<jupyter_start><jupyter_text>X_train<jupyter_code># Convirtiendo las imagenes 2 arrays
minimo = 0
maximo = 15000
#X_train = skimage.io.imread(X_train_path[0][0]) 
X_train = path2array(X_train_path,minimo,maximo)
print(X_train.shape)
#im_test = skimage.io.imread(X_train_path[0][0])
#im_test = cv2.imread(X_train_path[0][500], cv2.COLOR_BGR2RGB)
PIL.Image.fromarray(X_train[1000])
gaussiana = cv2.GaussianBlur(X_train[14000], (3,3), 0)	
canny = cv2.Canny(gaussiana, 10,300)
gamma = adjust_gamma(gaussiana,0.5)
canny = cv2.Canny(gamma, 90,150)
PIL.Image.fromarray(gamma)
temp = skimage.io.imread('IMG/center_2018_11_20_17_12_32_854.jpg')[60:-25,:,:]
PIL.Image.fromarray(X_train[36000])

# Save file
filename = os.path.join("data/", "{}.npy".format("X_train2"))
np.save(filename,X_train)<jupyter_output><empty_output><jupyter_text>Y_train<jupyter_code>#Take data with just belong to the center camera >> Angulos
#Y_train = Y_train.iloc[img_center_index]
Y_train = angle_dt
Y_train_ = np.array(Y_train)
Y_train = np.append(Y_train_[minimo:maximo],-Y_train_[minimo:maximo],axis=0)
Y_train = np.append(Y_train, Y_train_[minimo:maximo],axis=0)
# Save file
filename = os.path.join("data/", "{}.npy".format("Y_train2"))
np.save(filename,Y_train)
Y_train.shape
!sudo chown -R $USER:$USER IMG<jupyter_output><empty_output>
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '15582', 'license': 'MIT', 'n_tokens_mistral': 613, 'n_tokens_neox': 574, 'n_words': 75, 'path': 'data_collect/data_preprocesing_train (1).ipynb', 'repo_name': 'DavidCastilloAlvarado/Self_driving_car', 'size': '1311'}