uki-dZddlZddlmZmZmZmZddlZddlZ ddl m Z ddl m Z ddl m Z ddlmZddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddl m!Z!ddl"m#Z#ddl"m$Z$ddl"m%Z%ddl&m'Z'ddl(m)Z)GddejTZ+dZ,e-e+j\e+j^fe+j\e+j`fe+j\e+jbfe+j^e+jdfe+j^e+jffe+j`e+jdfe+j`e+jhfe+jde+jjfe+jbe+jffe+jbe+jhfe+jfe+jjfe+jhe+jjfg Z6ejnd Gd!d"e8Z9ejndd#Gd$d%e9Z:ejndd#Gd&d'e9Z;ejndd#Gd(d)e9Z<ejndd#Gd*d+e9Z=e:e;e=e<d,Z>d-e?fd.Z@d/e?d0e9fd1ZAejnd Gd2d3e8ZBGd4d5e'ZCy)6zMediaPipe Objectron.N)ListTuple NamedTupleOptional)#constant_side_packet_calculator_pb2)gate_calculator_pb2)split_vector_calculator_pb2)image_to_tensor_calculator_pb2)inference_calculator_pb2)$tensors_to_detections_calculator_pb2) tensors_to_floats_calculator_pb2)#tensors_to_landmarks_calculator_pb2)ssd_anchors_calculator_pb2)association_calculator_pb2)&collection_has_min_size_calculator_pb2))detection_label_id_to_text_calculator_pb2)"detections_to_rects_calculator_pb2)"landmark_projection_calculator_pb2)"local_file_contents_calculator_pb2)"non_max_suppression_calculator_pb2)"rect_transformation_calculator_pb2)thresholding_calculator_pb2) landmark_pb2)annotation_data_pb2)'frame_annotation_to_rect_calculator_pb2)-lift_2d_frame_annotation_to_3d_calculator_pb2) SolutionBase)download_utilsc4eZdZdZdZdZdZdZdZdZ dZ d Z d Z y ) BoxLandmarkzThe 9 3D box landmarks.rN) __name__ __module__ __qualname____doc__CENTERBACK_BOTTOM_LEFTFRONT_BOTTOM_LEFT BACK_TOP_LEFTFRONT_TOP_LEFTBACK_BOTTOM_RIGHTFRONT_BOTTOM_RIGHTBACK_TOP_RIGHTFRONT_TOP_RIGHT_/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/mediapipe/python/solutions/objectron.pyr r 3s8 &-../r7r z2mediapipe/modules/objectron/objectron_cpu.binarypbT) auto_attribsc"eZdZUeed<eed<y)ObjectronModel model_path label_nameN)r)r*r+str__annotations__r6r7r8r;r;_s //r7r;)r9frozenc*eZdZUdZeed<dZeed<y) ShoeModelz?mediapipe/modules/objectron/object_detection_3d_sneakers.tfliter<Footwearr=Nr)r*r+r<r>r?r=r6r7r8rBrBes;*c<*cr7rBc*eZdZUdZeed<dZeed<y) ChairModelz T r7ceZdZUejed<ej ed<ejed<ejed<ejed<y)ObjectronOutputs landmarks_2d landmarks_3drotation translationscaleN) r)r*r+rNormalizedLandmarkListr? LandmarkListnpndarrayr6r7r8rZrZs8333))) JJzz r7rZceZdZdZ ddedededededeeefdeeefd e eeefffd Z d e jd e ffd Zdej d eefdZxZS) ObjectronzMediaPipe Objectron. MediaPipe Objectron processes an RGB image and returns the 3D box landmarks and 2D rectangular bounding box of each detected object. static_image_modemax_num_objectsmin_detection_confidencemin_tracking_confidence model_name focal_lengthprincipal_point image_sizec |\} } |\} } |,|ddz } |ddz }| | z } | |z } | | z | z } | |z |z } t|}t| t|j|j || d||| | | | ddgy) aInitializes a MediaPipe Objectron class. Args: static_image_mode: Whether to treat the input images as a batch of static and possibly unrelated images, or a video stream. max_num_objects: Maximum number of objects to detect. min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for object detection to be considered successful. min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the box landmarks to be considered tracked successfully. model_name: Name of model to use for predicting box landmarks, currently support {'Shoe', 'Chair', 'Cup', 'Camera'}. focal_length: Camera focal length `(fx, fy)`, by default is defined in NDC space. To use focal length (fx_pixel, fy_pixel) in pixel space, users should provide image_size = (image_width, image_height) to enable conversions inside the API. principal_point: Camera principal point (px, py), by default is defined in NDC space. To use principal point (px_pixel, py_pixel) in pixel space, users should provide image_size = (image_width, image_height) to enable conversions inside the API. image_size (Optional): size (image_width, image_height) of the input image , ONLY needed when use focal_length and principal_point in pixel space. Raises: ConnectionError: If the objectron open source model can't be downloaded from the MediaPipe Github repo. Nrg@r!)box_landmark_model_pathallowed_labelsrguse_prev_landmarks)zLobjectdetectionoidv4subgraph__TensorsToDetectionsCalculator.min_score_threshz5boxlandmarksubgraph__ThresholdingCalculator.thresholdz6Lift2DFrameAnnotationTo3DCalculator.normalized_focal_xz6Lift2DFrameAnnotationTo3DCalculator.normalized_focal_yz@Lift2DFrameAnnotationTo3DCalculator.normalized_principal_point_xz@Lift2DFrameAnnotationTo3DCalculator.normalized_principal_point_ydetected_objects)binary_graph_path side_inputscalculator_paramsoutputs)rXsuper__init___BINARYPB_FILE_PATHr<r=)selfrfrgrhrirjrkrlrmfxfypxpy half_width half_heightmodel __class__s r8rxzObjectron.__init__sLFB FBa=3&jqMC'k ?b b:o  +b; + -b j )E G-','7'7#...&7"7  )(%'%'/1/1 $$1&r7imagerTct|d|i}|jr"|j|j|_|Sd|_|S)a Processes an RGB image and returns the box landmarks and rectangular bounding box of each detected object. Args: image: An RGB image represented as a numpy ndarray. Raises: RuntimeError: If the underlying graph throws any error. ValueError: If the input image is not three channel RGB. Returns: A NamedTuple object with a "detected_objects" field that contains a list of detected 3D bounding boxes. Each detected box is represented as an "ObjectronOutputs" instance. r) input_dataN)rwprocessrr_convert_format)rzrresultsrs r8rzObjectron.processsR go'5)9o:G!%!5!5g6N6N!Og N"&g Nr7inputsc t}|jD]Q}tjtj|j d}tj|j }tj|j}tj}tj}|jD]} | j} |jj| j| j | j"} |jj| j| j | j$|j't)|||||T|S)N)r#r#)xy)rrz)r_)list annotationsrbreshapearrayr]r^r_rr`ra keypointspoint_2dlandmarkaddrrpoint_3drappendrZ) rzr new_outputs annotationr]r^r_r[r\keypointrrs r8rzObjectron._convert_format s'&K((O BHHZ%8%896BhHHZ334khhz''(e!88:l!..0l **L($$!!HJJ(**!=$$!!HJJ(** !K L), *2KuNOO" r7)Fr%g?gGz?rM)?r)rN)r)r*r+r,boolintfloatr>rrrxrbrcrrrFrameAnnotationrrZr __classcell__)rs@r8reres*/&'1404!'3=6@7;J&"&J& #J&*/J&). 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