Face Mesh using Python step by step

What is Face Mesh?

Face Mesh is a face geometry solution that estimates 468 3D face landmarks in real-time even on mobile devices. It employs machine learning (ML) to infer the 3D surface geometry, requiring only a single camera input without the need for a dedicated depth sensor. Utilizing lightweight model architectures together with GPU acceleration throughout the pipeline, the solution delivers real-time performance-critical for live experiences.
Additionally, the solution is bundled with the Face Geometry module that bridges the gap between face landmark estimation and useful real-time augmented reality (AR) applications. It establishes a metric 3D space and uses the face landmark screen positions to estimate face geometry within that space. The face geometry data consists of common 3D geometry primitives, including a face pose transformation matrix and a triangular face mesh. Under the hood, a lightweight statistical analysis method called Procrustes Analysis is employed to drive a robust, performant, and portable logic. The analysis runs on a CPU and has a minimal speed/memory footprint on top of the ML model inference.

Let's Make Face Mesh

#1 Installing Requirments

 Requirments

# Mediapipe

#Open Cv

Open CMD and Type

# Pip Install Mediapipe

# Pip Install Opencv

then Press Enter

#2 Write Code

First, open any Code editor and create a file

Then Start Writing Code

import cv2

import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils

mp_drawing_styles = mp.solutions.drawing_styles

mp_face_mesh = mp.solutions.face_mesh

# For static images:

IMAGE_FILES = []

drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)

with mp_face_mesh.FaceMesh(

    static_image_mode=True,

    max_num_faces=1,

    min_detection_confidence=0.5) as face_mesh:

  for idx, file in enumerate(IMAGE_FILES):

    image = cv2.imread(file)

    # Convert the BGR image to RGB before processing.

    results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    # Print and draw face mesh landmarks on the image.

    if not results.multi_face_landmarks:

      continue

    annotated_image = image.copy()

    for face_landmarks in results.multi_face_landmarks:

      print('face_landmarks:', face_landmarks)

      mp_drawing.draw_landmarks(

          image=annotated_image,

          landmark_list=face_landmarks,

          connections=mp_face_mesh.FACEMESH_TESSELATION,

          landmark_drawing_spec=None,

          connection_drawing_spec=mp_drawing_styles

          .get_default_face_mesh_tesselation_style())

      mp_drawing.draw_landmarks(

          image=annotated_image,

          landmark_list=face_landmarks,

          connections=mp_face_mesh.FACEMESH_CONTOURS,

          landmark_drawing_spec=None,

          connection_drawing_spec=mp_drawing_styles

          .get_default_face_mesh_contours_style())

    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)

# For webcam input:

drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)

cap = cv2.VideoCapture(0)

with mp_face_mesh.FaceMesh(

    min_detection_confidence=0.5,

    min_tracking_confidence=0.5) as face_mesh:

  while cap.isOpened():

    success, image = cap.read()

    if not success:

      print("Ignoring empty camera frame.")

      # If loading a video, use 'break' instead of 'continue'.

      continue

    # Flip the image horizontally for a later selfie-view display, and convert

    # the BGR image to RGB.

    image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)

    # To improve performance, optionally mark the image as not writeable to

    # pass by reference.

    image.flags.writeable = False

    results = face_mesh.process(image)

    # Draw the face mesh annotations on the image.

    image.flags.writeable = True

    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    if results.multi_face_landmarks:

      for face_landmarks in results.multi_face_landmarks:

        mp_drawing.draw_landmarks(

            image=image,

            landmark_list=face_landmarks,

            connections=mp_face_mesh.FACEMESH_TESSELATION,

            landmark_drawing_spec=None,

            connection_drawing_spec=mp_drawing_styles

            .get_default_face_mesh_tesselation_style())

        mp_drawing.draw_landmarks(

            image=image,

            landmark_list=face_landmarks,

            connections=mp_face_mesh.FACEMESH_CONTOURS,

            landmark_drawing_spec=None,

            connection_drawing_spec=mp_drawing_styles

            .get_default_face_mesh_contours_style())

    cv2.imshow('MediaPipe FaceMesh', image)

    if cv2.waitKey(5) & 0xFF == 27:

      break

cap.release()

#3 Run The Code

If you have any problem then contact me on telegram @odewithyash