devarshi16

"  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')

devarshi16

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')

devarshi16

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)

devarshi16

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):

devarshi16

# 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
#

devarshi16

import os
import json
import sys
import random

data = {}

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

devarshi16

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']

devarshi16

import os
import random
TASK_SIZE = 25

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

devarshi16

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")

devarshi16