PCV | Open source Python module for computer vision | Computer Vision library

 by   jesolem Python Version: Current License: BSD-2-Clause

kandi X-RAY | PCV Summary

kandi X-RAY | PCV Summary

PCV is a Python library typically used in Artificial Intelligence, Computer Vision applications. PCV 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.

PCV is a pure Python library for computer vision based on the book "Programming Computer Vision with Python" by Jan Erik Solem. More details on the book (and a pdf version of the latest draft) can be found at programmingcomputervision.com.
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              PCV has a medium active ecosystem.
              It has 1881 star(s) with 671 fork(s). There are 167 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 23 open issues and 11 have been closed. On average issues are closed in 137 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of PCV is current.

            kandi-Quality Quality

              PCV has 0 bugs and 0 code smells.

            kandi-Security Security

              PCV has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              PCV code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              PCV is licensed under the BSD-2-Clause License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              PCV releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              PCV saves you 1425 person hours of effort in developing the same functionality from scratch.
              It has 3185 lines of code, 275 functions and 73 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed PCV and discovered the below as its top functions. This is intended to give you an instant insight into PCV implemented functionality, and help decide if they suit your requirements.
            • Generator that tracks features
            • Detects the corners of the image
            • Calculate the smoothing point
            • Match two descions
            • Compute similarity between two images
            • Plot matches between two images
            • Concatenate two images together
            • Performs rigid alignment
            • Compute rotation matrix
            • Calculate the error per point
            • Normalize points
            • R Plot an ephemeral line
            • Compute the e - dipole
            • Compute the P_from_essential
            • Symmetric skew
            • Train the model
            • Project the image
            • Triangulate the triangle
            • Pepify a directory tree
            • Center
            • Convert homogonal coordinates into homogonal coordinates
            • Calculates the calibration
            • Compute homography of the homography
            • Generate points for a cube
            • Classify a point
            • Compute p_from_matrix
            Get all kandi verified functions for this library.

            PCV Key Features

            No Key Features are available at this moment for PCV.

            PCV Examples and Code Snippets

            安装
            Pythondot img1Lines of Code : 1dot img1no licencesLicense : No License
            copy iconCopy
            python setup.py install
              

            Community Discussions

            QUESTION

            how can i replace mean instead of missing values in python
            Asked 2021-May-26 at 13:07

            In the code below, i'm trying to replace mean instead of missing values but i can't get a result for my attempts because this data includes special characters which is "?". When there is no question marks in the data this code works data.fillna(data.mean()). When i tried to impute method, i got the following error:

            ValueError: Cannot use mean strategy with non-numeric data: could not convert string to float:

            Also this data includes string columns with missing values, how can i fix missing values in the string columns (column rbc for example)?

            here is my data: https://easyupload.io/te2mbc

            ...

            ANSWER

            Answered 2021-May-16 at 05:44

            The fact that you have '?' characters in columns 'sod' and 'pot' make pandas parse those columns as strings, so even if you do

            Source https://stackoverflow.com/questions/67552362

            QUESTION

            javascript sort array of string by value after special character
            Asked 2021-May-05 at 07:21

            I want to sort by array which contains value having colon (:)

            This is the below input

            ...

            ANSWER

            Answered 2021-May-05 at 07:21

            You could collect all groups and single values and return a flat array.

            Source https://stackoverflow.com/questions/67386157

            QUESTION

            Replace missing values with the mean of each variable Python
            Asked 2021-May-03 at 21:01

            I would like to: Replace missing values with the mean I've tried this :

            ...

            ANSWER

            Answered 2021-May-03 at 20:25

            [Edit]

            I understand the actual problem, fillna(df.mean()) fills the nan values with mean of the column. You have columns with all nan values. So you get the nan values after fillna(df.mean()) method.

            I tried your method with a random example shown in below;

            Source https://stackoverflow.com/questions/67375183

            QUESTION

            Converting a pandas Dataframe to a list of dictionary
            Asked 2021-Apr-25 at 10:47

            I have a Dataframe consisting of medical records which looks like this

            My plan is to convert it to a list of dictionary(s) which would look something like this

            ...

            ANSWER

            Answered 2021-Apr-25 at 10:33

            QUESTION

            Add view at point on screen in LinearLayout
            Asked 2021-Feb-24 at 19:17

            I'm trying to dynamically add views to a view on different points on screen using LinearLayout. This the view I'm trying to add the views to:

            ...

            ANSWER

            Answered 2021-Feb-24 at 19:17

            Ended up using RelativeLayout instead of LinearLayout with alignWithParent = true, updated add view function:

            Source https://stackoverflow.com/questions/66352979

            QUESTION

            Create 2 variables which gives me the values in var1_A to var1_E corresponding to the highest values in var2_A to var2_E
            Asked 2021-Feb-15 at 17:11

            I have a dataset similar to the one created throuhg the code below (this is a sample of the real dataset).

            I need to create two columns (let's call them "ideol_1" and "ideol_2") which gives me the values in "ideolparty" that are correspondent to the two highest columns with similar letters (A:E) in columns "pcv_PR". So, if we have in pcv_PR_A the highest value among pcv_PR_A:pcv_PR_E, the value of "ideolparty_A" should be in column "ideol1" for that row, while if pcv_PR_B is the second highest then "ideol1" should yield the value of "ideolpartyB".

            To make the sample data:

            ...

            ANSWER

            Answered 2021-Feb-15 at 09:11

            Here is an idea with pivot_longer and pivot_wider:

            Source https://stackoverflow.com/questions/66198124

            QUESTION

            Epochs loss is coming as NaN
            Asked 2020-Jul-11 at 09:35
            import tensorflow as tf
            from tensorflow import keras
            import pandas as pd
            from sklearn.model_selection import train_test_split
            import numpy as np
            xi=pd.read_csv(r'/content/ckd_full.csv')
            xi=xi.drop(columns=['sg','su','pc','pcc','pcv','rbcc','wbcc'])
            y=xi[['class']]
            y['class']=y['class'].replace(to_replace=(r'ckd',r'notckd'), value=(1,0))
            x=xi.drop(columns=['class'])
            
            
            x['rbc']=x['rbc'].replace(to_replace=(r'normal',r'abnormal'), value=(1,0))
            x['ba']=x['ba'].replace(to_replace=(r'present',r'notpresent'), value=(1,0))
            x['htn']=x['htn'].replace(to_replace=(r'yes',r'no'), value=(1,0))
            x['dm']=x['dm'].replace(to_replace=(r'yes',r'no'), value=(1,0))
            x['cad']=x['cad'].replace(to_replace=(r'yes',r'no'), value=(1,0))
            x['pe']=x['pe'].replace(to_replace=(r'yes',r'no'), value=(1,0))
            x['ane']=x['ane'].replace(to_replace=(r'yes',r'no'), value=(1,0))
            x['appet']=x['appet'].replace(to_replace=(r'good',r'poor'), value=(1,0))
            x[x=="?"]=np.nan
            d=['age', 'bp', 'al', 'rbc', 'ba', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'htn', 'dm','cad', 'appet', 'pe', 'ane']
            
            for i in d:
              x[i] = x[i].astype(float)
            x.fillna(x.median(),inplace=True) 
            xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.025)
            #begin the model
            
            
            model=keras.models.Sequential()
            model.add(keras.layers.Dense(150,input_dim = 17, activation=tf.nn.relu))
            #model.add(keras.layers.Dropout(0.5))
            model.add(keras.layers.Dense(100,input_dim = 17, activation=tf.nn.relu))
            model.add(keras.layers.Dense(50,input_dim = 17, activation=tf.nn.relu))
            model.add(keras.layers.Dense(10,input_dim = 17, activation=tf.nn.relu))
            model.add(keras.layers.Dense(5,input_dim = 17, activation=tf.nn.relu))
            model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
            model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # specifiying hyperparameters
            xtrain_tensor = tf.convert_to_tensor(xtrain, dtype=tf.float32)
            ytrain_tensor = tf.convert_to_tensor(ytrain, dtype=tf.float32)
            model.fit(xtrain_tensor , ytrain_tensor , epochs=100, batch_size=128, validation_split = 0.15, shuffle=True, verbose=2) # load the model
            #es = tf.keras.callbacks.EarlyStopping(monitor='accuracy', mode='min', verbose=1,patience=5)
            model.save('NephrologistLite') # save the model with a unique name
            myModel=tf.keras.models.load_model('NephrologistLite')  # make an object of the model
            
            ...

            ANSWER

            Answered 2020-Jul-11 at 09:35

            You have 1 record in row #403 of your dataset (on the 2nd cell) which is causing the NaN gradient problem. Just delete it from your dataset and you will be good to go.

            In addition: I removed the input_dims from the hidden layers as it isn't necessary. Tried to keep the code as close as possible to your code.

            Source https://stackoverflow.com/questions/62845291

            QUESTION

            how to use anti_join for many variables at the same time?
            Asked 2020-Jul-10 at 01:22

            I want to remove some values of n variables contained in data1 (blood) from data2 (outlier).

            ...

            ANSWER

            Answered 2020-Jul-10 at 01:22

            You can use Map here to replace the df2 values present in df1 with NA.

            Source https://stackoverflow.com/questions/62825599

            QUESTION

            ValueError: could not convert string to float: 'ane'
            Asked 2020-Jul-05 at 05:13
            print(xtest.head())
            print("predicted as",myModel.predict(xtest))
            
            ...

            ANSWER

            Answered 2020-Jul-05 at 05:13

            you should try this code I replaced smae code in colab also.

            Source https://stackoverflow.com/questions/62736905

            QUESTION

            I want to convert a dictionary to pandas dataFrame
            Asked 2020-Jul-04 at 19:27
            di={'ind': 1, 'age': 59, 'bp': 70, 'sg': 1.01, 'al': 1.0, 'su': 3.0, 'rbc': 0.0, 'pc': 0.0, 'pcc': 0.0, 'ba': 0.0, 'bgr': 424.0, 'bu': 55.0, 'sc': 1.7, 'sod': 138.0, 'pot': 4.5, 'hemo': 12.0, 'pcv': 37.0, 'wbcc': 10200.0, 'rbcc': 4.1, 'htn': 1.0, 'dm': 1.0, 'cad': 1.0, 'appet': 1.0, 'pe': 0.0, 'ane': 1.0}
            
            ...

            ANSWER

            Answered 2020-Jul-04 at 19:24

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

            Vulnerabilities

            No vulnerabilities reported

            Install PCV

            Open a terminal in the PCV directory and run (with sudo if needed on your system):. Now you should be able to do. in your Python session or script. Try one of the sample code examples to check that the installation works.

            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/jesolem/PCV.git

          • CLI

            gh repo clone jesolem/PCV

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            git@github.com:jesolem/PCV.git

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