for the predicted bounding boxes. Defaults to `(255, 235, 59)`. line_thickness: The thickness of the bounding box lines. Defaults to 2. font_scale: The scale of the font used for labels. Defaults to 1.0. text_thickness: The thickness of the bounding box text. Defaults to `line_thickness`. class_mapping: A dictionary mapping class IDs to class names. Used f or both ground truth and predicted boxes if `ground_truth_mapping` and `prediction_mapping` are not provided. Defaults to `None`. ground_truth_mapping: A dictionary mapping class IDs to class names specifically for ground truth boxes. Overrides `class_mapping` for ground truth. Defaults to `None`. prediction_mapping: A dictionary mapping class IDs to class names specifically for predicted boxes. Overrides `class_mapping` for predictions. Defaults to `None`. legend: A boolean indicating whether to show a legend. Defaults to `False`. legend_handles: A list of matplotlib `Patch` objects to use for the legend. If this is provided, the `legend` argument will be ignored. Defaults to `None`. rows: The number of rows in the image gallery. Required if the images are not batched. Defaults to `None`. cols: The number of columns in the image gallery. Required if the images are not batched. Defaults to `None`. data_format: The image data format `"channels_last"` or `"channels_first"`. Defaults to the Keras backend data format. kwargs: Additional keyword arguments to be passed to `keras.visualization.plot_image_gallery`. Returns: The output of `keras.visualization.plot_image_gallery`. Raises: ValueError: If `images` is not a 4D tensor/array or if both `legend` a nd `legend_handles` are specified. ImportError: if matplotlib is not installed Nz~The `plot_bounding_box_gallery` function requires the `matplotlib` package. Please install it with `pip install matplotlib`.z;`images` must be batched 4D tensor. Received: images.shape=channels_first)rr)bounding_box_formatline_thicknesstext_thickness font_scale)color class_mappingz\Only pass `legend` OR `legend_handles` to `keras.visualization.plot_bounding_box_gallery()`.go@z Ground Truth)rlabel Prediction) value_rangelegend_handlesrowscols)r ImportErrorrimage_data_formatrshapelen ValueError transposeconvert_to_numpy functoolspartialrPatchnparrayr)imagesry_truey_predr true_color pred_colorrrrrground_truth_mappingprediction_mappinglegendrr r! data_formatkwargs images_shapeplotted_imagesdraw_fnr;e/home/ubuntu/.local/lib/python3.10/site-packages/keras/src/visualization/plot_bounding_box_gallery.pyplot_bounding_box_gallerys|P        r=)NNrr r rrNNNNFNNNN)r)numpyr, keras.srcrrkeras.src.api_exportr+keras.src.visualization.draw_bounding_boxesr*keras.src.visualization.plot_image_galleryr matplotlibrr"r=r;r;r;r<s<