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import gradio as gr
import cv2
import numpy as np
import mediapipe as mp
# Initialize MediaPipe Pose
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(static_image_mode=True)
mp_drawing = mp.solutions.drawing_utils
mp_pose_landmark = mp_pose.PoseLandmark
def detect_pose(image):
# Convert to RGB
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Run pose detection
result = pose.process(image_rgb)
keypoints = {}
if result.pose_landmarks:
# Draw landmarks on image
mp_drawing.draw_landmarks(image, result.pose_landmarks, mp_pose.POSE_CONNECTIONS)
# Get image dimensions
height, width, _ = image.shape
# Extract specific landmarks
landmark_indices = {
'left_shoulder': mp_pose_landmark.LEFT_SHOULDER,
'right_shoulder': mp_pose_landmark.RIGHT_SHOULDER,
'left_hip': mp_pose_landmark.LEFT_HIP,
'right_hip': mp_pose_landmark.RIGHT_HIP
}
for name, index in landmark_indices.items():
lm = result.pose_landmarks.landmark[index]
x, y = int(lm.x * width), int(lm.y * height)
keypoints[name] = (x, y)
# Draw a circle + label for debug
cv2.circle(image, (x, y), 5, (0, 255, 0), -1)
cv2.putText(image, name, (x + 5, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
return image, keypoints
# Gradio interface
iface = gr.Interface(
fn=detect_pose,
inputs=gr.Image(type="numpy", label="Upload Full-Body Image"),
outputs=[
gr.Image(type="numpy", label="Pose Visualization"),
gr.JSON(label="Extracted Keypoints")
],
title="Virtual Try-On - Pose Detection",
description="Detects body keypoints using MediaPipe Pose and visualizes them. Shoulders and hips are labeled."
)
if __name__ == "__main__":
iface.launch() |