shivanandmn

import pandas as pd
import numpy as np
import cv2
image_files = pd.DataFrame(columns=range(784)).add_prefix('pixels_')
for i in range(1, 6):
    r_image = cv2.imread(f'images/{i}.JPG')
    numpy_image = cv2.cvtColor(r_image, cv2.COLOR_BGR2GRAY)
    image = cv2.resize(numpy_image, (28, 28)).astype(np.float32)
    image = image.reshape(-1)
    image_files.loc[f'image_{i}', 'pixels_0':] = image

shivanandmn

class LightningModel(pl.LightningModule):
    def __init__(self):
      super().__init__()

    def forward(self,x):
      pass

    def configure_optimizers(self):
      pass

shivanandmn

class LightningDataModule(pl.LightningDataModule):
  def __init__(self):
    super().__init__()
  
  def prepare_data(self):
    pass
  
  def setup(self,stage=None):
    pass
  

shivanandmn

dataset = LightningDataModule()
pl_model = LightningModel()
trainer = pl.Trainer(max_epochs=10)
trainer.fit(model=pl_model,datamodule=dataset)
#trainer.test(test_dataset) 

shivanandmn

# changing the names of the files in a directory
import os
import re

path = os.getcwd()
k = os.listdir(path)
for files in k:
    x = re.search("\d+", files)
    if x is None:
        continue

shivanandmn

#install boost library
!sudo apt-get install libboost-dev
#boost-root or boost-include-directory may be here /usr/include/boost
####follow below with example
"""Consider your installing mesh from https://github.com/MPI-IS/mesh, it has makefile(filename:makefile)"""
#--Cloning repository
!git clone https://github.com/MPI-IS/mesh.git 
#--Providing permission to admin rights
!chmod 755 /content/mesh/Makefile
#--move to its project root directory by following command

shivanandmn

#install upgraded to get full-dataset, otherwise you may get sample of it. 
!pip install -q kaggle==1.5.6

#upload kaggle.json file
from google.colab import files
files.upload()

#must create ~/.kaggle directory and copy kaggle.json to it.
!mkdir ~/.kaggle
!cp kaggle.json ~/.kaggle

shivanandmn

#pip install dlib
#download pretrained dlib file and unzip 
#$wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
#$bzip2 -d /content/shape_predictor_68_face_landmarks.dat.bz2

import sys
import numpy as np 
import cv2 
#below line of code is used only in colab
from google.colab.patches import cv2_imshow

shivanandmn

import webbrowser
import pandas as pd 
df = pd.read_csv("sub.csv")
for i in df["files"].values.tolist():
    urls = "http://127.0.0.1:5000/question/"+str(i[:-3])
    #opens url in default browser, you can also use open_tab()
    webbrowser.get().open(url=urls)

shivanandmn

git branch -a #get all branch
git checkout master #get to the master branch
git checkout -b new_branch #(create and get) new branch
  git push -u origin new_branch  #push changes to new_branch to repo. for first push -u will set upstream/tracker.

git pull origin guru #pull the repo from guru branch
git add . # add present changes 
git commit -m "fdhkfjd" # commit all the added changes.
  git push origin guru #then push it