Vision | As of now , OpenCV codes and snippets | Computer Vision library

 by   jayrambhia C++ Version: Current License: No License

kandi X-RAY | Vision Summary

kandi X-RAY | Vision Summary

Vision is a C++ library typically used in Artificial Intelligence, Computer Vision, OpenCV applications. Vision has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

As of now, OpenCV codes and snippets that I’m working on.

            kandi-support Support

              Vision has a low active ecosystem.
              It has 73 star(s) with 78 fork(s). There are 9 watchers for this library.
              It had no major release in the last 6 months.
              There are 2 open issues and 0 have been closed. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of Vision is current.

            kandi-Quality Quality

              Vision has no bugs reported.

            kandi-Security Security

              Vision has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              Vision does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              Vision releases are not available. You will need to build from source code and install.

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            Vision Key Features

            No Key Features are available at this moment for Vision.

            Vision Examples and Code Snippets

            Vision Transformer for Small Datasets
            pypidot img1Lines of Code : 30dot img1no licencesLicense : No License
            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

            Community Discussions


            General approach to parsing text with special characters from PDF using Tesseract?
            Asked 2021-Jun-15 at 20:17

            I would like to extract the definitions from the book The Navajo Language: A Grammar and Colloquial Dictionary by Young and Morgan. They look like this (very blurry):

            I tried running it through the Google Cloud Vision API, and got decent results, but it doesn't know what to do with these "special" letters with accent marks on them, or the curls and lines on/through them. And because of the blurryness (there are no alternative sources of the PDF), it gets a lot of them wrong. So I'm thinking of doing it from scratch in Tesseract. Note the term is bold and the definition is not bold.

            How can I use Node.js and Tesseract to get basically an array of JSON objects sort of like this:



            Answered 2021-Jun-15 at 20:17

            Tesseract takes a lang variable that you can expand to include different languages if they're installed. I've used the UB Mannheim ( installation which includes a ton of languages supported.

            To get better and more accurate results, the best thing to do is to process the image before handing it to Tesseract. Set a white/black threshold so that you have black text on white background with no shading. I'm not sure how to do this in Node, but I've done it with Python's OpenCV library.

            If that font doesn't get you decent results with the out of the box, then you'll want to train your own, yes. This blog post walks through the process in great detail: It revolves around using the jTessBoxEditor to hand-label the objects detected in the images you're using.

            Edit: In brief, the process to train your own:

            1. Install jTessBoxEditor ( Requires Java Runtime installed as well.
            2. Collect your training images. They want to be .tiffs. I found I got fairly accurate results with not a whole lot of images that had a good sample of all the characters I wanted to detect. Maybe 30/40 images. It's tedious, so you don't want to do TOO many, but need enough in order to get a good sampling.
            3. Use jTessBoxEditor to merge all the images into a single .tiff
            4. Create a training label file (.box)j. This is done with Tesseract itself. tesseract your_language.font.exp0.tif your_language.font.exp0 makebox
            5. Now you can open the box file in jTessBoxEditor and you'll see how/where it detected the characters. Bounding boxes and what character it saw. The tedious part: Hand fix all the bounding boxes and characters to accurately represent what is in the images. Not joking, it's tedious. Slap some tv episodes up and just churn through it.
            6. Train the tesseract model itself
            • save a file: font_properties who's content is font 0 0 0 0 0
            • run the following commands:

            tesseract num.font.exp0.tif font_name.font.exp0 nobatch box.train


            shapeclustering -F font_properties -U unicharset -O font_name.unicharset

            mftraining -F font_properties -U unicharset -O font_name.unicharset


            You should, in there close to the end see some output that looks like this:

            Master shape_table:Number of shapes = 10 max unichars = 1 number with multiple unichars = 0

            That number of shapes should roughly be the number of characters present in all the image files you've provided.

            If it went well, you should have 4 files created: inttemp normproto pffmtable shapetable. Rename them all with the prefix of your_language from before. So e.g. your_language.inttemp etc.

            Then run:

            combine_tessdata your_language

            The file: your_language.traineddata is the model. Copy that into your Tesseract's data folder. On Windows, it'll be like: C:\Program Files x86\tesseract\4.0\tessdata and on Linux it's probably something like /usr/shared/tesseract/4.0/tessdata.

            Then when you run Tesseract, you'll pass the lang=your_language. I found best results when I still passed an existing language as well, so like for my stuff it was still English I was grabbing, just funny fonts. So I still wanted the English as well, so I'd pass: lang=your_language+eng.



            Can VNImageRequestHandler accepts MLMultiArray as an input? (Without converting to UIImage)
            Asked 2021-Jun-15 at 09:01

            I have two MLModels in my app. The first one is generating an MLMultiArray output which is meant to be used as the second model input.
            As I'm trying to make things as performance-best as possible. I was thinking about using VNImageRequestHandler to feed it with the first model output (MLMultiArray) and use Vision resize and rectOfIntersent to avoid converting the first input to an image, crop features, to avoid the need to convert the first output to image, do everything manually and use the regular image initializer.

            Something like that:



            Answered 2021-Jun-15 at 09:01

            Vision uses images (hence the name ;-) ). If you don't want to use images, you need to use the Core ML API directly.

            If the output from the first model really is an image, it's easiest to change that model's output type to an image so that you get a CVPixelBuffer instead of an MLMultiArray. Then you can directly pass this CVPixelBuffer into the next model using Vision.



            How and where can i freeze classifier layer?
            Asked 2021-Jun-12 at 20:29

            If I need to freeze the output layer of this model which is doing the classification as I don't need it.



            Answered 2021-Jun-11 at 15:33

            You are confusing a few things here (I think)

            Freezing layers

            You freeze the layer if you don't want them to be trained (and don't want them to be part of the graph also).

            Usually we freeze part of the network creating features, in your case it would be everything up to self.head.

            After that, we usually only train bottleneck (self.head in this case) to fine-tune it for the task at hand.

            In case of your model it would be:



            Python image types, shapes, and channels for segmentation
            Asked 2021-Jun-07 at 07:07

            I am using this tutorial for instance segmentation in PyTorch. The test data the tutorial uses includes images and accompanying image masks from a dataset available here. I have an example of one of the image masks from that data set here (example data for this question). That mask looks like this by default in the dataset:

            The tutorial uses this code:



            Answered 2021-Jun-07 at 07:07

            Following is an example how to create a grayscale image representing classes for a segmentation task or similar.

            On some black background, draw some shapes with fill values in the range of 1, ..., #classes. For visualization purposes, this mask is plotted as perceived as a regular grayscale image as well as scaled to the said value range – to emphasize that the mask looks all black in general, but there's actual content in it. This mask is saved as a lossless PNG image, and then opened using Pillow, and converted to mode P. Last step is to set up a proper palette for the desired number of colors, and apply that palette using Image.putpalette.



            Google Vision API - Running Explicit Content Detection (Safe Search) Using a URL
            Asked 2021-Jun-07 at 01:23

            I'm having trouble running an image from a URL through the Vision API's Safe Search/Explicit Content Detection. Python Samples can be found here:


            If I were to save the below in a python file - what is the best way to run it? I tried !python safe-search-uri but it's not working. Maybe I'm missing some of the code or running it the wrong way?

            Sample Code from above github:



            Answered 2021-Jun-07 at 01:23

            If you just executed the code snippet you included in your question, you are not passing the value uri to the code properly. You need to parse the arguments you are passing on your python command. You can do this by adding argparse.



            Sequential filling of a jagged array. How does this code work?
            Asked 2021-Jun-06 at 11:48

            I was going through one of the solutions on the jagged array and could not follow a few of the below lines. Can anyone help me in understanding the below-the-line how the count is being utilized here. I do understand basics of Java but not getting a vision why count is exactly used here.

            Program to demonstrate 2-D jagged array in Java:



            Answered 2021-Jun-06 at 09:53

            You can add output to this code. The count variable is needed to sequentially fill the array with integers from 0 and so on.



            Difference in histograms of a colored image using plt.plot v/s plt.hist [Python]
            Asked 2021-Jun-06 at 08:34

            I used the code below to generate histograms of a colored image using 2 methods :

            Method 1 :-

            1. Using cv2.calcHist() function to calculate the frequency
            2. Using plt.plot() to generate a line plot of the frequency

            Method 2 :-

            1. Using plt.hist() function to calculate and generate the histogram (I added bin=250 so that the 2 histograms are consistent)

            Observation : Both histograms are roughly similar. The 1st histogram (using plt.plot) looks pretty smooth. However the 2nd histogram (using plt.hist) has additional spikes and drops.

            Question : Since the image only has int values there shouldn't be inconsistent binning. What is the reason for these additional spikes and drops in the histogram-2 ?



            Answered 2021-Jun-06 at 08:34

            bins=250 creates 251 equally spaced bin edges between the lowest and highest values. These don't align well with the discrete values. When the difference between highest and lowest is larger than 250, some bins will be empty. When the difference is smaller than 250, some bins will get the values for two adjacent numbers, creating a spike. Also, when superimposing histograms, it is handy that all histograms use exactly the same bin edges.

            You need the bins to be exactly between the integer values, setting bins=np.arange(-0.5, 256, 1) would achieve such. Alternatively, you can use seaborn's histplot(...., discrete=True).

            Here is some code with smaller numbers to illustrate what's happening.



            Cannot send my output from to template in django
            Asked 2021-Jun-05 at 18:02

            what my application does is capture live images and extract some values using google vision ocr. I get data from an external python script and i want to show the output in html text field.

            here is my template



            Answered 2021-Jun-05 at 18:02

            assuming your urlpatterns looks like this: path('external/', views.external, name='ext_data')



            SQLGrammarException:error executing work ORA-01722: invalid number in SQL | Is it SQL Injection possible?
            Asked 2021-Jun-03 at 20:15

            I am a web Application tester, While testing one of the requests i have found some SQL error when I break the lines. Someone suggested to me that SQL injection is possible but I am not sure. Can anyone suggest that is any risk of SQL injection in this CASE?

            #SQL ERROR#



            Answered 2021-Jun-03 at 20:15

            I'm not sure what "when I break the lines" means here. It doesn't appear that there is a SQL injection attack here. It looks like the JSON passed in the request has an enrollStatus of "2000 and select sleep(10)))); --" rather than, presumably, "2000". The query that is reported in the error message is using bind variables so this is just Oracle saying "hey, I'm comparing this thing you're passing in against a numeric value but I can't seem to convert it to a number". The actual value you're passing in doesn't appear in the query so it's not a SQL injection attack.

            Now, from a security standpoint, the error message is leaking a whole lot of information about the underlying schema which I'd be uncomfortable handing an attacker. It is possible that knowing that information would help an attacker to craft a SQL injection attack somewhere else in the system or to access data they're not authorized to access.



            How to adjust sparql query to return even brief info
            Asked 2021-Jun-03 at 07:26

            I am new on this side, the question-asking side, so please tell me if you need any additional information.

            I have a dataset with 2900 entries consisting mostly Dutch and Flemish poets. I want to add information to this dataframe by querying wikidata; gender, nationality, day of birth, day of death. Now how many poets can two small countries have? Not all of them are to be found on wikidata (I'm going to take care of that later), and for the ones that are, the info is sometimes very scarce.

            I have used the following query:



            Answered 2021-Jun-03 at 07:24

            The intuition of using OPTIONAL is correct. You have to add it for every single information that you want to consider optional (i.e. not necessary).

            Furthermore, for avoiding false positives, I think you should also use rdfs:label instead of a generic ?label (which can refer to any property).


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


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