PCV | Open source Python module for computer vision | Computer Vision library
kandi X-RAY | PCV Summary
kandi X-RAY | PCV Summary
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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Top functions reviewed by kandi - BETA
- 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
PCV Key Features
PCV Examples and Code Snippets
Community Discussions
Trending Discussions on PCV
QUESTION
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 05:44The fact that you have '?' characters in columns 'sod' and 'pot' make pandas parse those columns as strings, so even if you do
QUESTION
I want to sort by array which contains value having colon (:)
This is the below input
...ANSWER
Answered 2021-May-05 at 07:21You could collect all groups and single values and return a flat array.
QUESTION
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;
QUESTION
ANSWER
Answered 2021-Apr-25 at 10:33Check out to_dict(orient = 'records')
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_dict.html
QUESTION
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 19:17Ended up using RelativeLayout
instead of LinearLayout
with alignWithParent = true, updated add view function:
QUESTION
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 09:11Here is an idea with pivot_longer
and pivot_wider
:
QUESTION
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 09:35You 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.
QUESTION
I want to remove some values of n variables contained in data1 (blood) from data2 (outlier).
...ANSWER
Answered 2020-Jul-10 at 01:22You can use Map
here to replace the df2
values present in df1
with NA
.
QUESTION
print(xtest.head())
print("predicted as",myModel.predict(xtest))
...ANSWER
Answered 2020-Jul-05 at 05:13you should try this code I replaced smae code in colab also.
QUESTION
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 19:24You can try
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