Have you ever wondered how quickly our cellular devices recognize QR codes and scan them within seconds? Well, that’s the magic of computer vision techniques. There are so many such instances we come across in our everyday lives but are unaware of the technicalities behind them. Today, we are about to dive into the technical roller coaster of the OpenCV library.
If you are someone who is looking to expand their knowledge in this field or if you are someone who is a tech master, we are sure that you will make the most out of this tutorial and learn one or two new things about it. Many IT training institutions these days also provide deep learning of such software as part of their curriculum. Even if you are someone who has no prior experience or knowledge in this domain, these curriculums shape you into an industry-ready individual.
Welcome to this techy ride. Let’s see the world through the lens of computer vision.


What is OpenCV?
OpenCV is a renowned Open Source Computer Vision and machine learning software library. It was developed with the perspective of delivering an interconnected infrastructure for applications in computer vision and speeding up the use of machine perception in consumer products. It provides comprehensive tools and algorithms to process and analyze images and videos. To be more specific, its recent spike in usage can be seen via object detection, video capturing, image processing, real-time face recognition, and tracking moving objects.
OpenCV was first created by Intel in 1999 and later developed collaboratively with help from Willow Garage, Itseez, and is currently supported by OpenCV.org. Although OpenCV is written in C++, it also offers interfaces and bindings for a number of other programming languages, such as Python, Java, and MATLAB. The widely used “cv2” Python interface offers an easy way to integrate OpenCV functionalities into Python-based projects.

What is Computer Vision? 
The ability for computers to perceive the world as we do is known as computer vision. It is a field of AI that enables computers to understand and interpret images and videos. It is built upon different algorithms and techniques to facilitate the meaningful extraction of visual information.
Computer vision is a domain in which computers try to replicate the human optical approach. As emphasized before, computer vision is a branch of AI. The process of computer vision includes image acquisition, pre-processing, feature extraction, object detection and recognition, and scene understanding. This entire process, in other words, leads to the operation of computer vision.

The information gathered by computer vision is later converted into computer language, making it simple for the machine to understand and perceive, which makes it easier to gather multi-dimensional information. The main purpose of computer vision is for machines to learn the process of capturing information through pixels.

Without any further ado, let’s begin with the OpenCV tutorial:

  1. Setting up OpenCV with Python:
    A. Installing OpenCV and required dependencies:
    There are a few different ways through which you can install OpenCV and here is one of them:
    You can install OpenCV on Windows using pip. Installing OpenCV in Python involves a few steps, including the installation of required dependencies. Here is a quick start guide to assist you:
  1. Install Python:

   – Go to https://www.python.org/, the official website for Python, and download the most recent version of the language for your operating system.

   – Follow the directions that the Python installer gives you.

  • Install pip:

   – Pip is a package manager for Python that makes it simple to install outside libraries. It usually comes bundled with Python installations.

  •  Install OpenCV dependencies:

   – Before installing OpenCV, a number of dependencies must be installed. The most frequent dependencies are Matplotlib (for visualizations) and NumPy (for numerical operations). Use the following pip commands to install them:

pip install numpy
pip install matplotlib  
  • Install OpenCV:

   – OpenCV can be installed using pip. Run the following command:

  pip install opencv-python

     This command installs the precompiled OpenCV binaries for Python.

            B. Verifying and importing OpenCV in Python :

   – To check if OpenCV is installed correctly, open a Python interpreter or create a Python script.

   – Import the OpenCV module and print its version to ensure it is successfully imported without any errors:

python
import cv2
print(cv2.__version__) 

  A successful installation is indicated if the version prints flawlessly.

That’s it! The required dependencies and OpenCV have now been installed in Python.

     2. Basic Image Operations:
        In this module, we are going to look into the basic operations of computer vision
        with the help of OpenCV.

        A. Loading and Displaying Images:
When a photo on your computer is loading at the same time, the computer is creating a path for it to open, and as the computer reads the image, it creates a memory of the same. Once the image is loaded, it is displayed on your screen.

import cv2   # Load an image image = cv2.imread(‘image.jpg’)   # Display the image cv2.imshow(‘Image’, image) cv2.waitKey(0) cv2.destroyAllWindows()

Replace ‘image.jpg’ with the path to your own image file.

         B. Accessing Pixel Values and Channels:  
An image contains tiny grids of squares, and each square contains a color. These squares are called pixels. We are able to retrieve the specific color of a pixel at a specific location by accessing pixel values. Red, green, and blue (RGB) are the three colors that each pixel channel has. One can access these channels individually to see how much red, green, and blue are present.

import cv2   # Load an image image = cv2.imread(‘image.jpg’)   # Access pixel value at a specific location pixel = image[100, 100]  # Row 100, Column 100   # Access specific channel (B, G, R) blue = image[100, 100, 0] green = image[100, 100, 1] red = image[100, 100, 2]


        C. Modifying Image Properties:
The properties of an image can be modified. We can resize the image, make it small or long, change the shape of the image, or change its width or height. Additionally, we can also change the color space of an image. Color space is referred to as the color representation in an image, for example, RGB or grayscale. Converting the color space allows one to view the image in different ways.

import cv2   # Load an image image = cv2.imread(‘image.jpg’)   # Get image properties height, width, channels = image. shape   # Resize the image resized_image = cv2.resize(image, (new_width, new_height))   # Convert color space (e.g., BGR to grayscale) gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)


 You can replace new_width and new_height with the desired dimensions for resizing.

         D. Saving Images:
Saving an image is similar to saving the image after editing it in a photo editor app. After modifying the image, in order to save the image, you specify a filename and a location where the image is to be saved, and the computer writes the image data to a file.

import cv2   # Load an image image = cv2.imread(‘image.jpg’)   # Modify the image (e.g., convert to grayscale) gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)   # Save the modified image cv2.imwrite(‘modified_image.jpg’, gray_image)


You can replace ‘modified_image.jpg’ with the desired path and filename for saving the image.

     3. Image Filtering and Enhancements:

A. Applying various Image Filters:
Digital effects that are applied to an image to either enhance or alter the image are as follows:

  1. Blur Filter: In a blur effect, one can blur the image, and make it smoother by reducing the high-frequency details. It can be used to reduce noise or create a soft effect.
  2. Sharpen Filter: With the help of a sharpen filter, we can sharpen the edges and details and make them appear more defined.
  3. Edge Detection Filter: It makes highlights and makes the edges in an image even more stipulated.

B. Histogram Equalization for Image Enhancement:
Histogram Equalization for Image Enhancements is especially used in fixing uneven lighting or low-contrast images. It overall changes the appearance of the image. It redistributes the pixel intensities across the entire range, enhancing both dark and bright areas.

C. Denoising Techniques (Gaussian, Median Filter):
Images captured in the real world often contain a lot of noise, which affects the quality of the image and further leads to degradation of the image. The denoising technique aims to reduce the noise in the image, and it uses the following two techniques:

  1. Gaussian Filter: This filter helps to smooth the image by averaging the pixel values in a neighborhood, effectively reducing random noise.
  2. Median Filter: Replaces each pixel with the median value of its neighboring pixels. It is effective in removing salt-and-pepper-type noise.

In OpenCV with Python, you can utilize these image-processing techniques using the following functions:

import cv2   # Applying Filters blurred_image = cv2.blur(image, (ksize, ksize))  # Blur filter sharpened_image = cv2.filter2D(image, -1, kernel)  # Sharpen filter edges = cv2.Canny(image, threshold1, threshold2)  # Edge detection   # Histogram Equalization equalized_image = cv2.equalizeHist(image)   # Denoising Techniques denoised_image = cv2.GaussianBlur(image, (ksize, ksize), sigmaX)  # Gaussian filter denoised_image = cv2.medianBlur(image, ksize)  # Median filter

You would need to adjust the parameters (ksize, kernel, thresholds, and sigmaX) based on your specific requirements.

     4. Image Transformation and Geometric Operations:
A. Resizing, Cropping, and Rotating Images:

  1. Resizing: Resizing an image means making the image smaller or larger. In simpler words, you can change the dimensions of the image. It is done by specifying the desired width and height of the image.
  2. Cropping: Cropping refers to the action where one can select the specific region of interest (ROI) and extract only that part of the image.
  3. Rotating: Rotating an image involves changing the orientation or angle of the image, one can change its angle by moving it clockwise or anti-clockwise.

B. Affine and Perspective Transformations:
      1. Affine Transformations: Affine transformations are responsible for preserving parallel lines and ratios of distances. Affine transformations take care of rotating, scaling, and shearing the image. These transformations are used to change the orientation, position, and size of an object in an image.

      2. Perspective Transformations: Perspective Transformation acts in changing the viewpoint of the image. It involves adjusting the angle through which the image is seen or correcting or stimulating the visual perspective of the image.

C. Image warping and homography:

  1. Image warping: Image warping refers to the digital manipulation of the pixels of an image. Through image warping one can deform the image in order to get the desired shape. And hence, one can distort or transform an image.
  2. Homography: Homography is a transformation that relates corresponding points in two different images or two different perspectives of the same image. For homography to work, both images must be in the same plane for enabling mapping of the points, allowing geometric alignment and registration.

In OpenCV with Python, you can perform these operations using the following functions:

import cv2 import numpy as np   # Resizing, Cropping, and Rotating resized_image = cv2.resize(image, (new_width, new_height)) cropped_image = image[y_start:y_end, x_start:x_end] rotated_image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)  # or cv2.ROTATE_90_COUNTERCLOCKWISE   # Affine and Perspective Transformations M = cv2.getAffineTransform(src_points, dst_points) warped_image = cv2.warpAffine(image, M, (width, height)) M = cv2.getPerspectiveTransform(src_points, dst_points) warped_image = cv2.warpPerspective(image, M, (width, height))   # Image Warping and Homography M, mask = cv2.findHomography(src_points, dst_points) warped_image = cv2.warpPerspective(image, M, (width, height))


Ensure that you define the necessary parameters (new_width, new_height, x_start, x_end, y_start, y_end, src_points, dst_points, etc.) according to your specific requirements.


5. Feature Detection and Description:

  1. Corner Detection (Harris Corner Detector):
    The Corner Detection feature identifies the corner or interest points in an image. The Harris corner detector is a famous algorithm to detect corners. It identifies the change in the intensity of the angle in an image, which is indicative of corners.

import cv2   # Load an image image = cv2.imread(‘image.jpg’)   # Convert to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)   # Apply Harris corner detection corners = cv2.cornerHarris(gray_image, blockSize, ksize, k)   # Threshold and mark the corners corners = cv2.dilate(corners, None) image[corners > threshold * corners.max()] = [0, 0, 255]  # Mark corners in red   # Display the image with corners cv2.imshow(‘Corners’, image) cv2.waitKey(0) cv2.destroyAllWindows()
  1. Feature extraction (SIFT, SURF, ORB):
    Feature extraction algorithms identify distinctive points in an image and save them for further analysis or matching. OpenCV provides various feature extraction algorithms, including SIFT (Scale- Invariant Feature Transform), SURF (Speeded-Up Robust Features), and ORB (Oriented FAST and rotated BRIEF). These algorithms extract key points and descriptors that represent the features of an image.










import cv2   # Load an image image = cv2.imread(‘image.jpg’)   # Convert to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)   # Create the feature detector detector = cv2.xfeatures2d.SIFT_create()  # or cv2.xfeatures2d.SURF_create(), cv2.ORB_create()   # Detect keypoints and compute descriptors keypoints, descriptors = detector.detectAndCompute(gray_image, None)   # Draw keypoints on the image image_with_keypoints = cv2.drawKeypoints(image, keypoints, None)   # Display the image with keypoints cv2.imshow(‘Image with Keypoints’, image_with_keypoints) cv2.waitKey(0) cv2.destroyAllWindows()
  1. Matching Features Between Images:
    Matching features help you find corresponding points between different images.
    OpenCV provides different algorithms to identify corresponding points between images like brute-force, FLANN (Fast Library for Approximate Nearest Neighbors)
import cv2   # Load two images image1 = cv2.imread(‘image1.jpg’) image2 = cv2.imread(‘image2.jpg’)   # Convert images to grayscale gray_image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) gray_image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)   # Create the feature detector and descriptor extractor detector = cv2.xfeatures2d.SIFT_create() matcher = cv2.DescriptorMatcher_create(cv2.DescriptorMatcher_FLANNBASED)   # Detect keypoints and compute descriptors for both images
keypoints1

6. Object Detection and Tracking:
    A. Haar Cascades for Face and Object Detection:
        In order to identify particular objects or features in pictures or videos, classifiers based on machine learning called Haar cascades can be trained. OpenCV provides pre-trained Haar cascades for face detection and other objects. Here’s an example of using the Haar cascade for face detection:

import cv2   # Load the Haar cascade xml file for face detection face_cascade = cv2.CascadeClassifier(‘haarcascade_frontalface_default.xml’)   # Load an image or video frame frame = cv2.imread(‘image.jpg’)   # Convert the frame to grayscale gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)   # Perform face detection faces = face_cascade.detectMultiScale(gray_frame, scaleFactor, minNeighbors)   # Draw rectangles around the detected faces for (x, y, w, h) in faces:     cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), thickness)   # Display the frame with detected faces cv2.imshow(‘Faces’, frame) cv2.waitKey(0) cv2.destroyAllWindows()


You need to download the Haar cascade xml file for face detection (e.g., ‘haarcascade_frontalface_default.xml’) and adjust the parameters (scaleFactor, minNeighbors, thickness) based on your specific needs.


B. Implementing object tracking algorithms (Optical Flow, CAMShift):
    The tracking of objects in consecutive frames is enabled by object tracking     algorithms.OpenCV provides various object tracking algorithms, including Optical Flow and CAMShift.
 1. Optical Flow: Optical Flow enables tracking the motion of objects by analyzing the movement of pixels between consecutive frames. The velocity vector of pixels in an image is estimated by the Optical Flow motion, allowing you to track a motion.

import cv2   # Load two consecutive frames frame1 = cv2.imread(‘frame1.jpg’) frame2 = cv2.imread(‘frame2.jpg’)   # Convert frames to grayscale gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)   # Calculate optical flow flow = cv2.calcOpticalFlowFarneback(gray1, gray2, None, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags)   # Display the optical flow # You can visualize the flow using quiver plots or other methods cv2.imshow(‘Optical Flow’, flow) cv2.waitKey(0) cv2.destroyAllWindows()


You can adjust the parameters (pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags) according to your requirements.

2. CAMShift: (Continuously Adaptive Mean Shift) is an object-tracking algorithm that combines color information with the mean shift algorithm. It is able to track objects even when their scale or orientation changes over time.

import cv2   # Load an image or video frame frame = cv2.imread(‘image.jpg’)   # Define the region of interest (ROI) to track x, y, w, h = 100, 100, 200, 200 roi = frame[y:y+h, x:x+w]   # Convert the ROI to HSV color space

7. Image Segmentation:
A. Thresholding and Binary Image Segmentation:
 Thresholding is a known technique used to segment an image into distinct regions based on pixel intensity. A grayscale image is transformed into a binary image, where pixels are categorized as either foreground or background depending on a predetermined threshold value.

import cv2   # Load an image image = cv2.imread(‘image.jpg’)   # Convert the image to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)   # Apply thresholding _, binary_image = cv2.threshold(gray_image, threshold_value, max_value, threshold_type)   # Display the binary image cv2.imshow(‘Binary Image’, binary_image) cv2.waitKey(0) cv2.destroyAllWindows()


Adjust the parameters (threshold_value, max_value, threshold_type) based on your specific requirements.

B. Contour Detection and Extraction:
    Contour detection involves identifying and extracting the boundaries of objects in an image. It can be used to segment objects based on their shapes or extract shape-related features.

import cv2   # Load a binary image binary_image = cv2.imread(‘binary_image.jpg’, cv2.IMREAD_GRAYSCALE)   # Find contours in the binary image contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)   # Draw contours on the original image image_with_contours = cv2.drawContours(image, contours, -1, (0, 255, 0), thickness)   # Display the image with contours cv2.imshow(‘Image with Contours’, image_with_contours) cv2.waitKey(0) cv2.destroyAllWindows()


You can adjust the parameters (cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE, thickness) as per your specific needs.

C. Region-based segmentation (watershed algorithm):
The watershed algorithm is a region-based segmentation that separates the different objects or regions in an image based on the presence of markers and boundaries. It treats the image as a topographic map and simulates flooding to determine the object.

import cv2 import numpy as np   # Load an image image = cv2.imread(‘image.jpg’)   # Convert the image to grayscale gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)   # Apply thresholding _, binary_image = cv2.threshold(gray_image, threshold_value, max_value, threshold_type)   # Perform morphological operations (optional) kernel = np.ones((3, 3), np.uint8) opening = cv2.morphologyEx(binary_image, cv2.MORPH_OPEN, kernel, iterations)   # Perform the watershed algorithm markers = cv2.connectedComponents(opening)[1] markers = markers + 1 markers[opening == 255] = 0 markers = cv2.watershed(image, markers)   # Outline the segmented regions image[markers == -1] = [0, 0, 255]   # Display the segmented image cv2.imshow(‘Segmented Image’, image) cv2.waitKey(0) cv2.destroyAllWindows()

Adjust the parameters (threshold_value, max_value, threshold_type, iterations) and experiment with the morphological operations based on your specific requirements.

8. Advanced Topics:
A. Deep learning-based image processing with OpenCV:
    A module called DNN (Deep Neutral Networks) is provided by OpenCV that allows you to use deep learning models for various image processing tasks, such as object detection, image classification, and image segmentation. This module supports deep learning modules like PyTorch, TensorFlow, and Caffe.

import cv2   # Load the pre-trained deep learning model net = cv2.dnn.readNet(‘model.weights’, ‘model.cfg’)   # Load an image image = cv2.imread(‘image.jpg’)   # Preprocess the image blob = cv2.dnn.blobFromImage(image, scalefactor, size, mean, swapRB, crop)   # Set the input for the deep learning model net.setInput(blob)   # Perform forward pass and get the predictions predictions = net.forward()   # Process the predictions (e.g., object detection, image classification)   # Display the results cv2.imshow(‘Result’, image) cv2.waitKey(0) cv2.destroyAllWindows()

You need to download the pre-trained model files (e.g., ‘model.weights’ and ‘model.cfg’) and adjust the parameters (scalefactor, size, mean, swapRB, crop) according to the requirements of the specific deep learning model.

B. Camera Calibration and 3D Reconstruction :
   Camera calibration is the process of estimating the parameters of a camera that relate pixel coordinates to the 3D world coordinates. With the aid of the Structure from Motion (SfM) method, OpenCV offers functions for camera calibration, distortion removal, and 3D reconstruction.

import cv2   # Load calibration images (multiple images of a calibration pattern taken from different viewpoints)   # Define the calibration pattern (e.g., chessboard corners) pattern_size = (8, 6)  # Number of inner corners in the calibration pattern   # Prepare object points object_points = [] for i in range(pattern_size[1]):     for j in range(pattern_size[0]):         object_points.append((j, i, 0))   object_points = np.array(object_points, dtype=np.float32)   # Find corners in the calibration images image_points = [] for image_file in calibration_images:     image = cv2.imread(image_file)     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)     ret, corners = cv2.findChessboardCorners(gray, pattern_size, None)     if ret:         image_points.append(corners)   # Perform camera calibration ret, camera_matrix, distortion_coefficients, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, image_size, None, None)   # Perform undistortion on an image image = cv2.imread(‘image.jpg’) undistorted_image = cv2.undistort(image, camera_matrix, distortion_coefficients)   # Perform 3D reconstruction (e.g., using Structure from Motion)   # Display the results cv2.imshow(‘Undistorted Image’, undistorted_image) cv2.waitKey(0) cv2.destroyAllWindows()


You need to provide calibration images with a known calibration pattern, define the pattern size, and adjust the parameters based on your specific setup.

C. Augmented Reality and AR Applications:

By adding virtual objects or data to the real-world view that was captured by a camera, OpenCV can be used to create augmented reality applications. You can detect and track features in the camera view, estimate the camera pose, and render virtual objects accordingly.

import cv2 import numpy as np   # Load the camera intrinsic parameters (camera matrix) camera_matrix = np.load(‘camera_matrix.npy’)   # Load the virtual object model or

9. Real-world Examples and Projects:
   A. Building an image classifier using OpenCV and machine learning:

  1. Prepare your dataset: Collect a dataset of images with corresponding labels for training and testing. Make sure the labels on the images are accurate.
  2. Extract features: Use OpenCV to extract features from the images. Common techniques include Histogram of Oriented Gradients (HOG) or Scale-Invariant Feature Transform (SIFT). These features will serve as inputs to your machine-learning algorithm.
  3. Train a machine learning model: Use a machine learning algorithm such as Support Vector Machines (SVM), Random Forests, or Convolutional Neural Networks (CNNs) to train your model. You can use libraries like scikit-learn or TensorFlow to implement the machine learning algorithm.
  4. Evaluate the model: Evaluate the performance of your trained model using a separate test dataset. Measure metrics such as accuracy, precision, recall, or F1 score to assess the model’s performance.
  5. Save and use the model: Once your model is trained and evaluated, you can save it to disk for later use. This saved model can be loaded and used to classify new images.

B. Creating a Real-Time Face Detection and Recognition System:

To create a real-time face detection and recognition system using OpenCV, you can follow these steps:

  1. Face Detection:

Use OpenCV’s pre-trained Haar cascades or deep learning models to detect faces in an image or video frames. Once the faces are detected, you can extract the face regions for further processing.

  • Face Recognition:

 Train a machine learning model, such as a Support Vector Machine (SVM) or Convolutional Neural Network (CNN), using a dataset of labeled face images. This model will learn to recognize different individuals based on their facial features.

  • Real-Time Processing:

Capture video frames using a webcam or any other video source. Apply face detection to detect faces in each frame. For each detected face, use your trained face recognition model to recognize the person.

  • Display the Results:

Overlay bounding boxes around the detected faces and display the recognized person’s name or ID. You can also maintain a database of known individuals and update it with new faces that the system encounters.

C. Implementing Optical Character Recognition (OCR) using OpenCV in Python:

To implement OCR using OpenCV in Python, you can follow these steps:

  1. Preprocess the Image:

Convert the input image to grayscale. Apply any necessary preprocessing techniques like noise removal, image thresholding, or morphological operations to enhance the text regions.

  • Text Detection:

Use techniques like the Stroke Width Transform (SWT), Maximally Stable Extremal Regions (MSER), or the EAST (Efficient and Accurate Scene Text) algorithm to detect text regions in the preprocessed image.

  • Text Recognition:

Extract each detected text region and pass it to an OCR engine like Tesseract. Tesseract is an open-source OCR engine widely used in the industry. You can use the pytesseract library to interface with Tesseract in Python.

  • Post-processing:

Apply post-processing techniques like language modeling, spell-checking, or regular expressions to improve the accuracy and correctness of the recognized text.

  • Display the Results:

Overlay the recognized text on the original image or output it as plain text. You can also perform further analysis or tasks based on the recognized text. Remember to install the necessary libraries and dependencies for machine learning, face recognition, and OCR before starting the implementation.
    
Conclusion:
Through this, we come to an end to this OpenCV tutorial, where we were able to learn and understand OpenCV. For a better and more detailed understanding, you can get enrolled in various IT Training Institute. I hope you found this blog interesting and extremely useful.

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