Code: app.py

from flask import Flask, Response
import cv2app = Flask(__name__)
video = cv2.VideoCapture(0)@app.route('/')
def index():
    return "Default Message"def gen(video):
    while True:
        success, image = video.read()
        ret, jpeg = cv2.imencode('.jpg', image)
        frame = jpeg.tobytes()
        yield (b'--frame\r\n'
               b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')@app.route('/video_feed')
def video_feed():
    global video
    return Response(gen(video),
                    mimetype='multipart/x-mixed-replace; boundary=frame')if __name__ == '__main__':
    app.run(host='0.0.0.0', port=2204, threaded=True)

RUN: python app.py

In this tutorial, I will explain how to expose the rest api using python flask in Google Colab.

I have used ngrok for making the public ip from our local. So download the ngrok and extract it.

!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip!unzip -o ngrok-stable-linux-amd64.zip

‘!’ command used for run the linux command in colab jupyter

from IPython import get_ipython

‘get_ipython’ used for run the linux command in background

app.py

In the jupyter 6 and 7 cell used for run the flask app.py and ngrok

Cell 8 used for see the public ip. Once you got the ngrok public ip, paste into the browser and see the output.

This is the complete google colab code.

Chat application but it’s a one way messaging system

This is blog explain how to steaming the message using redis. The Redis is a python library to streaming message with publisher and subscriber. This is a one way chat, don’t have the reply option in this application.

Sender only send a message to receiver and it’s doesn’t receive a message from any receiver.
Receiver only receive a message and it’s doesn’t send a message to any sender.

Full source code GitHub link is here.

Instructions of the setup:

Installing Steps:

sudo apt-get install redis-server
pip install redis
pip install flask
pip install flask_cors

Running…

Speed ​​and accuracy are both improved

The Yolov4 released by Alexey Bochkovskiy and there are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets.

Paper Yolo v4: https://arxiv.org/abs/2004.10934

New Features:

1. Weighted-Residual-Connections (WRC)
2. Cross-Stage-Partial-connections (CSP)
3. Cross mini-Batch Normalization (CmBN)
4. Self-adversarial-training (SAT)
5. Mish activation
6. Mosaic…

Before you will check this blog when you are going to read this blog. Because I have explained how to track the object using Euclidean distance by referring to the previous frame.

In this blog explains how to track each object by referring to the ’n’ previous frame. The ’n’ means the number of frames to refer. Now I would take 8 frames to refer.

I have explained to the detection and tracking techniques in my previous blog which is I mentioned above.

Concept:

Only save the 8 frames of middle points of each bounding box and corresponding labels to find…

In my previous blog, I have explained what is object detection and how to detect and track multiple objects.

In this blog, I explain how to track each object from the video. I have assigned the unique label(1,2,3, etc) to each object and track it by reference with the previous frame.

Detection Techniques:

In yolov3 detect the objects from the image. It will return the coordinates of each object. The coordinates are x, y, width and height of each object.

This blog explains, how to train and get the named entity from my own training data using spacy and python.

This blog explains, what is spacy and how to get the named entity recognition using spacy. Now I have to train my own training data to identify the entity from the text.

In before I don’t use any annotation tool for annotating the entity from the text. But I have created one tool is called spaCy NER Annotator. The main reason for making this tool is to reduce the annotation time. This tool more helped to annotate the NER. I…

What is spaCy(v2):

spaCy is an open-source software library for advanced Natural Language Processing, written in the programming languages Python and Cython. The library is published under the MIT license and currently offers statistical neural network models for English, German, Spanish, Portuguese, French, Italian, Dutch and multi-language NER, as well as tokenization for various other languages.
spaCy v2.0 features new neural models for tagging, parsing and entity recognition. The models have been designed and implemented from scratch specifically for spaCy, to give you an unmatched balance of speed, size and accuracy.
Convolutional layers with residual connections, layer normalization and maxout non-linearity are used, giving much…