From b96a861da8cdea13d8cc79a7d08dcc0a4116c16d Mon Sep 17 00:00:00 2001 From: Max Pinheiro Date: Thu, 28 Oct 2021 14:14:00 -0400 Subject: [PATCH 1/6] begin separating modules --- src/media/modules/module_descriptions.json | 66 ++++++++++++++++++- .../modules/text/computer-vision-intro.json | 49 ++++++++++++++ src/media/modules/text/gabor-filter.json | 27 ++++++++ src/media/modules/text/gaussian-blur.json | 54 +++++++++++++++ .../modules/text/histogram-of-gradients.json | 27 ++++++++ src/media/modules/text/sobel-filter.json | 27 ++++++++ src/modulePage/ModulePage.tsx | 32 +++++---- 7 files changed, 268 insertions(+), 14 deletions(-) create mode 100644 src/media/modules/text/computer-vision-intro.json create mode 100644 src/media/modules/text/gabor-filter.json create mode 100644 src/media/modules/text/gaussian-blur.json create mode 100644 src/media/modules/text/histogram-of-gradients.json create mode 100644 src/media/modules/text/sobel-filter.json diff --git a/src/media/modules/module_descriptions.json b/src/media/modules/module_descriptions.json index 428d147..0caa7ef 100644 --- a/src/media/modules/module_descriptions.json +++ b/src/media/modules/module_descriptions.json @@ -12,6 +12,66 @@ "path": "computer-vision", "active": true }, + { + "number": 8.1, + "title": "Intro to Images and Kernels", + "dropdownTitle": "Images and Kernels", + "body": "How do we represent the data contained in an image? What is a kernel, and how do we use them to extract features from images?", + "bgColor": "bg-darkblue", + "textColor": "text-white", + "margin": "ml-64", + "imgSrc": "mod8", + "path": "images-and-kernels", + "active": true + }, + { + "number": 8.2, + "title": "Image Blurring: The Gaussian Blur", + "dropdownTitle": "Gaussian Blur", + "body": "Here, we explore an popular instance of a kernel called the Gaussian Blur, which (as its name suggests) is used to blur an image.", + "bgColor": "bg-offwhite", + "textColor": "text-darkblue", + "margin": "mr-64", + "imgSrc": "mod8", + "path": "gaussian-blur", + "active": true + }, + { + "number": 8.3, + "title": "Directional Filtering: The Gabor Filter", + "dropdownTitle": "Gabor Filter", + "body": "This section explores yet another kernel called the Gabor Filter, which extracts portions of an image that follow a specific directional pattern", + "bgColor": "bg-lightblue", + "textColor": "text-darkblue", + "margin": "ml-64", + "imgSrc": "mod8", + "path": "gabor-filter", + "active": true + }, + { + "number": 8.4, + "title": "Edge Detection: The Sobel Filter", + "dropdownTitle": "Sobel Filter", + "body": "In order for AI to detect an object within an image, we search for edges in the image to give us a sense of the shape of the object. We use the Sobel Filter to detect these edges.", + "bgColor": "bg-darkblue", + "textColor": "text-white", + "margin": "mr-64", + "imgSrc": "mod8", + "path": "sobel-filter", + "active": true + }, + { + "number": 8.4, + "title": "Histogram of (Oriented) Gradients", + "dropdownTitle": "Histogram of Gradients", + "body": "While a Sobel Filter can give us information about edges in a singular direction, we need to know about the edges in all directions simultaneously to fully understand the shape of the image. The Histogram of Gradients allows us to collect the edges in all different directions to see which is most prominent.", + "bgColor": "bg-lightblue", + "textColor": "text-darkblue", + "margin": "ml-64", + "imgSrc": "mod8", + "path": "histogram-of-gradients", + "active": true + }, { "number": 9, "title": "Recognition as a Classification Problem", @@ -19,7 +79,7 @@ "body": "Once features have been detected and, perhaps combined into “higher-order features”, machine vision algorithms are often tasked to perform classification–that is, to determine what an array of features is “of”. Does this bundle of features in an image indicate the presence of a dog? Does that bundle of features over there indicate a cat? A set of features can be thought of as a location in a “state space”. Nearby locations in state space tend to correspond to similar objects or scenes. Features of many dogs of the same breed may form a “cluster” in state space. We will explore clusters of features in some simple state spaces and begin to ask how different “naturally occurring” clusters (corresponding to dogs and cats in our example) might be detected by a machine.", "bgColor": "bg-darkblue", "textColor": "text-white", - "margin": "ml-64", + "margin": "mr-64", "imgSrc": "mod9", "path": "classification", "active": true @@ -31,7 +91,7 @@ "body": "Today’s remarkable advances in the power of artificial neural networks leverage foundations laid in 1943 by the McCulloch and Pitts neuron model and Rosenblatt’s 1958 software implementation of the first perceptron. We will examine the architecture and functions of the simplest trainable pattern classifier.This classifier is so simple, in fact, that it cannot effectively separate clusters that are not “linearly separable”. Two clusters in a two-dimensional state space that cannot be separated by a single straight line are examples of non-linearly separable classes. What does the perceptron do when confronted with a data-set that is not linearly separable?", "bgColor": "bg-offwhite", "textColor": "text-darkblue", - "margin": "mr-64", + "margin": "ml-64", "imgSrc": "mod10", "path": "perceptrons", "active": false @@ -43,7 +103,7 @@ "body": "The problem of trying to separate clusters that are not linearly separable has been known for more than half a century, and many variations of multi-layer perceptrons continue to be developed to address and overcome limitations of earlier approaches. Multi-layered artificial neural networks are often said to have “hidden layers” where features are iteratively “weighted” in various ways until a desired classification performance is reached. We will consider a simple example of how hidden layers help to solve the “exclusive-OR problem” and explore the “credit assignment problem.” How does an algorithm know which weights to change and by how much, in order to promote learning for improved classification by a neural network?", "bgColor": "bg-darkblue", "textColor": "text-white", - "margin": "ml-64", + "margin": "mr-64", "imgSrc": "mod11", "path": "neural-nets", "active": false diff --git a/src/media/modules/text/computer-vision-intro.json b/src/media/modules/text/computer-vision-intro.json new file mode 100644 index 0000000..c736c89 --- /dev/null +++ b/src/media/modules/text/computer-vision-intro.json @@ -0,0 +1,49 @@ +{ + "title": "Intro to Computer Vision: Images and Kernels", + "sections": [ + { + "title": "What is Computer Vision?", + "colorScheme": "dark", + "subsections": [ + { + "title": "", + "body": "In order to extract valuable information from an image, computers must be able to process images and detect features in those images based on some criteria. As humans, we do this all the time - if we see an animal, we can identify it as a cat because it has fur, perked up ears, a small nose, and bright eyes. We extract features/traits of the objects in our view that allow us to identify those objects.", + "imgSrc": "blank" + }, + { + "title": "", + "body": "Computer vision is conceptually not all that different! To identify features in an image, a small window called a *kernel* slides across the image and at every step, makes a calculation.", + "imgSrc": "blank" + }, + { + "title": "", + "body": "Depending on what trait the kernel is supposed to capture, that calculation reveals whether that trait is present at the current location of the window or not. These calculations are then joined into a new image that represents the result of sliding that window across the original image.", + "imgSrc": "blank" + } + ], + "demoComp": "" + }, + { + "title": "What is a Kernel?", + "colorScheme": "light", + "subsections": [ + { + "title": "Subsection 1", + "body": "Let's take a step back and define what an image is first. An image is made up of individual pixels which each have a value that represents the color, and those pixels are organized into a grid of values to give us the resulting image.", + "imgSrc": "animation1" + }, + { + "title": "Subsection 2", + "body": "Kernels are not all the different from images in that sense - we specify a much smaller grid of numbers that, based on their value and organization are capable of extracting a \"feature\" from the part of the image the kernel is currently sitting on. A kernel might find the edges of an object in an image, sharpen the details of an image, or smooth the image out.", + "imgSrc": "animation2" + }, + { + "title": "Subsection 3", + "body": "The way this feature extraction occurs is by making a 'window' the size of the kernel around a pixel in the original image. We then multiply the original image's pixel values by the numbers in the kernel, and sum them all up. We compute this sum for all pixels by sliding this 'window' across every pixel in the image and repeating the math at every step. As we slide across the image, we stitch together the new values after applying the kernel to create a brand new image!", + "imgSrc": "animation3" + } + ], + "demoComp": "" + } + ] +} \ No newline at end of file diff --git a/src/media/modules/text/gabor-filter.json b/src/media/modules/text/gabor-filter.json new file mode 100644 index 0000000..a64e899 --- /dev/null +++ b/src/media/modules/text/gabor-filter.json @@ -0,0 +1,27 @@ +{ + "title": "Gabor Filter", + "sections": [ + { + "title": "Gabor Filter", + "colorScheme": "dark", + "subsections": [ + { + "title": "Subsection 1", + "body": "The Gabor Filter is a bit more complicated in its definition but the features that it aims to extract are just as intuitive as the Gaussian Blur. Instead of wanting to extract the overall structure of the image, now we are interested in extracting portions of an image that follow a specific directional pattern. The canonical example of this is wanting to extract stripes on a zebra that are in a particular direction.", + "imgSrc": "blank" + }, + { + "title": "Subsection 2", + "body": "The gist of how the filter works is that we can detect certain orientations by modifying our angle θ and specifying the size of the window we slide across the image. We can also specify a frequency or magnitude for the directionality that assesses how strong the directionality is as a filter.", + "imgSrc": "blank" + }, + { + "title": "Subsection 3", + "body": "The resulting image contains only the windows of the image that contain features that follow the angle specified by θ. This can be highly effective when scanning images which contain text where we only want to retain the text in the image. The reason for this is that images generally have lower directional content as they are smoother relative to text.", + "imgSrc": "blank" + } + ], + "demoComp": "GaborDemo" + } + ] +} \ No newline at end of file diff --git a/src/media/modules/text/gaussian-blur.json b/src/media/modules/text/gaussian-blur.json new file mode 100644 index 0000000..36fd53f --- /dev/null +++ b/src/media/modules/text/gaussian-blur.json @@ -0,0 +1,54 @@ +{ + "title": "Gaussian Blur", + "sections": [ + { + "title": "Gaussian Blur", + "colorScheme": "dark", + "subsections": [ + { + "title": "Subsection 1", + "body": "As its name suggests, the Gaussian blur is used for blurring. This is useful to make sure that computer vision algorithms don't focus too much on the details of an image. The same way we don't recognize a cell phone by its serial number, an image processing system should focus on the big picture when trying to recognize objects.", + "imgSrc": "blank" + }, + { + "title": "Subsection 2", + "body": "The definition of a Gaussian Blur Filter comes from the famous Gaussian/Normal/Bell curve as though it were projected into 3D. The values of the kernel are based on the density of (area under) the bell curve at the location of the current kernel entry. For example, the center of the kernel is the center of the bell curve where the density is the highest; therefore, the largest value of the kernel should be at the center. Conversely, values on the edges of the kernel should be relatively the smallest.", + "imgSrc": "blank" + }, + { + "title": "Subsection 3", + "body": "We can control the change in the magnitude of the values from the center to the outsides of the kernel (spread of the bell curve) by modifying the standard deviation of the distribution using the σ (sigma) parameter. The last thing we want to make sure about the kernel is that the values sum up to approximately 1. If we didn't do this, then the image would end up brighter or darker than intended (called the image's \"energy\"). We can do this by summing up all of the values and dividing every entry by the sum.", + "imgSrc": "blank" + }, + { + "title": "Subsection 3", + "body": "This kernel defocuses the middle pixel of the window by averaging its value with its neighbors. Done across the whole image, you retain the picture's overall structure without sharp details. This achieves our original goal of making a blurred image.", + "imgSrc": "blank" + } + ], + "demoComp": "GaussianBlurDemo" + }, + { + "title": "Difference of Gaussians", + "colorScheme": "light", + "subsections": [ + { + "title": "Subsection 1", + "body": "The motivation behind the Difference of Gaussians technique is to detect edges in an image. This is particularly useful for segmenting objects from one another or creating a coloring book from pictures on your phone!", + "imgSrc": "blank" + }, + { + "title": "Subsection 2", + "body": "A difference of Gaussians Kernel builds directly upon what we have already covered in Gaussian Blurring. We take two images processed using Gaussian Blur filters with different standard deviations (σ, sigma) and subtract them from each other. We want the image that we are subtracting from to have been processed with a Gaussian Blur filter that has a smaller standard deviation (less spread) than the image that we are subtracting.", + "imgSrc": "blank" + }, + { + "title": "Subsection 3", + "body": "You can think of this technique as extracting what changes most when you blur. If you think about, the sharpest parts of the image, or, the edges of objects, undergo the most change when blurring. When finding the difference between two images at different sigma values, the sharpest parts of the image, or the edges, are going to be the only thing left.", + "imgSrc": "blank" + } + ], + "demoComp": "DiffOfGaussian" + } + ] +} \ No newline at end of file diff --git a/src/media/modules/text/histogram-of-gradients.json b/src/media/modules/text/histogram-of-gradients.json new file mode 100644 index 0000000..9abbc9b --- /dev/null +++ b/src/media/modules/text/histogram-of-gradients.json @@ -0,0 +1,27 @@ +{ + "title": "Histogram of Gradients", + "sections": [ + { + "title": "Histogram of Gradients", + "colorScheme": "dark", + "subsections": [ + { + "title": "Subsection 1", + "body": "", + "imgSrc": "blank" + }, + { + "title": "Subsection 2", + "body": "", + "imgSrc": "blank" + }, + { + "title": "Subsection 3", + "body": "", + "imgSrc": "blank" + } + ], + "demoComp": "" + } + ] +} \ No newline at end of file diff --git a/src/media/modules/text/sobel-filter.json b/src/media/modules/text/sobel-filter.json new file mode 100644 index 0000000..d82cd0f --- /dev/null +++ b/src/media/modules/text/sobel-filter.json @@ -0,0 +1,27 @@ +{ + "title": "Sobel Filter", + "sections": [ + { + "title": "Sobel Filter", + "colorScheme": "dark", + "subsections": [ + { + "title": "Subsection 1", + "body": "", + "imgSrc": "blank" + }, + { + "title": "Subsection 2", + "body": "", + "imgSrc": "blank" + }, + { + "title": "Subsection 3", + "body": "", + "imgSrc": "blank" + } + ], + "demoComp": "" + } + ] +} \ No newline at end of file diff --git a/src/modulePage/ModulePage.tsx b/src/modulePage/ModulePage.tsx index 21821fb..5f04556 100644 --- a/src/modulePage/ModulePage.tsx +++ b/src/modulePage/ModulePage.tsx @@ -5,6 +5,11 @@ import module8 from '../media/modules/module_8.json'; import module9 from '../media/modules/module_9.json'; import module10 from '../media/modules/module_10.json'; import module11 from '../media/modules/module_11.json'; +import cvIntroModule from '../media/modules/text/computer-vision-intro.json'; +import gaborModule from '../media/modules/text/gabor-filter.json'; +import gaussianModule from '../media/modules/text/gaussian-blur.json'; +import sobelModule from '../media/modules/text/sobel-filter.json'; +import hogModule from '../media/modules/text/histogram-of-gradients.json'; export interface ModuleSubsectionType { title: string; @@ -20,15 +25,23 @@ interface ModuleSectionType { } interface Module { - number: number; title: string; sections: ModuleSectionType[]; } -const modules: Record = { +// eslint-disable-next-line +type moduleName = 'computer-vision' | 'images-and-kernels' | 'gaussian-blur' | 'gabor-filter' | 'sobel-filter' | 'histogram-of-gradients' | 'classification' | 'perceptron' | 'neural-nets'; +const modules: Record = { 'computer-vision': module8, - classification: module9, - perceptrons: module10, + 'images-and-kernels': cvIntroModule, + 'gaussian-blur': gaussianModule, + 'gabor-filter': gaborModule, + 'sobel-filter': sobelModule, + 'histogram-of-gradients': hogModule, + // eslint-disable-next-line + 'classification': module9, + // eslint-disable-next-line + 'perceptron': module10, 'neural-nets': module11, }; @@ -38,14 +51,11 @@ const modules: Record = { */ export default function ModulePage( - props: RouteComponentProps<{ module: string }>, + props: RouteComponentProps<{ module: moduleName }>, ) { - const { - match: { - params: { module }, - }, - } = props; - const curModule = modules[module]; + // eslint-disable-next-line + const module = props.match.params.module; + const curModule: Module = modules[module]; if (!curModule) { return ( From 9afad1dbe3b9a9e077a6bbb270d9970d6337fc0b Mon Sep 17 00:00:00 2001 From: Max Pinheiro Date: Sun, 31 Oct 2021 13:40:26 -0400 Subject: [PATCH 2/6] separate cv module routes, sobel filter page --- .../combinedSobelKernelExampleDark.png | Bin 0 -> 72185 bytes .../combinedSobelKernelExampleLight.png | Bin 0 -> 58522 bytes .../computerVision/vertSobelExampleDark.png | Bin 0 -> 115369 bytes .../computerVision/vertSobelExampleLight.png | Bin 0 -> 115297 bytes src/media/modules/text/sobel-filter.json | 16 ++++++++-------- src/modulePage/ModuleSection.tsx | 14 ++++++++++++++ .../computerVision/sobelFilter/sobelFilter.tsx | 8 ++++---- 7 files changed, 26 insertions(+), 12 deletions(-) create mode 100644 src/media/modules/computerVision/combinedSobelKernelExampleDark.png create mode 100644 src/media/modules/computerVision/combinedSobelKernelExampleLight.png create mode 100644 src/media/modules/computerVision/vertSobelExampleDark.png create mode 100644 src/media/modules/computerVision/vertSobelExampleLight.png diff --git a/src/media/modules/computerVision/combinedSobelKernelExampleDark.png b/src/media/modules/computerVision/combinedSobelKernelExampleDark.png new file mode 100644 index 0000000000000000000000000000000000000000..b6f242a181f170b07e21924618bfc24241c0bb21 GIT binary patch literal 72185 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z(Rc9gxL_{tY1+%OLw~W%$&qREq&S7OEdP>JSm{gBFN|+)k$<;$Q)KzK+cZU%DYE>a zz| Date: Sun, 31 Oct 2021 14:08:29 -0400 Subject: [PATCH 3/6] responsive width image selector --- src/index.css | 10 ++++++++++ .../computerVision/common/InteractiveFilter.tsx | 4 ++-- 2 files changed, 12 insertions(+), 2 deletions(-) diff --git a/src/index.css b/src/index.css index ef00dc4..38fe593 100644 --- a/src/index.css +++ b/src/index.css @@ -116,6 +116,16 @@ input[type="range"]:focus { width: 60vw; } +.responsive-img-width { + width: 60vw; +} + +@media (min-width:768px) { + .responsive-img-width { + width: 35vw; + } +} + .sobel-image-width { width: 60vw; } diff --git a/src/modules/computerVision/common/InteractiveFilter.tsx b/src/modules/computerVision/common/InteractiveFilter.tsx index 7ea0710..a8ec30c 100644 --- a/src/modules/computerVision/common/InteractiveFilter.tsx +++ b/src/modules/computerVision/common/InteractiveFilter.tsx @@ -65,13 +65,13 @@ const InteractiveFilter: React.FC = ({
input Date: Tue, 2 Nov 2021 12:36:22 -0400 Subject: [PATCH 4/6] revise sobel filter text --- src/media/modules/text/sobel-filter.json | 2 +- src/modulePage/ModulePage.tsx | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/media/modules/text/sobel-filter.json b/src/media/modules/text/sobel-filter.json index c5b5a4f..2b4fe25 100644 --- a/src/media/modules/text/sobel-filter.json +++ b/src/media/modules/text/sobel-filter.json @@ -17,7 +17,7 @@ }, { "title": "Subsection 3", - "body": "To get a more accurate understanding about the shape of the image, we need to extract the image gradient in the four primary edge directions: vertical, horizontal, diagonal up (45 degrees), and diagonal down (-45 degrees). The demo below calculates the images resulted by filtering by the eight sobel kernels (4 directions, 2 filters per direction for dark-to-light and light-to-dark).", + "body": "To get a more accurate understanding about the shape of the image, we need to extract the image gradient in the four primary edge directions: vertical, horizontal, diagonal up (45 degrees), and diagonal down (-45 degrees). The demo below calculates the images resulting from filtering by the eight Sobel kernels (4 directions, 2 filters per direction for dark-to-light and light-to-dark) for a stop sign image. Note that you can select other images to filter from the box below.", "imgSrc": "blank" } ], diff --git a/src/modulePage/ModulePage.tsx b/src/modulePage/ModulePage.tsx index 5f04556..2296a63 100644 --- a/src/modulePage/ModulePage.tsx +++ b/src/modulePage/ModulePage.tsx @@ -59,7 +59,7 @@ export default function ModulePage( if (!curModule) { return ( -
+ From b29caae924fcbae67654dfc0f6d6799f27b68434 Mon Sep 17 00:00:00 2001 From: Max Pinheiro Date: Tue, 2 Nov 2021 17:48:14 -0400 Subject: [PATCH 5/6] cleanup module descriptions, images for modules, bold capability in json --- src/landingPage/ModuleIntro.tsx | 23 +++- src/media/modules/module_descriptions.json | 20 +-- src/media/modules/previews/gabor.png | Bin 0 -> 99420 bytes src/media/modules/previews/gausBlur.png | Bin 0 -> 19416 bytes src/media/modules/previews/hog.png | Bin 0 -> 71350 bytes src/media/modules/previews/kernel.png | Bin 0 -> 71610 bytes src/media/modules/{ => previews}/mod10.png | Bin src/media/modules/{ => previews}/mod11.png | Bin src/media/modules/{ => previews}/mod8.png | Bin src/media/modules/{ => previews}/mod9.png | Bin src/media/modules/previews/sobel.png | Bin 0 -> 99727 bytes src/media/modules/text/sobel-filter.json | 2 +- src/modulePage/ModulePage.tsx | 8 +- src/modulePage/ModuleSection.tsx | 141 +++++++++++---------- 14 files changed, 109 insertions(+), 85 deletions(-) create mode 100644 src/media/modules/previews/gabor.png create mode 100644 src/media/modules/previews/gausBlur.png create mode 100644 src/media/modules/previews/hog.png create mode 100644 src/media/modules/previews/kernel.png rename src/media/modules/{ => previews}/mod10.png (100%) rename src/media/modules/{ => previews}/mod11.png (100%) rename src/media/modules/{ => previews}/mod8.png (100%) rename src/media/modules/{ => previews}/mod9.png (100%) create mode 100644 src/media/modules/previews/sobel.png diff --git a/src/landingPage/ModuleIntro.tsx b/src/landingPage/ModuleIntro.tsx index 0da9a16..8c6d4c1 100644 --- a/src/landingPage/ModuleIntro.tsx +++ b/src/landingPage/ModuleIntro.tsx @@ -1,8 +1,13 @@ import React from 'react'; -import mod8 from '../media/modules/mod8.png'; -import mod9 from '../media/modules/mod9.png'; -import mod10 from '../media/modules/mod10.png'; -import mod11 from '../media/modules/mod11.png'; +import mod8 from '../media/modules/previews/mod8.png'; +import mod9 from '../media/modules/previews/mod9.png'; +import mod10 from '../media/modules/previews/mod10.png'; +import mod11 from '../media/modules/previews/mod11.png'; +import kernel from '../media/modules/previews/kernel.png'; +import gausBlur from '../media/modules/previews/gausBlur.png'; +import gabor from '../media/modules/previews/gabor.png'; +import sobel from '../media/modules/previews/sobel.png'; +import hog from '../media/modules/previews/hog.png'; function getCoverImage(imgName: string) { switch (imgName) { @@ -14,6 +19,16 @@ function getCoverImage(imgName: string) { return mod10; case 'mod11': return mod11; + case 'kernel': + return kernel; + case 'gausBlur': + return gausBlur; + case 'gabor': + return gabor; + case 'sobel': + return sobel; + case 'hog': + return hog; default: return undefined; } diff --git a/src/media/modules/module_descriptions.json b/src/media/modules/module_descriptions.json index 0caa7ef..6fc9ec7 100644 --- a/src/media/modules/module_descriptions.json +++ b/src/media/modules/module_descriptions.json @@ -16,11 +16,11 @@ "number": 8.1, "title": "Intro to Images and Kernels", "dropdownTitle": "Images and Kernels", - "body": "How do we represent the data contained in an image? What is a kernel, and how do we use them to extract features from images?", + "body": "To begin, we discuss how an image is represented as data so it can be used in computer vision algorithms. We also introduce the kernel, and how it relates to the task of extracting features from images.", "bgColor": "bg-darkblue", "textColor": "text-white", "margin": "ml-64", - "imgSrc": "mod8", + "imgSrc": "kernel", "path": "images-and-kernels", "active": true }, @@ -28,11 +28,11 @@ "number": 8.2, "title": "Image Blurring: The Gaussian Blur", "dropdownTitle": "Gaussian Blur", - "body": "Here, we explore an popular instance of a kernel called the Gaussian Blur, which (as its name suggests) is used to blur an image.", + "body": "Here we explore the Gaussian Blur, a popular computer vision technique that blurs an image. It uses a kernel with values obtained from the Gaussian function.", "bgColor": "bg-offwhite", "textColor": "text-darkblue", "margin": "mr-64", - "imgSrc": "mod8", + "imgSrc": "gausBlur", "path": "gaussian-blur", "active": true }, @@ -40,11 +40,11 @@ "number": 8.3, "title": "Directional Filtering: The Gabor Filter", "dropdownTitle": "Gabor Filter", - "body": "This section explores yet another kernel called the Gabor Filter, which extracts portions of an image that follow a specific directional pattern", + "body": "This section explores another computer vision technique called the Gabor Filter, which extracts portions of an image that follow a specific directional pattern.", "bgColor": "bg-lightblue", "textColor": "text-darkblue", "margin": "ml-64", - "imgSrc": "mod8", + "imgSrc": "gabor", "path": "gabor-filter", "active": true }, @@ -52,11 +52,11 @@ "number": 8.4, "title": "Edge Detection: The Sobel Filter", "dropdownTitle": "Sobel Filter", - "body": "In order for AI to detect an object within an image, we search for edges in the image to give us a sense of the shape of the object. We use the Sobel Filter to detect these edges.", + "body": "In order to detect an object within an image, we search for edges in the image to give us a sense of the shape of the object. We use the Sobel Filter to detect edges in a particular direction in an image.", "bgColor": "bg-darkblue", "textColor": "text-white", "margin": "mr-64", - "imgSrc": "mod8", + "imgSrc": "sobel", "path": "sobel-filter", "active": true }, @@ -64,11 +64,11 @@ "number": 8.4, "title": "Histogram of (Oriented) Gradients", "dropdownTitle": "Histogram of Gradients", - "body": "While a Sobel Filter can give us information about edges in a singular direction, we need to know about the edges in all directions simultaneously to fully understand the shape of the image. The Histogram of Gradients allows us to collect the edges in all different directions to see which is most prominent.", + "body": "While a Sobel Filter can give us information about edges in a singular direction, we need to know about the edges in all directions simultaneously to fully understand the shape of the image. The Histogram of Gradients allows us to collect the edges in all different directions.", "bgColor": "bg-lightblue", "textColor": "text-darkblue", "margin": "ml-64", - "imgSrc": "mod8", + "imgSrc": "hog", "path": "histogram-of-gradients", "active": true }, diff --git a/src/media/modules/previews/gabor.png b/src/media/modules/previews/gabor.png new file mode 100644 index 0000000000000000000000000000000000000000..3eea63d508bd1d34747289d274c039712b120e8e GIT binary patch literal 99420 zcmYhibyU;u`#-*oZh_I=8%RsnC`n;}3L+h%pycQposy%J7%)KT5EKNYV<4>{odVKG zm*2hK=l411_Xpz~;+*YzUe|R$>W+gOXi<!9XhXDirk;g9Q}0bIpU>2u4UpOz#3qn8E$FZV>A7+LNx9-9AqN#a3zI^6sJ{Z$-(8uIvZH1y6tS@~*fTwdMDX>(FR zL80vIU`gd$L&M*?+w1d#z1`jUx$*HMx6-n*GTd)D<{O+wlNIFU&sYO@>$DCQzJ9K# zsMu+~KHHldJ?HSAGMdEwSHVQnQRm%lxt)JhqRL3p-3AOwjS{*ojI_2EhC+%$z+~?*w zUG0+n?wzy6y?)4gZRo$d+vGe?EOaS!&&B#HPuUOApcS&w(DhWU{O1)vl2RFQZ}@z> z#XC5_gK8l2dRi_OPa@%1OH`80=@O(oXBy>QO9s zg}UM?=dT54ZGW#3oy9-zJ@T}dN$l_Y7WJJ?Y?18!2xWS8b@gxGw9j|*`E_m_R2Ic^ 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); - case 'KMeans': + case "KMeans": return ; default: return
; @@ -185,20 +186,20 @@ const ModuleSection: React.FC = ({ demoComp, }) => { const scheme = - colorScheme === 'dark' + colorScheme === "dark" ? { - bgColor: 'bg-moduleDarkBlue', - titleColor: 'text-modulePaleBlue', - headingColor: 'text-moduleTeal', - bodyColor: 'text-moduleOffwhite', - labelColorHex: '#CBD9F2', + bgColor: "bg-moduleDarkBlue", + titleColor: "text-modulePaleBlue", + headingColor: "text-moduleTeal", + bodyColor: "text-moduleOffwhite", + labelColorHex: "#CBD9F2", } : { - bgColor: 'bg-modulePaleBlue', - titleColor: 'text-moduleNavy', - headingColor: 'text-moduleDarkBlue', - bodyColor: 'text-moduleNavy', - labelColorHex: '#394D73', + bgColor: "bg-modulePaleBlue", + titleColor: "text-moduleNavy", + headingColor: "text-moduleDarkBlue", + bodyColor: "text-moduleNavy", + labelColorHex: "#394D73", }; return ( @@ -214,34 +215,42 @@ const ModuleSection: React.FC = ({ {sections.map((section, index) => (
-

- {section.body || lorem} -

+ {section.body?.includes("") ? ( +

+

+ ) : ( +

+ {section.body || lorem} +

+ )}
From 2b08d9713e8bf071ac8f1a47b0f129ea9426f387 Mon Sep 17 00:00:00 2001 From: Max Pinheiro Date: Tue, 2 Nov 2021 17:54:04 -0400 Subject: [PATCH 6/6] clean style on gabor filter page --- src/media/modules/text/gabor-filter.json | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/media/modules/text/gabor-filter.json b/src/media/modules/text/gabor-filter.json index a64e899..380c66e 100644 --- a/src/media/modules/text/gabor-filter.json +++ b/src/media/modules/text/gabor-filter.json @@ -2,8 +2,8 @@ "title": "Gabor Filter", "sections": [ { - "title": "Gabor Filter", - "colorScheme": "dark", + "title": "", + "colorScheme": "light", "subsections": [ { "title": "Subsection 1", @@ -24,4 +24,4 @@ "demoComp": "GaborDemo" } ] -} \ No newline at end of file +}

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gTZIX}%Jo0S$BRC(*%$6@NbvPgNmH>>!94K)0IlqB1^@s6 literal 0 HcmV?d00001 diff --git a/src/media/modules/text/sobel-filter.json b/src/media/modules/text/sobel-filter.json index 2b4fe25..509bcd3 100644 --- a/src/media/modules/text/sobel-filter.json +++ b/src/media/modules/text/sobel-filter.json @@ -17,7 +17,7 @@ }, { "title": "Subsection 3", - "body": "To get a more accurate understanding about the shape of the image, we need to extract the image gradient in the four primary edge directions: vertical, horizontal, diagonal up (45 degrees), and diagonal down (-45 degrees). The demo below calculates the images resulting from filtering by the eight Sobel kernels (4 directions, 2 filters per direction for dark-to-light and light-to-dark) for a stop sign image. Note that you can select other images to filter from the box below.", + "body": "To get a more accurate understanding about the shape of the image, we need to extract the image gradient in the four primary edge directions: vertical, horizontal, diagonal up (45 degrees), and diagonal down (-45 degrees). The demo below calculates the images resulting from filtering by the eight Sobel kernels (4 directions, 2 filters per direction for dark-to-light and light-to-dark) for a stop sign image. Note that you can select other images to filter from the box below.", "imgSrc": "blank" } ], diff --git a/src/modulePage/ModulePage.tsx b/src/modulePage/ModulePage.tsx index 2296a63..a13ee37 100644 --- a/src/modulePage/ModulePage.tsx +++ b/src/modulePage/ModulePage.tsx @@ -9,7 +9,7 @@ import cvIntroModule from '../media/modules/text/computer-vision-intro.json'; import gaborModule from '../media/modules/text/gabor-filter.json'; import gaussianModule from '../media/modules/text/gaussian-blur.json'; import sobelModule from '../media/modules/text/sobel-filter.json'; -import hogModule from '../media/modules/text/histogram-of-gradients.json'; +// import hogModule from '../media/modules/text/histogram-of-gradients.json'; export interface ModuleSubsectionType { title: string; @@ -31,13 +31,13 @@ interface Module { // eslint-disable-next-line type moduleName = 'computer-vision' | 'images-and-kernels' | 'gaussian-blur' | 'gabor-filter' | 'sobel-filter' | 'histogram-of-gradients' | 'classification' | 'perceptron' | 'neural-nets'; -const modules: Record = { +const modules: Record = { 'computer-vision': module8, 'images-and-kernels': cvIntroModule, 'gaussian-blur': gaussianModule, 'gabor-filter': gaborModule, 'sobel-filter': sobelModule, - 'histogram-of-gradients': hogModule, + 'histogram-of-gradients': null, // eslint-disable-next-line 'classification': module9, // eslint-disable-next-line @@ -55,7 +55,7 @@ export default function ModulePage( ) { // eslint-disable-next-line const module = props.match.params.module; - const curModule: Module = modules[module]; + const curModule: Module | null = modules[module]; if (!curModule) { return ( diff --git a/src/modulePage/ModuleSection.tsx b/src/modulePage/ModuleSection.tsx index 65bebed..c1d7ee9 100644 --- a/src/modulePage/ModuleSection.tsx +++ b/src/modulePage/ModuleSection.tsx @@ -1,34 +1,35 @@ -import React from 'react'; -import SobelFilterDemo from '../modules/computerVision/sobelFilter/SobelFilterDemo'; -import GaussianBlurDemo from '../modules/computerVision/gaussianBlur/GaussianBlurDemo'; -import GaborDemo from '../modules/computerVision/gaborFilter/gaborFilter'; -import DiffOfGaussianDemo from '../modules/computerVision/diffofgaussian/DiffOfGaussian'; -import HaarWaveletDemo from '../modules/computerVision/haarWavelet/HaarWaveletDemo'; -import { ImageSelectableDemo } from '../modules/computerVision/imageSelector/ImageSelectableDemo'; +/* eslint-disable */ +import React from "react"; +import SobelFilterDemo from "../modules/computerVision/sobelFilter/SobelFilterDemo"; +import GaussianBlurDemo from "../modules/computerVision/gaussianBlur/GaussianBlurDemo"; +import GaborDemo from "../modules/computerVision/gaborFilter/gaborFilter"; +import DiffOfGaussianDemo from "../modules/computerVision/diffofgaussian/DiffOfGaussian"; +import HaarWaveletDemo from "../modules/computerVision/haarWavelet/HaarWaveletDemo"; +import { ImageSelectableDemo } from "../modules/computerVision/imageSelector/ImageSelectableDemo"; import PCADemo, { RawDataTable, SelectableAxisChart, StaticAxisChart, config as pcaConfig, -} from '../modules/stateSpaces/pca/PCA'; +} from "../modules/stateSpaces/pca/PCA"; import KMeans, { KMeansStepExample, InteractiveClusteringExample, -} from '../modules/stateSpaces/kmeans'; -import blank from '../media/modules/blank.png'; -import animation1 from '../media/modules/computerVision/animation-1.gif'; -import animation2 from '../media/modules/computerVision/animation-2.gif'; -import animation3 from '../media/modules/computerVision/animation-3.gif'; -import combinedSobelKernelExampleLight from '../media/modules/computerVision/combinedSobelKernelExampleLight.png'; -import combinedSobelKernelExampleDark from '../media/modules/computerVision/combinedSobelKernelExampleDark.png'; -import vertSobelExampleLight from '../media/modules/computerVision/sobelKernels/vertical_lighttodark.png'; -import vertSobelExampleDark from '../media/modules/computerVision/sobelKernels/vertical_darktolight.png'; +} from "../modules/stateSpaces/kmeans"; +import blank from "../media/modules/blank.png"; +import animation1 from "../media/modules/computerVision/animation-1.gif"; +import animation2 from "../media/modules/computerVision/animation-2.gif"; +import animation3 from "../media/modules/computerVision/animation-3.gif"; +import combinedSobelKernelExampleLight from "../media/modules/computerVision/combinedSobelKernelExampleLight.png"; +import combinedSobelKernelExampleDark from "../media/modules/computerVision/combinedSobelKernelExampleDark.png"; +import vertSobelExampleLight from "../media/modules/computerVision/sobelKernels/vertical_lighttodark.png"; +import vertSobelExampleDark from "../media/modules/computerVision/sobelKernels/vertical_darktolight.png"; // import lightVertSobelExampleLight from '../media/modules/computerVision/vertSobelExampleLight.png'; // import lightVertSobelExampleDark from '../media/modules/computerVision/vertSobelExampleDark.png'; const lorem = - 'Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.'; + "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum."; interface ColorScheme { bgColor: string; @@ -46,21 +47,21 @@ interface ModuleSubsection { function GetImage(imgName: string) { switch (imgName) { - case 'blank': + case "blank": return blank; - case 'animation1': + case "animation1": return animation1; - case 'animation2': + case "animation2": return animation2; - case 'animation3': + case "animation3": return animation3; - case 'sobelKernelLight': + case "sobelKernelLight": return vertSobelExampleLight; - case 'sobelKernelDark': + case "sobelKernelDark": return vertSobelExampleDark; - case 'combinedSobelKernelLight': + case "combinedSobelKernelLight": return combinedSobelKernelExampleLight; - case 'combinedSobelKernelDark': + case "combinedSobelKernelDark": return combinedSobelKernelExampleDark; default: } @@ -70,7 +71,7 @@ function getDemo(comp: string, scheme: ColorScheme) { const demoArgs = { labelColor: scheme.titleColor }; switch (comp) { - case 'GaussianBlurDemo': + case "GaussianBlurDemo": return ( ); - case 'GaborDemo': + case "GaborDemo": return ( ); - case 'DiffOfGaussian': + case "DiffOfGaussian": return ( ); - case 'HaarWaveletDemo': + case "HaarWaveletDemo": return ( ); - case 'SobelFilterDemo': + case "SobelFilterDemo": return ( ); - case 'PCADemo': + case "PCADemo": return ; - case 'RawDataTable': + case "RawDataTable": return ; - case 'StaticAxisChart': + case "StaticAxisChart": return ( ); - case 'SelectableAxisChart': + case "SelectableAxisChart": return ( ); - case 'PCASelectableAxisChart': + case "PCASelectableAxisChart": return ( ); - case 'InteractiveKMeans': + case "InteractiveKMeans": return (