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MediaPipe Team
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@@ -201,8 +201,8 @@ A list of 21 hand landmarks on the right hand, in the same representation as
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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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@@ -219,74 +219,75 @@ mp_drawing = mp.solutions.drawing_utils
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mp_holistic = mp.solutions.holistic
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# For static images:
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holistic = mp_holistic.Holistic(static_image_mode=True)
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for idx, file in enumerate(file_list):
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image = cv2.imread(file)
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image_hight, image_width, _ = image.shape
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# Convert the BGR image to RGB before processing.
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results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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with mp_holistic.Holistic(static_image_mode=True) as holistic:
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for idx, file in enumerate(file_list):
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image = cv2.imread(file)
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image_height, image_width, _ = image.shape
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# Convert the BGR image to RGB before processing.
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results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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if results.pose_landmarks:
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print(
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f'Nose coordinates: ('
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f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
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f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_hight})'
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)
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# Draw pose, left and right hands, and face landmarks on the image.
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annotated_image = image.copy()
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mp_drawing.draw_landmarks(
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annotated_image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
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mp_drawing.draw_landmarks(
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annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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mp_drawing.draw_landmarks(
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annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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mp_drawing.draw_landmarks(
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annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
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cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
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holistic.close()
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if results.pose_landmarks:
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print(
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f'Nose coordinates: ('
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f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
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f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'
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)
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# Draw pose, left and right hands, and face landmarks on the image.
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annotated_image = image.copy()
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mp_drawing.draw_landmarks(
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annotated_image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
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mp_drawing.draw_landmarks(
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annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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mp_drawing.draw_landmarks(
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annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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# Use mp_holistic.UPPER_BODY_POSE_CONNECTIONS for drawing below when
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# upper_body_only is set to True.
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mp_drawing.draw_landmarks(
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annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
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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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holistic = mp_holistic.Holistic(
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min_detection_confidence=0.5, min_tracking_confidence=0.5)
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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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with mp_holistic.Holistic(
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5) as holistic:
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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 = holistic.process(image)
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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 = holistic.process(image)
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# Draw landmark annotation 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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mp_drawing.draw_landmarks(
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image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
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mp_drawing.draw_landmarks(
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image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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mp_drawing.draw_landmarks(
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image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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mp_drawing.draw_landmarks(
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image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
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cv2.imshow('MediaPipe Holistic', image)
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if cv2.waitKey(5) & 0xFF == 27:
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break
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holistic.close()
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# Draw landmark annotation 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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mp_drawing.draw_landmarks(
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image, results.face_landmarks, mp_holistic.FACE_CONNECTIONS)
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mp_drawing.draw_landmarks(
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image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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mp_drawing.draw_landmarks(
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image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
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mp_drawing.draw_landmarks(
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image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS)
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cv2.imshow('MediaPipe Holistic', 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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@@ -407,7 +408,5 @@ on how to build MediaPipe examples.
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* Google AI Blog:
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[MediaPipe Holistic - Simultaneous Face, Hand and Pose Prediction, on Device](https://ai.googleblog.com/2020/12/mediapipe-holistic-simultaneous-face.html)
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* [Models and model cards](./models.md#holistic)
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[Colab]:https://mediapipe.page.link/holistic_py_colab
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[web demo]:https://code.mediapipe.dev/codepen/holistic
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* [Web demo](https://code.mediapipe.dev/codepen/holistic)
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* [Python Colab](https://mediapipe.page.link/holistic_py_colab)
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