2VhddlZddlmZddlmZddlmZddlmZ ddl Z ed d dZ dZ y#e $rdZ Y"wxYw) N)backend)ops) keras_export)convert_formatz'keras.visualization.draw_bounding_boxesc Vt td|xsi}|xs|}|xstj}t j |} t | dk7rtd| t|tstd|dt|d|vsd|vrtd ||d k(rd } d } nd } d } | | } | | } |j}t||d| | }t j|jd}t j|d}t j|d}d|vrt j|d}nd}g}|j d}t!|D]#}||}||}||}t!|j dD]}||jd\}}}}||jd}|d k(r6t#|t#|t#|t#|f\}}}}t#|}tj$|||f||f||||vs||}||||}|d|d}t'||||\}} tj(|||| ftj*||||j-|&t/j0|dS)a! Draws bounding boxes on images. This function draws bounding boxes on a batch of images. It supports different bounding box formats and can optionally display class labels and confidences. Args: images: A batch of images as a 4D tensor or NumPy array. Shape should be `(batch_size, height, width, channels)`. bounding_boxes: A dictionary containing bounding box data. Should have the following keys: - `boxes`: A tensor or array of shape `(batch_size, num_boxes, 4)` containing the bounding box coordinates in the specified format. - `labels`: A tensor or array of shape `(batch_size, num_boxes)` containing the class labels for each bounding box. - `confidences` (Optional): A tensor or array of shape `(batch_size, num_boxes)` containing the confidence scores for each bounding box. bounding_box_format: A string specifying the format of the bounding boxes. Refer [keras-io](TODO) class_mapping: A dictionary mapping class IDs (integers) to class labels (strings). Used to display class labels next to the bounding boxes. Defaults to None (no labels displayed). color: A tuple or list representing the RGB color of the bounding boxes. For example, `(255, 0, 0)` for red. Defaults to `(128, 128, 128)`. line_thickness: An integer specifying the thickness of the bounding box lines. Defaults to `2`. text_thickness: An integer specifying the thickness of the text labels. Defaults to `1`. font_scale: A float specifying the scale of the font used for text labels. Defaults to `1.0`. data_format: A string, either `"channels_last"` or `"channels_first"`, specifying the order of dimensions in the input images. Defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last". Returns: A NumPy array of the annotated images with the bounding boxes drawn. The array will have the same shape as the input `images`. Raises: ValueError: If `images` is not a 4D tensor/array, if `bounding_boxes` is not a dictionary, or if `bounding_boxes` does not contain `"boxes"` and `"labels"` keys. TypeError: If `bounding_boxes` is not a dictionary. ImportError: If `cv2` (OpenCV) is not installed. Nz|The `draw_bounding_boxes` function requires the `cv2` package (OpenCV). Please install it with `pip install opencv-python`.z;`images` must be batched 4D tensor. Received: images.shape=z<`bounding_boxes` should be a dict. Received: bounding_boxes=z of type boxeslabelsza`bounding_boxes` should be a dict containing 'boxes' and 'labels' keys. Received: bounding_boxes= channels_lastxyxyuint8 confidencesrint32z | z.2f)imgtextorgfontFace fontScalecolor thickness)axis)cv2 ImportErrorrimage_data_formatrshapelen ValueError isinstancedict TypeErrortypecopyrconvert_to_numpyastyperangeint rectangle_find_text_locationputTextFONT_HERSHEY_SIMPLEXappendnpstack)!imagesbounding_boxesbounding_box_format class_mappingrline_thicknesstext_thickness font_scale data_format images_shapeh_axisw_axisheightwidthr r rresult batch_sizei_image_box_classbox_ix1y1x2y2clabelconffont_x1font_y1s! [/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/visualization/draw_bounding_boxes.pydraw_bounding_boxesrOs)z { M  "'RM(.<!:!:!-+HI  FaJ :  Qx4::a=) E!%[//8NBBu $$W-ABw Wc"gs2wB?NBBAA MM&2r(RHe^ LM!%a(*&q>%0D$gSc 3E#6J$   '* 55(,' 8  fA B 88F ##czt|dz}|dz }|d|zz dkDr||dz fS|}d}||z|d|zz|z|zfS)N r)r))xyr7r font_heighttarget_y line_offset static_offsets rNr+r+skj2o&K1uH1{?#a'!a%xKM M Q_ +m; rP)N)r\r\rTg?N) numpyr/ keras.srcrrkeras.src.api_exportrLkeras.src.layers.preprocessing.image_preprocessing.bounding_boxes.convertersrrrrOr+rPrNrcsk- 78  Q$9Q$h s Cs;AA