WpfdZddlmZddlZddlmZddlmZddlmZddl m Z ddl m Z dd l m Z dd l m Z dd l mZdd lmZdd lmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z ddlm!Z!ddlm"Z"ddlm#Z#ddlm$Z$ddlm%Z%ddlm&Z&dd lm'Z'd!Z(d"Z)d#Z*Gd$d%eZ+dS)&zMediaPipe Face Mesh.) NamedTupleN)#constant_side_packet_calculator_pb2)gate_calculator_pb2)split_vector_calculator_pb2)image_to_tensor_calculator_pb2)inference_calculator_pb2)(tensors_to_classification_calculator_pb2)$tensors_to_detections_calculator_pb2)#tensors_to_landmarks_calculator_pb2)ssd_anchors_calculator_pb2)association_calculator_pb2)"detections_to_rects_calculator_pb2)#landmarks_refinement_calculator_pb2)logic_calculator_pb2)"non_max_suppression_calculator_pb2)"rect_transformation_calculator_pb2)thresholding_calculator_pb2) SolutionBase)FACEMESH_CONTOURS)FACEMESH_FACE_OVAL)FACEMESH_IRISES)FACEMESH_LEFT_EYE)FACEMESH_LEFT_EYEBROW)FACEMESH_LEFT_IRIS) FACEMESH_LIPS) FACEMESH_NOSE)FACEMESH_RIGHT_EYE)FACEMESH_RIGHT_EYEBROW)FACEMESH_RIGHT_IRIS)FACEMESH_TESSELATIONiiz@mediapipe/modules/face_landmark/face_landmark_front_cpu.binarypbcLeZdZdZ d fd Zdejdeffd ZxZ S) FaceMeshzMediaPipe Face Mesh. MediaPipe Face Mesh processes an RGB image and returns the face landmarks on each detected face. Please refer to https://solutions.mediapipe.dev/face_mesh#python-solution-api for usage examples. F?cntt||| d||ddgdS)ajInitializes a MediaPipe Face Mesh 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/face_mesh#static_image_mode. max_num_faces: Maximum number of faces to detect. See details in https://solutions.mediapipe.dev/face_mesh#max_num_faces. refine_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/face_mesh#refine_landmarks. min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for face detection to be considered successful. See details in https://solutions.mediapipe.dev/face_mesh#min_detection_confidence. min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the face landmarks to be considered tracked successfully. See details in https://solutions.mediapipe.dev/face_mesh#min_tracking_confidence. ) num_faceswith_attentionuse_prev_landmarks)zrfacedetectionshortrangecpu__facedetectionshortrange__facedetection__TensorsToDetectionsCalculator.min_score_threshz1facelandmarkcpu__ThresholdingCalculator.thresholdmulti_face_landmarks)binary_graph_path side_inputscalculator_paramsoutputsN)super__init___BINARYPB_FILE_PATH)selfstatic_image_mode max_num_facesrefine_landmarksmin_detection_confidencemin_tracking_confidence __class__s d/var/www/html/nettyfy-visnx/env/lib/python3.11/site-packages/mediapipe/python/solutions/face_mesh.pyr/zFaceMesh.__init__Fsc2 GG-&.&7"7  )'   (( * * * * *imagereturncLtd|iS)aProcesses an RGB image and returns the face landmarks on each detected face. 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 "multi_face_landmarks" field that contains the face landmarks on each detected face. r:) input_data)r.process)r1r:r7s r8r>zFaceMesh.processns" 77??w&6? 7 77r9)Fr#Fr$r$) __name__ __module__ __qualname____doc__r/npndarrayrr> __classcell__)r7s@r8r"r"<s"' %(+'* &*&*&*&*&*&*P82:8*8888888888r9r"),rBtypingrnumpyrCmediapipe.calculators.corerrrmediapipe.calculators.tensorrrr r r mediapipe.calculators.tfliter mediapipe.calculators.utilr rrrrrrmediapipe.python.solution_baser0mediapipe.python.solutions.face_mesh_connectionsrrrrrrrrrrrr FACEMESH_NUM_LANDMARKS"FACEMESH_NUM_LANDMARKS_WITH_IRISESr0r"r9r8rQsKJJJJJ::::::BBBBBBGGGGGGAAAAAAQQQQQQMMMMMMLLLLLLCCCCCCAAAAAAIIIIIIJJJJJJ;;;;;;IIIIIIIIIIIIBBBBBB777777NNNNNNOOOOOOLLLLLLNNNNNNRRRRRROOOOOOJJJJJJJJJJJJOOOOOOSSSSSSPPPPPPQQQQQQ%("XA8A8A8A8A8|A8A8A8A8A8r9