模型與流程

上一篇文章介紹了MediaPipe中手勢關鍵點檢測與簡單的手勢識別，本文介紹如何試用MediaPipe實現人臉3D點云數據提取，提取的數據為人臉468點位。

整個流程如下：

輸的468點是3D坐標，值在0~1之間，其中z表示深度，Python函數支持下面的參數配置：

max_num_faces 默認為1，表示支持最大人臉檢測數目
min_detection_confidence 最小檢測置信度，默認0.5
min_tracking_confidence 最小跟蹤置信度，默認0.5

人臉3D點云提取

基于MediaPipe的python版本函數，在官方教程的基礎上，我稍微修改了一下，代碼如下：

import?cv2
import?mediapipe?as?mp
mp_drawing?=?mp.solutions.drawing_utils
mp_face_mesh?=?mp.solutions.face_mesh


#?For?webcam?input:
drawing_spec?=?mp_drawing.DrawingSpec(thickness=1,?circle_radius=1)
cap?=?cv2.VideoCapture("D:/images/video/face_mesh.mp4")
with?mp_face_mesh.FaceMesh(
????max_num_faces=4,
????min_detection_confidence=0.5,
????min_tracking_confidence=0.5)?as?face_mesh:
??while?cap.isOpened():
????success,?frame?=?cap.read()
????if?not?success:
??????print("Ignoring?empty?camera?frame.")
??????#?If?loading?a?video,?use?'break'?instead?of?'continue'.
??????break

????h,?w,?c?=?frame.shape
????image?=?cv2.resize(frame,?(w?//2,?h//2))
????#?To?improve?performance,?optionally?mark?the?image?as?not?writeable?to
????#?pass?by?reference.
????image?=?cv2.cvtColor(image,?cv2.COLOR_BGR2RGB)
????results?=?face_mesh.process(image)

????#?Draw?the?face?mesh?annotations?on?the?image.
????image?=?cv2.cvtColor(image,?cv2.COLOR_RGB2BGR)
????if?results.multi_face_landmarks:
??????for?face_landmarks?in?results.multi_face_landmarks:
????????mp_drawing.draw_landmarks(
????????????image=image,
????????????landmark_list=face_landmarks)
????????mp_drawing.draw_landmarks(
????????????image=image,
????????????landmark_list=face_landmarks)
????????mp_drawing.draw_landmarks(
????????????image=image,
????????????landmark_list=face_landmarks
????????)
????#?Flip?the?image?horizontally?for?a?selfie-view?display.
????cv2.imshow('MediaPipe?Face?Mesh',?image)
????if?cv2.waitKey(5)?&?0xFF?==?27:
??????break
cap.release()

運行結果如下：

編輯：黃飛

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