Convert Image into Matrix - Like a Pro!
Summary
TLDRThis video explores how computers perceive images, transforming what humans see as simple visuals into complex mathematical structures. Starting with the concept of pixels, the video explains how binary numbers represent black-and-white images, while grayscale and RGB systems handle more complex visuals. The RGB format, which measures red, green, and blue intensities, is explained using practical examples. The video also shows how to create matrices to represent these images mathematically. Viewers will learn how to understand and convert images into numbers, preparing them to process images using Python in future videos.
Takeaways
- 😀 Pixels are the smallest controllable elements of a digital image, and they provide a coordinate system for images.
- 🖼️ A 7x7 pixel image with only black and white colors can be represented in binary form using 0s and 1s, where 0 stands for black and 1 for white.
- 📏 Grayscale images use a range of numbers from 0 to 255 to represent different shades of gray, with 0 being black and 255 being white.
- 🎨 Grayscale values can be calculated by determining the percentage of black in a color and using that to find the corresponding value on the 0-255 scale.
- 🌈 RGB (Red, Green, Blue) format is used to describe colorful images, with each color channel ranging from 0 to 255 to represent the intensity of that color.
- 🔴 The color white in RGB is represented by the highest value (255, 255, 255), while black is represented by the lowest value (0, 0, 0).
- 🟡 The color yellow is represented in RGB by the values (255, 255, 0), indicating maximum intensity for red and green and none for blue.
- 📊 RGB images are represented mathematically as three-dimensional matrices, with each dimension representing one of the color channels.
- 👨🏫 The script promises to show viewers how to process images with Python in a future video, indicating a follow-up tutorial on practical image processing.
- 🔑 Understanding the numeric representation of colors and pixels is fundamental to computer vision and image processing tasks.
Q & A
What is the smallest controllable element of an image called?
-The smallest controllable element of an image is called a pixel.
How does a computer perceive an image that looks like a smiley face to humans?
-To a computer, an image that looks like a smiley face to humans appears as a complex piece of math.
What is the size of the image used in the video to explain the concept of pixels?
-The size of the image used in the video is 7 pixels by 7 pixels.
In the context of the video, what does the binary value '0' represent in an image?
-In the context of the video, the binary value '0' represents the color black in an image.
How is the grayscale format different from the binary format when describing an image?
-The grayscale format uses numbers from 0 to 255 to describe the intensity of black, whereas the binary format uses only 1 or 0.
What is the grayscale value that represents 50% black?
-The grayscale value that represents 50% black is 128.
How is the RGB format different from grayscale in terms of color representation?
-The RGB format measures the intensity of red, green, and blue colors on a particular pixel, using values from 0 to 255 for each channel, whereas grayscale uses a single value from 0 to 255 to represent shades of black and white.
What RGB values represent the color white?
-The RGB values that represent the color white are 255 for red, 255 for green, and 255 for blue.
How are RGB values used to represent a color that is not a shade of gray?
-RGB values are used to represent a color that is not a shade of gray by having different values for the red, green, and blue channels, where one or more channels have values other than the maximum or minimum.
What is a three-dimensional matrix in the context of RGB images?
-A three-dimensional matrix in the context of RGB images is a matrix where the first dimension holds the values for the red channel, the second dimension holds the green values, and the third dimension holds the blue values.
How can you determine the color of a pixel by looking at its RGB values?
-You can determine the color of a pixel by looking at its RGB values by observing which channel's value is the highest or which colors are more dominant. For example, a high value in the red channel with lower values in green and blue would suggest a color closer to red.
Outlines
😀 Understanding Pixels and Binary Images
This paragraph introduces the concept of pixels as the fundamental building blocks of digital images. It explains that pixels are the smallest controllable elements, which when magnified, appear as tiny squares that cannot be further divided. The video aims to demonstrate how these pixels can be converted into a mathematical format. The size of the image is given as 7x7 pixels, and since it contains only black and white colors, it is best represented in binary, using 1s and 0s to denote white and black pixels, respectively. The process of assigning numeric values to each pixel based on its color is detailed, and the resulting data structure is described as a matrix, which is a simplified representation of the image using only numbers and coordinates.
📊 Grayscale and Color Intensity Representation
The second paragraph delves into the representation of images with more than two colors, focusing on grayscale. It explains that grayscale uses a range of numbers from 0 to 255 to represent different shades of black, with 0 being black and 255 being white. The paragraph illustrates how to calculate the grayscale value for different percentages of black, using the example of 50% black being represented by the value 128. It also discusses how to determine the grayscale value for more complex percentages, such as 15% black, which is calculated to be approximately 218. The process of filling in the grayscale values for an image is described, leading to a matrix representation that captures the grayscale intensity of the image.
🌈 RGB Format and Color Channel Intensity
The final paragraph discusses the RGB format used to describe colorful images. It explains that RGB stands for red, green, and blue, and each color channel is measured on a scale from 0 to 255, representing the intensity of each color in a pixel. The paragraph provides examples of how to represent colors like white, black, and yellow in RGB, emphasizing the importance of the order of values. It also touches on how to adjust the intensity of a color by modifying the values of its respective channels. The paragraph concludes with a description of how to represent an RGB image mathematically, using a three-dimensional matrix where each dimension corresponds to a color channel. The process of selecting a specific pixel and understanding the combined effect of its red, green, and blue channel values to produce a specific color is explained.
Mindmap
Keywords
💡Pixel
💡Binary
💡Grayscale
💡RGB
💡Matrix
💡Color Intensity
💡Coordinates
💡Shades of Gray
💡Color Channels
💡Three-Dimensional Matrix
Highlights
Introduction to how computers perceive images as complex math.
Explanation of pixels as the smallest controllable element of an image.
Conversion of an image into a 7x7 matrix of binary values.
Binary representation of colors with 1 for white and 0 for black.
The concept of using matrices to represent image data.
Introduction to grayscale images and their representation with numbers from 0 to 255.
Calculating grayscale values based on the percentage of black.
Representing 50% black with a grayscale value of 128.
Calculating more complex grayscale values using percentage and ratio.
Filling in a grayscale image matrix with calculated values.
Transition to handling images with more than two colors using RGB format.
Description of RGB format measuring the intensity of red, green, and blue colors.
Representing white and black in RGB with the values 255 and 0 respectively.
Explanation of how RGB values create shades of gray when all channels have the same value.
Representing the color yellow in RGB with values 255, 255, 0.
Creating a three-dimensional matrix to represent an RGB image.
Filling in the RGB matrix with values for red, green, and blue channels.
Concept of selecting a pixel's color by accessing its RGB values in the matrix.
Anticipation of processing images with Python in the next video.
Transcripts
hi everybody it's been a while but i'm
back and today we're going to talk about
images and more specifically about how
computers perceive
images so to us humans
this looks like a smiley face to a
computer however
the exact same image looks like a very
complex piece of math
so in this video i'm going to show you
how to
make this conversion and even though it
looks
a little bit intimidating at first it is
actually super
simple i promise let's start with a
quick intro to the most
basic building block of digital images
the pixel
a pixel is the smallest controllable
element of an image
so if we take this image for example and
we zoom
in we'll eventually end up seeing all
these tiny squares
and if we keep zooming in the squares
will grow larger
but they'll never split into any more
squares
that's because we're looking at the
smallest unit of the image
so this would be one pixel this would be
another pixel
and same goes for the rest of these
squares in fact
pixels provide us with a system of
coordinates
and with that in mind let's begin
exploring it by converting
this image into math we see that the
size of the image
is 7 pixels by 7 pixels and because the
image consists of only two colors black
and white
the best way to describe it would be in
a binary form
meaning we can only use one or zero
to describe the color computers in
general
will always prefer binary input because
bits are also binary
but we'll talk about it more in a
different video so what does
1 and 0 mean in our case in a binary
image
zero stands for black and one stands for
white
which is also equivalent to false and
true by the way
and if we focus on our image and start
filling in the values
all the white pixels will become one and
all the black pixels will become
zero so we can see how each pixel
suddenly got a numeric value so now
if we want to check the value of the
fourth pixel in the fourth row
we'll get one and if we're checking
which value the second
pixel in the fifth row holds that will
be zero
so now when we understand the values and
coordinates of our image
we can finally get rid of the background
and only keep the numbers
when we do that we see that we got this
beautiful data structure
which we call a matrix pretty cool eh
but what happens if our image contains
more than just two colors
and we can no longer describe it in a
binary form
let's take this monochromatic image as
an example
we see right away that this particular
image
has one two three four five six seven
different colors and all these colors
are shades of gray
in this case the best color format to
describe this image
would be grayscale so the grayscale
format is using numbers
anywhere from 0 to 255
to describe the intensity of black where
once again
0 is black and 255 is white
so it's the highest contrast possible
with black
so first let's fill in the colors that
we already know
so 255 is white and 0
is of course black any other shade of
gray
can be represented with numbers from 1
to 254
so let's say that we want to find out
how we represent
a color that's only 50 black which is
the color we see in the very center of
our image
to calculate this we would have to
divide our number of available values by
two and because zero also represents a
color which is black
our total number of available values
would be 256
rather than 255. therefore
50 black in grayscale is represented by
the value
128 which we can now also fill in
but let's try to calculate a slightly
more complicated percentage
let's say we want to find out what 15
black would look like first we subtract
15 percent from 100
and we get 85 percent then we'll divide
100 by 85 to get the ratio we need
and then we'll do the exact same as in
the above example
divide 256 which is the total number of
available values
by 1.176 which is the ratio we just got
and the result is 217.68
which we can definitely round up and get
218.
and again we can fill it in in our image
in the appropriate spots
and by using the exact same principles
we can fill in the rest of the values
and if you'll trace these back you can
also figure out which percentage of
black i've used for these colors
so coordinate wise let's quickly check
what's the value of the fifth pixel
on row number three and we can see it's
128
so let's get rid of the background add a
bunch of commas
and two huge square brackets and we got
our matrix again
this time it represents a grayscale
image rather than binary
cool so we're done with the easy part
because so far we've been measuring the
intensity of
one particular shade but what happens
when we need to measure the intensity of
colors
across several shades let's take this
colorful image as an example
we can clearly see one two three four
five six
colors this time however these
are not just different shades of the
same color they are
actually very particular shades of red
blue
yellow and green so how exactly are we
supposed to handle it
we actually have special formats to deal
with these sort of situations
it is common to use the rgb format to
describe a colorful
image so rgb actually stands
for red green and blue and it measures
the intensity of
each of these colors on a particular
pixel
here we are also using values from 0 to
255
the only difference is we'll be checking
across three different color channels
and not just one so i'll explain it with
an example
so let's say that we want to find out
how we describe the color
white in rgb okay so
we already know that white was
represented by the top most numeric
value in grayscale
it is also true for rgb however
since we're measuring the intensity of
red green
and blue we'll say that white is
represented
by a set of three values 255 for red
255 for green and 255 for blue
in this particular order this is very
important
similarly if we want to represent the
color black
with rgb we'll get the value 0 on the
red channel
0 on the green channel and 0 on the blue
channel
and as a matter of fact whenever you see
an rgb
color mix consisting of three identical
values
for an example rgb 777 or
rgb 50 5050 you'll always be looking at
a shade of gray
and this is because no color is
overpowering the other
okay but what happens if we want to
represent the color yellow
which luckily we happen to have in our
image we already know
that it would take in three values one
for each channel
and also we know that these three values
will not be identical because yellow is
not a shade of gray
so the numeric values which produce
yellow are 255
on the red channel on the green channel
and finally zero on the blue channel
and this particular combination is
actually as yellow as possible
because we're using the top most and the
bottom most values available
but what if our shade is somewhere in
between if we take this green color for
example
it is represented by 0 for the red
channel
200 on the green and 20 on the blue
so potentially we can make this green
even greener
if we boost the value of the green
channel to 255 or 250 for example
well so far it's all rainbows and
butterflies but how on earth
are we going to represent this rgb image
mathematically
and will actually start from this green
color now because each rgb
image has three different color channels
we'll need to multiply the image by the
amount of channels
in our case we'll have three of them
when the left copy
represents red the center copy
represents green
and the right copy blue and if you
remember
this green was produced by rgb 0
220 which as you can see we can easily
fill in across our channels
and as a matter of fact each of the
green pixels will hold the exact same
values
now we also know that black was rgb000
and that white was rgb 255 255.
255. and also yellow if you guys
remember
is 255 for the red 255 for the green and
0 for the blue
and we can fill in the red which will
obviously have a higher intensity of red
as opposed to green and blue while this
particular cyan shade
will have a higher intensity of green
and blue rather than red
so you can kind of guesstimate which
color you're getting
just by looking at the values so okay
now all the numeric values are in place
we can get rid of all the image copies
in the background
now you may think we're getting three
different matrices here
however what we are actually getting is
a single matrix
with three different dimensions aka
a three-dimensional matrix where the
first dimension
holds the values for the red channel of
the image the second dimension
holds the green values the third
dimension holds the blue values
in a multi-dimensional matrix we stagger
the channels
one behind the other to form this
complex data structure
for example if we want to select a
particular pixel in our image
let's say the sixth pixel on row number
three
we are actually selecting all three
channels at once
and we see that together they produce
the color rgb
100 220 and 230
which is probably cyan because the green
and blue values are overpowering the red
thank you so much for watching you guys
i'll see you next time where
i'll show you how to process images with
python
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