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ImageDenoising.jl
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ImageDenoising.jl
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### A Pluto.jl notebook ###
# v0.19.16
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
el
end
end
# ╔═╡ 6510127c-0459-4df5-9b35-60e22ffcc6dd
begin
using Images
using PlutoUI
end
# ╔═╡ b1e77776-a8bd-4138-b9e5-6928391b4a96
md"""
## Noising and Denoising of webcam images
In this notebook, you can take snapshots with your webcam and apply a noise filter to them. Afterward, you can apply another filter for denoising them.
"""
# ╔═╡ 9ae2876f-433b-44a5-8826-9bfb28dbdc54
md"""
#### 1. Import libraries and define noising/denoising functions.
"""
# ╔═╡ 9f7422f4-2abb-4990-a948-f0d3343bdb94
function noisify(x::Number, s)
clamp(x + s * rand(-1.:1), 0., 1.)
end
# ╔═╡ a2453e52-7151-11ed-3873-c91173205bd5
function noisify(color::AbstractRGB, s)
RGB(noisify(color.r, s), noisify(color.g, s), noisify(color.b, s))
end
# ╔═╡ c8a043a0-5459-4da0-a6dd-c15aa6024710
function noisify(image::AbstractMatrix, s)
[noisify(pixel, s) for pixel in image]
end
# ╔═╡ 74a2174c-7c7c-4502-88a1-651874f9a79d
function median(arr::Array{Float64, 1})
# Sort the array
arr = sort(arr)
# Get the length of the array
n = length(arr)
# Check if the length is even or odd
if iseven(n)
# If even, return the average of the two middle elements
(arr[n÷2] + arr[n÷2+1])/2
else
# If odd, return the middle element
arr[n÷2+1]
end
end
# ╔═╡ 8e565dc3-ea79-4307-82e3-2081cea0410f
function median(patch::AbstractMatrix)
red_channel = vec([pixel.r for pixel in patch])
green_channel = vec([pixel.g for pixel in patch])
blue_channel = vec([pixel.b for pixel in patch])
return RGB(median(red_channel), median(green_channel), median(blue_channel))
end
# ╔═╡ 3073f4f6-1fcb-4a8e-86d7-bcce2b286238
function median_filter(image::AbstractMatrix, filter_size::Int64)
height, width = size(image)
image_new = deepcopy(image)
for i in 1:height
for j in 1:width
window = image[max(1, i - filter_size):min(height, i + filter_size),
max(1, j - filter_size):min(width, j + filter_size)]
image_new[i, j] = median(window)
end
end
return image_new
end
# ╔═╡ b903485c-4686-41cc-b0c6-2d2e403df607
md"""
#### 2. Apply Noise to webcam snapshot
"""
# ╔═╡ 43bd0ec5-d70f-4c6d-902a-5897d3096703
@bind noise Slider(0:0.01:1, show_value=true)
# ╔═╡ b2918929-7102-4106-ac87-5ade00891fe8
@bind filter_size Slider(2:7, show_value=true)
# ╔═╡ 7f547b5c-083c-48d2-950e-da3fabe6252d
function camera_input(;max_size=400, default_url="https://i.imgur.com/SUmi94P.png")
"""
<span class="pl-image waiting-for-permission">
<style>
.pl-image.popped-out {
position: fixed;
top: 0;
right: 0;
z-index: 5;
}
.pl-image #video-container {
width: 250px;
}
.pl-image video {
border-radius: 1rem 1rem 0 0;
}
.pl-image.waiting-for-permission #video-container {
display: none;
}
.pl-image #prompt {
display: none;
}
.pl-image.waiting-for-permission #prompt {
width: 250px;
height: 200px;
display: grid;
place-items: center;
font-family: monospace;
font-weight: bold;
text-decoration: underline;
cursor: pointer;
border: 5px dashed rgba(0,0,0,.5);
}
.pl-image video {
display: block;
}
.pl-image .bar {
width: inherit;
display: flex;
z-index: 6;
}
.pl-image .bar#top {
position: absolute;
flex-direction: column;
}
.pl-image .bar#bottom {
background: black;
border-radius: 0 0 1rem 1rem;
}
.pl-image .bar button {
flex: 0 0 auto;
background: rgba(255,255,255,.8);
border: none;
width: 2rem;
height: 2rem;
border-radius: 100%;
cursor: pointer;
z-index: 7;
}
.pl-image .bar button#shutter {
width: 3rem;
height: 3rem;
margin: -1.5rem auto .2rem auto;
}
.pl-image video.takepicture {
animation: pictureflash 200ms linear;
}
@keyframes pictureflash {
0% {
filter: grayscale(1.0) contrast(2.0);
}
100% {
filter: grayscale(0.0) contrast(1.0);
}
}
</style>
<div id="video-container">
<div id="top" class="bar">
<button id="stop" title="Stop video">✖</button>
<button id="pop-out" title="Pop out/pop in">⏏</button>
</div>
<video playsinline autoplay></video>
<div id="bottom" class="bar">
<button id="shutter" title="Click to take a picture">📷</button>
</div>
</div>
<div id="prompt">
<span>
Enable webcam
</span>
</div>
<script>
// based on https://github.com/fonsp/printi-static (by the same author)
const span = currentScript.parentElement
const video = span.querySelector("video")
const popout = span.querySelector("button#pop-out")
const stop = span.querySelector("button#stop")
const shutter = span.querySelector("button#shutter")
const prompt = span.querySelector(".pl-image #prompt")
const maxsize = $(max_size)
const send_source = (source, src_width, src_height) => {
const scale = Math.min(1.0, maxsize / src_width, maxsize / src_height)
const width = Math.floor(src_width * scale)
const height = Math.floor(src_height * scale)
const canvas = html`<canvas width=\${width} height=\${height}>`
const ctx = canvas.getContext("2d")
ctx.drawImage(source, 0, 0, width, height)
span.value = {
width: width,
height: height,
data: ctx.getImageData(0, 0, width, height).data,
}
span.dispatchEvent(new CustomEvent("input"))
}
const clear_camera = () => {
window.stream.getTracks().forEach(s => s.stop());
video.srcObject = null;
span.classList.add("waiting-for-permission");
}
prompt.onclick = () => {
navigator.mediaDevices.getUserMedia({
audio: false,
video: {
facingMode: "environment",
},
}).then(function(stream) {
stream.onend = console.log
window.stream = stream
video.srcObject = stream
window.cameraConnected = true
video.controls = false
video.play()
video.controls = false
span.classList.remove("waiting-for-permission");
}).catch(function(error) {
console.log(error)
});
}
stop.onclick = () => {
clear_camera()
}
popout.onclick = () => {
span.classList.toggle("popped-out")
}
shutter.onclick = () => {
const cl = video.classList
cl.remove("takepicture")
void video.offsetHeight
cl.add("takepicture")
video.play()
video.controls = false
console.log(video)
send_source(video, video.videoWidth, video.videoHeight)
}
document.addEventListener("visibilitychange", () => {
if (document.visibilityState != "visible") {
clear_camera()
}
})
// Set a default image
const img = html`<img crossOrigin="anonymous">`
img.onload = () => {
console.log("helloo")
send_source(img, img.width, img.height)
}
img.src = "$(default_url)"
console.log(img)
</script>
</span>
""" |> HTML
end
# ╔═╡ a220bcb5-f9e1-471e-ac9d-4119cec2d71e
@bind cam_data camera_input()
# ╔═╡ a41de4c7-4a4c-4095-b2af-22408754252d
function process_raw_camera_data(raw_camera_data)
# the raw image data is a long byte array, we need to transform it into something
# more "Julian" - something with more _structure_.
# The encoding of the raw byte stream is:
# every 4 bytes is a single pixel
# every pixel has 4 values: Red, Green, Blue, Alpha
# (we ignore alpha for this notebook)
# So to get the red values for each pixel, we take every 4th value, starting at
# the 1st:
reds_flat = UInt8.(raw_camera_data["data"][1:4:end])
greens_flat = UInt8.(raw_camera_data["data"][2:4:end])
blues_flat = UInt8.(raw_camera_data["data"][3:4:end])
# but these are still 1-dimensional arrays, nicknamed 'flat' arrays
# We will 'reshape' this into 2D arrays:
width = raw_camera_data["width"]
height = raw_camera_data["height"]
# shuffle and flip to get it in the right shape
reds = reshape(reds_flat, (width, height))' / 255.0
greens = reshape(greens_flat, (width, height))' / 255.0
blues = reshape(blues_flat, (width, height))' / 255.0
# we have our 2D array for each color
# Let's create a single 2D array, where each value contains the R, G and B value of
# that pixel
RGB.(reds, greens, blues)
end
# ╔═╡ 7131580d-f695-4882-91f5-e4255831141c
noisy_cam = noisify(process_raw_camera_data(cam_data), noise)
# ╔═╡ 7f3977e0-4277-403d-86d1-7be158deed13
median_filter(noisy_cam, filter_size)
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
[compat]
Images = "~0.25.2"
PlutoUI = "~0.7.49"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
julia_version = "1.8.3"
manifest_format = "2.0"
project_hash = "6e1f72fbab5fc349895d482941291c1a91287cd5"
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[[deps.AbstractPlutoDingetjes]]
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