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Face and Hand Landmarks Detection using Python – Mediapipe, OpenCV

Last Updated : 10 Jan, 2023
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In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for different computer vision applications such as sign language detection, drowsiness detection, etc.

Required Libraries

  • Mediapipe is a cross-platform library developed by Google that provides amazing ready-to-use ML solutions for computer vision tasks.
  • OpenCV library in python is a computer vision library that is widely used for image analysis, image processing, detection, recognition, etc.

Installing required libraries

pip install opencv-python mediapipe msvc-runtime

Below is the step-wise approach for Face and Hand landmarks detection

STEP-1: Import all the necessary libraries, In our case only two libraries are required.

Python3

# Import Libraries
import cv2
import time
import mediapipe as mp
                      
                      
                                                    
                       
                  
                  
 
  
 STEP-2: Initializing Holistic model and Drawing utils for detecting and drawing landmarks on the image.
  
 
 
Python3
 
                   
                      
                          
                              
                                  
                                                                            
                                                                                                                  
                                                                            
                                                                                                                                                    
                              
                          
                      
                      
# Grabbing the Holistic Model from Mediapipe and
# Initializing the Model
mp_holistic = mp.solutions.holistic
holistic_model = mp_holistic.Holistic(
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5
)
 
# Initializing the drawing utils for drawing the facial landmarks on image
mp_drawing = mp.solutions.drawing_utils
                      
                          
                  
                  
                      
                                                    
                      
                      
                                                    
                       
                  
                  
 
  
Let us look into the parameters for the Holistic Model:
  
Holistic(   static_image_mode=False,    model_complexity=1,    smooth_landmarks=True,    min_detection_confidence=0.5,    min_tracking_confidence=0.5 )
  • static_image_mode: It is used to specify whether the input images must be treated as static images or as a video stream. The default value is False.
  • model_complexity: It is used to specify the complexity of the pose landmark model: 0, 1, or 2. As the model complexity of the model increases the landmark accuracy and latency increase. The default value is 1.
  • smooth_landmarks: This parameter is used to reduce the jitter in the prediction by filtering pose landmarks across different input images. The default value is True.
  • min_detection_confidence: It is used to specify the minimum confidence value with which the detection from the person-detection model needs to be considered as successful. Can specify a value in [0.0,1.0]. The default value is 0.5.
  • min_tracking_confidence: It is used to specify the minimum confidence value with which the detection from the landmark-tracking model must be considered as successful. Can specify a value in [0.0,1.0]. The default value is 0.5.
  
STEP-3: Detecting Face and Hand landmarks from the image. Holistic model processes the image and produces landmarks for Face, Left Hand, Right Hand and also detects the Pose of the 
  
  1. Capture the frames continuously from the camera using OpenCV.
  2. Convert the BGR image to an RGB image and make predictions using initialized holistic model.
  3. The predictions made by the holistic model are saved in the results variable from which we can access the landmarks using results.face_landmarks, results.right_hand_landmarks, results.left_hand_landmarks respectively.
  4. Draw the detected landmarks on the image using the draw_landmarks function from drawing utils.
  5. Display the resulting Image.
 
 
Python3
 
                   
                      
                          
                              
                                  
                                                                            
                                                                                                                  
                                                                            
                                                                                                                                                    
                              
                          
                      
                      
# (0) in VideoCapture is used to connect to your computer's default camera
capture = cv2.VideoCapture(0)
 
# Initializing current time and precious time for calculating the FPS
previousTime = 0
currentTime = 0
 
while capture.isOpened():
    # capture frame by frame
    ret, frame = capture.read()
 
    # resizing the frame for better view
    frame = cv2.resize(frame, (800, 600))
 
    # Converting the from BGR to RGB
    image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
    # Making predictions using holistic model
    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    results = holistic_model.process(image)
    image.flags.writeable = True
 
    # Converting back the RGB image to BGR
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
 
    # Drawing the Facial Landmarks
    mp_drawing.draw_landmarks(
      image,
      results.face_landmarks,
      mp_holistic.FACEMESH_CONTOURS,
      mp_drawing.DrawingSpec(
        color=(255,0,255),
        thickness=1,
        circle_radius=1
      ),
      mp_drawing.DrawingSpec(
        color=(0,255,255),
        thickness=1,
        circle_radius=1
      )
    )
 
    # Drawing Right hand Land Marks
    mp_drawing.draw_landmarks(
      image, 
      results.right_hand_landmarks, 
      mp_holistic.HAND_CONNECTIONS
    )
 
    # Drawing Left hand Land Marks
    mp_drawing.draw_landmarks(
      image, 
      results.left_hand_landmarks, 
      mp_holistic.HAND_CONNECTIONS
    )
     
    # Calculating the FPS
    currentTime = time.time()
    fps = 1 / (currentTime-previousTime)
    previousTime = currentTime
     
    # Displaying FPS on the image
    cv2.putText(image, str(int(fps))+" FPS", (10, 70), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
 
    # Display the resulting image
    cv2.imshow("Facial and Hand Landmarks", image)
 
    # Enter key 'q' to break the loop
    if cv2.waitKey(5) & 0xFF == ord('q'):
        break
 
# When all the process is done
# Release the capture and destroy all windows
capture.release()
cv2.destroyAllWindows()
                      
                          
                  
                  
                      
                                                    
                      
                      
                                                    
                       
                  
                  
 
  
The holistic model produces 468 Face landmarks, 21 Left-Hand landmarks, and 21 Right-Hand landmarks. The individual landmarks can be accessed by specifying the index of the required landmark. Example: results.left_hand_landmarks.landmark[0]. You can get the index of all the individual landmarks using the below code:
  
 
 
Python3
 
                   
                      
                          
                              
                                  
                                                                            
                                                                                                                  
                                                                            
                                                                                                                                                    
                              
                          
                      
                      
# Code to access landmarks
for landmark in mp_holistic.HandLandmark:
    print(landmark, landmark.value)
 
print(mp_holistic.HandLandmark.WRIST.value)
                      
                          
                  
                  
                      
                                                    
                      
                      
                                                    
                       
                  
                  
 
HandLandmark.WRIST 0 HandLandmark.THUMB_CMC 1 HandLandmark.THUMB_MCP 2 HandLandmark.THUMB_IP 3 HandLandmark.THUMB_TIP 4 HandLandmark.INDEX_FINGER_MCP 5 HandLandmark.INDEX_FINGER_PIP 6 HandLandmark.INDEX_FINGER_DIP 7 HandLandmark.INDEX_FINGER_TIP 8 HandLandmark.MIDDLE_FINGER_MCP 9 HandLandmark.MIDDLE_FINGER_PIP 10 HandLandmark.MIDDLE_FINGER_DIP 11 HandLandmark.MIDDLE_FINGER_TIP 12 HandLandmark.RING_FINGER_MCP 13 HandLandmark.RING_FINGER_PIP 14 HandLandmark.RING_FINGER_DIP 15 HandLandmark.RING_FINGER_TIP 16 HandLandmark.PINKY_MCP 17 HandLandmark.PINKY_PIP 18 HandLandmark.PINKY_DIP 19 HandLandmark.PINKY_TIP 20 0
Hand Landmarks and their Indices
  
OUTPUT: 
    
    

 
   
                                                                                      

                                                                                
                                          
                                                                                  
                                                                  
                              
                                
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                                          Detecting COVID-19 From Chest X-Ray Images using CNN                                      
                                  
                                                          
                          
                              
                                          
                                              
                                                  
                                                  
                                                      
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                                                      venugopalkadamba                                                
                                                  
                                                  
              
              
  
                                            
                                                                                        
                                          
                                                                                            
                                                                                                                                                  
                                                                                                                            
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      Recommender Systems provide personalized suggestions for items that are most relevant to each user by predicting preferences according to user's past choices. They are used in various areas like movies, music, news, search queries, etc. These recommendations are made in two ways: Collaborative filte
                        
                        
                                                  4 min read                  
                        
    •                       
      How can Tensorflow be used with abalone dataset to build a sequential model?                      
                            
      In this article, we will learn how to build a sequential model using TensorFlow in Python to predict the age of an abalone. We may wonder what is an abalone. Answer to this question is that it is a kind of snail. Generally, the age of an Abalone is determined by the physical examination of the abalo
                        
                        
                                                  8 min read                  
                        
    Computer Vision Projects
    •                       
      OCR of Handwritten digits | OpenCV                      
                            
      OCR which stands for Optical Character Recognition is a computer vision technique used to identify the different types of handwritten digits that are used in common mathematics. To perform OCR in OpenCV we will use the KNN algorithm which detects the nearest k neighbors of a particular data point an
                        
                        
                                                  2 min read                  
                        
    •                       
      Cartooning an Image using OpenCV - Python                      
                            
      Instead of sketching images by hand we can use OpenCV to convert a image into cartoon image. In this tutorial you'll learn how to turn any image into a cartoon. We will apply a series of steps like: Smoothing the image (like a painting)Detecting edges (like a sketch)Combining both to get a cartoon e
                        
                        
                                                  2 min read                  
                        
    •                       
      Count number of Object using Python-OpenCV                      
                            
      In this article, we will use image processing to count the number of Objects using OpenCV in Python. Google Colab link: https://colab.research.google.com/drive/10lVjcFhdy5LVJxtSoz18WywM92FQAOSV?usp=sharing Module neededOpenCv: OpenCv is an open-source library that is useful for computer vision appli
                        
                        
                                                  3 min read                  
                        
    •                       
      Count number of Faces using Python - OpenCV                      
                            
      Prerequisites: Face detection using dlib and openCV In this article, we will use image processing to detect and count the number of faces. We are not supposed to get all the features of the face. Instead, the objective is to obtain the bounding box through some methods i.e. coordinates of the face i
                        
                        
                                                  3 min read                  
                        
    •                       
      Text Detection and Extraction using OpenCV and OCR                      
                            
      Optical Character Recognition (OCR) is a technology used to extract text from images which is used in applications like document digitization, license plate recognition and automated data entry. In this article, we explore how to detect and extract text from images using OpenCV for image processing
                        
                        
                                                  2 min read                  
                        
    •                       
      FaceMask Detection using TensorFlow in Python                      
                            
      In this article, we’ll discuss our two-phase COVID-19 face mask detector, detailing how our computer vision/deep learning pipeline will be implemented. We’ll use this Python script to train a face mask detector and review the results. Given the trained COVID-19 face mask detector, we’ll proceed to i
                        
                        
                                                  9 min read                  
                        
    •                       
      Dog Breed Classification using Transfer Learning                      
                            
      In this tutorial, we will demonstrate how to build a dog breed classifier using transfer learning. This method allows us to use a pre-trained deep learning model and fine-tune it to classify images of different dog breeds. Why to use Transfer Learning for Dog Breed ClassificationTransfer learning is
                        
                        
                                                  9 min read                  
                        
    •                       
      Flower Recognition Using Convolutional Neural Network                      
                            
      Convolutional Neural Network (CNN) are a type of deep learning model specifically designed for processing structured grid data such as images. In this article we will build a CNN model to classify different types of flowers from a dataset containing images of various flowers like roses, daisies, dan
                        
                        
                                                  6 min read                  
                        
    •                       
      Emojify using Face Recognition with Machine Learning                      
                            
      In this article, we will learn how to implement a modification app that will show an emoji of expression which resembles the expression on your face. This is a fun project based on computer vision in which we use an image classification model in reality to classify different expressions of a person.
                        
                        
                                                  7 min read                  
                        
    •                       
      Cat & Dog Classification using Convolutional Neural Network in Python                      
                            
      Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi
                        
                        
                                                  5 min read                  
                        
    •                       
      Traffic Signs Recognition using CNN and Keras in Python                      
                            
      We always come across incidents of accidents where drivers' Overspeed or lack of vision leads to major accidents. In winter, the risk of road accidents has a 40-50% increase because of the traffic signs' lack of visibility. So here in this article, we will be implementing Traffic Sign recognition us
                        
                        
                                                  6 min read                  
                        
    •                       
      Lung Cancer Detection using Convolutional Neural Network (CNN)                      
                            
      Computer Vision is one of the applications of deep neural networks that helps us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using a simple Convolution Neural Netwo
                        
                        
                                                  7 min read                  
                        
    •                       
      Lung Cancer Detection Using Transfer Learning                      
                            
      Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using the Transfer Learning technique
                        
                        
                                                  8 min read                  
                        
    •                       
      Pneumonia Detection using Deep Learning                      
                            
      In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta
                        
                        
                                                  7 min read                  
                        
    •                       
      Detecting Covid-19 with Chest X-ray                      
                            
      COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic
                        
                        
                                                  9 min read                  
                        
    •                       
      Skin Cancer Detection using TensorFlow                      
                            
      In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us
                        
                        
                                                  5 min read                  
                        
    •                       
      Age Detection using Deep Learning in OpenCV                      
                            
      The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'
                        
                        
                                                  5 min read                  
                        
    •                       
      Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV                      
                            
      In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff
                        
                        
                                                  4 min read                  
                        
    •                       
      Detecting COVID-19 From Chest X-Ray Images using CNN                      
                            
      A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T
                        
                        
                                                  5 min read                  
                        
    •                       
      Image Segmentation Using TensorFlow                      
                            
      Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,
                        
                        
                                                  8 min read                  
                        
    •                       
      License Plate Recognition with OpenCV and Tesseract OCR                      
                            
      License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar
                        
                        
                                                  5 min read                  
                        
    •                       
      Detect and Recognize Car License Plate from a video in real time                      
                            
      Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro
                        
                        
                                                  11 min read                  
                        
    •                       
      Residual Networks (ResNet) - Deep Learning                      
                            
      After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble
                        
                        
                                                  9 min read                  
                        
    Natural Language Processing Projects
    •                       
      Twitter Sentiment Analysis using Python                      
                            
      This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
                        
                        
                                                  10 min read                  
                        
    •                       
      Facebook Sentiment Analysis using python                      
                            
      This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
                        
                        
                                                  6 min read                  
                        
    •                       
      Next Sentence Prediction using BERT                      
                            
      Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
                        
                        
                                                  5 min read                  
                        
    •                       
      Hate Speech Detection using Deep Learning                      
                            
      There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
                        
                        
                                                  7 min read                  
                        
    •                       
      Image Caption Generator using Deep Learning on Flickr8K dataset                      
                            
      Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
                        
                        
                                                  12 min read                  
                        
    •                       
      Movie recommendation based on emotion in Python                      
                            
      Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their
                        
                        
                                                  4 min read                  
                        
    •                       
      Speech Recognition in Python using Google Speech API                      
                            
      Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API. Step 1: Install Requir
                        
                        
                                                  2 min read                  
                        
    •                       
      Voice Assistant using python                      
                            
      As we know Python is a suitable language for scriptwriters and developers. Let’s write a script for Voice Assistant using Python. The query for the assistant can be manipulated as per the user’s need. Speech recognition is the process of converting audio into text. This is commonly used in voice ass
                        
                        
                                                  11 min read                  
                        
    •                       
      Human Activity Recognition - Using Deep Learning Model                      
                            
      Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
                        
                        
                                                  6 min read                  
                        
    •                       
      Fine-tuning BERT model for Sentiment Analysis                      
                            
      Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
                        
                        
                                                  7 min read                  
                        
    •                       
      Sentiment Classification Using BERT                      
                            
      BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
                        
                        
                                                  13 min read                  
                        
    •                       
      Sentiment Analysis with an Recurrent Neural Networks (RNN)                      
                            
      Recurrent Neural Networks (RNNs) excel in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews as
                        
                        
                                                  3 min read                  
                        
    •                       
      Building an Autocorrector Using NLP in Python                      
                            
      Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
                        
                        
                                                  4 min read                  
                        
    •                       
      Python | NLP analysis of Restaurant reviews                      
                            
      Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach
                        
                        
                                                  7 min read                  
                        
    •                       
      Restaurant Review Analysis Using NLP and SQLite                      
                            
      Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei
                        
                        
                                                  9 min read                  
                        
    •                       
      Twitter Sentiment Analysis using Python                      
                            
      This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
                        
                        
                                                  10 min read                  
                        
    Clustering Projects
    •                       
      Customer Segmentation using Unsupervised Machine Learning in Python                      
                            
      Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning
                        
                        
                                                  5 min read                  
                        
    •                       
      Music Recommendation System Using Machine Learning                      
                            
      When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona
                        
                        
                                                  4 min read                  
                        
    •                       
      K means Clustering - Introduction                      
                            
      K-Means Clustering is an Unsupervised Machine Learning algorithm which groups the unlabeled dataset into different clusters. The article aims to explore the fundamentals and working of k means clustering along with its implementation. Understanding K-means ClusteringK-means clustering is a technique
                        
                        
                                                  6 min read                  
                        
    •                       
      Image Segmentation using K Means Clustering                      
                            
      Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.  Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im
                        
                        
                                                  2 min read                  
                        
    Recommender System Project
    •                       
      AI Driven Snake Game using Deep Q Learning                      
                            
      Content has been removed from this Article
                        
                        
                                                  1 min read                  
                        
                                 
                              
                                                                                
                                  
                                                                                                            
                              
                            
                              
                                                                                      
                                
                              
                                                                                    
                      
                  
              
                  
          
              
                  
                      
                                        
                   
                            
                                  
                              
                                  
              
          
              
                        
  	  	  	
  		
  					
  			
  			
  					
  	
    	  	
  		
  					
  			
  			
  			
  				
  	
  	  	        	  	
  		
  					
  			
  			    	  	
  		
  		   		
  	
  
                    
          
      
      
          
              
              
          
      
  
    
                        	
    
      
                
          
                    
              
                
                                  
                        
        
      
    
      
        
        
            
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