Wpf" ddZddlmZddlZddlmZddlmZddlmZddl m Z ddl m Z dd l m Z dd l m Z dd l mZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z ddlm!Z!ddl"m#Z#ddl$m%Z%ddl&m'Z'ddl(m)Z)dZ*dZ+GddeZ,dS) zMediaPipe Holistic.) NamedTupleN)#constant_side_packet_calculator_pb2)gate_calculator_pb2)split_vector_calculator_pb2)image_to_tensor_calculator_pb2)inference_calculator_pb2)(tensors_to_classification_calculator_pb2) tensors_to_floats_calculator_pb2)#tensors_to_landmarks_calculator_pb2)ssd_anchors_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)switch_container_pb2)roi_tracking_calculator_pb2) SolutionBase)download_utils)FACEMESH_CONTOURS)FACEMESH_TESSELATION) HandLandmark)HAND_CONNECTIONS) PoseLandmark)POSE_CONNECTIONSzBmediapipe/modules/holistic_landmark/holistic_landmark_cpu.binarypbcv|dkrtjddS|dkrtjddSdS)zoDownloads the pose landmark lite/heavy model from the MediaPipe Github repo if it doesn't exist in the package.rz9mediapipe/modules/pose_landmark/pose_landmark_lite.tflitez:mediapipe/modules/pose_landmark/pose_landmark_heavy.tfliteN)rdownload_oss_model)model_complexitys c/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/mediapipe/python/solutions/holistic.py!_download_oss_pose_landmark_modelr!7sp%CEEEEE1%DFFFFFcReZdZdZ d fd Zdejdeffd ZxZ S) Holistica'MediaPipe Holistic. MediaPipe Holistic processes an RGB image and returns pose landmarks, left and right hand landmarks, and face mesh landmarks on the most prominent person detected. Please refer to https://solutions.mediapipe.dev/holistic#python-solution-api for usage examples. FT?c t|tt||o| ||o| || d||dgddS)aInitializes a MediaPipe Holistic object. Args: static_image_mode: Whether to treat the input images as a batch of static and possibly unrelated images, or a video stream. See details in https://solutions.mediapipe.dev/holistic#static_image_mode. model_complexity: Complexity of the pose landmark model: 0, 1 or 2. See details in https://solutions.mediapipe.dev/holistic#model_complexity. smooth_landmarks: Whether to filter landmarks across different input images to reduce jitter. See details in https://solutions.mediapipe.dev/holistic#smooth_landmarks. enable_segmentation: Whether to predict segmentation mask. See details in https://solutions.mediapipe.dev/holistic#enable_segmentation. smooth_segmentation: Whether to filter segmentation across different input images to reduce jitter. See details in https://solutions.mediapipe.dev/holistic#smooth_segmentation. refine_face_landmarks: Whether to further refine the landmark coordinates around the eyes and lips, and output additional landmarks around the irises. Default to False. See details in https://solutions.mediapipe.dev/holistic#refine_face_landmarks. min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for person detection to be considered successful. See details in https://solutions.mediapipe.dev/holistic#min_detection_confidence. min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the pose landmarks to be considered tracked successfully. See details in https://solutions.mediapipe.dev/holistic#min_tracking_confidence. )rsmooth_landmarksenable_segmentationsmooth_segmentationrefine_face_landmarksuse_prev_landmarks)zQposelandmarkcpu__posedetectioncpu__TensorsToDetectionsCalculator.min_score_threshznposelandmarkcpu__poselandmarkbyroicpu__tensorstoposelandmarksandsegmentation__ThresholdingCalculator.threshold)pose_landmarkspose_world_landmarksleft_hand_landmarksright_hand_landmarksface_landmarkssegmentation_mask)binary_graph_path side_inputscalculator_paramsoutputsN)r!super__init___BINARYPB_FILE_PATH) selfstatic_image_moderr(r)r*r+min_detection_confidencemin_tracking_confidence __class__s r r8zHolistic.__init__MsH&&6777 GG- 0 0 J9J5J#6#=,=(=%:&7"7  )'      #     r"imagereturnctd|i}|jr$|jjD]}|d|jr$|jjD]}|d|S)a.Processes an RGB image and returns the pose landmarks, left and right hand landmarks, and face landmarks on the most prominent person detected. Args: image: An RGB image represented as a numpy ndarray. Raises: RuntimeError: If the underlying graph throws any error. ValueError: If the input image is not three channel RGB. Returns: A NamedTuple with fields describing the landmarks on the most prominate person detected: 1) "pose_landmarks" field that contains the pose landmarks. 2) "pose_world_landmarks" field that contains the pose landmarks in real-world 3D coordinates that are in meters with the origin at the center between hips. 3) "left_hand_landmarks" field that contains the left-hand landmarks. 4) "right_hand_landmarks" field that contains the right-hand landmarks. 5) "face_landmarks" field that contains the face landmarks. 6) "segmentation_mask" field that contains the segmentation mask if "enable_segmentation" is set to true. r?) input_datapresence)r7processr-landmark ClearFieldr.)r:r?resultsrEr>s r rDzHolistic.processs0ggoo'5)9o::G(,5(((J''''#(2;(((J'''' Nr")Fr%TFTFr&r&) __name__ __module__ __qualname____doc__r8npndarrayrrD __classcell__)r>s@r r$r$Bs"' ! $#(#'%*(+'*9 9 9 9 9 9 v2:*r"r$)-rKtypingrnumpyrLmediapipe.calculators.corerrrmediapipe.calculators.tensorrrr r r mediapipe.calculators.tfliter mediapipe.calculators.utilr rrrrmediapipe.framework.toolr/mediapipe.modules.holistic_landmark.calculatorsrmediapipe.python.solution_basermediapipe.python.solutionsr0mediapipe.python.solutions.face_mesh_connectionsrr mediapipe.python.solutions.handsr,mediapipe.python.solutions.hands_connectionsrmediapipe.python.solutions.poser+mediapipe.python.solutions.pose_connectionsrr9r!r$r"r r_s9 KJJJJJ::::::BBBBBBGGGGGGAAAAAAQQQQQQIIIIIILLLLLLCCCCCCIIIIIIIIIIIIIIIIIIIIIIIIIIIIII999999WWWWWW877777555555NNNNNNQQQQQQ999999IIIIII888888HHHHHH[FFFeeeee|eeeeer"