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MediaPipe Team
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@@ -185,8 +185,8 @@ following steps are executed in the given order:
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The geometry pipeline is implemented as a MediaPipe
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[calculator](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc).
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For your convenience, the face geometry pipeline calculator is bundled together
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with the face landmark module into a unified MediaPipe
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[subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_front_gpu.pbtxt).
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with corresponding metadata into a unified MediaPipe
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[subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt).
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The face geometry format is defined as a Protocol Buffer
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[message](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/face_geometry.proto).
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@@ -264,8 +264,8 @@ magnitude of `z` uses roughly the same scale as `x`.
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### Python Solution API
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Please first follow general [instructions](../getting_started/python.md) to
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install MediaPipe Python package, then learn more in the companion [Colab] and
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the following usage example.
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install MediaPipe Python package, then learn more in the companion
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[Python Colab](#resources) and the following usage example.
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Supported configuration options:
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@@ -281,74 +281,73 @@ mp_drawing = mp.solutions.drawing_utils
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mp_face_mesh = mp.solutions.face_mesh
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# For static images:
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face_mesh = mp_face_mesh.FaceMesh(
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
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with mp_face_mesh.FaceMesh(
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static_image_mode=True,
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max_num_faces=1,
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min_detection_confidence=0.5)
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
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for idx, file in enumerate(file_list):
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image = cv2.imread(file)
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# Convert the BGR image to RGB before processing.
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results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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min_detection_confidence=0.5) as face_mesh:
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for idx, file in enumerate(file_list):
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image = cv2.imread(file)
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# Convert the BGR image to RGB before processing.
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results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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# Print and draw face mesh landmarks on the image.
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if not results.multi_face_landmarks:
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continue
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annotated_image = image.copy()
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for face_landmarks in results.multi_face_landmarks:
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print('face_landmarks:', face_landmarks)
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mp_drawing.draw_landmarks(
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image=annotated_image,
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landmark_list=face_landmarks,
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connections=mp_face_mesh.FACE_CONNECTIONS,
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landmark_drawing_spec=drawing_spec,
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connection_drawing_spec=drawing_spec)
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cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
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face_mesh.close()
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# For webcam input:
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face_mesh = mp_face_mesh.FaceMesh(
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min_detection_confidence=0.5, min_tracking_confidence=0.5)
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
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cap = cv2.VideoCapture(0)
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while cap.isOpened():
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success, image = cap.read()
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if not success:
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print("Ignoring empty camera frame.")
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# If loading a video, use 'break' instead of 'continue'.
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continue
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# Flip the image horizontally for a later selfie-view display, and convert
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# the BGR image to RGB.
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image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
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# To improve performance, optionally mark the image as not writeable to
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# pass by reference.
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image.flags.writeable = False
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results = face_mesh.process(image)
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# Draw the face mesh annotations on the image.
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image.flags.writeable = True
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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if results.multi_face_landmarks:
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# Print and draw face mesh landmarks on the image.
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if not results.multi_face_landmarks:
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continue
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annotated_image = image.copy()
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for face_landmarks in results.multi_face_landmarks:
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print('face_landmarks:', face_landmarks)
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mp_drawing.draw_landmarks(
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image=image,
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image=annotated_image,
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landmark_list=face_landmarks,
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connections=mp_face_mesh.FACE_CONNECTIONS,
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landmark_drawing_spec=drawing_spec,
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connection_drawing_spec=drawing_spec)
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cv2.imshow('MediaPipe FaceMesh', image)
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if cv2.waitKey(5) & 0xFF == 27:
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break
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face_mesh.close()
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cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
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# For webcam input:
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
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cap = cv2.VideoCapture(0)
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with mp_face_mesh.FaceMesh(
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5) as face_mesh:
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while cap.isOpened():
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success, image = cap.read()
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if not success:
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print("Ignoring empty camera frame.")
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# If loading a video, use 'break' instead of 'continue'.
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continue
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# Flip the image horizontally for a later selfie-view display, and convert
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# the BGR image to RGB.
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image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
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# To improve performance, optionally mark the image as not writeable to
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# pass by reference.
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image.flags.writeable = False
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results = face_mesh.process(image)
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# Draw the face mesh annotations on the image.
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image.flags.writeable = True
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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if results.multi_face_landmarks:
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for face_landmarks in results.multi_face_landmarks:
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mp_drawing.draw_landmarks(
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image=image,
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landmark_list=face_landmarks,
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connections=mp_face_mesh.FACE_CONNECTIONS,
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landmark_drawing_spec=drawing_spec,
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connection_drawing_spec=drawing_spec)
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cv2.imshow('MediaPipe FaceMesh', image)
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if cv2.waitKey(5) & 0xFF == 27:
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break
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cap.release()
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```
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### JavaScript Solution API
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Please first see general [introduction](../getting_started/javascript.md) on
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MediaPipe in JavaScript, then learn more in the companion [web demo] and the
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following usage example.
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MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
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and the following usage example.
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Supported configuration options:
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@@ -503,7 +502,5 @@ only works for a single face. For visual reference, please refer to *Fig. 4*.
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[OBJ](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/data/canonical_face_model.obj),
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[UV visualization](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png)
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* [Models and model cards](./models.md#face_mesh)
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[Colab]:https://mediapipe.page.link/face_mesh_py_colab
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[web demo]:https://code.mediapipe.dev/codepen/face_mesh
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* [Web demo](https://code.mediapipe.dev/codepen/face_mesh)
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* [Python Colab](https://mediapipe.page.link/face_mesh_py_colab)
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