See this, or withsystem['python python_script.py'];
This was the top result for my Google search:link
I have a Python code.how can I convert it into matlab code or is there any option of executing the same using matlab
import cv2
from PIL import Image
import numpy as np
from glob import glob
import os
def main[]:
# path of the folder containing the raw images
inPath =["Z://Randomimages"]
# path of the folder that will contain the modified image
outPath =["Z:/normaltogray"]
for files in os.walk[inPath]:
for imagePath in os.listdir[inPath]:
if not imagePath.endswith[".jpg"]:
print["{} file is not an expected file".format[imagePath]]
continue
inputPath = os.path.join[inPath, imagePath]
img = np.array[Image.open[inputPath]]
if imagePath.startswith['T1_E1']:
roi = img[1360:1470,850:2700]
elif imagePath.startswith['T1_E2']:
roi= img[1370:1450,920:2770]
gray=cv2.cvtColor[roi,cv2.COLOR_BGR2GRAY]
fullOutPath = os.path.join[outPath,imagePath]
cv2.imwrite[fullOutPath,gray]
print[fullOutPath]
cv2.waitKey[0]
cv2.destroyAllWindows[]
# Driver Function
if __name__ == '__main__':
main[]
#
This may help you. There are a number of ducoments and videos related to using MATLAB with Python.
def Lagrange[x, y, n, xx]:
sum = 0
for i in range[0, n + 1]:
product = y[i]
for j in range[0, n + 1]:
if [i != j]:
product = product * [xx - x[j]] / [x[i] - x[j]]
sum += product
return sum
def Trapezoidal[h, n, f]:
sum = f[0]
for i in range [1, n]:
sum = sum + 2 * f[i]
sum = sum + f[n]
ans = h * sum / 2
return ans
Is there way to convert this python code to matlab code?
it's too hard to me :[
how to convert python to matlab???
this is code what I want to convert.
from sklearn.model_selection import train_test_split
import keras
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
np.random.seed[3]
# number of wine classes
classifications = 3
# load dataset
dataset = np.loadtxt['wine.csv', delimiter=","]
# split dataset into sets for testing and training
X = dataset[:,1:14]
Y = dataset[:,0:1]
x_train, x_test, y_train, y_test = train_test_split[X, Y, test_size=0.66, random_state=5]
# convert output values to one-hot
y_train = keras.utils.to_categorical[y_train-1, classifications]
y_test = keras.utils.to_categorical[y_test-1, classifications]
# creating model
model = Sequential[]
model.add[Dense[10, input_dim=13, activation='relu']]
model.add[Dense[8, activation='relu']]
model.add[Dense[6, activation='relu']]
model.add[Dense[6, activation='relu']]
model.add[Dense[4, activation='relu']]
model.add[Dense[2, activation='relu']]
model.add[Dense[classifications, activation='softmax']]
# compile and fit model
model.compile[loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy']]
model.fit[x_train, y_train, batch_size=15, epochs=2500, validation_data=[x_test, y_test]]
please!
Answers [2]
# Metamodel regression
X_train, X_test, y_train, y_test = \
train_test_split[LDB1.iloc[:,:-1], LDB1["d"], test_size=0.4, random_state=42]
clf = make_pipeline[SplineTransformer[],
MLPRegressor[alpha=0.0001, hidden_layer_sizes = [20, 10], max_iter = 500000,
activation = 'relu', verbose = 'True', learning_rate_init=0.01]]
a = clf.fit[X_train, y_train]
y_pred = clf.predict[X_test]
plt.figure[]
# plt.scatter[X_train[P]]
plt.scatter[X_test["P"], y_test.tolist[], label="Test values"]
plt.scatter[X_test["P"], y_pred, label="Predicted values"] # plot network output
plt.title["P vs d [Predicted and test values"]
plt.legend[]
For Deep Learning there are a few ways to import and export networks into MATLAB.
MATLAB has a direct Tensorflow Importer you could use to import the network:
//www.mathworks.com/help/deeplearning/ref/importtensorflownetwork.html
For other frameworks, you can import and export via ONNX:
Regards,
Deep Learning Product Manager, MathWorks
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