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import gradio as gr
import cv2
import mediapipe as mp
import numpy as np
# Initialize Mediapipe Pose Estimation
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=True, model_complexity=2)
mp_drawing = mp.solutions.drawing_utils
def estimate_pose(image):
# Convert image from BGR (OpenCV) to RGB
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Perform pose detection
results = pose.process(image_rgb)
if not results.pose_landmarks:
return image # No pose found, return the original image
# Draw pose landmarks on the image
annotated_image = image.copy()
mp_drawing.draw_landmarks(
annotated_image,
results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=2),
connection_drawing_spec=mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2),
)
return annotated_image
# Gradio Interface
interface = gr.Interface(
fn=estimate_pose,
inputs=gr.Image(type="numpy", label="Upload an Image"),
outputs=gr.Image(type="numpy", label="Pose Landmarks Image"),
title="Human Pose Estimation",
description="Upload an image to detect and visualize human pose landmarks.",
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch()
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