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import mediapipe as mp |
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import gradio as gr |
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import cv2 |
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import torch |
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torch.hub.download_url_to_file('https://i.imgur.com/9koaC6b.png', 'pose.png') |
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import mediapipe as mp |
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mp_holistic = mp.solutions.holistic |
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mp_drawing = mp.solutions.drawing_utils |
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1) |
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def inference(image): |
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with mp_holistic.Holistic(static_image_mode=True, min_detection_confidence=0.5, model_complexity=2) as holistic: |
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results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) |
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image_hight, image_width, _ = image.shape |
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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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annotated_image = image.copy() |
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mp_drawing.draw_landmarks(annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) |
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mp_drawing.draw_landmarks(annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) |
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mp_drawing.draw_landmarks( |
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image=annotated_image, |
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landmark_list=results.face_landmarks, |
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connections=mp_holistic.FACEMESH_TESSELATION, |
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landmark_drawing_spec=drawing_spec, |
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connection_drawing_spec=drawing_spec) |
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mp_drawing.draw_landmarks( |
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image=annotated_image, |
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landmark_list=results.pose_landmarks, |
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connections=mp_holistic.POSE_CONNECTIONS, |
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landmark_drawing_spec=drawing_spec, |
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connection_drawing_spec=drawing_spec) |
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return annotated_image |
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title = "Holistic Tracking" |
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description = "Gradio demo for Holistic Tracking, Simultaneous and semantically consistent tracking of 33 pose, 21 per-hand, and 468 facial landmarks. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." |
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article = "<p style='text-align: center'><a href='https://ai.googleblog.com/2020/12/mediapipe-holistic-simultaneous-face.html'>MediaPipe Holistic — Simultaneous Face, Hand and Pose Prediction, on Device</a> | <a href='https://github.com/google/mediapipe'>Github Repo</a></p>" |
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gr.Interface( |
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inference, |
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[gr.inputs.Image(label="Input")], |
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gr.outputs.Image(type="pil", label="Output"), |
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title=title, |
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description=description, |
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article=article, |
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examples=[ |
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["pose.png"] |
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] |
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).launch() |
