Initial commit

app.py

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+import streamlit as st
+import mediapipe as mp
+import cv2
+from PIL import Image
+import numpy as np
+
+# Initialize MediaPipe Face Mesh
+mp_face_mesh = mp.solutions.face_mesh
+mp_drawing = mp.solutions.drawing_utils
+drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
+
+# Face Mesh inference
+def process_image(image):
+    with mp_face_mesh.FaceMesh(
+        static_image_mode=True,
+        max_num_faces=2,
+        min_detection_confidence=0.5) as face_mesh:
+        # Convert the image to RGB for MediaPipe processing
+        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        results = face_mesh.process(rgb_image)
+
+        # Annotate the image
+        annotated_image = image.copy()
+        if results.multi_face_landmarks:
+            for face_landmarks in results.multi_face_landmarks:
+                mp_drawing.draw_landmarks(
+                    image=annotated_image,
+                    landmark_list=face_landmarks,
+                    connections=mp_face_mesh.FACEMESH_TESSELATION,
+                    landmark_drawing_spec=drawing_spec,
+                    connection_drawing_spec=drawing_spec
+                )
+        return annotated_image
+
+# Streamlit interface
+st.title("Face Mesh with MediaPipe")
+st.write("""
+This app uses MediaPipe's Face Mesh to detect facial landmarks. 
+Upload an image to get started.
+""")
+
+# Image upload
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    image = np.array(Image.open(uploaded_file))
+
+    # Process and display results
+    processed_image = process_image(image)
+    st.image(cv2.cvtColor(processed_image, cv2.COLOR_BGR2RGB), caption="Annotated Image", use_container_width=True)