upload media_pipe module

media_pipe/__init__.py

@@ -0,0 +1,111 @@
+import os
+import cv2
+import numpy as np
+
+from .mp_utils  import LMKExtractor
+from .draw_util import FaceMeshVisualizer
+from .pose_util import project_points_with_trans, matrix_to_euler_and_translation, euler_and_translation_to_matrix
+
+
+class FaceMeshDetector:
+    """
+    Class for face mesh detection and landmark extraction.
+    """
+    def __init__(self) -> None:
+        self.lmk_extractor = LMKExtractor()
+        self.vis = FaceMeshVisualizer(forehead_edge=False, iris_edge=False, iris_point=True)
+        
+    def __call__(self, image: np.array):
+        frame_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        try:
+            face_result = self.lmk_extractor(frame_bgr)
+            face_result['width'] = frame_bgr.shape[1]
+            face_result['height'] = frame_bgr.shape[0]
+        except:
+            face_result = None
+
+        if face_result is None:
+            return np.zeros_like(frame_bgr), None
+
+        lmks = face_result['lmks'].astype(np.float32)
+        motion = self.vis.draw_landmarks((frame_bgr.shape[1], frame_bgr.shape[0]), lmks, normed=True)
+        
+        return motion, face_result
+
+
+def smooth_pose_seq(pose_seq, window_size=5):
+    smoothed_pose_seq = np.zeros_like(pose_seq)
+
+    for i in range(len(pose_seq)):
+        start = max(0, i - window_size // 2)
+        end = min(len(pose_seq), i + window_size // 2 + 1)
+        smoothed_pose_seq[i] = np.mean(pose_seq[start:end], axis=0)
+
+    return smoothed_pose_seq
+    
+
+class FaceMeshAlign():
+
+    def __init__(self):
+        self.vis = FaceMeshVisualizer(forehead_edge=False, iris_edge=False, iris_point=True)
+
+    def _scale_iris(self, lmks, verts):
+        r_iris_ids = [468, 469, 470, 471, 472]
+        l_iris_ids = [473, 474, 475, 476, 477]
+        r_eye_ids = [33,7,163,144,145,153,154,155,246,161,160,159,158,157,173,133]
+        l_eye_ids = [249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,263]
+
+        def scale_iris(iris_ids, eye_ids):
+            iris_lmks, eye_lmks, eye_verts = lmks[iris_ids], lmks[eye_ids], verts[eye_ids]
+            x0, y0, x1, y1 = np.min(eye_lmks[:, 0]), np.min(eye_lmks[:, 1]), np.max(eye_lmks[:, 0]), np.max(eye_lmks[:, 1])
+            iris_lmks[:, 0] = (iris_lmks[:, 0] - x0) / (x1 - x0)
+            iris_lmks[:, 1] = (iris_lmks[:, 1] - y0) / (y1 - y0)
+            
+            iris_verts = np.zeros((5, 2))
+            x0, y0, x1, y1 = np.min(eye_verts[:, 0]), np.min(eye_verts[:, 1]), np.max(eye_verts[:, 0]), np.max(eye_verts[:, 1])
+            iris_verts[:, 0] = iris_lmks[:, 0] * (x1 - x0) + x0
+            iris_verts[:, 1] = iris_lmks[:, 1] * (y1 - y0) + y0
+
+            return iris_verts
+
+        r_iris_verts = scale_iris(r_iris_ids, r_eye_ids)
+        l_iris_verts = scale_iris(l_iris_ids, l_eye_ids)
+        verts = np.vstack((verts, r_iris_verts, l_iris_verts))
+        return verts
+
+    def __call__(self, ref_result, temp_results):
+        width, height = ref_result['width'], ref_result['height']
+        # prepare template data
+        trans_mat_arr = np.array([x['trans_mat'] for x in temp_results])
+        verts_arr = np.array([x['lmks3d'] for x in temp_results])
+        bs_arr = np.array([x['bs'] for x in temp_results])
+        min_bs_idx = np.argmin(bs_arr.sum(1))
+        
+        # compute delta pose
+        pose_arr = np.zeros([trans_mat_arr.shape[0], 6])
+
+        for i in range(pose_arr.shape[0]):
+            euler_angles, translation_vector = matrix_to_euler_and_translation(trans_mat_arr[i]) # real pose of source
+            pose_arr[i, :3] = euler_angles
+            pose_arr[i, 3:6] = translation_vector
+        
+        init_tran_vec = ref_result['trans_mat'][:3, 3] # init translation of tgt
+        pose_arr[:, 3:6] = pose_arr[:, 3:6] - pose_arr[0, 3:6] + init_tran_vec # (relative translation of source) + (init translation of tgt)
+
+        pose_arr_smooth = smooth_pose_seq(pose_arr, window_size=1)
+        pose_mat_smooth = [euler_and_translation_to_matrix(pose_arr_smooth[i][:3], pose_arr_smooth[i][3:6]) for i in range(pose_arr_smooth.shape[0])]    
+        pose_mat_smooth = np.array(pose_mat_smooth)
+
+        # face retarget
+        verts_arr = verts_arr - verts_arr[min_bs_idx] + ref_result['lmks3d']
+        # project 3D mesh to 2D landmark
+        projected_vertices = project_points_with_trans(verts_arr, pose_mat_smooth, [height, width])
+                
+        pose_list = []
+        for i, verts in enumerate(projected_vertices):
+            verts = self._scale_iris(temp_results[i]['lmks'], verts)
+            lmk_img = self.vis.draw_landmarks((width, height), verts, normed=False)
+            pose_list.append(lmk_img)
+        pose_list = np.array(pose_list)[1:]
+
+        return pose_list

media_pipe/draw_util.py

@@ -0,0 +1,173 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+from mediapipe.framework.formats import landmark_pb2
+
+class FaceMeshVisualizer:
+    def __init__(self, forehead_edge=False, iris_edge=False, iris_point=False):
+        self.mp_drawing = mp.solutions.drawing_utils
+        mp_face_mesh = mp.solutions.face_mesh
+        self.mp_face_mesh = mp_face_mesh
+        self.forehead_edge = forehead_edge
+
+        DrawingSpec = mp.solutions.drawing_styles.DrawingSpec
+        f_thick = 1
+        f_rad = 1
+        right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
+        right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
+        right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
+        left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
+        # head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        head_draw = DrawingSpec(color=(0, 0, 0), thickness=f_thick, circle_radius=f_rad)
+        
+        mouth_draw_obl = DrawingSpec(color=(10, 180, 20), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_obr = DrawingSpec(color=(20, 10, 180), thickness=f_thick, circle_radius=f_rad)
+        
+        mouth_draw_ibl = DrawingSpec(color=(100, 100, 30), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_ibr = DrawingSpec(color=(100, 150, 50), thickness=f_thick, circle_radius=f_rad)
+        
+        mouth_draw_otl = DrawingSpec(color=(20, 80, 100), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_otr = DrawingSpec(color=(80, 100, 20), thickness=f_thick, circle_radius=f_rad)
+        
+        mouth_draw_itl = DrawingSpec(color=(120, 100, 200), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_itr = DrawingSpec(color=(150 ,120, 100), thickness=f_thick, circle_radius=f_rad)
+        
+        FACEMESH_LIPS_OUTER_BOTTOM_LEFT = [(61,146),(146,91),(91,181),(181,84),(84,17)]
+        FACEMESH_LIPS_OUTER_BOTTOM_RIGHT = [(17,314),(314,405),(405,321),(321,375),(375,291)]
+        
+        FACEMESH_LIPS_INNER_BOTTOM_LEFT = [(78,95),(95,88),(88,178),(178,87),(87,14)]
+        FACEMESH_LIPS_INNER_BOTTOM_RIGHT = [(14,317),(317,402),(402,318),(318,324),(324,308)]
+        
+        FACEMESH_LIPS_OUTER_TOP_LEFT = [(61,185),(185,40),(40,39),(39,37),(37,0)]
+        FACEMESH_LIPS_OUTER_TOP_RIGHT = [(0,267),(267,269),(269,270),(270,409),(409,291)]
+        
+        FACEMESH_LIPS_INNER_TOP_LEFT = [(78,191),(191,80),(80,81),(81,82),(82,13)]
+        FACEMESH_LIPS_INNER_TOP_RIGHT = [(13,312),(312,311),(311,310),(310,415),(415,308)]
+        
+        FACEMESH_CUSTOM_FACE_OVAL = [(176, 149), (150, 136), (356, 454), (58, 132), (152, 148), (361, 288), (251, 389), (132, 93), (389, 356), (400, 377), (136, 172), (377, 152), (323, 361), (172, 58), (454, 323), (365, 379), (379, 378), (148, 176), (93, 234), (397, 365), (149, 150), (288, 397), (234, 127), (378, 400), (127, 162), (162, 21)]
+
+        # mp_face_mesh.FACEMESH_CONTOURS has all the items we care about.
+        face_connection_spec = {}
+        if self.forehead_edge:
+            for edge in mp_face_mesh.FACEMESH_FACE_OVAL:
+                face_connection_spec[edge] = head_draw
+        else:
+            for edge in FACEMESH_CUSTOM_FACE_OVAL:
+                face_connection_spec[edge] = head_draw
+        for edge in mp_face_mesh.FACEMESH_LEFT_EYE:
+            face_connection_spec[edge] = left_eye_draw
+        for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW:
+            face_connection_spec[edge] = left_eyebrow_draw
+        for edge in mp_face_mesh.FACEMESH_RIGHT_EYE:
+            face_connection_spec[edge] = right_eye_draw
+        for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
+            face_connection_spec[edge] = right_eyebrow_draw
+        if iris_edge:
+            for edge in mp_face_mesh.FACEMESH_LEFT_IRIS:
+               face_connection_spec[edge] = left_iris_draw
+            for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
+               face_connection_spec[edge] = right_iris_draw
+        # for edge in mp_face_mesh.FACEMESH_LIPS:
+        #     face_connection_spec[edge] = mouth_draw
+        
+        # for edge in FACEMESH_LIPS_OUTER_BOTTOM_LEFT:
+        #     face_connection_spec[edge] = mouth_draw_obl
+        # for edge in FACEMESH_LIPS_OUTER_BOTTOM_RIGHT:
+        #     face_connection_spec[edge] = mouth_draw_obr
+        for edge in FACEMESH_LIPS_INNER_BOTTOM_LEFT:
+            face_connection_spec[edge] = mouth_draw_ibl
+        for edge in FACEMESH_LIPS_INNER_BOTTOM_RIGHT:
+            face_connection_spec[edge] = mouth_draw_ibr
+        # for edge in FACEMESH_LIPS_OUTER_TOP_LEFT:
+        #     face_connection_spec[edge] = mouth_draw_otl
+        # for edge in FACEMESH_LIPS_OUTER_TOP_RIGHT:
+        #     face_connection_spec[edge] = mouth_draw_otr
+        for edge in FACEMESH_LIPS_INNER_TOP_LEFT:
+            face_connection_spec[edge] = mouth_draw_itl
+        for edge in FACEMESH_LIPS_INNER_TOP_RIGHT:
+            face_connection_spec[edge] = mouth_draw_itr
+
+        self.iris_point = iris_point
+        
+        self.face_connection_spec = face_connection_spec
+
+    def draw_pupils(self, image, landmark_list, drawing_spec, halfwidth: int = 2):
+        """We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all
+        landmarks.  Until our PR is merged into mediapipe, we need this separate method."""
+        if len(image.shape) != 3:
+            raise ValueError("Input image must be H,W,C.")
+        image_rows, image_cols, image_channels = image.shape
+        if image_channels != 3:  # BGR channels
+            raise ValueError('Input image must contain three channel bgr data.')
+        for idx, landmark in enumerate(landmark_list.landmark):
+            if (
+                    (landmark.HasField('visibility') and landmark.visibility < 0.9) or
+                    (landmark.HasField('presence') and landmark.presence < 0.5)
+            ):
+                continue
+            if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0:
+                continue
+            image_x = int(image_cols*landmark.x)
+            image_y = int(image_rows*landmark.y)
+            draw_color = None
+            if isinstance(drawing_spec, Mapping):
+                if drawing_spec.get(idx) is None:
+                    continue
+                else:
+                    draw_color = drawing_spec[idx].color
+            elif isinstance(drawing_spec, DrawingSpec):
+                draw_color = drawing_spec.color
+            image[image_y-halfwidth:image_y+halfwidth, image_x-halfwidth:image_x+halfwidth, :] = draw_color
+    
+    def draw_iris_points(self, image, point_list, halfwidth=2, normed=False):
+        color = (255, 0, 0)
+        for point in point_list:
+            if normed:
+                x, y = int(point[0] * image.shape[1]), int(point[1] * image.shape[0])
+            else:
+                x, y = int(point[0]), int(point[1])
+            image[y-halfwidth:y+halfwidth, x-halfwidth:x+halfwidth, :] = color
+        return image
+
+    def draw_landmarks(self, image_size, keypoints, normed=False):
+        ini_size = image_size #[512, 512]
+        image = np.zeros([ini_size[1], ini_size[0], 3], dtype=np.uint8)
+        new_landmarks = landmark_pb2.NormalizedLandmarkList()
+        for i in range(keypoints.shape[0]):
+            landmark = new_landmarks.landmark.add()
+            if normed:
+                landmark.x = keypoints[i, 0]
+                landmark.y = keypoints[i, 1]
+            else:
+                landmark.x = keypoints[i, 0] / image_size[0]
+                landmark.y = keypoints[i, 1] / image_size[1]
+            landmark.z = 1.0
+
+        self.mp_drawing.draw_landmarks(
+            image=image,
+            landmark_list=new_landmarks,
+            connections=self.face_connection_spec.keys(),
+            landmark_drawing_spec=None,
+            connection_drawing_spec=self.face_connection_spec
+        )
+        
+        if self.iris_point:
+            image = self.draw_iris_points(image, [keypoints[473], keypoints[468]], halfwidth=3, normed=normed)
+        
+        return image
+    
+    def draw_mask(self, image_size, keypoints, normed=False):
+        mask = np.zeros([image_size[1], image_size[0], 3], dtype=np.uint8)
+        if normed:
+            keypoints[:, 0] *= image_size[0]
+            keypoints[:, 1] *= image_size[1]
+        
+        head_idxs = [21, 162, 127, 234, 93, 132, 58, 172, 136, 150, 149, 176, 148, 152, 377, 400, 378, 379, 365, 397, 288, 361, 323, 454, 356, 389]
+        head_points = np.array(keypoints[head_idxs, :2], np.int32)
+
+        mask = cv2.fillPoly(mask, [head_points], (255, 255, 255))
+        mask = np.array(mask) / 255.0
+
+        return mask

media_pipe/face_landmark.py

@@ -0,0 +1,3305 @@
+# Copyright 2023 The MediaPipe Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MediaPipe face landmarker task."""
+
+import dataclasses
+import enum
+from typing import Callable, Mapping, Optional, List
+
+import numpy as np
+
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.framework.formats import matrix_data_pb2
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet as packet_module
+# pylint: disable=unused-import
+from mediapipe.tasks.cc.vision.face_geometry.proto import face_geometry_pb2
+# pylint: enable=unused-import
+from mediapipe.tasks.cc.vision.face_landmarker.proto import face_landmarker_graph_options_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import landmark as landmark_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_FaceLandmarkerGraphOptionsProto = (
+    face_landmarker_graph_options_pb2.FaceLandmarkerGraphOptions
+)
+_LayoutEnum = matrix_data_pb2.MatrixData.Layout
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks'
+_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS'
+_BLENDSHAPES_STREAM_NAME = 'blendshapes'
+_BLENDSHAPES_TAG = 'BLENDSHAPES'
+_FACE_GEOMETRY_STREAM_NAME = 'face_geometry'
+_FACE_GEOMETRY_TAG = 'FACE_GEOMETRY'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+class Blendshapes(enum.IntEnum):
+  """The 52 blendshape coefficients."""
+
+  NEUTRAL = 0
+  BROW_DOWN_LEFT = 1
+  BROW_DOWN_RIGHT = 2
+  BROW_INNER_UP = 3
+  BROW_OUTER_UP_LEFT = 4
+  BROW_OUTER_UP_RIGHT = 5
+  CHEEK_PUFF = 6
+  CHEEK_SQUINT_LEFT = 7
+  CHEEK_SQUINT_RIGHT = 8
+  EYE_BLINK_LEFT = 9
+  EYE_BLINK_RIGHT = 10
+  EYE_LOOK_DOWN_LEFT = 11
+  EYE_LOOK_DOWN_RIGHT = 12
+  EYE_LOOK_IN_LEFT = 13
+  EYE_LOOK_IN_RIGHT = 14
+  EYE_LOOK_OUT_LEFT = 15
+  EYE_LOOK_OUT_RIGHT = 16
+  EYE_LOOK_UP_LEFT = 17
+  EYE_LOOK_UP_RIGHT = 18
+  EYE_SQUINT_LEFT = 19
+  EYE_SQUINT_RIGHT = 20
+  EYE_WIDE_LEFT = 21
+  EYE_WIDE_RIGHT = 22
+  JAW_FORWARD = 23
+  JAW_LEFT = 24
+  JAW_OPEN = 25
+  JAW_RIGHT = 26
+  MOUTH_CLOSE = 27
+  MOUTH_DIMPLE_LEFT = 28
+  MOUTH_DIMPLE_RIGHT = 29
+  MOUTH_FROWN_LEFT = 30
+  MOUTH_FROWN_RIGHT = 31
+  MOUTH_FUNNEL = 32
+  MOUTH_LEFT = 33
+  MOUTH_LOWER_DOWN_LEFT = 34
+  MOUTH_LOWER_DOWN_RIGHT = 35
+  MOUTH_PRESS_LEFT = 36
+  MOUTH_PRESS_RIGHT = 37
+  MOUTH_PUCKER = 38
+  MOUTH_RIGHT = 39
+  MOUTH_ROLL_LOWER = 40
+  MOUTH_ROLL_UPPER = 41
+  MOUTH_SHRUG_LOWER = 42
+  MOUTH_SHRUG_UPPER = 43
+  MOUTH_SMILE_LEFT = 44
+  MOUTH_SMILE_RIGHT = 45
+  MOUTH_STRETCH_LEFT = 46
+  MOUTH_STRETCH_RIGHT = 47
+  MOUTH_UPPER_UP_LEFT = 48
+  MOUTH_UPPER_UP_RIGHT = 49
+  NOSE_SNEER_LEFT = 50
+  NOSE_SNEER_RIGHT = 51
+
+
+class FaceLandmarksConnections:
+  """The connections between face landmarks."""
+
+  @dataclasses.dataclass
+  class Connection:
+    """The connection class for face landmarks."""
+
+    start: int
+    end: int
+
+  FACE_LANDMARKS_LIPS: List[Connection] = [
+      Connection(61, 146),
+      Connection(146, 91),
+      Connection(91, 181),
+      Connection(181, 84),
+      Connection(84, 17),
+      Connection(17, 314),
+      Connection(314, 405),
+      Connection(405, 321),
+      Connection(321, 375),
+      Connection(375, 291),
+      Connection(61, 185),
+      Connection(185, 40),
+      Connection(40, 39),
+      Connection(39, 37),
+      Connection(37, 0),
+      Connection(0, 267),
+      Connection(267, 269),
+      Connection(269, 270),
+      Connection(270, 409),
+      Connection(409, 291),
+      Connection(78, 95),
+      Connection(95, 88),
+      Connection(88, 178),
+      Connection(178, 87),
+      Connection(87, 14),
+      Connection(14, 317),
+      Connection(317, 402),
+      Connection(402, 318),
+      Connection(318, 324),
+      Connection(324, 308),
+      Connection(78, 191),
+      Connection(191, 80),
+      Connection(80, 81),
+      Connection(81, 82),
+      Connection(82, 13),
+      Connection(13, 312),
+      Connection(312, 311),
+      Connection(311, 310),
+      Connection(310, 415),
+      Connection(415, 308),
+  ]
+
+  FACE_LANDMARKS_LEFT_EYE: List[Connection] = [
+      Connection(263, 249),
+      Connection(249, 390),
+      Connection(390, 373),
+      Connection(373, 374),
+      Connection(374, 380),
+      Connection(380, 381),
+      Connection(381, 382),
+      Connection(382, 362),
+      Connection(263, 466),
+      Connection(466, 388),
+      Connection(388, 387),
+      Connection(387, 386),
+      Connection(386, 385),
+      Connection(385, 384),
+      Connection(384, 398),
+      Connection(398, 362),
+  ]
+
+  FACE_LANDMARKS_LEFT_EYEBROW: List[Connection] = [
+      Connection(276, 283),
+      Connection(283, 282),
+      Connection(282, 295),
+      Connection(295, 285),
+      Connection(300, 293),
+      Connection(293, 334),
+      Connection(334, 296),
+      Connection(296, 336),
+  ]
+
+  FACE_LANDMARKS_LEFT_IRIS: List[Connection] = [
+      Connection(474, 475),
+      Connection(475, 476),
+      Connection(476, 477),
+      Connection(477, 474),
+  ]
+
+  FACE_LANDMARKS_RIGHT_EYE: List[Connection] = [
+      Connection(33, 7),
+      Connection(7, 163),
+      Connection(163, 144),
+      Connection(144, 145),
+      Connection(145, 153),
+      Connection(153, 154),
+      Connection(154, 155),
+      Connection(155, 133),
+      Connection(33, 246),
+      Connection(246, 161),
+      Connection(161, 160),
+      Connection(160, 159),
+      Connection(159, 158),
+      Connection(158, 157),
+      Connection(157, 173),
+      Connection(173, 133),
+  ]
+
+  FACE_LANDMARKS_RIGHT_EYEBROW: List[Connection] = [
+      Connection(46, 53),
+      Connection(53, 52),
+      Connection(52, 65),
+      Connection(65, 55),
+      Connection(70, 63),
+      Connection(63, 105),
+      Connection(105, 66),
+      Connection(66, 107),
+  ]
+
+  FACE_LANDMARKS_RIGHT_IRIS: List[Connection] = [
+      Connection(469, 470),
+      Connection(470, 471),
+      Connection(471, 472),
+      Connection(472, 469),
+  ]
+
+  FACE_LANDMARKS_FACE_OVAL: List[Connection] = [
+      Connection(10, 338),
+      Connection(338, 297),
+      Connection(297, 332),
+      Connection(332, 284),
+      Connection(284, 251),
+      Connection(251, 389),
+      Connection(389, 356),
+      Connection(356, 454),
+      Connection(454, 323),
+      Connection(323, 361),
+      Connection(361, 288),
+      Connection(288, 397),
+      Connection(397, 365),
+      Connection(365, 379),
+      Connection(379, 378),
+      Connection(378, 400),
+      Connection(400, 377),
+      Connection(377, 152),
+      Connection(152, 148),
+      Connection(148, 176),
+      Connection(176, 149),
+      Connection(149, 150),
+      Connection(150, 136),
+      Connection(136, 172),
+      Connection(172, 58),
+      Connection(58, 132),
+      Connection(132, 93),
+      Connection(93, 234),
+      Connection(234, 127),
+      Connection(127, 162),
+      Connection(162, 21),
+      Connection(21, 54),
+      Connection(54, 103),
+      Connection(103, 67),
+      Connection(67, 109),
+      Connection(109, 10),
+  ]
+
+  FACE_LANDMARKS_CONTOURS: List[Connection] = (
+      FACE_LANDMARKS_LIPS
+      + FACE_LANDMARKS_LEFT_EYE
+      + FACE_LANDMARKS_LEFT_EYEBROW
+      + FACE_LANDMARKS_RIGHT_EYE
+      + FACE_LANDMARKS_RIGHT_EYEBROW
+      + FACE_LANDMARKS_FACE_OVAL
+  )
+
+  FACE_LANDMARKS_TESSELATION: List[Connection] = [
+      Connection(127, 34),
+      Connection(34, 139),
+      Connection(139, 127),
+      Connection(11, 0),
+      Connection(0, 37),
+      Connection(37, 11),
+      Connection(232, 231),
+      Connection(231, 120),
+      Connection(120, 232),
+      Connection(72, 37),
+      Connection(37, 39),
+      Connection(39, 72),
+      Connection(128, 121),
+      Connection(121, 47),
+      Connection(47, 128),
+      Connection(232, 121),
+      Connection(121, 128),
+      Connection(128, 232),
+      Connection(104, 69),
+      Connection(69, 67),
+      Connection(67, 104),
+      Connection(175, 171),
+      Connection(171, 148),
+      Connection(148, 175),
+      Connection(118, 50),
+      Connection(50, 101),
+      Connection(101, 118),
+      Connection(73, 39),
+      Connection(39, 40),
+      Connection(40, 73),
+      Connection(9, 151),
+      Connection(151, 108),
+      Connection(108, 9),
+      Connection(48, 115),
+      Connection(115, 131),
+      Connection(131, 48),
+      Connection(194, 204),
+      Connection(204, 211),
+      Connection(211, 194),
+      Connection(74, 40),
+      Connection(40, 185),
+      Connection(185, 74),
+      Connection(80, 42),
+      Connection(42, 183),
+      Connection(183, 80),
+      Connection(40, 92),
+      Connection(92, 186),
+      Connection(186, 40),
+      Connection(230, 229),
+      Connection(229, 118),
+      Connection(118, 230),
+      Connection(202, 212),
+      Connection(212, 214),
+      Connection(214, 202),
+      Connection(83, 18),
+      Connection(18, 17),
+      Connection(17, 83),
+      Connection(76, 61),
+      Connection(61, 146),
+      Connection(146, 76),
+      Connection(160, 29),
+      Connection(29, 30),
+      Connection(30, 160),
+      Connection(56, 157),
+      Connection(157, 173),
+      Connection(173, 56),
+      Connection(106, 204),
+      Connection(204, 194),
+      Connection(194, 106),
+      Connection(135, 214),
+      Connection(214, 192),
+      Connection(192, 135),
+      Connection(203, 165),
+      Connection(165, 98),
+      Connection(98, 203),
+      Connection(21, 71),
+      Connection(71, 68),
+      Connection(68, 21),
+      Connection(51, 45),
+      Connection(45, 4),
+      Connection(4, 51),
+      Connection(144, 24),
+      Connection(24, 23),
+      Connection(23, 144),
+      Connection(77, 146),
+      Connection(146, 91),
+      Connection(91, 77),
+      Connection(205, 50),
+      Connection(50, 187),
+      Connection(187, 205),
+      Connection(201, 200),
+      Connection(200, 18),
+      Connection(18, 201),
+      Connection(91, 106),
+      Connection(106, 182),
+      Connection(182, 91),
+      Connection(90, 91),
+      Connection(91, 181),
+      Connection(181, 90),
+      Connection(85, 84),
+      Connection(84, 17),
+      Connection(17, 85),
+      Connection(206, 203),
+      Connection(203, 36),
+      Connection(36, 206),
+      Connection(148, 171),
+      Connection(171, 140),
+      Connection(140, 148),
+      Connection(92, 40),
+      Connection(40, 39),
+      Connection(39, 92),
+      Connection(193, 189),
+      Connection(189, 244),
+      Connection(244, 193),
+      Connection(159, 158),
+      Connection(158, 28),
+      Connection(28, 159),
+      Connection(247, 246),
+      Connection(246, 161),
+      Connection(161, 247),
+      Connection(236, 3),
+      Connection(3, 196),
+      Connection(196, 236),
+      Connection(54, 68),
+      Connection(68, 104),
+      Connection(104, 54),
+      Connection(193, 168),
+      Connection(168, 8),
+      Connection(8, 193),
+      Connection(117, 228),
+      Connection(228, 31),
+      Connection(31, 117),
+      Connection(189, 193),
+      Connection(193, 55),
+      Connection(55, 189),
+      Connection(98, 97),
+      Connection(97, 99),
+      Connection(99, 98),
+      Connection(126, 47),
+      Connection(47, 100),
+      Connection(100, 126),
+      Connection(166, 79),
+      Connection(79, 218),
+      Connection(218, 166),
+      Connection(155, 154),
+      Connection(154, 26),
+      Connection(26, 155),
+      Connection(209, 49),
+      Connection(49, 131),
+      Connection(131, 209),
+      Connection(135, 136),
+      Connection(136, 150),
+      Connection(150, 135),
+      Connection(47, 126),
+      Connection(126, 217),
+      Connection(217, 47),
+      Connection(223, 52),
+      Connection(52, 53),
+      Connection(53, 223),
+      Connection(45, 51),
+      Connection(51, 134),
+      Connection(134, 45),
+      Connection(211, 170),
+      Connection(170, 140),
+      Connection(140, 211),
+      Connection(67, 69),
+      Connection(69, 108),
+      Connection(108, 67),
+      Connection(43, 106),
+      Connection(106, 91),
+      Connection(91, 43),
+      Connection(230, 119),
+      Connection(119, 120),
+      Connection(120, 230),
+      Connection(226, 130),
+      Connection(130, 247),
+      Connection(247, 226),
+      Connection(63, 53),
+      Connection(53, 52),
+      Connection(52, 63),
+      Connection(238, 20),
+      Connection(20, 242),
+      Connection(242, 238),
+      Connection(46, 70),
+      Connection(70, 156),
+      Connection(156, 46),
+      Connection(78, 62),
+      Connection(62, 96),
+      Connection(96, 78),
+      Connection(46, 53),
+      Connection(53, 63),
+      Connection(63, 46),
+      Connection(143, 34),
+      Connection(34, 227),
+      Connection(227, 143),
+      Connection(123, 117),
+      Connection(117, 111),
+      Connection(111, 123),
+      Connection(44, 125),
+      Connection(125, 19),
+      Connection(19, 44),
+      Connection(236, 134),
+      Connection(134, 51),
+      Connection(51, 236),
+      Connection(216, 206),
+      Connection(206, 205),
+      Connection(205, 216),
+      Connection(154, 153),
+      Connection(153, 22),
+      Connection(22, 154),
+      Connection(39, 37),
+      Connection(37, 167),
+      Connection(167, 39),
+      Connection(200, 201),
+      Connection(201, 208),
+      Connection(208, 200),
+      Connection(36, 142),
+      Connection(142, 100),
+      Connection(100, 36),
+      Connection(57, 212),
+      Connection(212, 202),
+      Connection(202, 57),
+      Connection(20, 60),
+      Connection(60, 99),
+      Connection(99, 20),
+      Connection(28, 158),
+      Connection(158, 157),
+      Connection(157, 28),
+      Connection(35, 226),
+      Connection(226, 113),
+      Connection(113, 35),
+      Connection(160, 159),
+      Connection(159, 27),
+      Connection(27, 160),
+      Connection(204, 202),
+      Connection(202, 210),
+      Connection(210, 204),
+      Connection(113, 225),
+      Connection(225, 46),
+      Connection(46, 113),
+      Connection(43, 202),
+      Connection(202, 204),
+      Connection(204, 43),
+      Connection(62, 76),
+      Connection(76, 77),
+      Connection(77, 62),
+      Connection(137, 123),
+      Connection(123, 116),
+      Connection(116, 137),
+      Connection(41, 38),
+      Connection(38, 72),
+      Connection(72, 41),
+      Connection(203, 129),
+      Connection(129, 142),
+      Connection(142, 203),
+      Connection(64, 98),
+      Connection(98, 240),
+      Connection(240, 64),
+      Connection(49, 102),
+      Connection(102, 64),
+      Connection(64, 49),
+      Connection(41, 73),
+      Connection(73, 74),
+      Connection(74, 41),
+      Connection(212, 216),
+      Connection(216, 207),
+      Connection(207, 212),
+      Connection(42, 74),
+      Connection(74, 184),
+      Connection(184, 42),
+      Connection(169, 170),
+      Connection(170, 211),
+      Connection(211, 169),
+      Connection(170, 149),
+      Connection(149, 176),
+      Connection(176, 170),
+      Connection(105, 66),
+      Connection(66, 69),
+      Connection(69, 105),
+      Connection(122, 6),
+      Connection(6, 168),
+      Connection(168, 122),
+      Connection(123, 147),
+      Connection(147, 187),
+      Connection(187, 123),
+      Connection(96, 77),
+      Connection(77, 90),
+      Connection(90, 96),
+      Connection(65, 55),
+      Connection(55, 107),
+      Connection(107, 65),
+      Connection(89, 90),
+      Connection(90, 180),
+      Connection(180, 89),
+      Connection(101, 100),
+      Connection(100, 120),
+      Connection(120, 101),
+      Connection(63, 105),
+      Connection(105, 104),
+      Connection(104, 63),
+      Connection(93, 137),
+      Connection(137, 227),
+      Connection(227, 93),
+      Connection(15, 86),
+      Connection(86, 85),
+      Connection(85, 15),
+      Connection(129, 102),
+      Connection(102, 49),
+      Connection(49, 129),
+      Connection(14, 87),
+      Connection(87, 86),
+      Connection(86, 14),
+      Connection(55, 8),
+      Connection(8, 9),
+      Connection(9, 55),
+      Connection(100, 47),
+      Connection(47, 121),
+      Connection(121, 100),
+      Connection(145, 23),
+      Connection(23, 22),
+      Connection(22, 145),
+      Connection(88, 89),
+      Connection(89, 179),
+      Connection(179, 88),
+      Connection(6, 122),
+      Connection(122, 196),
+      Connection(196, 6),
+      Connection(88, 95),
+      Connection(95, 96),
+      Connection(96, 88),
+      Connection(138, 172),
+      Connection(172, 136),
+      Connection(136, 138),
+      Connection(215, 58),
+      Connection(58, 172),
+      Connection(172, 215),
+      Connection(115, 48),
+      Connection(48, 219),
+      Connection(219, 115),
+      Connection(42, 80),
+      Connection(80, 81),
+      Connection(81, 42),
+      Connection(195, 3),
+      Connection(3, 51),
+      Connection(51, 195),
+      Connection(43, 146),
+      Connection(146, 61),
+      Connection(61, 43),
+      Connection(171, 175),
+      Connection(175, 199),
+      Connection(199, 171),
+      Connection(81, 82),
+      Connection(82, 38),
+      Connection(38, 81),
+      Connection(53, 46),
+      Connection(46, 225),
+      Connection(225, 53),
+      Connection(144, 163),
+      Connection(163, 110),
+      Connection(110, 144),
+      Connection(52, 65),
+      Connection(65, 66),
+      Connection(66, 52),
+      Connection(229, 228),
+      Connection(228, 117),
+      Connection(117, 229),
+      Connection(34, 127),
+      Connection(127, 234),
+      Connection(234, 34),
+      Connection(107, 108),
+      Connection(108, 69),
+      Connection(69, 107),
+      Connection(109, 108),
+      Connection(108, 151),
+      Connection(151, 109),
+      Connection(48, 64),
+      Connection(64, 235),
+      Connection(235, 48),
+      Connection(62, 78),
+      Connection(78, 191),
+      Connection(191, 62),
+      Connection(129, 209),
+      Connection(209, 126),
+      Connection(126, 129),
+      Connection(111, 35),
+      Connection(35, 143),
+      Connection(143, 111),
+      Connection(117, 123),
+      Connection(123, 50),
+      Connection(50, 117),
+      Connection(222, 65),
+      Connection(65, 52),
+      Connection(52, 222),
+      Connection(19, 125),
+      Connection(125, 141),
+      Connection(141, 19),
+      Connection(221, 55),
+      Connection(55, 65),
+      Connection(65, 221),
+      Connection(3, 195),
+      Connection(195, 197),
+      Connection(197, 3),
+      Connection(25, 7),
+      Connection(7, 33),
+      Connection(33, 25),
+      Connection(220, 237),
+      Connection(237, 44),
+      Connection(44, 220),
+      Connection(70, 71),
+      Connection(71, 139),
+      Connection(139, 70),
+      Connection(122, 193),
+      Connection(193, 245),
+      Connection(245, 122),
+      Connection(247, 130),
+      Connection(130, 33),
+      Connection(33, 247),
+      Connection(71, 21),
+      Connection(21, 162),
+      Connection(162, 71),
+      Connection(170, 169),
+      Connection(169, 150),
+      Connection(150, 170),
+      Connection(188, 174),
+      Connection(174, 196),
+      Connection(196, 188),
+      Connection(216, 186),
+      Connection(186, 92),
+      Connection(92, 216),
+      Connection(2, 97),
+      Connection(97, 167),
+      Connection(167, 2),
+      Connection(141, 125),
+      Connection(125, 241),
+      Connection(241, 141),
+      Connection(164, 167),
+      Connection(167, 37),
+      Connection(37, 164),
+      Connection(72, 38),
+      Connection(38, 12),
+      Connection(12, 72),
+      Connection(38, 82),
+      Connection(82, 13),
+      Connection(13, 38),
+      Connection(63, 68),
+      Connection(68, 71),
+      Connection(71, 63),
+      Connection(226, 35),
+      Connection(35, 111),
+      Connection(111, 226),
+      Connection(101, 50),
+      Connection(50, 205),
+      Connection(205, 101),
+      Connection(206, 92),
+      Connection(92, 165),
+      Connection(165, 206),
+      Connection(209, 198),
+      Connection(198, 217),
+      Connection(217, 209),
+      Connection(165, 167),
+      Connection(167, 97),
+      Connection(97, 165),
+      Connection(220, 115),
+      Connection(115, 218),
+      Connection(218, 220),
+      Connection(133, 112),
+      Connection(112, 243),
+      Connection(243, 133),
+      Connection(239, 238),
+      Connection(238, 241),
+      Connection(241, 239),
+      Connection(214, 135),
+      Connection(135, 169),
+      Connection(169, 214),
+      Connection(190, 173),
+      Connection(173, 133),
+      Connection(133, 190),
+      Connection(171, 208),
+      Connection(208, 32),
+      Connection(32, 171),
+      Connection(125, 44),
+      Connection(44, 237),
+      Connection(237, 125),
+      Connection(86, 87),
+      Connection(87, 178),
+      Connection(178, 86),
+      Connection(85, 86),
+      Connection(86, 179),
+      Connection(179, 85),
+      Connection(84, 85),
+      Connection(85, 180),
+      Connection(180, 84),
+      Connection(83, 84),
+      Connection(84, 181),
+      Connection(181, 83),
+      Connection(201, 83),
+      Connection(83, 182),
+      Connection(182, 201),
+      Connection(137, 93),
+      Connection(93, 132),
+      Connection(132, 137),
+      Connection(76, 62),
+      Connection(62, 183),
+      Connection(183, 76),
+      Connection(61, 76),
+      Connection(76, 184),
+      Connection(184, 61),
+      Connection(57, 61),
+      Connection(61, 185),
+      Connection(185, 57),
+      Connection(212, 57),
+      Connection(57, 186),
+      Connection(186, 212),
+      Connection(214, 207),
+      Connection(207, 187),
+      Connection(187, 214),
+      Connection(34, 143),
+      Connection(143, 156),
+      Connection(156, 34),
+      Connection(79, 239),
+      Connection(239, 237),
+      Connection(237, 79),
+      Connection(123, 137),
+      Connection(137, 177),
+      Connection(177, 123),
+      Connection(44, 1),
+      Connection(1, 4),
+      Connection(4, 44),
+      Connection(201, 194),
+      Connection(194, 32),
+      Connection(32, 201),
+      Connection(64, 102),
+      Connection(102, 129),
+      Connection(129, 64),
+      Connection(213, 215),
+      Connection(215, 138),
+      Connection(138, 213),
+      Connection(59, 166),
+      Connection(166, 219),
+      Connection(219, 59),
+      Connection(242, 99),
+      Connection(99, 97),
+      Connection(97, 242),
+      Connection(2, 94),
+      Connection(94, 141),
+      Connection(141, 2),
+      Connection(75, 59),
+      Connection(59, 235),
+      Connection(235, 75),
+      Connection(24, 110),
+      Connection(110, 228),
+      Connection(228, 24),
+      Connection(25, 130),
+      Connection(130, 226),
+      Connection(226, 25),
+      Connection(23, 24),
+      Connection(24, 229),
+      Connection(229, 23),
+      Connection(22, 23),
+      Connection(23, 230),
+      Connection(230, 22),
+      Connection(26, 22),
+      Connection(22, 231),
+      Connection(231, 26),
+      Connection(112, 26),
+      Connection(26, 232),
+      Connection(232, 112),
+      Connection(189, 190),
+      Connection(190, 243),
+      Connection(243, 189),
+      Connection(221, 56),
+      Connection(56, 190),
+      Connection(190, 221),
+      Connection(28, 56),
+      Connection(56, 221),
+      Connection(221, 28),
+      Connection(27, 28),
+      Connection(28, 222),
+      Connection(222, 27),
+      Connection(29, 27),
+      Connection(27, 223),
+      Connection(223, 29),
+      Connection(30, 29),
+      Connection(29, 224),
+      Connection(224, 30),
+      Connection(247, 30),
+      Connection(30, 225),
+      Connection(225, 247),
+      Connection(238, 79),
+      Connection(79, 20),
+      Connection(20, 238),
+      Connection(166, 59),
+      Connection(59, 75),
+      Connection(75, 166),
+      Connection(60, 75),
+      Connection(75, 240),
+      Connection(240, 60),
+      Connection(147, 177),
+      Connection(177, 215),
+      Connection(215, 147),
+      Connection(20, 79),
+      Connection(79, 166),
+      Connection(166, 20),
+      Connection(187, 147),
+      Connection(147, 213),
+      Connection(213, 187),
+      Connection(112, 233),
+      Connection(233, 244),
+      Connection(244, 112),
+      Connection(233, 128),
+      Connection(128, 245),
+      Connection(245, 233),
+      Connection(128, 114),
+      Connection(114, 188),
+      Connection(188, 128),
+      Connection(114, 217),
+      Connection(217, 174),
+      Connection(174, 114),
+      Connection(131, 115),
+      Connection(115, 220),
+      Connection(220, 131),
+      Connection(217, 198),
+      Connection(198, 236),
+      Connection(236, 217),
+      Connection(198, 131),
+      Connection(131, 134),
+      Connection(134, 198),
+      Connection(177, 132),
+      Connection(132, 58),
+      Connection(58, 177),
+      Connection(143, 35),
+      Connection(35, 124),
+      Connection(124, 143),
+      Connection(110, 163),
+      Connection(163, 7),
+      Connection(7, 110),
+      Connection(228, 110),
+      Connection(110, 25),
+      Connection(25, 228),
+      Connection(356, 389),
+      Connection(389, 368),
+      Connection(368, 356),
+      Connection(11, 302),
+      Connection(302, 267),
+      Connection(267, 11),
+      Connection(452, 350),
+      Connection(350, 349),
+      Connection(349, 452),
+      Connection(302, 303),
+      Connection(303, 269),
+      Connection(269, 302),
+      Connection(357, 343),
+      Connection(343, 277),
+      Connection(277, 357),
+      Connection(452, 453),
+      Connection(453, 357),
+      Connection(357, 452),
+      Connection(333, 332),
+      Connection(332, 297),
+      Connection(297, 333),
+      Connection(175, 152),
+      Connection(152, 377),
+      Connection(377, 175),
+      Connection(347, 348),
+      Connection(348, 330),
+      Connection(330, 347),
+      Connection(303, 304),
+      Connection(304, 270),
+      Connection(270, 303),
+      Connection(9, 336),
+      Connection(336, 337),
+      Connection(337, 9),
+      Connection(278, 279),
+      Connection(279, 360),
+      Connection(360, 278),
+      Connection(418, 262),
+      Connection(262, 431),
+      Connection(431, 418),
+      Connection(304, 408),
+      Connection(408, 409),
+      Connection(409, 304),
+      Connection(310, 415),
+      Connection(415, 407),
+      Connection(407, 310),
+      Connection(270, 409),
+      Connection(409, 410),
+      Connection(410, 270),
+      Connection(450, 348),
+      Connection(348, 347),
+      Connection(347, 450),
+      Connection(422, 430),
+      Connection(430, 434),
+      Connection(434, 422),
+      Connection(313, 314),
+      Connection(314, 17),
+      Connection(17, 313),
+      Connection(306, 307),
+      Connection(307, 375),
+      Connection(375, 306),
+      Connection(387, 388),
+      Connection(388, 260),
+      Connection(260, 387),
+      Connection(286, 414),
+      Connection(414, 398),
+      Connection(398, 286),
+      Connection(335, 406),
+      Connection(406, 418),
+      Connection(418, 335),
+      Connection(364, 367),
+      Connection(367, 416),
+      Connection(416, 364),
+      Connection(423, 358),
+      Connection(358, 327),
+      Connection(327, 423),
+      Connection(251, 284),
+      Connection(284, 298),
+      Connection(298, 251),
+      Connection(281, 5),
+      Connection(5, 4),
+      Connection(4, 281),
+      Connection(373, 374),
+      Connection(374, 253),
+      Connection(253, 373),
+      Connection(307, 320),
+      Connection(320, 321),
+      Connection(321, 307),
+      Connection(425, 427),
+      Connection(427, 411),
+      Connection(411, 425),
+      Connection(421, 313),
+      Connection(313, 18),
+      Connection(18, 421),
+      Connection(321, 405),
+      Connection(405, 406),
+      Connection(406, 321),
+      Connection(320, 404),
+      Connection(404, 405),
+      Connection(405, 320),
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+
+
+@dataclasses.dataclass
+class FaceLandmarkerResult:
+  """The face landmarks detection result from FaceLandmarker, where each vector element represents a single face detected in the image.
+
+  Attributes:
+    face_landmarks: Detected face landmarks in normalized image coordinates.
+    face_blendshapes: Optional face blendshapes results.
+    facial_transformation_matrixes: Optional facial transformation matrix.
+  """
+
+  face_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  face_blendshapes: List[List[category_module.Category]]
+  facial_transformation_matrixes: List[np.ndarray]
+
+
+def _build_landmarker_result(
+    output_packets: Mapping[str, packet_module.Packet]
+) -> FaceLandmarkerResult:
+  """Constructs a `FaceLandmarkerResult` from output packets."""
+  face_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_NORM_LANDMARKS_STREAM_NAME]
+  )
+
+  face_landmarks_results = []
+  for proto in face_landmarks_proto_list:
+    face_landmarks = landmark_pb2.NormalizedLandmarkList()
+    face_landmarks.MergeFrom(proto)
+    face_landmarks_list = []
+    for face_landmark in face_landmarks.landmark:
+      face_landmarks_list.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
+      )
+    face_landmarks_results.append(face_landmarks_list)
+
+  face_blendshapes_results = []
+  if _BLENDSHAPES_STREAM_NAME in output_packets:
+    face_blendshapes_proto_list = packet_getter.get_proto_list(
+        output_packets[_BLENDSHAPES_STREAM_NAME]
+    )
+    for proto in face_blendshapes_proto_list:
+      face_blendshapes_categories = []
+      face_blendshapes_classifications = classification_pb2.ClassificationList()
+      face_blendshapes_classifications.MergeFrom(proto)
+      for face_blendshapes in face_blendshapes_classifications.classification:
+        face_blendshapes_categories.append(
+            category_module.Category(
+                index=face_blendshapes.index,
+                score=face_blendshapes.score,
+                display_name=face_blendshapes.display_name,
+                category_name=face_blendshapes.label,
+            )
+        )
+      face_blendshapes_results.append(face_blendshapes_categories)
+
+  facial_transformation_matrixes_results = []
+  if _FACE_GEOMETRY_STREAM_NAME in output_packets:
+    facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
+        output_packets[_FACE_GEOMETRY_STREAM_NAME]
+    )
+    for proto in facial_transformation_matrixes_proto_list:
+      if hasattr(proto, 'pose_transform_matrix'):
+        matrix_data = matrix_data_pb2.MatrixData()
+        matrix_data.MergeFrom(proto.pose_transform_matrix)
+        matrix = np.array(matrix_data.packed_data)
+        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
+        matrix = (
+            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
+        )
+        facial_transformation_matrixes_results.append(matrix)
+
+  return FaceLandmarkerResult(
+      face_landmarks_results,
+      face_blendshapes_results,
+      facial_transformation_matrixes_results,
+  )
+
+def _build_landmarker_result2(
+    output_packets: Mapping[str, packet_module.Packet]
+) -> FaceLandmarkerResult:
+  """Constructs a `FaceLandmarkerResult` from output packets."""
+  face_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_NORM_LANDMARKS_STREAM_NAME]
+  )
+
+  face_landmarks_results = []
+  for proto in face_landmarks_proto_list:
+    face_landmarks = landmark_pb2.NormalizedLandmarkList()
+    face_landmarks.MergeFrom(proto)
+    face_landmarks_list = []
+    for face_landmark in face_landmarks.landmark:
+      face_landmarks_list.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
+      )
+    face_landmarks_results.append(face_landmarks_list)
+
+  face_blendshapes_results = []
+  if _BLENDSHAPES_STREAM_NAME in output_packets:
+    face_blendshapes_proto_list = packet_getter.get_proto_list(
+        output_packets[_BLENDSHAPES_STREAM_NAME]
+    )
+    for proto in face_blendshapes_proto_list:
+      face_blendshapes_categories = []
+      face_blendshapes_classifications = classification_pb2.ClassificationList()
+      face_blendshapes_classifications.MergeFrom(proto)
+      for face_blendshapes in face_blendshapes_classifications.classification:
+        face_blendshapes_categories.append(
+            category_module.Category(
+                index=face_blendshapes.index,
+                score=face_blendshapes.score,
+                display_name=face_blendshapes.display_name,
+                category_name=face_blendshapes.label,
+            )
+        )
+      face_blendshapes_results.append(face_blendshapes_categories)
+
+  facial_transformation_matrixes_results = []
+  if _FACE_GEOMETRY_STREAM_NAME in output_packets:
+    facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
+        output_packets[_FACE_GEOMETRY_STREAM_NAME]
+    )
+    for proto in facial_transformation_matrixes_proto_list:
+      if hasattr(proto, 'pose_transform_matrix'):
+        matrix_data = matrix_data_pb2.MatrixData()
+        matrix_data.MergeFrom(proto.pose_transform_matrix)
+        matrix = np.array(matrix_data.packed_data)
+        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
+        matrix = (
+            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
+        )
+        facial_transformation_matrixes_results.append(matrix)
+
+  return FaceLandmarkerResult(
+      face_landmarks_results,
+      face_blendshapes_results,
+      facial_transformation_matrixes_results,
+  ), facial_transformation_matrixes_proto_list[0].mesh
+
+@dataclasses.dataclass
+class FaceLandmarkerOptions:
+  """Options for the face landmarker task.
+
+  Attributes:
+    base_options: Base options for the face landmarker task.
+    running_mode: The running mode of the task. Default to the image mode.
+      FaceLandmarker has three running modes: 1) The image mode for detecting
+      face landmarks on single image inputs. 2) The video mode for detecting
+      face landmarks on the decoded frames of a video. 3) The live stream mode
+      for detecting face landmarks on the live stream of input data, such as
+      from camera. In this mode, the "result_callback" below must be specified
+      to receive the detection results asynchronously.
+    num_faces: The maximum number of faces that can be detected by the
+      FaceLandmarker.
+    min_face_detection_confidence: The minimum confidence score for the face
+      detection to be considered successful.
+    min_face_presence_confidence: The minimum confidence score of face presence
+      score in the face landmark detection.
+    min_tracking_confidence: The minimum confidence score for the face tracking
+      to be considered successful.
+    output_face_blendshapes: Whether FaceLandmarker outputs face blendshapes
+      classification. Face blendshapes are used for rendering the 3D face model.
+    output_facial_transformation_matrixes: Whether FaceLandmarker outputs facial
+      transformation_matrix. Facial transformation matrix is used to transform
+      the face landmarks in canonical face to the detected face, so that users
+      can apply face effects on the detected landmarks.
+    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.
+  """
+
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  num_faces: int = 1
+  min_face_detection_confidence: float = 0.5
+  min_face_presence_confidence: float = 0.5
+  min_tracking_confidence: float = 0.5
+  output_face_blendshapes: bool = False
+  output_facial_transformation_matrixes: bool = False
+  result_callback: Optional[
+      Callable[[FaceLandmarkerResult, image_module.Image, int], None]
+  ] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _FaceLandmarkerGraphOptionsProto:
+    """Generates an FaceLandmarkerGraphOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = (
+        False if self.running_mode == _RunningMode.IMAGE else True
+    )
+
+    # Initialize the face landmarker options from base options.
+    face_landmarker_options_proto = _FaceLandmarkerGraphOptionsProto(
+        base_options=base_options_proto
+    )
+
+    # Configure face detector options.
+    face_landmarker_options_proto.face_detector_graph_options.num_faces = (
+        self.num_faces
+    )
+    face_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
+        self.min_face_detection_confidence
+    )
+
+    # Configure face landmark detector options.
+    face_landmarker_options_proto.min_tracking_confidence = (
+        self.min_tracking_confidence
+    )
+    face_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
+        self.min_face_detection_confidence
+    )
+    return face_landmarker_options_proto
+
+
+class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs face landmarks detection on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'FaceLandmarker':
+    """Creates an `FaceLandmarker` object from a TensorFlow Lite model and the default `FaceLandmarkerOptions`.
+
+    Note that the created `FaceLandmarker` instance is in image mode, for
+    detecting face landmarks on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `FaceLandmarker` object that's created from the model file and the
+      default `FaceLandmarkerOptions`.
+
+    Raises:
+      ValueError: If failed to create `FaceLandmarker` object from the
+        provided file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = FaceLandmarkerOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE
+    )
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(
+      cls, options: FaceLandmarkerOptions
+  ) -> 'FaceLandmarker':
+    """Creates the `FaceLandmarker` object from face landmarker options.
+
+    Args:
+      options: Options for the face landmarker task.
+
+    Returns:
+      `FaceLandmarker` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `FaceLandmarker` object from
+        `FaceLandmarkerOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+        empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME]
+        options.result_callback(
+            FaceLandmarkerResult([], [], []),
+            image,
+            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+        )
+        return
+
+      face_landmarks_result = _build_landmarker_result(output_packets)
+      timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp
+      options.result_callback(
+          face_landmarks_result,
+          image,
+          timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+      )
+
+    output_streams = [
+        ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_face_blendshapes:
+      output_streams.append(
+          ':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME])
+      )
+    if options.output_facial_transformation_matrixes:
+      output_streams.append(
+          ':'.join([_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME])
+      )
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
+        ],
+        output_streams=output_streams,
+        task_options=options,
+    )
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode
+            == _RunningMode.LIVE_STREAM
+        ),
+        options.running_mode,
+        packets_callback if options.result_callback else None,
+    )
+
+  def detect(
+      self,
+      image: image_module.Image,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> FaceLandmarkerResult:
+    """Performs face landmarks detection on the given image.
+
+    Only use this method when the FaceLandmarker is created with the image
+    running mode.
+
+    The image can be of any size with format RGB or RGBA.
+    TODO: Describes how the input image will be preprocessed after the yuv
+    support is implemented.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The face landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face landmarker detection failed to run.
+    """
+
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ),
+    })
+
+    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return FaceLandmarkerResult([], [], [])
+
+    return _build_landmarker_result2(output_packets)
+
+  def detect_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ):
+    """Performs face landmarks detection on the provided video frame.
+
+    Only use this method when the FaceLandmarker is created with the video
+    running mode.
+
+    Only use this method when the FaceLandmarker 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:
+      The face landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face landmarker detection failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
+        ),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+    })
+
+    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return FaceLandmarkerResult([], [], [])
+
+    return _build_landmarker_result2(output_packets)
+
+  def detect_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> None:
+    """Sends live image data to perform face landmarks detection.
+
+    The results will be available via the "result_callback" provided in the
+    FaceLandmarkerOptions. Only use this method when the FaceLandmarker is
+    created with the live stream running mode.
+
+    Only use this method when the FaceLandmarker 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 `FaceLandmarkerOptions`. The
+    `detect_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, face landmarker may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - The face landmarks detection results.
+      - The input image that the face landmarker 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
+      face landmarker has already processed.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
+        ),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+    })

media_pipe/mp_models/blaze_face_short_range.tflite

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+size 229746

media_pipe/mp_models/face_landmarker_v2_with_blendshapes.task

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+version https://git-lfs.github.com/spec/v1
+oid sha256:64184e229b263107bc2b804c6625db1341ff2bb731874b0bcc2fe6544e0bc9ff
+size 3758596

media_pipe/mp_models/pose_landmarker_heavy.task

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64437af838a65d18e5ba7a0d39b465540069bc8aae8308de3e318aad31fcbc7b
+size 30664242

media_pipe/mp_utils.py

@@ -0,0 +1,95 @@
+import os
+import numpy as np
+import cv2
+import time
+from tqdm import tqdm
+import multiprocessing
+import glob
+
+import mediapipe as mp
+from mediapipe import solutions
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+from . import face_landmark
+
+CUR_DIR = os.path.dirname(__file__)
+
+
+class LMKExtractor():
+    def __init__(self, FPS=25):
+        # Create an FaceLandmarker object.
+        self.mode = mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE
+        base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/face_landmarker_v2_with_blendshapes.task'))
+        base_options.delegate = mp.tasks.BaseOptions.Delegate.CPU
+        options = vision.FaceLandmarkerOptions(base_options=base_options,
+                                            running_mode=self.mode,
+                                            output_face_blendshapes=True,
+                                            output_facial_transformation_matrixes=True,
+                                            num_faces=1)
+        self.detector = face_landmark.FaceLandmarker.create_from_options(options)
+        self.last_ts = 0
+        self.frame_ms = int(1000 / FPS)
+
+        det_base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/blaze_face_short_range.tflite'))
+        det_options = vision.FaceDetectorOptions(base_options=det_base_options)
+        self.det_detector = vision.FaceDetector.create_from_options(det_options)
+                
+
+    def __call__(self, img):
+        frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
+        t0 = time.time()
+        if self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.VIDEO:
+            det_result = self.det_detector.detect(image)
+            if len(det_result.detections) != 1:
+                return None
+            self.last_ts += self.frame_ms
+            try:
+                detection_result, mesh3d = self.detector.detect_for_video(image, timestamp_ms=self.last_ts)
+            except:
+                return None
+        elif self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE:
+            # det_result = self.det_detector.detect(image)
+
+            # if len(det_result.detections) != 1:
+            #     return None
+            try:
+                detection_result, mesh3d = self.detector.detect(image)
+            except:
+                return None
+            
+        
+        bs_list = detection_result.face_blendshapes
+        if len(bs_list) == 1:
+            bs = bs_list[0]
+            bs_values = []
+            for index in range(len(bs)):
+                bs_values.append(bs[index].score)
+            bs_values = bs_values[1:] # remove neutral
+            trans_mat = detection_result.facial_transformation_matrixes[0]
+            face_landmarks_list = detection_result.face_landmarks
+            face_landmarks = face_landmarks_list[0]
+            lmks = []
+            for index in range(len(face_landmarks)):
+                x = face_landmarks[index].x
+                y = face_landmarks[index].y
+                z = face_landmarks[index].z
+                lmks.append([x, y, z])
+            lmks = np.array(lmks)
+            
+            lmks3d = np.array(mesh3d.vertex_buffer)
+            lmks3d = lmks3d.reshape(-1, 5)[:, :3]
+            mp_tris = np.array(mesh3d.index_buffer).reshape(-1, 3) + 1
+
+            return {
+                "lmks": lmks,
+                'lmks3d': lmks3d,
+                "trans_mat": trans_mat,
+                'faces': mp_tris,
+                "bs": bs_values
+            }
+        else:
+            # print('multiple faces in the image: {}'.format(img_path))
+            return None
+        

media_pipe/pose_util.py

@@ -0,0 +1,78 @@
+import math
+
+import numpy as np
+from scipy.spatial.transform import Rotation as R
+
+
+def create_perspective_matrix(aspect_ratio):
+    kDegreesToRadians = np.pi / 180.
+    near = 1
+    far = 10000
+    perspective_matrix = np.zeros(16, dtype=np.float32)
+
+    # Standard perspective projection matrix calculations.
+    f = 1.0 / np.tan(kDegreesToRadians * 63 / 2.)
+
+    denom = 1.0 / (near - far)
+    perspective_matrix[0] = f / aspect_ratio
+    perspective_matrix[5] = f
+    perspective_matrix[10] = (near + far) * denom
+    perspective_matrix[11] = -1.
+    perspective_matrix[14] = 1. * far * near * denom
+
+    # If the environment's origin point location is in the top left corner,
+    # then skip additional flip along Y-axis is required to render correctly.
+
+    perspective_matrix[5] *= -1.
+    return perspective_matrix
+
+
+def project_points(points_3d, transformation_matrix, pose_vectors, image_shape):
+    P = create_perspective_matrix(image_shape[1] / image_shape[0]).reshape(4, 4).T
+    L, N, _ = points_3d.shape
+    projected_points = np.zeros((L, N, 2))
+    for i in range(L):
+        points_3d_frame = points_3d[i]
+        ones = np.ones((points_3d_frame.shape[0], 1))
+        points_3d_homogeneous = np.hstack([points_3d_frame, ones])  
+        transformed_points = points_3d_homogeneous @ (transformation_matrix @ euler_and_translation_to_matrix(pose_vectors[i][:3], pose_vectors[i][3:])).T @ P
+        projected_points_frame = transformed_points[:, :2] / transformed_points[:, 3, np.newaxis] # -1 ~ 1
+        projected_points_frame[:, 0] = (projected_points_frame[:, 0] + 1) * 0.5 * image_shape[1] 
+        projected_points_frame[:, 1]  = (projected_points_frame[:, 1] + 1) * 0.5 * image_shape[0]
+        projected_points[i] = projected_points_frame
+    return projected_points
+
+
+def project_points_with_trans(points_3d, transformation_matrix, image_shape):
+    P = create_perspective_matrix(image_shape[1] / image_shape[0]).reshape(4, 4).T
+    L, N, _ = points_3d.shape
+    projected_points = np.zeros((L, N, 2))
+    for i in range(L):
+        points_3d_frame = points_3d[i]
+        ones = np.ones((points_3d_frame.shape[0], 1))
+        points_3d_homogeneous = np.hstack([points_3d_frame, ones])  
+        transformed_points = points_3d_homogeneous @ transformation_matrix[i].T @ P
+        projected_points_frame = transformed_points[:, :2] / transformed_points[:, 3, np.newaxis] # -1 ~ 1
+        projected_points_frame[:, 0] = (projected_points_frame[:, 0] + 1) * 0.5 * image_shape[1] 
+        projected_points_frame[:, 1]  = (projected_points_frame[:, 1] + 1) * 0.5 * image_shape[0]
+        projected_points[i] = projected_points_frame
+    return projected_points
+
+
+def euler_and_translation_to_matrix(euler_angles, translation_vector):
+    rotation = R.from_euler('xyz', euler_angles, degrees=True)
+    rotation_matrix = rotation.as_matrix()
+    
+    matrix = np.eye(4)
+    matrix[:3, :3] = rotation_matrix
+    matrix[:3, 3] = translation_vector
+    
+    return matrix
+
+
+def matrix_to_euler_and_translation(matrix):
+    rotation_matrix = matrix[:3, :3]
+    translation_vector = matrix[:3, 3]
+    rotation = R.from_matrix(rotation_matrix)
+    euler_angles = rotation.as_euler('xyz', degrees=True)
+    return euler_angles, translation_vector