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import cv2
import mediapipe as mp
import numpy as np
import gradio as gr
# Initialize MediaPipe Pose
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(
static_image_mode=False,
model_complexity=1,
enable_segmentation=False,
min_detection_confidence=0.5,
min_tracking_confidence=0.5
)
# Initialize MediaPipe Face Mesh
mp_face_mesh = mp.solutions.face_mesh
face_mesh = mp_face_mesh.FaceMesh(
static_image_mode=False,
max_num_faces=1,
refine_landmarks=True,
min_detection_confidence=0.5,
min_tracking_confidence=0.5
)
def process_frame(image):
"""
Processes a frame by:
1. Converting RGB to BGR for OpenCV.
2. Flipping the frame for a mirror view.
3. Creating a black background.
4. Drawing body landmarks and computing shoulder center.
5. Drawing facial mesh and extracting chin point.
6. Drawing a neck line from shoulder center to chin.
7. Converting the result back to RGB.
"""
frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
frame = cv2.flip(frame, 1)
output = np.zeros_like(frame)
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# --- Body Posture Analysis ---
pose_results = pose.process(rgb_frame)
shoulder_center = None
if pose_results.pose_landmarks:
h, w, _ = frame.shape
landmarks = [(int(lm.x * w), int(lm.y * h)) for lm in pose_results.pose_landmarks.landmark]
for connection in mp_pose.POSE_CONNECTIONS:
start_idx, end_idx = connection
if start_idx >= 11 and end_idx >= 11:
if start_idx < len(landmarks) and end_idx < len(landmarks):
cv2.line(output, landmarks[start_idx], landmarks[end_idx], (255, 255, 0), 2)
for i, pt in enumerate(landmarks):
if i >= 11:
cv2.circle(output, pt, 3, (255, 255, 0), -1)
if len(landmarks) > 12:
left_shoulder, right_shoulder = landmarks[11], landmarks[12]
shoulder_center = ((left_shoulder[0] + right_shoulder[0]) // 2,
(left_shoulder[1] + right_shoulder[1]) // 2)
cv2.circle(output, shoulder_center, 4, (0, 255, 255), -1)
# --- Facial Mesh Analysis ---
chin_point = None
fm_results = face_mesh.process(rgb_frame)
if fm_results.multi_face_landmarks:
for face_landmarks in fm_results.multi_face_landmarks:
h, w, _ = frame.shape
fm_points = [(int(lm.x * w), int(lm.y * h)) for lm in face_landmarks.landmark]
for connection in mp_face_mesh.FACEMESH_TESSELATION:
start_idx, end_idx = connection
if start_idx < len(fm_points) and end_idx < len(fm_points):
cv2.line(output, fm_points[start_idx], fm_points[end_idx], (0, 0, 255), 1)
for pt in fm_points:
cv2.circle(output, pt, 2, (0, 255, 0), -1)
if len(face_landmarks.landmark) > 152:
lm = face_landmarks.landmark[152]
chin_point = (int(lm.x * w), int(lm.y * h))
cv2.circle(output, chin_point, 4, (0, 0, 255), -1)
break # Process only the first detected face.
# --- Draw Neck Line ---
if shoulder_center and chin_point:
cv2.line(output, shoulder_center, chin_point, (0, 255, 255), 2)
return cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
# --- Gradio Interface for Live Webcam Inference ---
iface = gr.Interface(
fn=process_frame,
inputs=gr.Image(sources=["webcam"], streaming=True, label="Webcam Input"), # Live webcam stream
outputs=gr.Image(type="numpy", label="Processed Output"),
live=True,
title="Live Body Posture & Neck Analysis (No Face Pose)",
description="Real-time webcam analysis using MediaPipe Pose and Face Mesh with live inference."
)
iface.launch()
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