WpfZAdZddlZddlmZmZmZmZddlmZddlm Z ddl m Z ddl m Z ddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlm Z ej!Z"ej#Z$ej%Z&ej'Z(ej)Z*e j+Z,ej-Z.ej/Z0dZ1dZ2dZ3dZ4dZ5dZ6dZ7dZ8dZ9dZ:dZ;dZej=Gd d!Z?Gd"d#ej@ZAdS)$zMediaPipe image segmenter task.N)CallableListMappingOptional)packet_creator) packet_getter)image)packet)&tensors_to_segmentation_calculator_pb2)!image_segmenter_graph_options_pb2)segmenter_options_pb2)rect) base_options) task_info) doc_controls)base_vision_task_api)image_processing_options)vision_task_running_modeconfidence_masksCONFIDENCE_MASKS category_mask CATEGORY_MASKimage_in image_outIMAGE norm_rect_in NORM_RECTz/mediapipe.tasks.TensorsToSegmentationCalculatorz:mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraphicheZdZUdZdZeeeje d<dZ eeje d<dS)ImageSegmenterResultaWOutput result of ImageSegmenter. confidence_masks: multiple masks of float image where, for each mask, each pixel represents the prediction confidence, usually in the [0, 1] range. category_mask: a category mask of uint8 image where each pixel represents the class which the pixel in the original image was predicted to belong to. Nrr) __name__ __module__ __qualname____doc__rrr image_moduleImage__annotations__rm/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/mediapipe/tasks/python/vision/image_segmenter.pyrr?sT:>HT,"456===04-,,-44444r(rceZdZUdZeed<ejZeed<dZ e ed<dZ e ed<dZ e eeejegdfed <ejd efd ZdS) ImageSegmenterOptionsa7Options for the image segmenter task. Attributes: base_options: Base options for the image segmenter task. running_mode: The running mode of the task. Default to the image mode. Image segmenter task has three running modes: 1) The image mode for segmenting objects on single image inputs. 2) The video mode for segmenting objects on the decoded frames of a video. 3) The live stream mode for segmenting objects on a live stream of input data, such as from camera. output_confidence_masks: Whether to output confidence masks. output_category_mask: Whether to output category mask. result_callback: The user-defined result callback for processing live stream data. The result callback should only be specified when the running mode is set to the live stream mode. r running_modeToutput_confidence_masksFoutput_category_maskNresult_callbackreturnc|j}|jtjkrdnd|_t }t||S)z3Generates an ImageSegmenterOptions protobuf object.FT)rsegmenter_options)rto_pb2r, _RunningModeruse_stream_mode_SegmenterOptionsProto _ImageSegmenterGraphOptionsProto)selfbase_options_protosegmenter_options_protos r)r3zImageSegmenterOptions.to_pb2hsc*1133"l&888d&566 +'1   r()r r!r"r# _BaseOptionsr&r4rr,r-boolr.r/rrrr$r%intrdo_not_generate_docsr7r3r'r(r)r+r+Ns +1, 111"&4&&&$$$$ 8$l&8#>DE   $ 6   %$   r(r+c &eZdZdZdfd ZddZededdfdZede ddfd Z dd e j d e edefd Z dd e j d ed e edefdZ dd e j d ed e eddfdZedeefdZxZS)ImageSegmenteraClass that performs image segmentation on images. The API expects a TFLite model with mandatory TFLite Model Metadata. Input tensor: (kTfLiteUInt8/kTfLiteFloat32) - image input of size `[batch x height x width x channels]`. - batch inference is not supported (`batch` is required to be 1). - RGB and greyscale inputs are supported (`channels` is required to be 1 or 3). - if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the metadata for input normalization. Output tensors: (kTfLiteUInt8/kTfLiteFloat32) - list of segmented masks. - if `output_category_mask` is True, uint8 Image, Image vector of size 1. - if `output_confidence_masks` is True, float32 Image list of size `channels`. - batch is always 1 An example of such model can be found at: https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/2 r0Nctt|||||dS)z(Initializes the `ImageSegmenter` object.N)superr@__init___populate_labels)r8 graph_configr,packet_callback __class__s r)rCzImageSegmenter.__init__sF .$((lO r(cg|_|j}d}|jD]}t|jvr|rt dtdd}|jjtj }|j rjtt|j D]H}||j vrt d|d|j|j |jIdS)zPopulate the labelmap in TensorsToSegmentationCalculator to labels field. Raises: ValueError if there is an error during finding TensorsToSegmentationCalculator. FzThe graph has more than one .Tz"The labelmap has no expected key: N)_labels_runnerget_graph_confignode(_TENSORS_TO_SEGMENTATION_CALCULATOR_NAMEname ValueErroroptions Extensions+TensorsToSegmentationCalculatorOptionsProtoext label_itemsrangelenappend)r8rEfound_tensors_to_segmentationrMrQis r)rDzImageSegmenter._populate_labelssDL<0022L$)!!== 1TY > > ( =9===   )-%,) 7 ;    =W01122 = =a+++HAHHHIII L   3A 6 ; < < < <==r( model_pathct|}t|tj}||S)a[Creates an `ImageSegmenter` object from a TensorFlow Lite model and the default `ImageSegmenterOptions`. Note that the created `ImageSegmenter` instance is in image mode, for performing image segmentation on single image inputs. Args: model_path: Path to the model. Returns: `ImageSegmenter` object that's created from the model file and the default `ImageSegmenterOptions`. Raises: ValueError: If failed to create `ImageSegmenter` object from the provided file such as invalid file path. RuntimeError: If other types of error occurred. )model_asset_path)rr,)r;r+r4rcreate_from_options)clsr[rrQs r)create_from_model_pathz%ImageSegmenter.create_from_model_pathsF& <<.packets_callbacks . / 8 8 : :022  ( / > &I 3''!(!5'"( (   #3=   r(r rc |||d}|ttj|t tj|i}t}t|vr$tj |t|_ t|vr$tj|t|_|S)aPerforms the actual segmentation task on the provided MediaPipe Image. Args: image: MediaPipe Image. image_processing_options: Options for image processing. Returns: If the output_type is CATEGORY_MASK, the returned vector of images is per-category segmented image mask. If the output_type is CONFIDENCE_MASK, the returned vector of images contains only one confidence image mask. A segmentation result object that contains a list of segmentation masks as images. Raises: ValueError: If any of the input arguments is invalid. RuntimeError: If image segmentation failed to run. F roi_allowed)convert_to_normalized_rect_process_image_datar~r create_imager create_protor3rrhrrgrrjrir)r8r rnormalized_rectrbrns r)segmentzImageSegmenter.segments,55 %U6O--~:5AA ;  " " $ $! ! /N /00$66-:-I 6 7..*"^33*7*A 3 4++' r( timestamp_msc $|||d}|ttj||t zttj| |t zi}t}t|vr$tj |t|_t|vr$tj|t|_|S)aPerforms segmentation on the provided video frames. Only use this method when the ImageSegmenter is created with the video running mode. It's required to provide the video frame's timestamp (in milliseconds) along with the video frame. The input timestamps should be monotonically increasing for adjacent calls of this method. Args: image: MediaPipe Image. timestamp_ms: The timestamp of the input video frame in milliseconds. image_processing_options: Options for image processing. Returns: If the output_type is CATEGORY_MASK, the returned vector of images is per-category segmented image mask. If the output_type is CONFIDENCE_MASK, the returned vector of images contains only one confidence image mask. A segmentation result object that contains a list of segmentation masks as images. Raises: ValueError: If any of the input arguments is invalid. RuntimeError: If image segmentation failed to run. Fr)r_process_video_datar~rratrmrrr3rrhrrgrrjrir)r8r rrrrbrns r)segment_for_videoz ImageSegmenter.segment_for_videoBs :55 %U6O--~:5AADD 9 9    ;  " " $ $! ! "\: : ; ; /N/00$66-:-I 6 7..*"^33*7*A 3 4++' r(c T|||d}|ttj||t zttj| |t zidS)aSends live image data (an Image with a unique timestamp) to perform image segmentation. Only use this method when the ImageSegmenter is created with the live stream running mode. The input timestamps should be monotonically increasing for adjacent calls of this method. This method will return immediately after the input image is accepted. The results will be available via the `result_callback` provided in the `ImageSegmenterOptions`. The `segment_async` method is designed to process live stream data such as camera input. To lower the overall latency, image segmenter may drop the input images if needed. In other words, it's not guaranteed to have output per input image. The `result_callback` prvoides: - A segmentation result object that contains a list of segmentation masks as images. - The input image that the image segmenter runs on. - The input timestamp in milliseconds. Args: image: MediaPipe Image. timestamp_ms: The timestamp of the input image in milliseconds. image_processing_options: Options for image processing. Raises: ValueError: If the current input timestamp is smaller than what the image segmenter has already processed. FrN) r_send_live_stream_datar~rrrrmrrr3)r8r rrrs r) segment_asynczImageSegmenter.segment_asyncxsB55 %U6O ~:5AADD 9 9    ;  " " $ $! ! "\: : ; ; !r(c|jS)a|Get the category label list the ImageSegmenter can recognize. For CATEGORY_MASK type, the index in the category mask corresponds to the category in the label list. For CONFIDENCE_MASK type, the output mask list at index corresponds to the category in the label list. If there is no label map provided in the model file, empty label list is returned. )rJ)r8s r)labelszImageSegmenter.labelss  <r()r0Nrd)r r!r"r#rCrD classmethodrvr`r+r^r$r%r_ImageProcessingOptionsrrr=rrpropertyrr __classcell__)rGs@r)r@r@vs0====8,c,6F,,,;,0G)GGGG;GXEI++  +!))@ A+ ++++bEI 44  44!))@ A 4  4444tEI ++  ++!))@ A +  ++++Z  d3i    8     r(r@)Br# dataclassestypingrrrrmediapipe.pythonrr$mediapipe.python._framework_bindingsr r$r 5mediapipe.tasks.cc.vision.image_segmenter.calculatorsr /mediapipe.tasks.cc.vision.image_segmenter.protor r ,mediapipe.tasks.python.components.containersrmediapipe.tasks.python.corerbase_options_modulertask_info_module1mediapipe.tasks.python.core.optional_dependenciesr"mediapipe.tasks.python.vision.corerrimage_processing_options_modulerNormalizedRect_NormalizedRect BaseOptionsr;SegmenterOptionsr6ImageSegmenterGraphOptionsr7&TensorsToSegmentationCalculatorOptionsrSVisionTaskRunningModer4ImageProcessingOptionsrTaskInfor|rhrzrjr{r~reryrrrNr}rm dataclassrr+BaseVisionTaskApir@r'r(r)rs&%444444444444++++++******FFFFFF777777hhhhhh]]]]]]QQQQQQ======KKKKKKEEEEEEJJJJJJCCCCCCjjjjjjGGGGGG%". .?%@!+Q,(= 9P  % 2*,$"$  '5)P!%  5 5 5 5 5 5 5 5 $$$$$$$$N{{{{{);{{{{{r(