diff --git a/face detection mediapipe b/face detection mediapipe
new file mode 100644
index 0000000..afd361e
--- /dev/null
+++ b/face detection mediapipe	
@@ -0,0 +1,91 @@
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_holistic = mp.solutions.holistic
+
+# For static images:
+IMAGE_FILES = []
+BG_COLOR = (192, 192, 192) # gray
+with mp_holistic.Holistic(
+    static_image_mode=False,
+    model_complexity=2,
+    enable_segmentation=True,
+    smooth_segmentation=True,
+    refine_face_landmarks=True) as holistic:
+  for idx, file in enumerate(IMAGE_FILES):
+    image = cv2.imread(file)
+    image_height, image_width, _ = image.shape
+    # Convert the BGR image to RGB before processing.
+    results = holistic.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    if results.pose_landmarks:
+      print(
+          f'Nose coordinates: ('
+          f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].x * image_width}, '
+          f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'
+      )
+
+    annotated_image = image.copy()
+    # Draw segmentation on the image.
+    # To improve segmentation around boundaries, consider applying a joint
+    # bilateral filter to "results.segmentation_mask" with "image".
+    condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
+    bg_image = np.zeros(image.shape, dtype=np.uint8)
+    bg_image[:] = BG_COLOR
+    annotated_image = np.where(condition, annotated_image, bg_image)
+    # Draw pose, left and right hands, and face landmarks on the image.
+    mp_drawing.draw_landmarks(
+        annotated_image,
+        results.face_landmarks,
+        mp_holistic.FACEMESH_TESSELATION,
+        landmark_drawing_spec=None,
+        connection_drawing_spec=mp_drawing_styles
+        .get_default_face_mesh_tesselation_style())
+    mp_drawing.draw_landmarks(
+        annotated_image,
+        results.pose_landmarks,
+        mp_holistic.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles.
+        get_default_pose_landmarks_style())
+    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
+    # Plot pose world landmarks.
+    mp_drawing.plot_landmarks(
+        results.pose_world_landmarks, mp_holistic.POSE_CONNECTIONS)
+
+# For webcam input:
+cap = cv2.VideoCapture(0)
+with mp_holistic.Holistic(
+    min_detection_confidence=[0.0,0.1],
+    min_tracking_confidence=[0.0,0.1]) as holistic:
+    while cap.isOpened():
+       success, image = cap.read()
+       if not success:
+         print("Ignoring empty camera frame.")
+         break
+
+    image.flags.writeable = False
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = holistic.process(image)
+
+    # Draw landmark annotation on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    mp_drawing.draw_landmarks(
+        image,
+        results.face_landmarks,
+        mp_holistic.FACEMESH_CONTOURS,
+        landmark_drawing_spec=None,
+        connection_drawing_spec=mp_drawing_styles
+        .get_default_face_mesh_contours_style())
+    mp_drawing.draw_landmarks(
+        image,
+        results.pose_landmarks,
+        mp_holistic.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles
+        .get_default_pose_landmarks_style())
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Holistic', cv2.flip(image, 1))
+    if cv2.waitKey(5) & 0xFF == 27:
+       break
+cap.release()