Added rendered week 9
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<item>
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||||
<title>Week 9</title>
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<link>https://mmp.oscar.blue/posts/week-9/</link>
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<pubDate>Sun, 03 Apr 2022 13:13:48 +0100</pubDate>
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<description>This week I worked on getting focus detection working. I implemented basic laplatian blur detection1 and fast fourier blur detection2.
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Finding the threshold for both can be a challenge
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General pipeline # I continued to develop a general pipeline to fit all the filters in
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https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/&#160;&#x21a9;&#xfe0e;
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https://pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/&#160;&#x21a9;&#xfe0e;
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</description>
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</item>
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<item>
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<title>Week 8</title>
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<link>https://mmp.oscar.blue/posts/week-8/</link>
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<article class="markdown book-post">
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<h2>
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<a href="/posts/week-9/">Week 9</a>
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</h2>
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<h5>April 3, 2022</h5>
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<p>This week I worked on getting focus detection working. I implemented basic laplatian blur detection1 and fast fourier blur detection2.
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Finding the threshold for both can be a challenge
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General pipeline # I continued to develop a general pipeline to fit all the filters in
|
||||
https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/ ↩︎
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https://pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/ ↩︎
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||||
</p>
|
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</article>
|
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||||
<article class="markdown book-post">
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||||
<h2>
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<a href="/posts/week-8/">Week 8</a>
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<item>
|
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<title>Week 9</title>
|
||||
<link>https://mmp.oscar.blue/posts/week-9/</link>
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||||
<pubDate>Sun, 03 Apr 2022 13:13:48 +0100</pubDate>
|
||||
|
||||
<guid>https://mmp.oscar.blue/posts/week-9/</guid>
|
||||
<description>This week I worked on getting focus detection working. I implemented basic laplatian blur detection1 and fast fourier blur detection2.
|
||||
Finding the threshold for both can be a challenge
|
||||
General pipeline # I continued to develop a general pipeline to fit all the filters in
|
||||
https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/&#160;&#x21a9;&#xfe0e;
|
||||
https://pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/&#160;&#x21a9;&#xfe0e;
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||||
</description>
|
||||
</item>
|
||||
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||||
<item>
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||||
<title>Week 8</title>
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||||
<link>https://mmp.oscar.blue/posts/week-8/</link>
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||||
<meta name="description" content="This week is the first week of the project. I researched academic papers, existing code and dataset relating to the topic of determining aesthetics.
|
||||
|
|
@ -23,7 +23,7 @@ Code # Image Quality Assessment - Convolutional Neural Networks to predict the
|
|||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6bd18a0f048eff9ca273fd5ac40fe7159f145a30edf28a0847cab90265bd241a.js" integrity="sha256-a9GKDwSO/5yic/1axA/nFZ8UWjDt8ooIR8q5AmW9JBo="></script>
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
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|
||||
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|
|||
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|
|||
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|
||||
|
||||
<head>
|
||||
<meta name="generator" content="Hugo 0.95.0" />
|
||||
<meta name="generator" content="Hugo 0.93.2" />
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<meta name="description" content="This week I set up my repositories and starting writing some basic code.
|
||||
|
|
@ -19,7 +19,7 @@ Set up # Before starting any coding, I wanted to set up my remote git repositor
|
|||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6bd18a0f048eff9ca273fd5ac40fe7159f145a30edf28a0847cab90265bd241a.js" integrity="sha256-a9GKDwSO/5yic/1axA/nFZ8UWjDt8ooIR8q5AmW9JBo="></script>
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
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|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
<html lang="en" dir="ltr">
|
||||
|
||||
<head>
|
||||
<meta name="generator" content="Hugo 0.95.0" />
|
||||
<meta name="generator" content="Hugo 0.93.2" />
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<meta name="description" content="Filming footage # At the start of the week I went into town to film some practise footage to work with later (up until this point I had been experimenting with footage limited by my bedroom walls). I took some basic vertical and horizontal footage of the town - no nature or breach footage yet.
|
||||
|
|
@ -19,7 +19,7 @@ Gaining more useful information # I used the footage I had recorded at the star
|
|||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6bd18a0f048eff9ca273fd5ac40fe7159f145a30edf28a0847cab90265bd241a.js" integrity="sha256-a9GKDwSO/5yic/1axA/nFZ8UWjDt8ooIR8q5AmW9JBo="></script>
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
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|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
<html lang="en" dir="ltr">
|
||||
|
||||
<head>
|
||||
<meta name="generator" content="Hugo 0.95.0" />
|
||||
<meta name="generator" content="Hugo 0.93.2" />
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<meta name="description" content="This week I did some research into how to build a CNN from scratch, including the different type of layers, loss functions, learning rates, epochs and other core concepts.12
|
||||
|
|
@ -21,7 +21,7 @@ I also set up and created this blog with Hugo to document my progress and setup
|
|||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6bd18a0f048eff9ca273fd5ac40fe7159f145a30edf28a0847cab90265bd241a.js" integrity="sha256-a9GKDwSO/5yic/1axA/nFZ8UWjDt8ooIR8q5AmW9JBo="></script>
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
<!--
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
<html lang="en" dir="ltr">
|
||||
|
||||
<head>
|
||||
<meta name="generator" content="Hugo 0.95.0" />
|
||||
<meta name="generator" content="Hugo 0.93.2" />
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<meta name="description" content="Starting to write a CNN # This week I started to implement what I had learnt about CNNs in Week 4. At this point I hadn’t designed a CNN architecture to implement, instead I wanted to have a running model regardless of performance just to see if I could implement one and understand it.
|
||||
|
|
@ -19,7 +19,7 @@ Half way through implementation, I decided to look back at the existing research
|
|||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6bd18a0f048eff9ca273fd5ac40fe7159f145a30edf28a0847cab90265bd241a.js" integrity="sha256-a9GKDwSO/5yic/1axA/nFZ8UWjDt8ooIR8q5AmW9JBo="></script>
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
<!--
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
<html lang="en" dir="ltr">
|
||||
|
||||
<head>
|
||||
<meta name="generator" content="Hugo 0.95.0" />
|
||||
<meta name="generator" content="Hugo 0.93.2" />
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<meta name="description" content="This week I finished programming the basic CNN model using transfer learning. I decided to train it for 20 epochs to make sure there weren’t any runtime errors in my code. As I don’t own an Nvidia GPU (I have an AMD GPU), I couldn’t make use of the pytorch version that utilised CUDA to speed up processing. There is a RocM version of pytorch for AMD GPUs1 but RocM isn’t as mature as CUDA and only officially supports a small subset of Linux distributions.">
|
||||
|
|
@ -17,7 +17,7 @@
|
|||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6bd18a0f048eff9ca273fd5ac40fe7159f145a30edf28a0847cab90265bd241a.js" integrity="sha256-a9GKDwSO/5yic/1axA/nFZ8UWjDt8ooIR8q5AmW9JBo="></script>
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
<!--
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
<html lang="en" dir="ltr">
|
||||
|
||||
<head>
|
||||
<meta name="generator" content="Hugo 0.95.0" />
|
||||
<meta name="generator" content="Hugo 0.93.2" />
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<meta name="description" content="Now that I had successfully run my model without any runtime errors, the next step this week was finding some GPU compute so I can train my model on much more powerful hardware to accelerate the training.
|
||||
|
|
@ -19,7 +19,7 @@ My first idea was to use cloud computing. There are machine learning specific cl
|
|||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6bd18a0f048eff9ca273fd5ac40fe7159f145a30edf28a0847cab90265bd241a.js" integrity="sha256-a9GKDwSO/5yic/1axA/nFZ8UWjDt8ooIR8q5AmW9JBo="></script>
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
<!--
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
<html lang="en" dir="ltr">
|
||||
|
||||
<head>
|
||||
<meta name="generator" content="Hugo 0.95.0" />
|
||||
<meta name="generator" content="Hugo 0.93.2" />
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<meta name="description" content="Monday # Towards the end of last week ( Week 7), I managed to refactor my code in order to make it more portable. This allowed me to train my model on different machines. I ran my training script on the uni’s GPU compute successfully for 20 epochs. The next stage was to train it for longer and analyse the results. On the Monday morning I adjusted the parameters of my training script to train for 2000 epochs instead.">
|
||||
|
|
@ -17,7 +17,7 @@
|
|||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6bd18a0f048eff9ca273fd5ac40fe7159f145a30edf28a0847cab90265bd241a.js" integrity="sha256-a9GKDwSO/5yic/1axA/nFZ8UWjDt8ooIR8q5AmW9JBo="></script>
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
<!--
|
||||
|
|
|
|||
300
public/posts/week-9/index.html
Normal file
300
public/posts/week-9/index.html
Normal file
|
|
@ -0,0 +1,300 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="en" dir="ltr">
|
||||
|
||||
<head>
|
||||
<meta name="generator" content="Hugo 0.93.2" />
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<meta name="description" content="This week I worked on getting focus detection working. I implemented basic laplatian blur detection1 and fast fourier blur detection2.
|
||||
Finding the threshold for both can be a challenge
|
||||
General pipeline # I continued to develop a general pipeline to fit all the filters in
|
||||
https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/ ↩︎
|
||||
https://pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/ ↩︎
|
||||
">
|
||||
<meta name="theme-color" content="#FFFFFF"><meta property="og:title" content="Week 9" />
|
||||
<meta property="og:description" content="This week I worked on getting focus detection working. I implemented basic laplatian blur detection1 and fast fourier blur detection2.
|
||||
Finding the threshold for both can be a challenge
|
||||
General pipeline # I continued to develop a general pipeline to fit all the filters in
|
||||
https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/ ↩︎
|
||||
https://pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/ ↩︎
|
||||
" />
|
||||
<meta property="og:type" content="article" />
|
||||
<meta property="og:url" content="https://mmp.oscar.blue/posts/week-9/" /><meta property="article:section" content="posts" />
|
||||
<meta property="article:published_time" content="2022-04-03T13:13:48+01:00" />
|
||||
<meta property="article:modified_time" content="2022-04-03T13:13:48+01:00" />
|
||||
|
||||
<title>Week 9 | MMP | Oscar Pocock</title>
|
||||
<link rel="manifest" href="/manifest.json">
|
||||
<link rel="icon" href="/favicon.png" type="image/x-icon">
|
||||
<link rel="stylesheet" href="/book.min.68be0b7a9674f2a612ce0e9b2e9447ff4b7ac96546e06b642bfd3ded0ca490ef.css" integrity="sha256-aL4LepZ08qYSzg6bLpRH/0t6yWVG4GtkK/097QykkO8=">
|
||||
<script defer src="/en.search.min.6da28ba3214575fbb4a0161217790f59114174d4695200119be7db8ab6dccb49.js" integrity="sha256-baKLoyFFdfu0oBYSF3kPWRFBdNRpUgARm+fbirbcy0k="></script>
|
||||
|
||||
<script defer src="/sw.min.6f6f90fcb8eb1c49ec389838e6b801d0de19430b8e516902f8d75c3c8bd98739.js" integrity="sha256-b2+Q/LjrHEnsOJg45rgB0N4ZQwuOUWkC+NdcPIvZhzk="></script>
|
||||
<!--
|
||||
Made with Book Theme
|
||||
https://github.com/alex-shpak/hugo-book
|
||||
-->
|
||||
|
||||
|
||||
</head>
|
||||
|
||||
<body dir="ltr">
|
||||
<input type="checkbox" class="hidden toggle" id="menu-control" />
|
||||
<input type="checkbox" class="hidden toggle" id="toc-control" />
|
||||
<main class="container flex">
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<aside class="book-menu">
|
||||
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|
||||
|
||||
<nav>
|
||||
<h2 class="book-brand">
|
||||
<a href="/"><span>MMP | Oscar Pocock</span>
|
||||
</a>
|
||||
</h2>
|
||||
|
||||
|
||||
<div class="book-search">
|
||||
<input type="text" id="book-search-input" placeholder="Search" aria-label="Search" maxlength="64" data-hotkeys="s/" />
|
||||
<div class="book-search-spinner hidden"></div>
|
||||
<ul id="book-search-results"></ul>
|
||||
</div>
|
||||
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||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<ul>
|
||||
|
||||
|
||||
<li class="book-section-flat" >
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<a href="https://mmp.oscar.blue/docs/developer/" class="">Developer Documentation</a>
|
||||
|
||||
|
||||
|
||||
<ul>
|
||||
|
||||
</ul>
|
||||
|
||||
</li>
|
||||
|
||||
|
||||
|
||||
<li class="book-section-flat" >
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<a href="https://mmp.oscar.blue/docs/user/" class="">User Documentation</a>
|
||||
|
||||
|
||||
|
||||
<ul>
|
||||
|
||||
</ul>
|
||||
|
||||
</li>
|
||||
|
||||
|
||||
</ul>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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||||
|
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||||
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||||
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||||
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||||
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|
||||
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|
||||
Blog
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a href="https://teaching.dcs.aber.ac.uk/mmp" target="_blank" rel="noopener">
|
||||
Aberystwyth MMP Site
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a href="https://git.oscar.blue" target="_blank" rel="noopener">
|
||||
Source Code
|
||||
</a>
|
||||
</li>
|
||||
|
||||
</ul>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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|
||||
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||||
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||||
|
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||||
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||||
<strong>Week 9</strong>
|
||||
|
||||
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|
||||
|
||||
<img src="/svg/toc.svg" class="book-icon" alt="Table of Contents" />
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||||
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||||
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|
||||
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||||
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||||
|
||||
|
||||
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|
||||
|
||||
|
||||
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||||
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||||
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|
||||
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|
||||
</nav>
|
||||
|
||||
|
||||
|
||||
</aside>
|
||||
|
||||
|
||||
</header>
|
||||
|
||||
|
||||
|
||||
<article class="markdown">
|
||||
<h1>
|
||||
<a href="/posts/week-9/">Week 9</a>
|
||||
</h1>
|
||||
|
||||
<h5>April 3, 2022</h5>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<p>This week I worked on getting focus detection working. I implemented basic laplatian blur detection<sup id="fnref:1"><a href="#fn:1" class="footnote-ref" role="doc-noteref">1</a></sup> and fast fourier blur detection<sup id="fnref:2"><a href="#fn:2" class="footnote-ref" role="doc-noteref">2</a></sup>.</p>
|
||||
<p>Finding the threshold for both can be a challenge</p>
|
||||
<h1 id="general-pipeline">
|
||||
General pipeline
|
||||
<a class="anchor" href="#general-pipeline">#</a>
|
||||
</h1>
|
||||
<p>I continued to develop a general pipeline to fit all the filters in</p>
|
||||
<section class="footnotes" role="doc-endnotes">
|
||||
<hr>
|
||||
<ol>
|
||||
<li id="fn:1" role="doc-endnote">
|
||||
<p>
|
||||
<a href="https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/">https://pyimagesearch.com/2015/09/07/blur-detection-with-opencv/</a> <a href="#fnref:1" class="footnote-backref" role="doc-backlink">↩︎</a></p>
|
||||
</li>
|
||||
<li id="fn:2" role="doc-endnote">
|
||||
<p>
|
||||
<a href="https://pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/">https://pyimagesearch.com/2020/06/15/opencv-fast-fourier-transform-fft-for-blur-detection-in-images-and-video-streams/</a> <a href="#fnref:2" class="footnote-backref" role="doc-backlink">↩︎</a></p>
|
||||
</li>
|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -7,10 +7,13 @@
|
|||
<loc>https://mmp.oscar.blue/docs/user/</loc>
|
||||
</url><url>
|
||||
<loc>https://mmp.oscar.blue/posts/</loc>
|
||||
<lastmod>2022-03-27T13:13:48+01:00</lastmod>
|
||||
<lastmod>2022-04-03T13:13:48+01:00</lastmod>
|
||||
</url><url>
|
||||
<loc>https://mmp.oscar.blue/</loc>
|
||||
<lastmod>2022-03-27T13:13:48+01:00</lastmod>
|
||||
<lastmod>2022-04-03T13:13:48+01:00</lastmod>
|
||||
</url><url>
|
||||
<loc>https://mmp.oscar.blue/posts/week-9/</loc>
|
||||
<lastmod>2022-04-03T13:13:48+01:00</lastmod>
|
||||
</url><url>
|
||||
<loc>https://mmp.oscar.blue/posts/week-8/</loc>
|
||||
<lastmod>2022-03-27T13:13:48+01:00</lastmod>
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
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@ -15,7 +15,7 @@
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@ -1,10 +1 @@
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Reference in a new issue