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import mediapipe as mp
import gradio as gr
import cv2
import torch
# Images
torch.hub.download_url_to_file('https://i.imgur.com/9koaC6b.png', 'pose.png')
import mediapipe as mp
mp_holistic = mp.solutions.holistic
# Prepare DrawingSpec for drawing the face landmarks later.
mp_drawing = mp.solutions.drawing_utils
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
def inference(image):
# Run MediaPipe Holistic and draw pose landmarks.
with mp_holistic.Holistic(static_image_mode=True, min_detection_confidence=0.5, model_complexity=2) as holistic:
# Convert the BGR image to RGB and process it with MediaPipe Pose.
results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
# Print nose coordinates.
image_hight, image_width, _ = image.shape
if results.pose_landmarks:
print(
f'Nose coordinates: ('
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_hight})'
)
# Draw pose landmarks.
annotated_image = image.copy()
mp_drawing.draw_landmarks(annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
mp_drawing.draw_landmarks(annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS)
mp_drawing.draw_landmarks(
image=annotated_image,
landmark_list=results.face_landmarks,
connections=mp_holistic.FACEMESH_TESSELATION,
landmark_drawing_spec=drawing_spec,
connection_drawing_spec=drawing_spec)
mp_drawing.draw_landmarks(
image=annotated_image,
landmark_list=results.pose_landmarks,
connections=mp_holistic.POSE_CONNECTIONS,
landmark_drawing_spec=drawing_spec,
connection_drawing_spec=drawing_spec)
return annotated_image
title = "Holistic Tracking"
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."
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>"
gr.Interface(
inference,
[gr.inputs.Image(label="Input")],
gr.outputs.Image(type="pil", label="Output"),
title=title,
description=description,
article=article,
examples=[
["pose.png"]
]
).launch() |