i(dZddlmZddlZddlmZddlmZddl m Z m Z m Z m Z ddlmZmZdd lmZmZmZerd d lmZmZGd d e dZGdde dZeGdde ZdgZy)z( Image/Text processor class for OWL-ViT ) TYPE_CHECKINGN) BatchFeature) ImageInput) ImagesKwargsProcessingKwargsProcessorMixinUnpack)PreTokenizedInput TextInput) TensorTypeauto_docstringis_torch_available)&OwlViTImageGuidedObjectDetectionOutputOwlViTObjectDetectionOutputc"eZdZUdZedzed<y)OwlViTImagesKwargsaa query_images (`ImageInput`, *optional*): Query images to use for image-guided object detection. When provided, these images serve as visual queries to find similar objects in the main `images`. The query images override any text prompts, and the model performs image-to-image matching instead of text-to-image matching. N query_images)__name__ __module__ __qualname____doc__r__annotations__h/mnt/e/genesis-system/.venv/lib/python3.12/site-packages/transformers/models/owlvit/processing_owlvit.pyrr&st##rrF)totalc*eZdZUeed<ddiddidZy)OwlViTProcessorKwargs images_kwargspadding max_lengthreturn_tensorspt) text_kwargs common_kwargsN)rrrrr _defaultsrrrr r 1s'%% |  d Irr c eZdZdfd Ze ddedzdeezeezeezde e de fdZ dZ dd d d ed eeezdzd eeedzfdZ dd dd eded eeezdzfdZxZS)OwlViTProcessorNc &t|||yN)super__init__)selfimage_processor tokenizerkwargs __class__s rr.zOwlViTProcessor.__init__?s )4rimagestextr2returnc |jtfd|jji|}|dj dd}|dd}|| | t di}|t |ts#t |tr+t |dts|j|fi|dg}nt |tryt |dtrfg}td |D} |D]L} t| | k7r| d g| t| z zz} |j| fi|d} |j| Nn td |d k(rQtj|D cgc]} | d  c} d} tj|D cgc]} | d c} d} ng|dk(rWtrMddl}|j#|D cgc]} | d  c} d} |j#|D cgc]} | d c} d} n t d| |d <| |d<|$|j$|fi|dj&}d|i}|%|j$|fi|d}|j&|d<t)||Scc} wcc} wcc} wcc} w)a Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **query_pixel_values** -- Pixel values of the query images to be fed to a model. Returned when `query_images` is not `None`. tokenizer_init_kwargsr!rNr&r$zXYou have to specify at least one text or query image or image. All three cannot be none.rc32K|]}t|ywr,)len).0 text_singles r z+OwlViTProcessor.__call__..is%O;c+&6%Os zLInput text should be a string, a list of strings or a nested list of stringsnp input_ids)axisattention_maskr%)dimz/Target return tensor type could not be returnedquery_pixel_values pixel_values)data tensor_type) _merge_kwargsr r1 init_kwargspop ValueError isinstancestrlistmaxr:append TypeErrorr? concatenatertorchcatr0rEr)r/r4r5r2 output_kwargsrr$rF encodingsmax_num_queriesr<encodingr@rBrSrDimage_featuress r__call__zOwlViTProcessor.__call__Bs"+** ! "&.."<"<  %_599.$O &}56FG ,@!IIY&Wx '<&W]^I_ !&W`+a8H5E,F+agh!i !RSS )D %3D! "  #!5!5!5l!emTcFd!e!r!r (*<=D  1T11&[M/#>D >BB-,]0f'X+as I! I&8 I+ I0c:|jj|i|S)z This method forwards all its arguments to [`OwlViTImageProcessor.post_process`]. Please refer to the docstring of this method for more information. )r0 post_process)r/argsr2s rr\zOwlViTProcessor.post_processs" 1t##00$A&AAroutputsr threshold target_sizes text_labelsc|jj|||}|"t|t|k7r td|0t ||D]\}}|dDcgc]}|| } }| |d<!|S|D]}d|d< |Scc}w)a Converts the raw output of [`OwlViTForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. Args: outputs ([`OwlViTObjectDetectionOutput`]): Raw outputs of the model. threshold (`float`, *optional*, defaults to 0.1): Score threshold to keep object detection predictions. target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the batch. If unset, predictions will not be resized. text_labels (`list[list[str]]`, *optional*): List of lists of text labels for each image in the batch. If unset, "text_labels" in output will be set to `None`. Returns: `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: - "scores": The confidence scores for each predicted box on the image. - "labels": Indexes of the classes predicted by the model on the image. - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. - "text_labels": The text labels for each predicted bounding box on the image. )r^r_r`NzAMake sure that you pass in as many lists of text labels as imageslabelsra)r0post_process_object_detectionr:rKzip) r/r^r_r`raoutput image_outputimage_text_labelsiobject_text_labelss r&post_process_grounded_object_detectionz6OwlViTProcessor.post_process_grounded_object_detections<%%CCy|D   "s;'73v;'F`a a  "36v{3K A/ /DPQYDZ%[q&7&:%["%[.@ ]+ A !' 3 .2 ]+ 3 &\s Br nms_thresholdc@|jj||||S)a Converts the output of [`OwlViTForObjectDetection.image_guided_detection`] into the format expected by the COCO api. Args: outputs ([`OwlViTImageGuidedObjectDetectionOutput`]): Raw outputs of the model. threshold (`float`, *optional*, defaults to 0.0): Minimum confidence threshold to use to filter out predicted boxes. nms_threshold (`float`, *optional*, defaults to 0.3): IoU threshold for non-maximum suppression of overlapping boxes. target_sizes (`torch.Tensor`, *optional*): Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to None, predictions will not be unnormalized. Returns: `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: - "scores": The confidence scores for each predicted box on the image. - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. - "labels": Set to `None`. )r^r_rlr`)r0#post_process_image_guided_detection)r/r^r_rlr`s rrnz3OwlViTProcessor.post_process_image_guided_detections-:##GGy \hH  r)NN)g?NN)gg333333?N)rrrr.rrr r rNr r rrZr\floatr tuplerMrkrn __classcell__)r3s@rr*r*=s5%)Z^ICT!IC++d9o=EV@WWIC./ IC  ICICVB8<.2 ....!4;.5 . $s)_t+ .f"8<  9    !4;.5  rr*)rtypingrnumpyr?image_processing_utilsr image_utilsrprocessing_utilsrrr r tokenization_utils_baser r utilsr rrmodeling_owlvitrrrr r*__all__rrrr{s|!2% DCCd$U$ ,E f nf f R  r