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xdecoder/Instruct-X-Decoder

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rootutils__pycache__visualizer.cpython-38.pyc
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dd�Zddddd�dd�ZdCdd�ZdDd d!�ZdEd#d$�ZdFd%d&�ZdGdddd'd(�d)d*�ZdHddd+�d,d-�ZdId.d/�Zd0d1�ZdJd2d3�Zd4d5�Zd6d7�Zd8d9�Zd:d;�Zd<d=�Zd>d?�ZdS)Kra113    Visualizer that draws data about detection/segmentation on images.114 115    It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}`116    that draw primitive objects to images, as well as high-level wrappers like117    `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}`118    that draw composite data in some pre-defined style.119 120    Note that the exact visualization style for the high-level wrappers are subject to change.121    Style such as color, opacity, label contents, visibility of labels, or even the visibility122    of objects themselves (e.g. when the object is too small) may change according123    to different heuristics, as long as the results still look visually reasonable.124 125    To obtain a consistent style, you can implement custom drawing functions with the126    abovementioned primitive methods instead. If you need more customized visualization127    styles, you can process the data yourself following their format documented in128    tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not129    intend to satisfy everyone's preference on drawing styles.130 131    This visualizer focuses on high rendering quality rather than performance. It is not132    designed to be used for real-time applications.133    NrcCs�t�|��dd��tj�|_|dkr.t�d�}||_t	|j|d�|_134t�d�|_
tt�|j135j|j136j�dd|�|_d	|_||_t|_dS)137ae138        Args:139            img_rgb: a numpy array of shape (H, W, C), where H and W correspond to140                the height and width of the image respectively. C is the number of141                color channels. The image is required to be in RGB format since that142                is a requirement of the Matplotlib library. The image is also expected143                to be in the range [0, 255].144            metadata (Metadata): dataset metadata (e.g. class names and colors)145            instance_mode (ColorMode): defines one of the pre-defined style for drawing146                instances on an image.147        r��NZ__nonexist__)r��cpu�Z�148�)r#r$�clipr9r*r�rr�rxr�outputrp�deviceZ149cpu_device�max�sqrtr.r/�_default_font_size�_instance_mode�_KEYPOINT_THRESHOLD�keypoint_threshold)r=Zimg_rgbrxr�Z
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jdd�dk��nd��d}161�j|||||	|162d��jS)a�163        Draw instance-level prediction results on an image.164 165        Args:166            predictions (Instances): the output of an instance detection/segmentation167                model. Following fields will be used to draw:168                "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle").169 170        Returns:171            output (VisImage): image object with visualizations.172        �173pred_boxesNr��pred_classes�
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tj�}	�j	j|}182�j|	|t|183||d�qP�jS)	a�184        Draw semantic segmentation predictions/labels.185 186        Args:187            sem_seg (Tensor or ndarray): the segmentation of shape (H, W).188                Each value is the integer label of the pixel.189            area_threshold (int): segments with less than `area_threshold` are not drawn.190            alpha (float): the larger it is, the more opaque the segmentations are.191 192        Returns:193            output (VisImage): image object with visualizations.194        T)rfcs|t�jj�kSrB)rOrx�
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D�}Wntk206�rVd}YnX�j||||d��jS)a�207        Draw panoptic prediction annotations or results.208 209        Args:210            panoptic_seg (Tensor): of shape (height, width) where the values are ids for each211                segment.212            segments_info (list[dict] or None): Describe each segment in `panoptic_seg`.213                If it is a ``list[dict]``, each dict contains keys "id", "category_id".214                If None, category id of each pixel is computed by215                ``pixel // metadata.label_divisor``.216            area_threshold (int): stuff segments with less than `area_threshold` are not drawn.217 218        Returns:219            output (VisImage): image object with visualizations.220        r`cSsg|]}|d�qSr�rr&rrrr)�sz0Visualizer.draw_panoptic_seg.<locals>.<listcomp>Nr�rcSsg|]}|d�qS�r`rr&rrrr)scSsg|]}|d�qS)�scorerr&rrrr)scSsg|]}|�dd��qS��iscrowdr�r�r&rrrr)scs(g|] }��dd��jj|D���qS)cSsg|]}|d�qSr�rr&rrrr)sz;Visualizer.draw_panoptic_seg.<locals>.<listcomp>.<listcomp>r�r�rDrrr)s)r�r�r�r�)r^rxr�rrr�r�r�r�r�r�r�r�r�r�r3r�rOrt�KeyErrorr�r�r�)r=rvrwr�r��predrEr�Zcategory_idxr�r�Z
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|�}W5QRX|dk	�r�|d}t�|�}�j||ddd��jS)a236        Draw annotations/segmentaions in Detectron2 Dataset format.237 238        Args:239            dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format.240 241        Returns:242            output (VisImage): image object with visualizations.243        �annotationsN�segmentationrcSsg|]}|d�qS)r�rr&rrrr)1sz0Visualizer.draw_dataset_dict.<locals>.<listcomp>r�cSsg|]}|d�qS)r�rr&rrrr)5sr"rJcSs<g|]4}t|d�dkr0t�|d|dtj�n|d�qS)r\rI�	bbox_mode)rOr	�convert�XYXY_ABSr&rrrr):s��cSsg|]}|d�qSr�rr&rrrr)Bsr�cs(g|] }��dd��jj|D���qS)cSsg|]}|d�qSr�rr&rrrr)Esz;Visualizer.draw_dataset_dict.<locals>.<listcomp>.<listcomp>r�r�rDrrr)Ds�r�cSsg|]}|�dd��qSr�r�r&rrrr)Ms)r�r�r�)r�r�r�r�r�r��sem_seg_file_name�rbr*r}r�)r�r��pan_seg�pan_seg_file_name)�rgb2idrwr�)r�r#r�r%rOr�rrrxr�r�r
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|f}||t	�|jj|jj�}|j|dd�}t	�|ddddd�d|j}|j�|262||||d��q�|dk	�r�|D]}|�|��q�|jS)a263        Args:264            boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`,265                or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image,266                or a :class:`RotatedBoxes`,267                or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format268                for the N objects in a single image,269            labels (list[str]): the text to be displayed for each instance.270            masks (masks-like object): Supported types are:271 272                * :class:`detectron2.structures.PolygonMasks`,273                  :class:`detectron2.structures.BitMasks`.274                * list[list[ndarray]]: contains the segmentation masks for all objects in one image.275                  The first level of the list corresponds to individual instances. The second276                  level to all the polygon that compose the instance, and the third level277                  to the polygon coordinates. The third level should have the format of278                  [x0, y0, x1, y1, ..., xn, yn] (n >= 3).279                * list[ndarray]: each ndarray is a binary mask of shape (H, W).280                * list[dict]: each dict is a COCO-style RLE.281            keypoints (Keypoint or array like): an array-like object of shape (N, K, 3),282                where the N is the number of instances and K is the number of keypoints.283                The last dimension corresponds to (x, y, visibility or score).284            assigned_colors (list[matplotlib.colors]): a list of colors, where each color285                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'286                for full list of formats that the colors are accepted in.287        Returns:288            output (VisImage): image object with visualizations.289        rNcSsg|]}tddd��qS�Tr�r��maximumr�r'�_rrrr)�sz0Visualizer.overlay_instances.<locals>.<listcomp>r�)r�r�r�rr�cSsg|]}|���qSr)rYr&rrrr)�scsg|]}�|�qSrr�r'�k�r�rrr)�scsg|]}�|�qSrr�r'�idx)r�rrr)�scsg|]}�|�qSrrr�r�rrr)�s)r�r")r��left�center�(r���brightness_factor�{�G�z�?�{�G�z�?�333333�?rN)r��horizontal_alignment�	font_size) �_convert_boxesrO�_convert_masksr2�_convert_keypoints�ranger�r8�overlay_rotated_instancesr#�prodr$rqrs�draw_boxrG�draw_polygonr%r\�medianrE�nonzero�_SMALL_OBJECT_AREA_THRESHr�r.r�r/�_change_color_brightnessr�r��	draw_text�draw_and_connect_keypoints)r=r�r�r�r�r�r��
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lighter_colorr Zkeypoints_per_instancer)r�r�r�rr�is�&290291292�2934294295  296297298299300"��301����302	303zVisualizer.overlay_instancesc	s�t|�}�dkr"dd�t|�D��|dkr0|jS|dk	rX|dd�df|dd�df}t�|���}||}�dk	r��fdd�|D�nd��fdd�|D�}t|�D],}|j||||�dk	r̈|ndd	�q�|jS)304ag305        Args:306            boxes (ndarray): an Nx5 numpy array of307                (x_center, y_center, width, height, angle_degrees) format308                for the N objects in a single image.309            labels (list[str]): the text to be displayed for each instance.310            assigned_colors (list[matplotlib.colors]): a list of colors, where each color311                corresponds to each mask or box in the image. Refer to 'matplotlib.colors'312                for full list of formats that the colors are accepted in.313 314        Returns:315            output (VisImage): image object with visualizations.316        NcSsg|]}tddd��qSrrrrrrr)sz8Visualizer.overlay_rotated_instances.<locals>.<listcomp>rrrJcsg|]}�|�qSrrrrrrr)scsg|]}�|�qSrrrrrrr)s)r�r�)rOr$r�r#rqrs�draw_rotated_box_with_label)	r=r�r�r�r/rzr{r�r�r)r�r�rr%�s$ �z$Visualizer.overlay_rotated_instancescCs�i}|j�d�}t|�D]F\}}|\}}}||jkr|j||ftd�|r||}	||f||	<q|j�d�r�|jjD]\\}317}}|318|krt||krt||319\}
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