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#!/bin/bash
### Customization:
blue="\033[0;34m"
brightblue="\033[1;34m"
cyan="\033[0;36m"
brightcyan="\033[1;36m"
green="\033[0;32m"
brightgreen="\033[1;32m"
red="\033[0;31m"
brightred="\033[1;31m"

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Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

###Array ####Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

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Using Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

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import numpy as np
import matplotlib.pyplot as plt

x= range(1,15)
x1= range(2,16)
x2= range(3,17)
y= np.asarray([0.8219895422 , 0.8403141689 , 0.9581152138 , 0.9921465969 , 0.9921465969 , 0.9895288058, 0.9973822383 , 0.9895288158, 0.997382199 , 0.997382199 , 0.997382199, 0.9921466362, 0.9947644372, 0.9947643979])

variability = [0.0264397719, 0.0228848261, 0.0287958109, 0.0157067863, 0.0081151739, 0.0104711742, 0.0026177617, 0.0078533832, 0.002617801, 0.00011, 0.002346846, 0.002617801, 0.0052355628, 0.0052356021]
# example data

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import numpy as np

def computeIoU(y_pred_batch, y_true_batch):
    return np.mean(np.asarray([pixelAccuracy(y_pred_batch[i], y_true_batch[i]) for i in range(len(y_true_batch))])) 

def pixelAccuracy(y_pred, y_true):
    y_pred = np.argmax(np.reshape(y_pred,[N_CLASSES_PASCAL,img_rows,img_cols]),axis=0)
    y_true = np.argmax(np.reshape(y_true,[N_CLASSES_PASCAL,img_rows,img_cols]),axis=0)
    y_pred = y_pred * (y_true>0)
    

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# def predict_id(id, model, trs):
#     img = utils.M(id)
#     x = utils.stretch_n(img)

#     cnv = np.zeros((960, 960, 8)).astype(np.float32)
#     prd = np.zeros((n_classes, 960, 960)).astype(np.float32)
#     cnv[:img.shape[0], :img.shape[1], :] = x

#     for i in range(0, 6):
#         line = []

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import requests
from bs4 import BeautifulSoup

url = 'https://www.drikpanchang.com/muhurat/choghadiya.html?geoname-id=5375480'
chogadiya_selector = "body > div.dpPageWrapper > div.dpInnerWrapper > div.dpPHeaderWrapper > div.dpPHeaderContent.dpFlex > div.dpPHeaderLeftWrapper > div.dpPHeaderLeftContent.dpFlex > div:nth-child(2) > div.dpPHeaderLeftTitle"
time_selector = "body > div.dpPageWrapper > div.dpInnerWrapper > div.dpPHeaderWrapper > div.dpPHeaderContent.dpFlex > div.dpPHeaderLeftWrapper > div.dpPHeaderLeftContent.dpFlex > div:nth-child(2) > div:nth-child(2)"

response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')