Tuesday Morning Images With Quotes | Good Morning Tuesday Images for Whatsapp

June 15, 2021

 Tuesday Morning Images With Quotes

Tuesday Morning Images With Quotes, Good Morning Tuesday God Images for Whatsapp

Tuesday Morning Images With Quotes, Good Morning Tuesday God Images for Whatsapp

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Best Stocks to Buy in India for Long Term 2021

June 11, 2021

Best Stocks to Buy in India for Long Term 2021

I will recommend these stocks:

Comfortable Profit with Low Risk:

  1. Jubilant Food (Dominos Pizza company)
  2. PIDILITE (feviquick company)
  3. ASIAN Paint
  4. MUTHOOTFIN (Gold Finance Company)
  5. Polycab - (Cable wire company)
  6. IRCTC

Good Profit with Moderate Risk:

  1. IRCTC
  2. Jubilant Food

Huge Profit with Huge Risk:

  1. IEX (Energy Exchange of India)
  2. Dexion Tech (Contact manufacturer)
  3. Astrol Polytick
  5. Nazara Technologies
  6. IndiaMart Intermesh

A balanced Portfolio:

  1. Jubilant Food (Dominos Pizza company)
  2. PIDILITE (feviquick company)
  3. IRCTC
  5. IEX

If you like then you can follow me also.

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elasticsearch-dump with id and password

June 06, 2021

use like this
elasticdump --input https://user:password@my.es.com/index

elasticdump \
  --input=http://userid:passward@ \
  --output= \

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Top IOT Stocks in India | Top 5 Internet of Things (IoT) Stocks in India

June 03, 2021

Top IoT Stocks in India

  1. MosChip Technologies
  2. Tata Elxsi
  3. Subex Limited
  4. Cybermate Infotek
  5. Saksoft
  6. Persistent Systems
  7. LTI
  8. Kellton Tech
  9. L&T Technology Services
  10. Mphasis
  11. Happiest Minds Technologies
  12. Mastek

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Table Detection using Deep Learning

May 28, 2021

Step 1: Preprocessing Tables

We are using Luminoth(Tensorflow) as the backend. Luminoth only works on preprocessed(greyscale) images.  

Use the below script to convert images into grayscale images:


import osimport cv2import pandas as pdroot_dir = os.getcwd()file_list = ['train.csv', 'val.csv']image_source_dir = os.path.join(root_dir, 'data/images/')data_root = os.path.join(root_dir, 'data')for file in file_list:    image_target_dir = os.path.join(data_root, file.split(".")[0])    if not os.path.exists(image_target_dir):        os.mkdir(image_target_dir)    # read list of image files to process from file    image_list = pd.read_csv(os.path.join(data_root, file), header=None)[0]    print("Start preprocessing images")    for image in image_list:        # open image file        img = cv2.imread(os.path.join(image_source_dir, image))        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)        # perform transformations on image        b = cv2.distanceTransform(img, distanceType=cv2.DIST_L2, maskSize=5)        g = cv2.distanceTransform(img, distanceType=cv2.DIST_L1, maskSize=5)        r = cv2.distanceTransform(img, distanceType=cv2.DIST_C, maskSize=5)        # merge the transformed channels back to an image        transformed_image = cv2.merge((b, g, r))        target_file = os.path.join(image_target_dir, image)        print("Writing target file {}".format(target_file))        cv2.imwrite(target_file, transformed_image)print("Finished preprocessing images")

Step 2: Create TFRecords

To train our model we need to convert our preprocessed images into TFrecords.

run this script. create TFRecords

lumi dataset transform --type csv --data-dir data/ --output-dir tfdata/ --split train --split val --only-classes=table

Step 3: Training our Model

Start training the luminoth network
Run this script: start_traning.sh

lumi train -c config.yml


train:  # Name used to identify the run. Data inside `job_dir` will be stored under  # `run_name`.  run_name: table-area-detection-0.1  # Base directory in which model checkpoints & summaries (for Tensorboard) will  # be saved.  job_dir: jobs/  save_checkpoint_secs: 10  save_summaries_secs: 10  # Number of epochs (complete dataset batches) to run.  num_epochs: 10dataset:  type: object_detection  # From which directory to read the dataset.  dir: tfdata/classes-table/  image_preprocessing:    min_size: 600    max_size: 1024  data_augmentation:    - flip:        left_right: True        up_down: True        prob: 0.5model:  type: fasterrcnn  network:    # Total number of classes to predict.    num_classes: 1


It can take more than 1 day(24 hours) to train.

You will see last output like this:

INFO:tensorflow:Saving checkpoints for 3371 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3371, file: b'9527_018.png', train_loss: 2.469784736633301, in 23.64s
INFO:tensorflow:Saving checkpoints for 3372 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3372, file: b'9526_017.png', train_loss: 2.6023592948913574, in 21.86s
INFO:tensorflow:Saving checkpoints for 3373 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3373, file: b'1742_157.png', train_loss: 2.5478856563568115, in 22.36s
INFO:tensorflow:Saving checkpoints for 3374 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3374, file: b'9528_061.png', train_loss: 3.118919849395752, in 21.22s
INFO:tensorflow:Saving checkpoints for 3375 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3375, file: b'9516_001.png', train_loss: 3.0582146644592285, in 21.71s
INFO:tensorflow:Saving checkpoints for 3376 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3376, file: b'9509_040.png', train_loss: 2.7756423950195312, in 22.40s
INFO:tensorflow:Saving checkpoints for 3377 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3377, file: b'9508_065.png', train_loss: 3.152759552001953, in 22.57s
INFO:tensorflow:Saving checkpoints for 3378 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3378, file: b'5008_029.png', train_loss: 2.618196725845337, in 21.45s
INFO:tensorflow:Saving checkpoints for 3379 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3379, file: b'9518_018.png', train_loss: 2.6546759605407715, in 21.48s
INFO:tensorflow:Saving checkpoints for 3380 into jobs/table-area-detection-0.1/model.ckpt.
INFO:tensorflow:step: 3380, file: b'9515_024.png', train_loss: 3.292630434036255, in 21.28s
INFO:tensorflow:finished training after 10 epoch limit

To minimize training loss, you can train 20 epochs also.

Step 4: store last checkpoint

you have to store last checkpoint. it will be used in the prediction step.


lumi checkpoint create config.yml

Step 5: Prediction

You can use luminoth in two form:

  1. command line
  2. luminoth web server
  3. Using python

1. Command line:

Run this script on cmd:

lumi predict --checkpoint c2155084dca6 data/val/9541_023.png

Run this script to start Luminoth web server:

lumi server web --checkpoint c2155084dca6

3. Using Python:

Follow this Link:


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May 27, 2021


This thread will answer the most common question you ask yourselves several times, WHY should we be investing in stock markets.
If it brings clarity, share with people who are still thinking & retweet.

1. Inflation
Costs are rising and the benefit of buying influence of cash is diminishing. The inflation in India throughout the previous few years has been around 4-5%. In the event that you purchase good stocks,you can make a respectable steady return of 12-18%/annum

2. Put resources into Stocks Because You'll Make More Than Other Investments
Glancing back at history, by and large, financial backers have benefitted more from purchasing stocks than from purchasing bonds, purchasing a home, or most other venture alternatives.

3. Make money work for you
You simply need to sit idle after investing in good co.s. Your cash will develop as the organization flourishes. In the interim, you can utilize your opportunity to zero in on your essential work or in the manner in which you need.

4. They're Easy to Invest In
Stocks are quick, simple, and modest to exchange, though land and numerous different speculations are not. Stocks are frequently called "liquid resources," which simply implies they can be transformed into cash moderately rapidly.

5. Small cap
There is a typical misinterpretation among numerous individuals that they need an enormous sum to master the stock market. You can contribute a modest quantity of cash and begin getting returns. This alternative isn't accessible in other ventures like land.

6. Alternate revenue source aka diversification
Fundamentally, the more ways you need to bring in cash, the less you're in danger from falling into monetary difficulty if any one technique gets upset. Investing can be second kind of revenue.

7. Saving for retirement
Numerous individuals put resources into their retirement accounts. Instead putting resources into stocks or index funds can be an extraordinary method to put something aside for retirement.

8. You Can Own Part of a Company You Love
At the point when you purchase even a solitary portion of an organization, you're authoritatively a section proprietor.
You own a piece of business without even starting one.

Well,these were the key points of why one should be investing into stock markets!Simplifying your stock market journey with these threads.
Want more of them? Comment below the topic you need and don't forget to retweet.
Have a great evening.

You can follow @Abhishekkar_.

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endswith python list

May 18, 2021

Solution 1:
if file_name.endswith(tuple(extensions)):
Solution 2:
Though not widely known, str.endswith also accepts a tuple. You don't need to loop.
>>> 'test.mp3'.endswith(('.mp3', '.avi'))

Solution 3
if any((file_name.endswith(ext) for ext in extensions))

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