Day #292 - Gstream on Windows10

Note - Do "complete" instead of "typical" for both cases for it to work

This link was useful to experiment and follow.

Step 1. Download installer1 from link

Step 2. Download installer2 from link

Install both of them in Windows, Goto Folder - F:\gstreamer\1.0\x86_64\bin

Step #3 - Command
gst-launch-1.0.exe -v ksvideosrc device-index=0 ! video/x-raw, format=YUY2, width=320, heigh=240, framerate=30/1, pixel-aspect-ratio=1/1 ! videoconvert ! autovideosink

Stream Output

Next Post Stream from rasberry pi to windows

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Day #291 - Working with chatterbot - Windows 10

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Day #290 - Yolo from OpenCV DNN - Windows 10

OpenCV has examples to invoke Deep Network using readNet modules

Key Methods

• cv.dnn.readNet - Load the network
• cv.dnn.NMSBoxes - Non Max Suppression
• cv.dnn.blobFromImage - Input for Deep Network

Steps

Results

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Day #289 - Example code for Class Creation, Data Persistence, Email and Phone number Validation

Example code for Class Creation, Data Persistence, Email and Phone number validators

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Day #288 - Messaging using pyzmq

pip install pyzmq

Example - python usage

Output

Real world use case - imagezmq: Transporting OpenCV images

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Day #287 - Dlib Custom Detector

Learning's

• Aspect ratio needs to be maintained
• Trained for only one type of Object (Vim Dishwasher)
• Trained with just a few images 30 images of train and 5 images of test
• Environment - Windows 10, After all the Setup and Steps, It would take ~45 mins to develop for label, training, and testing

Data Set

• Custom Shelf and Vim dishwash Detection

Image Pre-requisites

• I resized data to 512 x 512 format to be consistent
• Place all images in train and test directory respectively before next steps

Label Training Data

• Img Lab Installation - Refer previous post
• Use Shift key to select rectangle
• Use Alt D to delete region
• Each tool has its own commands

Label Testing Data

Training Code

Test Code

Result

Make your own Haar Cascade on Windows | Quick & Simple

Making your own Haar Cascade Intro - OpenCV with Python for Image and Video Analysis 

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Day #286 - Working with imglab Annotation Tool

Working with imglab annotation tool.


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Learning Moments

Last three-four days, I was breaking my head for a segmentation task. There were a ton of tutorials. Everywhere I pick a code and ended up not working. Finally, I managed to segment it. When we sit and learn alone defintely there will be moments of long failures. Do whatever you do with a bit of curiosity and interest. Learning is an ongoing habit. We are not used to proper learning with focus, attention, curiosity, passion, and experimentation.  

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Day #285 - Experimenting with Unet Segmentation

U-Net

• Symmetric U-Shape - Convolutions + Poolings
• Up-Convolutions - Upsampled layers
• Encoder / Decoder
• Contraction / Expansion
• Skip Connections to learn pixel information

There are a ton of tutorials out there but it takes time to find to what works for us :) in our environment. I was experimenting on u-net based segmentation past few days. I will share my learnings on what worked for me.

Step 1 - The initial image is

I am interested in segmenting the parts (products)

Step 2 - The first step is to resize the image into 256 x 256 dimension

Step 3 - The Next Step is to binarize the image

This is the source image. The target image is

Step 4 - Tool - I used paint 3D and white brush in it to segment the required parts for my need

Step 5 - Follow the steps and create the train and label (source and segmented image)

Step 6 - Train the model, Got the repo and customized it link

Step 7 - The predictions for the test image are

Next Demo

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Day #284 - OpenCV Error in Windows server 2012

• Turn windows features on or off
• Skip the roles screen and directly go to Feature screen
• Select "Desktop Experience" under "User Interfaces and Infrastructure"

Think answer was useful link

Happy Learning!!!