Devarshi Aggarwal devarshi16

## init.vim
"  Personal neovim config file. NOTETOSELF: paste in ~/.config/nvim/
:set number
:set autoindent
:set tabstop=4
:set shiftwidth=4
:set smarttab
:set softtabstop=4
:set mouse=a

call plug#begin('~/.config/nvim/plugged')

## linearvslogistic.py
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from tqdm import tqdm
import requests
import gzip
from numpy import loadtxt,savetxt
np.seterr(divide='ignore',invalid='ignore')

## one_hot_compare.py
import numpy as np
import matplotlib.pyplot as plt
import random
import time

def one_hot(Y):
    data_size=Y.shape[0]
    classes=np.unique(Y).reshape(-1,1)
    num_classes=classes.shape[0]
    class_mappings=np.arange(0,max(Y)+1)

## image_processing.py
import cv2
import numpy as np

# Finds distance between two points
def points_distance(pt1,pt2):
    return (((pt1[0]-pt2[0])**2 + (pt1[1]-pt2[1])**2)**(1/2))

# Orders points clockwise
# Credits pyimagesearch.com
def order_points(pts):

## text_overlay.py
# This is the problem for First technical round for the role of Computer Vision Engineer at Vectorly
# More details at https://www.linkedin.com/jobs/view/1629909785/
#
# Write a function which will segment and extract the text overlay "Bart & Homer's EXCELLENT Adventure"
# Input image is at https://vectorly.io/demo/simpsons_frame0.png
# Output : Image with only the overlay visible and everything else white
#
# Note that you don't need to extract the text, the output is an image with only
# the overlay visible and everything else (background) white
#

## json_maker2.py
import os
import json
import sys
import random

data = {}

dir_names = sys.argv[1:]
current_dir = os.getcwd()
train = {}

## json_format.py
import os
import json
import cv2
import sys

textBB_items = ["Printed Key","Written Key","Check Box Key","Comment/Info" ]
fieldBB_items = [None,"detectorPrediction","None","Printed Value", "Written Value", "Check Box Value", "Logo", "Signature", "Photograph"]
extensions = ['.JPEG','.png','.jpg','.tif','.tiff']
folder_names = ['internal28','internal17','internal7','internal8','internal9']
#folder_names = ['internal7']

## packets.py
import os
import random
TASK_SIZE = 25

dir_names = [
	"cheque",
	"nach",
	"pan_and_aadhaar"
	]

## pdf_convert.py
from pdf2image import convert_from_path, convert_from_bytes
from PIL import Image
import os

files = os.listdir()
for f in files:
    if '.PDF' in f:
        imgs = convert_from_path(f)
        for i,im in enumerate(imgs):
            im.save('.'.join(f.split('.')[:-1])+"_"+str(i)+".JPEG","JPEG")

## aocr_test.ipynb

      
        
          
            
              
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                devarshi16
                / aocr_test.ipynb
            
            
              Last active
              December 19, 2019 13:18
            
              
                aocr package usage on colab
              
          
        
      
        
  
    
    

          
    
      
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	" Personal neovim config file. NOTETOSELF: paste in ~/.config/nvim/
	:set number
	:set autoindent
	:set tabstop=4
	:set shiftwidth=4
	:set smarttab
	:set softtabstop=4
	:set mouse=a

	call plug#begin('~/.config/nvim/plugged')
	import os
	import numpy as np
	import time
	import matplotlib.pyplot as plt
	from tqdm import tqdm
	import requests
	import gzip
	from numpy import loadtxt,savetxt
	np.seterr(divide='ignore',invalid='ignore')
	import numpy as np
	import matplotlib.pyplot as plt
	import random
	import time

	def one_hot(Y):
	data_size=Y.shape[0]
	classes=np.unique(Y).reshape(-1,1)
	num_classes=classes.shape[0]
	class_mappings=np.arange(0,max(Y)+1)
	import cv2
	import numpy as np

	# Finds distance between two points
	def points_distance(pt1,pt2):
	return (((pt1[0]-pt2[0])2 + (pt1[1]-pt2[1])2)**(1/2))

	# Orders points clockwise
	# Credits pyimagesearch.com
	def order_points(pts):
	# This is the problem for First technical round for the role of Computer Vision Engineer at Vectorly
	# More details at https://www.linkedin.com/jobs/view/1629909785/
	#
	# Write a function which will segment and extract the text overlay "Bart & Homer's EXCELLENT Adventure"
	# Input image is at https://vectorly.io/demo/simpsons_frame0.png
	# Output : Image with only the overlay visible and everything else white
	#
	# Note that you don't need to extract the text, the output is an image with only
	# the overlay visible and everything else (background) white
	#
	import os
	import json
	import sys
	import random

	data = {}

	dir_names = sys.argv[1:]
	current_dir = os.getcwd()
	train = {}
	import os
	import random
	TASK_SIZE = 25

	dir_names = [
	"cheque",
	"nach",
	"pan_and_aadhaar"
	]
	from pdf2image import convert_from_path, convert_from_bytes
	from PIL import Image
	import os

	files = os.listdir()
	for f in files:
	if '.PDF' in f:
	imgs = convert_from_path(f)
	for i,im in enumerate(imgs):
	im.save('.'.join(f.split('.')[:-1])+"_"+str(i)+".JPEG","JPEG")