#Image Preprocessing

[softlabsgroup05]

Explore the fundamental elements of computer vision in AI with our insightful guide. This simplified overview outlines the key components that enable machines to interpret and understand visual information, powering applications like image recognition and object detection. Perfect for those interested in unlocking the capabilities of artificial intelligence. Stay informed with Softlabs Group for more insightful content on cutting-edge technologies.

[getthrawnin]

"Forgive me, Father, I have sinned."

Cardinal!Thrawn

See all images on Patreon

Our process involves refining images to achieve a desired outcome, which requires skill. The original images were not of Thrawn; we edited them to resemble him.

[swkrullimaging]

DXO Pure Raw 4

Comparing DXO Pure Raw 4 to Topaz Photo AI version 2.4 in my real world comparison using it as a pre-processor to Adobe Camera Raw and Photoshop. The Problem Unfortunately it has come to my attention that all of my software licenses are expiring this month. That includes DXO Pure Raw 3, and all the Topaz products including Gigapixel, and Photo AI. The two stand alone products Sharpen AI and…

[dihalect]

finally went and started looking into nightshade because it sounds. dubious. apparently the project's full title is "nightshade: protecting copyright". it is absolutely wild that a bunch of internet communists saw that and went "yeah this is a good thing, we should totally rally around this"

[alpaca-clouds]

How Capitalism turned AI into something bad

AI "Art" sucks. AI "writing" sucks. Chat GPT sucks. All those fancy versions of "fancy predictive text" and "fancy predictive image generation" actually do suck a lot. Because they are bad at what they do - and they take jobs away from people, who would actually be good at them.

But at the same time I am also thinking about what kind of fucking dystopia we live in, that this had to turn out that way.

You know... I am an autistic guy, who has studied computer science for quite a while now. I have read a lot of papers and essays in my day about the development of AI and deep learning and what not. And I can tell you: There is stuff that AI is really good and helpful for.

Currently I am working a lot with the evaluation of satellite imagery and I can tell you: AI is making my job a ton easier. Sure, I could do that stuff manually, but it would be very boring and mind numbing. So, yeah, preprocessing the images with AI so that I just gotta look over the results the AI put out and confirm them? Much easier. Even though at times it means that my workday looks like this: I get to work, start the process on 50GB worth of satellite data, and then go look at tumblr for the rest of the day or do university stuff.

But the thing is that... You know. Creative stuff is actually not boring, manial stuff where folks are happy to have the work taken off their hands. Creative work is among those jobs that a lot of people find fulfilling. But from the feeling of fulfillment you cannot eat. But now AI is being used to push down the money folks in creative jobs can make.

I think movie and TV writing is a great example. When AI puts out a script, that script is barely sensible. Yet, the folks who actually make something useful out of it get paid less than they would, if they did it on their own.

Sure, in the US the WGA made it clear that they would not work with studios doing something like that - but the US is not the whole world. And in other countries it will definitely happen.

And that... kinda sucks.

And of course even outside of creative fields... There is definitely jobs that are going to get replaced by automation and artificial intelligence.

The irony is that once upon a time folks like Keynes were like: "OMG, we will get there one day and it is going to be great, because a machine is going to do your work, and you are gonna get paid for it." But the reality obviously is that: "A machine is going to do the work and the CEO is going to get an even bigger bonus, while you sleep on the streets, where police will then violate you for being homeless."

You know, looking at this from the point of view of Solarpunk: I absolutely think that there is a place in a Solarpunk future for AI. Even for some creative AI. But all under the assumption that first we are going to erradicate fucking capitalism. Because this does not work together with capitalism. We need to get rid of capitalism first. And no, I do not know how to start.

[kimsuyeon]

hi if its alright with you can I please ask your stage gif process 🩵 (I don't mind if its not too detailed but if you use vapoursynth or topaz or anything)

hiii! omg thank u for wanting to know 🥺🥺 i actually use two methods, depending on the source file (and how lazy i feel). either vapoursynth or mpv. i'll show u both!

long, the example gif has a flashy background, somewhat clear. i hope.

i source from .ts files on k24hr or twitter, fancams, and then the youtube version (which i then run through handbrake before vs or mpv) if there's absolutely no other choice. i try to avoid show music core because its backgrounds make sharpening hard, but i'm using one from there for this tutorial :) i will also use beyond live / blu-ray files when available (i.e. 4th world tour gifs i did of twice)

vapoursynth:

-> only use the deinterlace (60 slow) when it's 1080i 30fps! i use this on files from k24hrs. if the file is already 60 fps (i.e. those from srghkqud on twitter), i don't deinterlace or preprocess. i use finesharp on .7 any time i use vs!

this gif is 268 x 520 px! i leave the delay at .02 and set frame rate to 60fps. on the left, it is just changed to 60fps and run through vs with the above settings. the gif on the right is sharpened!

i change my sharpening settings on every stage (and most other sets as well)! i use an action, and then adjust by the background and quality of the source! this is using my stage sharpening - but i have removed a smart sharpen and reduced opacity of other filters i use on different (clearer) files!

show music core has these really visible leds in their background, so sharpening it tends to be harder! i avoid using my 500px smart sharpens when it looks like this, and use high pass and bigger radius smaller amount smart sharpens!

sharpening settings for above: 8.0 high pass on soft light blending (40% opacity), 241 .2 smart sharpen (60% opacity), 15 15 smart sharpen (50% opacity) i should also note that on really really pale stages, i use camera raw filter to fix whites / highlights before i add the other sharpening! this does really slow down export time and can be frustrating, so i reserve it for then!

then i color! sometimes i use ezgif (which hasn't lowered quality that i've noticed) to adjust the speed, if i feel it needs it! this gif has a fine speed so i've left it as is, but i normally speed up gifs by 120% on their speed feature to make the choreo look a little faster!

left: unsharpened except vs finesharp, colored right: sharpened, colored

my coloring focus is always restoring skin, everything else is pretty much purely stylistic. i try to leave colors in the background the same! i do really like making blonde hair pinker or more toned (since they're often yellow, i always make a point of essentially toning their hair for them - i.e. tsuki in dang! set)

when i color the rest of the set, i keep the first gif open next to it to make sure everything matches! i normally copy and paste the coloring group and adjust as needed :D

also, i check how it looks in tumblr on desktop + mobile, since web safe colors adjust the look of your gifs a lot (the ones with the little dots in the middle are web safe, everything else isn't) and try to fix what doesn't look quite right. i also ask my friends if something is wrong but i'm not sure what (mainly nini (@withyouth) so shout out to her for putting up w/ me, a big part of the stage gif process)!

mpv: -> i press 'd' and make sure it is deinterlacing (again, only 1080i ones get deinterlaced)! and then i screencap. i followed this guide on setting up mpv, and always use minimal compression settings for everything i screencap.

-> i turn my files into dicom files (you can just rename them on mac, on windows it is multistep) and then scipt->load multiple dicom files (faster than loading image into stack and, in my opinion, clearer too)!

for windows: -> alt+d in your screencap folder, cmd. enter. type ren *.* *.dcm into the window. enter. close the command window!

i make my frame animation + frames to layers. once it is on the timeline, i go ahead and crop before i do anything else. i tried to make the cropping like the vs gif, but it's not identical!

sharpening: since i didn't use finesharp, i can add more in ps. this is my normal stage sharpening with nothing added or removed!

left: deinterlaced, screencapped, cropped right: deinterlaced, screencapped, cropped, sharpened -> 8.0 high pass (soft light, 65%), 500 .3 smart sharpen, 241 .2 smart sharpen (60% opacity), 15 15 smart sharpen (50% opacity)

coloring: same one as the vs gifs!

comparison:

left: vapoursynth, fully complete right: dicom, fully complete

i normally use mpv, but sometimes i don't feel like waiting on the screencaps, or i think finesharp will add texture + depth that the original doesn't have (too smooth of a filter, whatever) so i use vs! my taeyeon mr. mr. and le sserafim rock ver. sets were both done via vapoursynth, though the sharpenings are different from each other and what it shown here (i change sharpening a lot, and sometimes by set... sorry.. KJHDFGJKH)!

anyways i hope this was helpful!!! thank u for asking it means a lot u want to know!! if u want more stuff answered or shown, u can always ask :D and i hope it's clear 😭😭 i know i ramble a lot

[anishmary]

In the subject of data analytics, this is the most important concept that everyone needs to understand. The capacity to draw insightful conclusions from data is a highly sought-after talent in today's data-driven environment. In this process, data analytics is essential because it gives businesses the competitive edge by enabling them to find hidden patterns, make informed decisions, and acquire insight. This thorough guide will take you step-by-step through the fundamentals of data analytics, whether you're a business professional trying to improve your decision-making or a data enthusiast eager to explore the world of analytics.

Step 1: Data Collection - Building the Foundation

Identify Data Sources: Begin by pinpointing the relevant sources of data, which could include databases, surveys, web scraping, or IoT devices, aligning them with your analysis objectives. Define Clear Objectives: Clearly articulate the goals and objectives of your analysis to ensure that the collected data serves a specific purpose. Include Structured and Unstructured Data: Collect both structured data, such as databases and spreadsheets, and unstructured data like text documents or images to gain a comprehensive view. Establish Data Collection Protocols: Develop protocols and procedures for data collection to maintain consistency and reliability. Ensure Data Quality and Integrity: Implement measures to ensure the quality and integrity of your data throughout the collection process.

Step 2: Data Cleaning and Preprocessing - Purifying the Raw Material

Handle Missing Values: Address missing data through techniques like imputation to ensure your dataset is complete. Remove Duplicates: Identify and eliminate duplicate entries to maintain data accuracy. Address Outliers: Detect and manage outliers using statistical methods to prevent them from skewing your analysis. Standardize and Normalize Data: Bring data to a common scale, making it easier to compare and analyze. Ensure Data Integrity: Ensure that data remains accurate and consistent during the cleaning and preprocessing phase.

Step 3: Exploratory Data Analysis (EDA) - Understanding the Data

Visualize Data with Histograms, Scatter Plots, etc.: Use visualization tools like histograms, scatter plots, and box plots to gain insights into data distributions and patterns. Calculate Summary Statistics: Compute summary statistics such as means, medians, and standard deviations to understand central tendencies. Identify Patterns and Trends: Uncover underlying patterns, trends, or anomalies that can inform subsequent analysis. Explore Relationships Between Variables: Investigate correlations and dependencies between variables to inform hypothesis testing. Guide Subsequent Analysis Steps: The insights gained from EDA serve as a foundation for guiding the remainder of your analytical journey.

Step 4: Data Transformation - Shaping the Data for Analysis

Aggregate Data (e.g., Averages, Sums): Aggregate data points to create higher-level summaries, such as calculating averages or sums. Create New Features: Generate new features or variables that provide additional context or insights. Encode Categorical Variables: Convert categorical variables into numerical representations to make them compatible with analytical techniques. Maintain Data Relevance: Ensure that data transformations align with your analysis objectives and domain knowledge.

Step 5: Statistical Analysis - Quantifying Relationships

Hypothesis Testing: Conduct hypothesis tests to determine the significance of relationships or differences within the data. Correlation Analysis: Measure correlations between variables to identify how they are related. Regression Analysis: Apply regression techniques to model and predict relationships between variables. Descriptive Statistics: Employ descriptive statistics to summarize data and provide context for your analysis. Inferential Statistics: Make inferences about populations based on sample data to draw meaningful conclusions.

Step 6: Machine Learning - Predictive Analytics

Algorithm Selection: Choose suitable machine learning algorithms based on your analysis goals and data characteristics. Model Training: Train machine learning models using historical data to learn patterns. Validation and Testing: Evaluate model performance using validation and testing datasets to ensure reliability. Prediction and Classification: Apply trained models to make predictions or classify new data. Model Interpretation: Understand and interpret machine learning model outputs to extract insights.

Step 7: Data Visualization - Communicating Insights

Chart and Graph Creation: Create various types of charts, graphs, and visualizations to represent data effectively. Dashboard Development: Build interactive dashboards to provide stakeholders with dynamic views of insights. Visual Storytelling: Use data visualization to tell a compelling and coherent story that communicates findings clearly. Audience Consideration: Tailor visualizations to suit the needs of both technical and non-technical stakeholders. Enhance Decision-Making: Visualization aids decision-makers in understanding complex data and making informed choices.

Step 8: Data Interpretation - Drawing Conclusions and Recommendations

Recommendations: Provide actionable recommendations based on your conclusions and their implications. Stakeholder Communication: Communicate analysis results effectively to decision-makers and stakeholders. Domain Expertise: Apply domain knowledge to ensure that conclusions align with the context of the problem.

Step 9: Continuous Improvement - The Iterative Process

Monitoring Outcomes: Continuously monitor the real-world outcomes of your decisions and predictions. Model Refinement: Adapt and refine models based on new data and changing circumstances. Iterative Analysis: Embrace an iterative approach to data analysis to maintain relevance and effectiveness. Feedback Loop: Incorporate feedback from stakeholders and users to improve analytical processes and models.

Step 10: Ethical Considerations - Data Integrity and Responsibility

Data Privacy: Ensure that data handling respects individuals' privacy rights and complies with data protection regulations. Bias Detection and Mitigation: Identify and mitigate bias in data and algorithms to ensure fairness. Fairness: Strive for fairness and equitable outcomes in decision-making processes influenced by data. Ethical Guidelines: Adhere to ethical and legal guidelines in all aspects of data analytics to maintain trust and credibility.

Data analytics is an exciting and profitable field that enables people and companies to use data to make wise decisions. You'll be prepared to start your data analytics journey by understanding the fundamentals described in this guide. To become a skilled data analyst, keep in mind that practice and ongoing learning are essential. If you need help implementing data analytics in your organization or if you want to learn more, you should consult professionals or sign up for specialized courses. The ACTE Institute offers comprehensive data analytics training courses that can provide you the knowledge and skills necessary to excel in this field, along with job placement and certification. So put on your work boots, investigate the resources, and begin transforming.

[deletedg1rl]

3rd July 2024

Goals:

Watch all Andrej Karpathy's videos

Watch AWS Dump videos

Watch 11-hour NLP video

Complete Microsoft GenAI course

GitHub practice

Topics:

1. Andrej Karpathy's Videos

Deep Learning Basics: Understanding neural networks, backpropagation, and optimization.

Advanced Neural Networks: Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and LSTMs.

Training Techniques: Tips and tricks for training deep learning models effectively.

Applications: Real-world applications of deep learning in various domains.

2. AWS Dump Videos

AWS Fundamentals: Overview of AWS services and architecture.

Compute Services: EC2, Lambda, and auto-scaling.

Storage Services: S3, EBS, and Glacier.

Networking: VPC, Route 53, and CloudFront.

Security and Identity: IAM, KMS, and security best practices.

3. 11-hour NLP Video

NLP Basics: Introduction to natural language processing, text preprocessing, and tokenization.

Word Embeddings: Word2Vec, GloVe, and fastText.

Sequence Models: RNNs, LSTMs, and GRUs for text data.

Transformers: Introduction to the transformer architecture and BERT.

Applications: Sentiment analysis, text classification, and named entity recognition.

4. Microsoft GenAI Course

Generative AI Fundamentals: Basics of generative AI and its applications.

Model Architectures: Overview of GANs, VAEs, and other generative models.

Training Generative Models: Techniques and challenges in training generative models.

Applications: Real-world use cases such as image generation, text generation, and more.

5. GitHub Practice

Version Control Basics: Introduction to Git, repositories, and version control principles.

GitHub Workflow: Creating and managing repositories, branches, and pull requests.

Collaboration: Forking repositories, submitting pull requests, and collaborating with others.

Advanced Features: GitHub Actions, managing issues, and project boards.

Detailed Schedule:

Wednesday:

2:00 PM - 4:00 PM: Andrej Karpathy's videos

4:00 PM - 6:00 PM: Break/Dinner

6:00 PM - 8:00 PM: Andrej Karpathy's videos

8:00 PM - 9:00 PM: GitHub practice

Thursday:

9:00 AM - 11:00 AM: AWS Dump videos

11:00 AM - 1:00 PM: Break/Lunch

1:00 PM - 3:00 PM: AWS Dump videos

3:00 PM - 5:00 PM: Break

5:00 PM - 7:00 PM: 11-hour NLP video

7:00 PM - 8:00 PM: Dinner

8:00 PM - 9:00 PM: GitHub practice

Friday:

9:00 AM - 11:00 AM: Microsoft GenAI course

11:00 AM - 1:00 PM: Break/Lunch

1:00 PM - 3:00 PM: Microsoft GenAI course

3:00 PM - 5:00 PM: Break

5:00 PM - 7:00 PM: 11-hour NLP video

7:00 PM - 8:00 PM: Dinner

8:00 PM - 9:00 PM: GitHub practice

Saturday:

9:00 AM - 11:00 AM: Andrej Karpathy's videos

11:00 AM - 1:00 PM: Break/Lunch

1:00 PM - 3:00 PM: 11-hour NLP video

3:00 PM - 5:00 PM: Break

5:00 PM - 7:00 PM: AWS Dump videos

7:00 PM - 8:00 PM: Dinner

8:00 PM - 9:00 PM: GitHub practice

Sunday:

9:00 AM - 12:00 PM: Complete Microsoft GenAI course

12:00 PM - 1:00 PM: Break/Lunch

1:00 PM - 3:00 PM: Finish any remaining content from Andrej Karpathy's videos or AWS Dump videos

3:00 PM - 5:00 PM: Break

5:00 PM - 7:00 PM: Wrap up remaining 11-hour NLP video

7:00 PM - 8:00 PM: Dinner

8:00 PM - 9:00 PM: Final GitHub practice and review

[erikabsworld]

Mastering MATLAB: Solving Challenging University Assignments

Welcome to another installment of our MATLAB assignment series! Today, we're diving into a challenging topic often encountered in university-level coursework: image processing. MATLAB's versatility makes it an invaluable tool for analyzing and manipulating images, offering a wide array of functions and capabilities to explore. In this blog, we'll tackle a complex problem commonly found in assignments, providing both a comprehensive explanation of the underlying concepts and a step-by-step guide to solving a sample question. So, let's roll up our sleeves and get ready to do your MATLAB assignment!

Understanding the Concept: Image processing in MATLAB involves manipulating digital images to extract useful information or enhance visual quality. One common task is image segmentation, which involves partitioning an image into meaningful regions or objects. This process plays a crucial role in various applications, including medical imaging, object recognition, and computer vision.

Sample Question: Consider an assignment task where you're given a grayscale image containing cells under a microscope. Your objective is to segment the image to distinguish individual cells from the background. This task can be challenging due to variations in cell appearance, noise, and lighting conditions.

Step-by-Step Guide:

1. Import the Image: Begin by importing the grayscale image into MATLAB using the 'imread' function.

image = imread('cells.jpg');

2. Preprocess the Image: To enhance the quality of the image and reduce noise, apply preprocessing techniques such as filtering or morphological operations.

filtered_image = medfilt2(image, [3 3]); % Apply median filtering

3. Thresholding: Thresholding is a fundamental technique for image segmentation. It involves binarizing the image based on a certain threshold value.

threshold_value = graythresh(filtered_image); % Compute threshold value binary_image = imbinarize(filtered_image, threshold_value); % Binarize image

4. Morphological Operations: Use morphological operations like erosion and dilation to refine the segmented regions and eliminate noise.

se = strel('disk', 3); % Define a structuring element morph_image = imclose(binary_image, se); % Perform closing operation

5. Identify Objects: Utilize functions like 'bwlabel' to label connected components in the binary image.

[label_image, num_objects] = bwlabel(morph_image); % Label connected components

6. Analyze Results: Finally, analyze the labeled image to extract relevant information about the segmented objects, such as their properties or spatial distribution.

props = regionprops(label_image, 'Area', 'Centroid'); % Extract object properties

How We Can Help:

Navigating through complex MATLAB assignments, especially in challenging topics like image processing, can be daunting for students. At matlabassignmentexperts.com, we understand the struggles students face and offer expert assistance to ensure they excel in their coursework. If you need someone to do your MATLAB assignment, we are here to help. Our team of experienced MATLAB tutors is dedicated to providing comprehensive guidance, from explaining fundamental concepts to assisting with assignment solutions. With our personalized approach and timely support, students can tackle even the most demanding assignments with confidence.

Conclusion:

In conclusion, mastering MATLAB for image processing assignments requires a solid understanding of fundamental concepts and proficiency in utilizing various functions and techniques. By following the step-by-step guide provided in this blog, you'll be well-equipped to tackle complex tasks and excel in your university assignments. Remember, at matlabassignmentexperts.com, we're here to support you every step of the way. So, go ahead and dive into your MATLAB assignment with confidence!

[muhammadarslanalvi]

Seeing Beyond the Pixel: An Introduction to Digital Image Processing

Have you ever stopped to wonder how that blurry picture from your phone gets transformed into a crystal-clear masterpiece on social media?

Or how scientists can analyze faraway galaxies using images captured by telescopes? The secret sauce behind these feats is Digital Image Processing (DIP)!

Imagine DIP (Digital Image Processing) as a cool toolbox for your digital images. It lets you manipulate and analyze them using powerful computer algorithms. You can think of it as giving your pictures a makeover, but on a whole new level.

The Image Makeover Process

DIP works in a series of steps, like a recipe for image perfection:

Snap Happy! (Image Acquisition) - This is where it all starts. You capture the image using a camera, scanner, or even a scientific instrument like a telescope!

Person taking a picture with smartphone

Picture Prep (Preprocessing) - Sometimes, images need a little prep work before the real magic happens. Think of it like trimming the edges or adjusting the lighting to ensure better analysis.

Person editing a photo on a computer

Enhance Me! (Enhancement) - Here's where your image gets a glow-up! Techniques like adjusting brightness, contrast, or sharpening details can make all the difference in clarity and visual appeal.

Blurry photo becoming clear after editing

Fixing the Funky (Restoration) - Did your old family photo get a little scratched or blurry over time? DIP can help remove those imperfections like a digital eraser, restoring the image to its former glory.

Scratched photo being restored

Info Time! (Analysis) - This is where things get interesting. DIP can actually extract information from the image, like identifying objects, recognizing patterns, or even measuring distances. Pretty cool, right?

Xray being analyzed by a doctor on a computer

Size Matters (Compression) - Ever struggled to send a massive photo via email? DIP can shrink the file size without losing too much detail, making it easier to store and share images efficiently.

Large image file being compressed

Voila! (Output) - The final step is presenting your masterpiece! This could be a stunningly clear picture, a detailed analysis report, or anything in between, depending on the purpose of the image processing.

Highquality image after processing

Real World Wow Factor

DIP isn't just about making pretty pictures (although that's a valuable application too!). It has a wide range of real-world uses that benefit various fields:

Medical Marvels (Medical Field) - DIP helps doctors analyze X-rays, MRIs, and other medical scans with greater accuracy and efficiency, leading to faster and more precise diagnoses.

Cosmic Companions (Astronomy) - Scientists use DIP to analyze images from space telescopes, revealing the secrets of stars, galaxies, and other wonders of the universe. By enhancing faint details and removing noise, DIP allows astronomers to peer deeper into the cosmos.

Space telescope capturing an image of a galaxy

Eagle Eye from Above (Remote Sensing) - Satellites use DIP to monitor Earth, tracking weather patterns, deforestation, and other environmental changes. By analyzing satellite imagery, researchers can gain valuable insights into the health of our planet.

Satellite image of Earth

Unlocking Your Face (Security Systems) - Facial recognition systems use DIP to identify people in images and videos, which can be used for security purposes or even to personalize user experiences.

Facial recognition system unlocking a phone

Selfie Magic (Consumer Electronics) - Your smartphone uses DIP to enhance your photos, automatically adjusting brightness, contrast, and other factors to make your selfies look their best.

Person taking a selfie

The Future's Looking Sharp

DIP is constantly evolving, thanks to advancements in Artificial Intelligence (AI). Imagine self-driving cars using DIP for super-accurate navigation in real-time, or virtual reality experiences that seamlessly blend real and digital worlds with exceptional clarity. The possibilities are endless!

So, the next time you look at an image, remember, there's a whole world of technology working behind the scenes to make it what it is. With DIP, we can truly see beyond the pixel and unlock the hidden potential of the visual world around us.

References:

Gonzalez, Rafael C., and Richard E. Woods. "Digital image processing." Pearson Education India, 2008.

Jain, Anil K. "Fundamentals of digital image processing." Prentice-Hall, Inc., 1989.

National Institute of Standards and Technology (NIST). "Digital Image Processing: An Introduction." https://www.amazon.com/Introduction-Digital-Image-Processing/dp/0134806743

U.S. Department of Energy (DOE). "Image Processing and Analysis." https://www.baeldung.com/cs/energy-image-processing

Patel, Meet, et al. "Image Processing Techniques in Medical Field: A Literature Review." Journal of Medical Physics, vol. 40, no. 4, 2019, pp. 140001. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3782694/

[ajcgames]

A little output

I spent my lunch programming rather than eating, which is very typical of me when I get deeply involved in a project. I must make sure I don't fall into that obsession trap again, which has caused many burnouts in the past! 😅

My current task was to get the new generation machine to spit items out onto a connected belt. This was mostly straightforward, though it was a little tricky getting my head around supporting rotation.

Ultimately it came down to making things simple. Machines can either have an input or an output, or both. Since the side with which the machine accepts either inputs and outputs is defined at the prefab level (i.e. it's fixed based on what I set the model to), I just had to write some code to shift those input/output 'sides' depending on which of the 4 possible directions the model was placed in.

As far as handling exactly where the machine should look for an input / output belt, I kinda cheated a bit on this one.

The reason this was a little more complex is because machines can have a footprint of more than just 1x1, they can be 2x2 as well. A 2x2 machine will have a inputs / outputs on specific grid positions around its outside perimeter. How do I allow for this whilst enabling rotation of the placed object?

This. This is my cheat.

The little blob in the above image is an object that represents which grid position I want the machine to look for a belt (specifically an output belt). Because this blob is a child object of the machine, when the machine rotates - so the blob does too. At runtime, when I configure the machine, I can simply read the real-world position of the blob to get its grid coordinates. Neat!

It's easier to see top-down here:

The machine lives at grid position [6,4] and the blob lives inside the machine object at position [1,0]. Translated to absolute world position the blob lives at [7,4] - exactly where I need to be checking for belts!

I'm sure there are much better ways of doing this, but this was a pretty straightforward solution that only requires a tiny amount of preprocessing when the object is first placed, after which no additional calculation is needed.

With the positioning and belt-lookup code added, it was just a case of writing up a short Machine class, from which I derived a special 'InputMachine' class that only spits out items of a specific type.

The result you can see below!

Where is this all leading?

I suppose it's a good a time as any to review my future plans for these developments. What good are little prototypes without some larger goal in mind?

In one of my earliest posts I details the kind of game I'm hoping to make. Mechanically it's not super complicated, but it required figuring out some of the technical stuff I've been showing in recent posts - namely conveyor belts and moving items around.

I'm hoping to create a game that fits in the automation / factory genre. You'll be playing the role of someone setting up an automated factory that has been deposited and unpacked on a remote asteroid. You place down drills (inputs) that dig and spit out raw materials into your factory, and you move these around and process them in various machines in the name of science!

As I said this isn't the most complex of concepts. From a programming complexity point of view some of the legwork has already done (problem solving belts, specifically). There are areas that still need consideration, but looking over what's left to come I'm quietly confident it falls within my skill set to execute.

A cut down the middle

I expect anybody familiar with game development is aware of the term 'vertical slice'. It refers to the practice of developing a small segment of a product, but to a very polished state that could be considered represetative of its final form.

You see this a lot in game development. Particularly at conferences and exhibitions where publishers want to whip up excitement about games that could be years away from release. But there should be some delineation between a vertical slice for a trailer, and a playable vertical slice. I'm aiming for the latter.

In order to decide how to put something like that together, I need a broader understanding of the scope of the game. A vertical slice should demonstrate all the key concepts in the game, but leaving room to expand those ideas later without changing the core experience too much. It doesn't have to look like the final game either, though I would like to put some effort in there where possible.

I'll probably end this post here for now, but I'll probably detail out the main efforts I'm likely to be aiming for in order to reach this vertical slice. I will, of course, continue posting updates when I can with more details about the game's development.

And if you read this far and found some small measure of interest in here, thanks for your time and have yourself a great day! 😊

[gmtasoftware]

AI Video Generator Like Synthesia

How to create it and how much it cost?

The need for customized media is on the rise. People prefer methods of video production that require minimal effort on their part. With the help of Synthesia, an AI-powered video generator, creating unique music videos is a breeze.

Synthesia can generate video content featuring humanoid characters playing music in real-time by employing machine learning algorithms. It’s a fresh approach to making interesting videos. How to Create an AI-Based Video Generator Like Synthesia and how much it costare the Subject of This Article. First, though, let’s define Synthesia.

Synthesia: what is it, and how does it work?

Synthesia is a software program that, using artificial intelligence, can generate videos. Using machine learning (ML) algorithms, Synthesia can instantly generate videos like any other AI software. Synthesia is an AI that creates videos based on your provided information in a script box. Regarding pre-AI video templates, this software’s AI video generator has some of the best options available.

Two simple actions — adding content and selecting a template — are required to make an AI-generated video. It takes what you provide, combines it with the AI video, and outputs a stunning result. Use the Synthesia AI video generator to create an advertisement or any other type of video. Synthesia is only the beginning of your adventure.

Features of Your Synthesia-Style Artificial Intelligence Video Creation Application

Here are some features to think about including in your AI-powered video generator:

· Recognizing images and videos: To generate relevant content, your AI video maker needs to be able to identify various image and video file formats.

· Processing of natural language: To make your AI video maker capable of comprehending and acting upon user text input, you should equip it with natural language processing (NLP) capabilities.

· Voice recognition: To generate videos with suitable voiceovers, your AI video generator should be able to recognize a wide variety of voices, accents, and languages.

· Audio components: Suppose you want your AI video generator to be able to make exciting and interesting videos. In that case, you should give it access to a large music and sound effects library.

· Personalization: Your AI video maker should be able to tailor its output to each user, considering their demographics, interests, and other data.

· Capability to make changes: The generated content from your AI video maker should be editable in terms of length, fonts, colours, and the addition of captions or subtitles.

· Multi-OS compatibility: Your AI video maker must produce content suitable for use on various websites, social media channels, and video hosting services.

· Simple incorporation: Integration with CMSes, MA tools, and social media sites is essential, and your AI video maker shouldn’t make that difficult.

How to Create an AI-Powered Video Maker That Competes with Synthesia

· Realize the Foundations of ML

Building an AI-powered video maker requires a deep familiarity with machine learning algorithms. AI video creators are based on machine learning algorithms. They aid computers in gaining insights from data and enhancing their functionality over time.

· Data Collection and Preprocessing

A significant quantity of data is required to train your machine-learning model — Utilise data related to that if you wish to create music videos, for instance. You can use pre-existing datasets as well as custom-built ones. To have clean and ready-to-train data, preprocessing is essential.

· Construct the Model’s Core Architecture

Your model’s efficiency relies heavily on its structure. Your model’s success relies on selecting an appropriate machine learning algorithm and carefully structuring its underlying data structures. Convolutional, pooling, and fully connected layers are all possible components of an architecture when training with a CNN.

· Model Training

Training the model comes after data collection, preprocessing, and model architecture design. Machine learning model training involves iteratively adjusting parameters to reduce error. This could take hours or days based on dataset size and model complexity.

· Put the Model to the Test

If you want to know how well your model did in practice, you can’t just stop with training. The model’s efficacy measures include accuracy, precision, recall, and F1 score. If the model doesn’t perform well enough, you should start over at Step 3 and alter its structure.

· Model Deployment

You can deploy your model when it meets your expectations. Amazon Web Services, Google Cloud, or Microsoft Azure can produce your model. Depending on your needs, local servers or mobile devices can run your model.

What You Need to Know About the Tech Behind Synthesia to Create Your AI-Powered Video Generator

· Frontend Technologies

Use current frontend web technologies like HTML, CSS, and JavaScript to create AI video generator software like Synthesia.

· Backend Technologies

To create an AI-powered video generator, you’ll need server-side scripting from Node.js, a server-side framework like Express.js, and a database like MongoDB.

· Artificial Intelligence Languages

Programming languages for artificial intelligence (AI) development include Python, Lisp, Java, JavaScript, Rust, C++, and R.

· Cloud Infrastructure

Cloud integration requires AWS, Azure, or GCP. Elastic Compute Cloud (EC2) lets you host your web-based AI video generator software, store user data in S3, and distribute files with CloudFront.

· Video Engineering Tools

AI video generator software development requires both Web RTC for real-time communication and video conferencing and FFmpeg, an open-source video editing software for processing and editing videos.

To create an AI video generator similar to Synthesia, how much would it cost?

It can cost anywhere from $5,000 to $100,000 or more to create an artificial intelligence video generator like Synthesia. The development of the AI algorithms alone contributes significantly to the overall price tag. The development of basic software for an AI video generator will cost at least $6,000 to $8,000, as it relies heavily on artificial intelligence and machine learning. Software development costs for sophisticated AI video generators can easily exceed $80,000.

These are, of course, just estimates, and the true development cost depends on several factors. A few examples of things that can drive up the price of creating AI-powered video generation software are:

· ML Video Maker Genre

· Customization Degree

· Productivity Quality

· Complexity of Datasets and Their Size

· Expenditures for Both Production and Upkeep

· Cost of Licences

These are the main drivers of the high price tag associated with creating an AI-powered video maker. A reliable development firm, however, can help you cut costs and save time.

AI has great potential, so many companies use it to boost profits. Furthermore, now is the time to grow your business into artificial intelligence by creating AI Video generator software like Synthesia.

The total price tag for developing an artificial intelligence video generator like Synthesia can fluctuate widely depending on several factors, such as the sophistication of the AI model, the features and functionalities sought after, the size and cost of the development team, and the need for ongoing server maintenance and upkeep. A ballpark figure for this kind of undertaking could be anywhere from hundreds to millions.

[ellocentlabsin]

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5. AI-driven Applications: We develop AI-powered applications that enhance user experiences and drive engagement. Our team ensures that the applications are user-friendly, secure, and optimized for performance.

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Moreover, we are committed to transparent communication with our clients, providing them with a clear understanding of the AI models and their implications.

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Innovation is at the core of our company. We invest in ongoing research and development to explore new frontiers in AI and ML. Our collaboration with academic institutions and industry partners fuels our drive to stay ahead in this ever-changing field.

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[katdbee]

Workflow for generating 25 images of a character concept using Automatic1111 and Control Net image diffusion method with txt2img;

Enable Control Net , Low VRAM, and Preview checkboxes on.

Select the Open Pose setting and choose the openpose_hand preprocessor. Feed it a good clean source image such as this render of figure I made in Design Doll. Click the explodey button to preprocess the image, and you'll get a spooky rave skeleton like this.

Low VRAM user (me, I am low VRAM) tip: Save that preprocessed image and then replace the source image with it. Change the preprocessor to none, and it saves a bit of time.

Lower the steps from 20 if you like. Choose the DPM++SDE Karras sampler if you like.

Choose X/Y/Z plot from the script drop down and pick the settings you like for the character chart about to be generated. in the top one posted I used X Nothing Y CFG scale 3-7 Z Clipskip 1,2,3,7,12

Thanks for reading.

[cadmusfly]

Thank you for the detailed reply wrt your AI art process! It's very interesting. Also disheartening, but that's not your fault.

In relation to the ask about my AI art process

Thanks?

I’ve been dabbling in amateur digital drawing for many years, which you can see some in #cadmus draws - I’ve never been very good at it, I have poor fine motor skills which were so bad I was given a computer for school exams and also very bad visual imagination probably bordering on aphantasia. The stuff I’m doing with AI art has evolved from my amateur drawing and uses what I’ve learned from that, in that I’ve always liked obnoxiously saturated colours and surreal imagery, and I use art programs to plan composition and colours (preprocessing?) as well as editing and fixing up (post processing?).

I don’t want to devalue or displace traditional dexterity and training based illustrative skills, which I hold deep respect for. Yeah, I sympathise with those who are scared that their craft and industry will be devalued and disrupted, and I don’t have very good answers except that I have philosophical disagreements with the many popular accusations against AI art, I think legal remedies prohibiting AI art will hurt more than help and I think unionising and labour responses like the current American screenwriting strikes is a much better way to deal with these things.

Yeah, there’s a much lower ceiling in terms of creating something that would be considered “polished” to an untrained eye in AI art. That makes it attractive to people who want quick gratification, there’s no denying that, as well as those who view it as a profitable opportunity and those who want to cut costs and displace working artists. Those same qualities, however, make it attractive to those who cannot engage with traditional dexterity-based illustrative skills for reasons of time or disability.

And there are ways to engage with AI art that definitely aren’t possible in other art, which I find deeply fascinating. What does subtracting the ocean from a mountain look like? If we’re converting images and words into numbers, can we convert other things into pictures or words? Midjourney is overtuned on a very conventional aesthetic preference, but I’ve seen people interact with it to actively work against that aesthetic preference to create stylebending monstrosities that I find deeply fascinating.

Don’t know where I’m going with this, but yeah.