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vision | Datasets , Transforms and Models specific to Computer Vision | Computer Vision library

 by   pytorch Python Version: v0.14.1 License: BSD-3-Clause

 by   pytorch Python Version: v0.14.1 License: BSD-3-Clause

kandi X-RAY | vision Summary

vision is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow applications. vision has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub, Maven.
Datasets, Transforms and Models specific to Computer Vision
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kandi-support Support

  • vision has a medium active ecosystem.
  • It has 13149 star(s) with 6468 fork(s). There are 385 watchers for this library.
  • There were 3 major release(s) in the last 6 months.
  • There are 672 open issues and 2025 have been closed. On average issues are closed in 97 days. There are 175 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of vision is v0.14.1
vision Support
Best in #Computer Vision
Average in #Computer Vision
vision Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • vision has 0 bugs and 0 code smells.
vision Quality
Best in #Computer Vision
Average in #Computer Vision
vision Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • vision has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • vision code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
vision Security
Best in #Computer Vision
Average in #Computer Vision
vision Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • vision is licensed under the BSD-3-Clause License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
vision License
Best in #Computer Vision
Average in #Computer Vision
vision License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • vision releases are available to install and integrate.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • vision saves you 11390 person hours of effort in developing the same functionality from scratch.
  • It has 31456 lines of code, 2354 functions and 200 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
vision Reuse
Best in #Computer Vision
Average in #Computer Vision
vision Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

kandi has reviewed vision and discovered the below as its top functions. This is intended to give you an instant insight into vision implemented functionality, and help decide if they suit your requirements.

  • Return a list of all supported extensions
    • Create a feature extractor .
      • Draws affine transformation .
        • Uses SSDL Lite320 .
          • Wrapper for FasterRCNN .
            • Generate a grid of tensors .
              • Read video from memory .
                • Construct an SSD 2000 model .
                  • Construct a KeypointRCNN .
                    • Apply a transformation to an image .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      vision Key Features

                      Datasets, Transforms and Models specific to Computer Vision

                      vision Examples and Code Snippets

                      See all related Code Snippets

                      Community Discussions

                      Trending Discussions on vision
                      • Camera calibration, focal length value seems too large
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                      • After updating Gradle to 7.0.2, Element type “manifest” must be followed by either attribute specifications, “>” or “/>” error
                      • Combining Object Detection with Text to Speech Code
                      • Android Huawei image segmentation not working on release build
                      • How to get post categories from an object in array
                      • How do you create a new AVAsset video that consists of only frames from given `CMTimeRange`s of another video?
                      • How do you implement React-native-gesture-handler with React Navigation 6.x Native Stack Navigator (RN>0.6)?
                      Trending Discussions on vision

                      QUESTION

                      Camera calibration, focal length value seems too large

                      Asked 2022-Mar-16 at 16:58

                      I tried a camera calibration with python and opencv to find the camera matrix. I used the following code from this link

                      https://automaticaddison.com/how-to-perform-camera-calibration-using-opencv/

                      import cv2 # Import the OpenCV library to enable computer vision
                      import numpy as np # Import the NumPy scientific computing library
                      import glob # Used to get retrieve files that have a specified pattern
                       
                      # Path to the image that you want to undistort
                      distorted_img_filename = r'C:\Users\uid20832\3.jpg'
                       
                      # Chessboard dimensions
                      number_of_squares_X = 10 # Number of chessboard squares along the x-axis
                      number_of_squares_Y = 7  # Number of chessboard squares along the y-axis
                      nX = number_of_squares_X - 1 # Number of interior corners along x-axis
                      nY = number_of_squares_Y - 1 # Number of interior corners along y-axis
                       
                      # Store vectors of 3D points for all chessboard images (world coordinate frame)
                      object_points = []
                       
                      # Store vectors of 2D points for all chessboard images (camera coordinate frame)
                      image_points = []
                       
                      # Set termination criteria. We stop either when an accuracy is reached or when
                      # we have finished a certain number of iterations.
                      criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
                       
                      # Define real world coordinates for points in the 3D coordinate frame
                      # Object points are (0,0,0), (1,0,0), (2,0,0) ...., (5,8,0)
                      object_points_3D = np.zeros((nX * nY, 3), np.float32)       
                       
                      # These are the x and y coordinates                                              
                      object_points_3D[:,:2] = np.mgrid[0:nY, 0:nX].T.reshape(-1, 2) 
                       
                      def main():
                           
                        # Get the file path for images in the current directory
                        images = glob.glob(r'C:\Users\Kalibrierung\*.jpg')
                           
                        # Go through each chessboard image, one by one
                        for image_file in images:
                        
                          # Load the image
                          image = cv2.imread(image_file)  
                       
                          # Convert the image to grayscale
                          gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  
                       
                          # Find the corners on the chessboard
                          success, corners = cv2.findChessboardCorners(gray, (nY, nX), None)
                           
                          # If the corners are found by the algorithm, draw them
                          if success == True:
                       
                            # Append object points
                            object_points.append(object_points_3D)
                       
                            # Find more exact corner pixels       
                            corners_2 = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)       
                             
                                  # Append image points
                            image_points.append(corners)
                       
                            # Draw the corners
                            cv2.drawChessboardCorners(image, (nY, nX), corners_2, success)
                       
                            # Display the image. Used for testing.
                            #cv2.imshow("Image", image) 
                           
                            # Display the window for a short period. Used for testing.
                            #cv2.waitKey(200) 
                                                                                                                                           
                        # Now take a distorted image and undistort it 
                        distorted_image = cv2.imread(distorted_img_filename)
                       
                        # Perform camera calibration to return the camera matrix, distortion coefficients, rotation and translation vectors etc 
                        ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(object_points, 
                                                                          image_points, 
                                                                          gray.shape[::-1], 
                                                                          None, 
                                                                          None)
                      

                      But I think I always get wrong parameters. My focal length is around 1750 in x and y direction from calibration. I think this couldnt be rigth, it is pretty much. The camera documentation says the focal lentgh is between 4-7 mm. But I am not sure, why it is so high from the calibration. Here are some of my photos for the calibration. Maybe something is wrong with them. I moved the chessboard under the camera in different directions, angles and high.

                      I was also wondering, why I dont need the size of the squares in the code. Can someone explains it to me or did I forgot this input somewhere?

                      enter image description here enter image description here enter image description here

                      ANSWER

                      Answered 2021-Sep-13 at 11:31

                      Your misconception is about "focal length". It's an overloaded term.

                      • "focal length" (unit mm) in the optical part: it describes the distance between the lens plane and image/sensor plane
                      • "focal length" (unit pixels) in the camera matrix: it describes a scale factor for mapping the real world to a picture of a certain resolution

                      1750 may very well be correct, if you have a high resolution picture (Full HD or something).

                      The calculation goes:

                      f [pixels] = (focal length [mm]) / (pixel pitch [µm / pixel])

                      (take care of the units and prefixes, 1 mm = 1000 µm)

                      Example: a Pixel 4a phone, which has 1.40 µm pixel pitch and 4.38 mm focal length, has f = ~3128.57 (= fx = fy).

                      Another example: A Pixel 4a has a diagonal Field of View of approximately 77.7 degrees, and a resolution of 4032 x 3024 pixels, so that's 5040 pixels diagonally. You can calculate:

                      f = (5040 / 2) / tan(~77.7° / 2)

                      f = ~3128.6 [pixels]

                      And that calculation you can apply to arbitrary cameras for which you know the field of view and picture size. Use horizontal FoV and horizontal resolution if the diagonal resolution is ambiguous. That can happen if the sensor isn't 16:9 but the video you take from it is cropped to 16:9... assuming the crop only crops vertically, and leaves the horizontal alone.


                      Why don't you need the size of the chessboard squares in this code? Because it only calibrates the intrinsic parameters (camera matrix and distortion coefficients). Those don't depend on the distance to the board or any other object in the scene.

                      If you were to calibrate extrinsic parameters, i.e. the distance of cameras in a stereo setup, then you would need to give the size of the squares.

                      Source https://stackoverflow.com/questions/69159247

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install vision

                      You can download it from GitHub, Maven.
                      You can use vision like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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                      • https://github.com/pytorch/vision.git

                      • gh repo clone pytorch/vision

                      • git@github.com:pytorch/vision.git

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