MediaPiple - Explored

Many interesting examples have been posted in mediapipe. Google Vision library for many vision tasks.

Bookmarking python examples link

• Mediapipe Objectron provides pre-trained models for shoe, chair, cup and camera
• Mediapipe pose estimation
• MediaPipe Face Detection Solution API
• mediapipe_face_mesh - Face Landmarks

Example Code 

	#!pip install mediapipe
	#Minor changes from https://colab.research.google.com/drive/1FCxIsJS9i58uAsgsLFqDwFmiPO14Z2Hd
	from google.colab import files
	uploaded = files.upload()
	import cv2
	from google.colab.patches import cv2_imshow
	import math
	import numpy as np
	DESIRED_HEIGHT = 480
	DESIRED_WIDTH = 480
	def resize_and_show(image):
	h, w = image.shape[:2]
	if h < w:
	img = cv2.resize(image, (DESIRED_WIDTH, math.floor(h/(w/DESIRED_WIDTH))))
	else:
	img = cv2.resize(image, (math.floor(w/(h/DESIRED_HEIGHT)), DESIRED_HEIGHT))
	cv2_imshow(img)
	# Read images with OpenCV.
	images = {name: cv2.imread(name) for name in uploaded.keys()}
	# Preview the images.
	for name, image in images.items():
	print(name)
	resize_and_show(image)
	import mediapipe as mp
	mp_face_mesh = mp.solutions.face_mesh
	# Load drawing_utils and drawing_styles
	mp_drawing = mp.solutions.drawing_utils
	mp_drawing_styles = mp.solutions.drawing_styles
	# Run MediaPipe Face Mesh.
	with mp_face_mesh.FaceMesh(
	static_image_mode=True,
	refine_landmarks=True,
	max_num_faces=2,
	min_detection_confidence=0.5) as face_mesh:
	for name, image in images.items():
	# Convert the BGR image to RGB and process it with MediaPipe Face Mesh.
	results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
	# Draw face landmarks of each face.
	print(f'Face landmarks of {name}:')
	if not results.multi_face_landmarks:
	continue
	annotated_image = image.copy()
	for face_landmarks in results.multi_face_landmarks:
	#use this landmark points for further analysis / other usecases
	print(face_landmarks)
	print('FACEMESH_IRISES')
	print(mp_face_mesh.FACEMESH_IRISES)
	print('FACEMESH_TESSELATION')
	print(mp_face_mesh.FACEMESH_TESSELATION)
	print('FACEMESH_CONTOURS')
	print(mp_face_mesh.FACEMESH_CONTOURS)
	print(face_landmarks)

view raw face_landmarks.py hosted with ❤ by GitHub

Keep Exploring!!!