app.py

import streamlit as st
import cv2
import mediapipe as mp
import numpy as np
from PIL import Image

# MediaPipe setup
mp_face_mesh = mp.solutions.face_mesh
mp_drawing = mp.solutions.drawing_utils
grid_color = (0, 255, 0)  # Green color
drawing_spec = mp_drawing.DrawingSpec(color=grid_color, thickness=1, circle_radius=1)

st.title("MediaPipe Face Mesh Detection")

# Upload image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_file is not None:
    # Read the image file
    image = Image.open(uploaded_file).convert("RGB")
    image = np.array(image)

    # Convert RGB to BGR for MediaPipe
    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    # Initialize MediaPipe FaceMesh
    with mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh:
        results = face_mesh.process(image_bgr)

        # Draw landmarks
        if results.multi_face_landmarks:
            for face_landmarks in results.multi_face_landmarks:
                mp_drawing.draw_landmarks(
                    image=image_bgr,
                    landmark_list=face_landmarks,
                    connections=mp_face_mesh.FACEMESH_TESSELATION,
                    landmark_drawing_spec=drawing_spec,
                    connection_drawing_spec=drawing_spec,
                )

    # Convert the image back to RGB for display in Streamlit
    image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
    st.image(image_rgb, channels="RGB", caption="Processed Image")