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

 by   pytorch Python Version: v0.15.2 License: BSD-3-Clause

kandi X-RAY | vision Summary

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

            kandi-support Support

              vision has a medium active ecosystem.
              It has 14123 star(s) with 6710 fork(s). There are 383 watchers for this library.
              It had no major release in the last 12 months.
              There are 718 open issues and 2143 have been closed. On average issues are closed in 44 days. There are 185 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of vision is v0.15.2

            kandi-Quality Quality

              vision has 0 bugs and 0 code smells.

            kandi-Security Security

              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.

            kandi-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.

            kandi-Reuse Reuse

              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.

            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.

            vision Key Features

            No Key Features are available at this moment for vision.

            vision Examples and Code Snippets

            Vision Transformer Detection-Citations
            Pythondot img1Lines of Code : 37dot img1License : Permissive (Apache-2.0)
            copy iconCopy
              title={Context autoencoder for self-supervised representation learning},
              author={Chen, Xiaokang and Ding, Mingyu and Wang, Xiaodi and Xin, Ying and Mo, Shentong and Wang, Yunhao and Han, Shumin and Luo, Ping and Zeng, Ga  
            Vision Transformer (ViT)-How do I use this model on an image?
            Pythondot img2Lines of Code : 33dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            import timm
            model = timm.create_model('vit_base_patch16_224', pretrained=True)
            import urllib
            from PIL import Image
            from import resolve_data_config
            from import create_transform
            config = resolve_dat  
            Vision Transformer for Small Datasets
            Pythondot img3Lines of Code : 30dot img3License : Permissive (MIT)
            copy iconCopy
            import torch
            from vit_pytorch.vit_for_small_dataset import ViT
            v = ViT(
                image_size = 256,
                patch_size = 16,
                num_classes = 1000,
                dim = 1024,
                depth = 6,
                heads = 16,
                mlp_dim = 2048,
                dropout = 0.1,
                emb_dropout = 0.1
            Upgrading pip to latest version using pip in conda using environment.yaml
            Pythondot img4Lines of Code : 13dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            name: temp_env
              - pytorch
              - conda-forge
              - python=3.7
              - pytorch::pytorch=1.11.0
              - pytorch::torchvision=0.12.0
              - pytorch::cpuonly
              - pip>=22.0.4
              - pip: 
                  - -e '.[dev]'
            How can I determine validation loss for faster RCNN (PyTorch)?
            Pythondot img5Lines of Code : 127dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from typing import Tuple, List, Dict, Optional
            import torch
            from torch import Tensor
            from collections import OrderedDict
            from torchvision.models.detection.roi_heads import fastrcnn_loss
            from torchvision.models.detection.rpn import concat_b
            How to access latest torchvision.models (e.g. ViT)?
            Pythondot img6Lines of Code : 10dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            # list all ViT models
            # list all convNext models
            # load ViT-B/16
            vit_b_16 = timm.create_model('vit_base_patch16_224', pretrained=True)
            # load conv next
            convnext = timm.create_model('
            Creating conda environment cause huge incompatible error with each other
            Pythondot img7Lines of Code : 24dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            name: neucon
              # You can use the TUNA mirror to speed up the installation if you are in mainland China.
              # -
              - pytorch
              - defaults
              - conda-forge
            copy iconCopy
            Precision: TP / (TP + FP)
            Recall:    TP / (TP + FN)
            F1:        2*Precision*Recall /(Precision + Recall)
            def iou(self,a,b):
                Calculates intersection over union for all sets
            Result type cast error when doing calculations with Pytorch model parameters
            Pythondot img9Lines of Code : 16dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            model = torchvision.models.densenet201(num_classes=10)
            params = model.state_dict()
            name = 'features.norm0.num_batches_tracked'
            print(id(params[name]))  # 140247785908560
            params[name] = params[name] + 0.1
            print(id(params[name]))  # 1402477
            Pytorch Custom dataloader: TypeError: pic should be PIL Image or ndarray. Got
            Pythondot img10Lines of Code : 5dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            te_data    =  np.ones([100, 32, 32, 3])
            te_targets =  np.ones([100])
            assert all(tensors[0].shape[0] == tensor.shape[0] for tensor in tensors)

            Community Discussions


            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




            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.



            React-native-vision-camera can't access to normal camera in back
            Asked 2022-Mar-07 at 07:02

            i am trying to use 'normal' camera on my iphone 11 pro. I use react-native-vision-camera. When i run this code:



            Answered 2022-Mar-07 at 07:02

            tl;dr - Single-lens smartphone cameras commonly have a wide-angle lens of roughly 22mm and 30mm equivalent. So basically, you would want to choose wide-angle, as this is the "normal" type.

            based on the react-native documentation, there are three Identifiers for a physical camera (one that exists on the back/front of the device):

            "ultra-wide-angle-camera" | "wide-angle-camera" | "telephoto-camera"

            "ultra-wide-angle-camera": A built-in camera with a shorter focal length than that of a wide-angle camera. (focal length between below 24mm)

            "wide-angle-camera": A built-in wide-angle camera. (focal length between 24mm and 35mm)

            "telephoto-camera": A built-in camera device with a longer focal length than a wide-angle camera. (focal length between above 85mm)

            now that we have that settled, let's take a look at cameras' focal lengths that are equivalent to phone cameras' focal length (resource)

            Camera type Focal length Angle-of-view Wide-angle 22mm to 30mm ~84° to ~62° Telephoto 50mm to 80mm ~40° to ~25° Ultrawide-angle 12mm to 18mm ~112° to ~90° Periscope 103mm to 125mm ~20° to ~16°

            what is considered a "normal" focal length is 35mm, so you should choose wide-angle since it is the closest (and eventually with the angle of view the user might be even closer to 35mm), further more the wide-angle is the most common focal-length for phone camera lens



            Create new boolean fields based on specific bigrams appearing in a tokenized pandas dataframe
            Asked 2022-Feb-16 at 20:47

            Looping over a list of bigrams to search for, I need to create a boolean field for each bigram according to whether or not it is present in a tokenized pandas series. And I'd appreciate an upvote if you think this is a good question!

            List of bigrams:



            Answered 2022-Feb-16 at 20:28

            You could use a regex and extractall:



            How do I remove background from an image like this?
            Asked 2022-Jan-12 at 17:44

            I want to remove the background, and draw the outline of the box shown in the image(there are multiple such images with a similar background) . I tried multiple methods in OpenCV, however I am unable to determine the combination of features which can help remove background for this image. Some of the approaches tried out were:

            • Edge Detection - Since the background itself has edges of its own, using edge detection on its own(such as Canny and Sobel) didnt seem to give good results.
            • Channel Filtering / Thresholding - Both the background and foreground have a similar white color, so I was unable to find a correct threshold to filter the foreground.
            • Contour Detection - Since the background itself has a lot of contours, just using the largest contour area, as is often used for background removal, also didnt work.

            I would be open to tools in Computer Vision or of Deep Learning (in Python) to solve this particular problem.



            Answered 2022-Jan-07 at 01:57
            The Concept

            This is one of the cases where it is really useful to fine-tune the kernels of which you are using to dilate and erode the canny edges detected from the images. Here is an example, where the dilation kernel is np.ones((4, 2)) and the erosion kernel is np.ones((13, 7)):

            The Code



            After updating Gradle to 7.0.2, Element type “manifest” must be followed by either attribute specifications, “>” or “/>” error
            Asked 2021-Dec-29 at 11:19

            So today I updated Android Studio to:



            Answered 2021-Jul-30 at 07:00

            Encountered the same problem. Update Huawei services. Please take care. Remember to keep your dependencies on the most up-to-date version. This problem is happening on Merged-Manifest.



            Combining Object Detection with Text to Speech Code
            Asked 2021-Dec-28 at 16:46

            I am trying to write an object detection + text-to-speech code to detect objects and produce a voice output on the raspberry pi 4. However, as of right now, I am trying to write a simple python script that incorporates both elements into a single .py file and preferably as a function. I will then run this script on the raspberry pi. I want to give credit to Murtaza's Workshop "Object Detection OpenCV Python | Easy and Fast (2020)" and for the Text to speech documentation for pyttsx3. I have attached the code below. I have tried running the program and I always keep getting errors with the Text to speech code (commented lines 33-36 for reference). I believe it is some looping error but I just can't seem to get the program to run continuously. For instance, if I run the code without the TTS part, it works fine. Otherwise, it runs for perhaps 3-5 seconds and suddenly stops. I am a beginner but highly passionate in computer vision, and any help is appreciated!



            Answered 2021-Dec-28 at 16:46

            I installed pyttsx3 using the two commands in the terminal on the Raspberry Pi:

            1. sudo apt update && sudo apt install espeak ffmpeg libespeak1
            2. pip install pyttsx3

            I followed the video to install pyttsx3. My functional code should also be listed above. My question should be resolved but hopefully useful to anyone looking to write a similar program. I have made minor tweaks to my code.



            Android Huawei image segmentation not working on release build
            Asked 2021-Dec-27 at 09:39

            I'm using Huawei image segmentation for background removal from images. This code work perfectly fine on debug build but it does not work on a release build. I don't understand what could be the case.




            Answered 2021-Dec-27 at 08:50

            Stuff like this usually happens when you have ProGuard enabled but not correctly configured. Make sure to add appropriate rules to file to prevent it from obfuscating relevant classes.

            Information about this is usually provided by the library developers. After a quick search I came up with this example. Sources seem to be documented well enough, so that it should not be a problem to find the correct settings.

            Keep in mind that you probably need to add rules for more than one library.



            How to get post categories from an object in array
            Asked 2021-Dec-16 at 09:19

            Im trying to filter posts by categories from this array



            Answered 2021-Dec-16 at 09:19

            You are getting the undefined error because for few of the cases the post_categories array is empty and if u try accessing the 0th element it will throw an error. So add a null check for the array length and for id something like below



            How do you create a new AVAsset video that consists of only frames from given `CMTimeRange`s of another video?
            Asked 2021-Dec-12 at 17:03

            Apple's sample code Identifying Trajectories in Video contains the following delegate callback:



            Answered 2021-Dec-12 at 17:03

            By the time you identify a trajectory in captured video frames or from frames decoded from a file you may not have the initial frames in memory any more, so the easiest way to create your file containing only trajectories is to keep the original file on hand, and then insert its trajectory snippets into an AVComposition which you then export using AVAssetExportSession.

            This sample captures frames from the camera, encodes them to a file whilst analysing them for trajectories and after 20 seconds, it closes the file and then creates the new file containing only trajectory snippets.

            If you're interested in detecting trajectories in a pre-existing file, it's not too hard to rewire this code.



            How do you implement React-native-gesture-handler with React Navigation 6.x Native Stack Navigator (RN>0.6)?
            Asked 2021-Nov-30 at 08:25

            So, I am building a prototype android app as an internship project for a startup in React Native v0.66. I was new to RN but not React when I set up the project. My choice for navigation fell upon React Navigation 6.x and their Native Stack Navigator because it performs better than the regular Stack Navigator, although is not as customizable according to docs.

            Now I want to use react-native-gesture-handler in my project. According to their docs,

            "If you are using a native navigation library like wix/react-native-navigation you need to follow a different setup for your Android app to work properly. The reason is that both native navigation libraries and Gesture Handler library need to use their own special subclasses of ReactRootView.

            Instead of changing Java code you will need to wrap every screen component using gestureHandlerRootHOC on the JS side. This can be done for example at the stage when you register your screens."

            I suppose this includes React Navigation-Native Stack Navigator as well? There is code example of how to implement RNGH with wix/react-native-navigation, but none, anywhere, for my case:



            Answered 2021-Nov-30 at 08:25

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


            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.


            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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