ChatGPT
https://www.tiziran.com/topics-and-projects/chatgpt
MindMap of ChatGPT Prompt Engineering for Developers by deeplearning.ai
https://learn.deeplearning.ai/chatgpt-prompt-eng
#ChatGPT #LLM #tiziran
Download source : https://github.com/tiziran/tiziran/blob/main/ChatGPT%20Prompt%20Engineering%20for%20Developers.md
# ChatGPT
* introduction
* two model
* based llm
* predict next word, based on text training data
* instruction tuned llm
* tries to follow instructions
* fine-tune on instructions and good attempts at following those instructions
* RLHF: reinforcement learning with human feedback
* principles
* 1: write clear and specific instructions
* 2: give the model time to think
* guidelines
* two model
* based llm
* predict next word, based on text training data
* instruction tuned llm
* tries to follow instructions
* fine-tune on instructions and good attempts at following those instructions
* RLHF: reinforcement learning with human feedback
* principles
* 1: write clear and specific instructions
* Tactic 1: Use delimiters
* Triple quotes: “””
* Triple backticks: ``` ``` ```
* Triple dashes: - - -
* Angle brackets: <>
* XML tags: <tag› ‹/tag›
* Tactic 2: ask for structured output HtML, JSON
* Tactic 3: check whether conditions are satisfied check assumptions required to do the task
* Tactic 4: few-shot prompting
* give successful examples of completing tasks, then ask model to perform the task
* 2: give the model time to think
* Tactic1: specify the steps to complete a task
step1:…
step2:…
…
stepN:…
* Tactic2: instruct the model to work out its own solution before rushing to a conclusion
* model limitations
* hallucination
* reducing hallucinations: fist find relevant information, then answer question.
* iterative
* idea; implementation(code/data)[prompt];experimental result; error analysis
* prompt guidelines
* be clear and specific
* analyze why result does not give desired output
* refine the idea and the prompt
* repeat
* iterative process
* try something
* analyze where the result does not give what you want
* clarify instructions, give more time to think
* refine prompts with a batch of examples
* summarizing
* Identify the following items from the review text:
- Sentiment (positive or negative)
- Is the reviewer expressing anger? (true or false)
- Item purchased by reviewer
- Company that made the item
* inferring
* Determine whether each item in the following list of topics is a topic in the text below, which is delimited with triple backticks.
* transforming
* proofread and correct this review. Make it more compelling. Ensure it follows APA style guide and targets an advanced reader. Output in markdown format.
* expanding
* You are a customer service AI assistant.
* temperature
* a. temperature=0 ; for tasks that require reliability, predictability
* b. temperature=0.3 ; for tasks that require variety
* b. temperature=0.7 ; for tasks that require high variety
* chatbot
* messages
1. system
2. user
3. assistant
* conclusion
* Principles
- Write clear and specific instructions
- Give the model time to "think"
- Iterative prompt development
- Capabilities: Summarizing, Inferring, Transforming, Expanding
- build a chatbot
https://www.tiziran.com/topics-and-projects/chatgpt
ChatGPT Prompts for Computer Vision/Image processing/pattern recognition. My thoughts on the response ChatGPT provided to my inquiries in the field of computer vision/image processing.
#ChatGPT #Computer_Vision #Image_processing
https://www.tiziran.com/topics-and-projects/chatgpt
you can ask:
Explain code
Improve previous code
Rewrite code using correct style
Simplify code
Find bugs in code
Translate code
what are the key takeaways from " name of the book "
How do I become computer vision engineer?
write a bash command to find the name of all images files in a directory
How do I know how many lines of code I've committed to a Git repository
I want to make a revolutionary computer vision app. give me some good app names.
i want to hire a developer for computer vision app. we agree. he deliver the first draft in two weeks, offer 3 iterations free of charge. any iteration after will be charged at 100 Euro/Hour. Write a contract for us.
write a resume/CV/cover letter for me.
ask some question about interview,
write a code in c, python, rust which
what is the time complexity of this function
write a unit test for the function
how can i improve the code
convert this python code to C++
generate dummy data for the ....
Question:
What are the best libraries for computer vision?
ChatGPT:
There are several popular libraries for computer vision, including:
OpenCV: an open-source library with a wide range of functionality, including image processing, video analysis, and object detection.
TensorFlow: an open-source library developed by Google for machine learning and deep learning, which includes a module for computer vision tasks.
scikit-image: an image processing library for Python that is built on top of NumPy and SciPy.
PIL (Python Imaging Library) : It is also one of the most widely used library for image processing and manipulation.
MATLAB: a proprietary programming language and environment for numerical computation and visualization, which has a strong focus on image processing and computer vision.
Dlib: A modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
It is worth noting that the choice of library depends on the specific task and the requirements of your project.
My thought :
Great answer. I have been utilizing OpenCV for over 15 years and it is the premier library for computer vision. Other libraries may be useful, but they are not as comprehensive and practical as OpenCV.
Question: What types of images does OpenCV support?
ChatGPT:
OpenCV supports a wide variety of image types, including:
Grayscale images: images with only one channel of intensity values, often used for tasks such as edge detection and image thresholding.
RGB (Red-Green-Blue) images: images with three channels of color information, often used for tasks such as color-based object detection and image manipulation.
Binary images: images with only two intensity values, often used for tasks such as object segmentation and morphological operations.
16-bit images: images with 16 bits per channel, often used for tasks such as high dynamic range imaging.
32-bit floating point images: images with 32 bits per channel, often used for tasks such as image filtering and image arithmetic operations.
It should be noted that OpenCV is a very powerful library and can handle many other image types as well.
My thought :
Great answer. I have utilized various types of images in OpenCV and it supports a wide range of formats and types. However, I should mention that OpenCV loads color images as BGR.
Question: Why and how can we convert an image from Cartesian to polar coordinates using OpenCV?
ChatGPT:
In image processing, it is sometimes useful to represent an image in polar coordinates instead of the typical Cartesian coordinates. This can be beneficial for certain types of image analysis and manipulation, such as image registration and image enhancement. In OpenCV, the process of converting an image from Cartesian to polar coordinates can be achieved using the cv2.linearPolar() function. This function takes in the image, the center point of the image, the magnitude of the polar transformation, and a flag indicating whether the output image should be inverted. The function then returns the transformed image in polar coordinates.
My thought :
Great answer.
Question: Why do we use meshgrid in OpenCV?
ChatGPT:
Meshgrid is a utility function in OpenCV that is used to create a rectangular grid out of an array of x and y coordinates. This grid can then be used for various operations, such as transforming and warping images, or for creating masks for filtering or segmentation. Additionally, it can be used for creating the coordinates for a 2D array of pixels in an image, which is useful for various types of image processing tasks.
My thought :
Okay, that's a good answer. In many projects, I use the meshgrid function and implement floating meshgrid map functions for use in image processing applications where I need to, for example, change the image's Cartesian to polar system.
Question: What are some ways to optimize and increase the speed of OpenCV C++ image processing?
ChatGPT:
There are several ways to optimize and increase the speed of image processing using OpenCV C++:
Use the built-in OpenCV functions and avoid using for loops as much as possible, as they can be slow.
Use the appropriate data types and data structures for your image processing task.
Use the OpenCV parallel_for_ function to parallelize your code and take advantage of multi-core processors.
Use the OpenCV's GPU module to perform image processing on the GPU.
Avoid unnecessary memory allocation and deallocation by reusing memory where possible.
Use OpenCV's performance optimization techniques such as vectorization, unrolling loops, and using lookup tables.
Use profilers such as gprof, valgrind, or Visual Studio Profiler to identify and optimize performance bottlenecks in your code.
Use prebuilt libraries instead of building from source when possible to take advantage of performance optimizations.
It's worth noting that, optimizing image processing may require a combination of these approaches, and the best option will depend on the specific task and the requirements of the project.
My thought :
Okay, that's a good answer. cv::setNumThreads(4); cv::parallel_for_ ; I also compiled OpenCV based on different hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, Intel® Distribution of OpenVINO™ Toolkit, NVIDIA Jetson Nano ( 2GB and 4GB ram)
Question: What are some ways to optimize and increase the speed of OpenCV C++ image processing?
ChatGPT:
There are several ways to optimize and increase the speed of image processing using OpenCV C++:
Use the built-in OpenCV functions and avoid using for loops as much as possible, as they can be slow.
Use the appropriate data types and data structures for your image processing task.
Use the OpenCV parallel_for_ function to parallelize your code and take advantage of multi-core processors.
Use the OpenCV's GPU module to perform image processing on the GPU.
Avoid unnecessary memory allocation and deallocation by reusing memory where possible.
Use OpenCV's performance optimization techniques such as vectorization, unrolling loops, and using lookup tables.
Use profilers such as gprof, valgrind, or Visual Studio Profiler to identify and optimize performance bottlenecks in your code.
Use prebuilt libraries instead of building from source when possible to take advantage of performance optimizations.
It's worth noting that, optimizing image processing may require a combination of these approaches, and the best option will depend on the specific task and the requirements of the project.
My thought :
Okay, that's a good answer. cv::setNumThreads(4); cv::parallel_for_
https://github.com/pirahansiah/cvtest
CVTest library to compare images for similarity/Image comparison
A) modified images:
feature-based (SIFT)/Detectors of salient points/areas
Key-point Matching
Histogram method
Key-points + Decision Trees
Simple euclidean distance
(Normalised) Cross Correlation
OpenCV PSNR,SSIM,
B) file-hash based (md5, sha1, etc) for exact duplicates
C) perceptual hashing (phash) for rescaled images
Introduction to Image Processing
What is Image Processing
Digital Image Representation
Applications of Image Processing
Image Enhancement Techniques
Spatial Domain Techniques
Histogram Processing
Point Processing
Frequency Domain Techniques
Filtering in the Frequency Domain
Image Sharpening
Image Restoration Techniques
Degradation and Restoration Model
Noise Models
Restoration in the Spatial Domain
Restoration in the Frequency Domain
Image Compression Techniques
Lossless Compression
Lossy Compression
Image Compression Standards
Image Segmentation
Edge Detection
Thresholding
Region-based Segmentation
Clustering-based Segmentation
Object Recognition and Computer Vision
Feature Extraction
Object Representation
Object Classification
Applications of Image Processing
Medical Image Processing
Remote Sensing
Industrial Inspection
Computer Vision
Conclusion
Future of Image Processing
References.
Table of Contents
Chapter 1: Introduction to Computer Vision
What is Computer Vision?
History of Computer Vision
Applications of Computer Vision
Key concepts and terminology
Chapter 2: Image Processing Fundamentals
Digital image representation
Image enhancement techniques
Image segmentation
Image restoration
Chapter 3: Feature Detection and Extraction
Feature detection techniques
Feature extraction methods
Feature matching and tracking
Chapter 4: Object Recognition
Object detection and localization
Object classification
Deep learning for object recognition
Chapter 5: Motion Analysis and Tracking
Motion estimation and tracking
Optical flow
Object tracking
Chapter 6: 3D Computer Vision
3D reconstruction
Stereo vision
3D object recognition
Chapter 7: Applications of Computer Vision
Computer vision in robotics
Computer vision in autonomous vehicles
Computer vision in medical imaging
Computer vision in security and surveillance
Chapter 8: Current Trends and Future Directions
Deep learning in computer vision
Augmented reality and virtual reality
Computer vision on edge devices
Ethical considerations in computer vision
Appendix: Resources for Further Study
Books and articles
Online tutorials and courses
Computer vision libraries and frameworks
Computer vision competitions and challenges.
Chapter 1: Introduction to Pattern Recognition
Overview of pattern recognition
Applications of pattern recognition
Types of pattern recognition
Chapter 2: Fundamentals of Pattern Recognition
Statistical pattern recognition
Machine learning and pattern recognition
Feature extraction and representation
Chapter 3: Supervised Learning
Linear discriminant analysis
Support vector machines
Neural networks
Decision trees
Random forests
Chapter 4: Unsupervised Learning
Clustering algorithms
Principal component analysis
Independent component analysis
Chapter 5: Hybrid Approaches
Ensemble methods
Boosting
Bagging
Chapter 6: Evaluation and Validation
Performance measures
Cross-validation
Overfitting and underfitting
Chapter 7: Applications of Pattern Recognition
Computer vision
Speech recognition
Natural language processing
Bioinformatics
Chapter 8: Future Directions and Challenges
Deep learning
Reinforcement learning
Transfer learning
Big data and pattern recognition
Appendix A: Resources and Tools for Pattern Recognition
Software packages
Datasets
Online tutorials and courses
Appendix B: Glossary of Terms
Commonly used terms and definitions in pattern recognition.
Chapter 1: Introduction to Cloud Computing for Computer Vision
Definition of cloud computing
Advantages and disadvantages of using cloud computing for computer vision
Overview of the book
Chapter 2: Setting Up a Cloud Environment for Computer Vision
Choosing a cloud provider
Setting up a virtual machine
Installing necessary software and packages
Chapter 3: Image Processing and Analysis in the Cloud
Image processing techniques and algorithms
Implementing image processing and analysis in a cloud environment
Examples of image processing applications in the cloud
Chapter 4: Object Detection and Recognition in the Cloud
Techniques for object detection and recognition
Implementing object detection and recognition in a cloud environment
Examples of object detection and recognition applications in the cloud
Chapter 5: Video Processing and Analysis in the Cloud
Techniques for video processing and analysis
Implementing video processing and analysis in a cloud environment
Examples of video processing and analysis applications in the cloud
Chapter 6: Cloud-Based Deep Learning for Computer Vision
Introduction to deep learning
Implementing deep learning in a cloud environment
Examples of deep learning applications in computer vision in the cloud
Chapter 7: Cloud-Based Augmented Reality and Virtual Reality
Introduction to augmented reality and virtual reality
Implementing AR/VR in a cloud environment
Examples of AR/VR applications in the cloud
Chapter 8: Security and Privacy in Cloud-Based Computer Vision
Security concerns in cloud computing
Protecting data and models in the cloud
Compliance and regulations for cloud-based computer vision
Chapter 9: Conclusion
Recap of key points
Future directions for cloud-based computer vision
Additional resources for further learning.
Introduction
What is cloud computing
How cloud computing is used in image processing
Benefits of using cloud solutions for image processing
Chapter 1: Understanding Cloud Services for Image Processing
Types of cloud services for image processing
Comparison of cloud providers for image processing
Cost considerations for cloud image processing
Chapter 2: Setting Up a Cloud Environment for Image Processing
Choosing the right cloud provider
Setting up a virtual machine
Configuring network and storage options
Chapter 3: Image Processing Workflows on the Cloud
Pre-processing and cleaning of images
Feature extraction and analysis
Post-processing and visualization
Chapter 4: Use Cases for Cloud Image Processing
Medical imaging
Surveillance and security
Remote sensing and satellite imagery
Industrial automation
Chapter 5: Best Practices for Cloud Image Processing
Security and data privacy
Scalability and performance
Monitoring and troubleshooting
Conclusion
Future trends in cloud image processing
Resources for further learning
References
Introduction
Overview of IoT and image processing
Purpose and scope of the book
Chapter 1: Fundamentals of IoT and Image Processing
Overview of IoT and its components
Overview of image processing and its applications
Intersection of IoT and image processing
Chapter 2: IoT for Image Capture and Transmission
IoT enabled image capture devices
Network protocols for image transmission
Security considerations for image transmission
Chapter 3: Image Processing Algorithms for IoT
Feature detection and extraction algorithms
Image compression techniques
Machine learning algorithms for image processing
Chapter 4: IoT for Image Analysis and Processing
Image analysis and processing in real-time
IoT platforms for image analysis and processing
Case studies of IoT applications in image analysis and processing
Chapter 5: IoT for Computer Vision and Augmented Reality
Overview of computer vision and augmented reality
IoT applications in computer vision and augmented reality
Challenges and future developments in IoT for computer vision and augmented reality
Chapter 6: IoT for Medical Image Processing
Overview of medical image processing
IoT applications in medical imaging
Challenges and future developments in IoT for medical image processing
Conclusion
Summary of key points
Future developments and research opportunities in IoT for image processing
References
List of sources used in the book
Appendix
Glossary of terms
Introduction
Chapter 1: Understanding the Basics of Computer Vision
Understanding the concepts of computer vision
The history of computer vision
The applications of computer vision in IoT devices
Chapter 2: Implementing Computer Vision in IoT Devices
Understanding the hardware and software requirements for computer vision in IoT devices
The different types of computer vision algorithms and their use cases
Best practices for implementing computer vision in IoT devices
Chapter 3: Computer Vision for Object Detection and Recognition
Understanding the different types of object detection and recognition algorithms
How to train and optimize object detection and recognition algorithms for IoT devices
Real-world applications of object detection and recognition in IoT devices
Chapter 4: Computer Vision for Face Detection and Recognition
Understanding the different types of face detection and recognition algorithms
How to train and optimize face detection and recognition algorithms for IoT devices
Real-world applications of face detection and recognition in IoT devices
Chapter 5: Computer Vision for Image and Video Processing
Understanding the different types of image and video processing algorithms
How to optimize image and video processing algorithms for IoT devices
Real-world applications of image and video processing in IoT devices
Chapter 6: Computer Vision for Augmented Reality
Understanding the concepts of augmented reality
How to implement augmented reality in IoT devices
Real-world applications of augmented reality in IoT devices
Chapter 7: Future Trends and Developments in Computer Vision for IoT Devices
The future of computer vision in IoT devices
Emerging technologies and trends in computer vision for IoT devices
Challenges and opportunities for computer vision in IoT devices
Conclusion
References
Index
Introduction to Computer Vision for IoT and Cloud
Overview of computer vision in IoT and cloud applications
Importance of computer vision in modern technology
Fundamentals of Computer Vision
Understanding image processing and analysis
Key concepts and techniques in computer vision
Computer Vision in IoT Applications
Smart cameras and surveillance systems
Object detection and tracking in IoT devices
Computer vision in drones and robots
Computer Vision in Cloud Applications
Cloud-based image and video processing
Computer vision as a service (CVaaS)
Machine learning and deep learning in cloud-based computer vision
Challenges and Opportunities in Computer Vision for IoT and Cloud
Security and privacy concerns
Scalability and performance limitations
Emerging trends and future developments
Case Studies and Applications
Real-world examples of computer vision in IoT and cloud
Industry use cases and potential applications
Conclusion and Future Directions
Summary of key takeaways
Future research and development opportunities in computer vision for IoT and cloud.
Machine vision and machine learning with cloud for IoT devices is an exciting and rapidly growing field that combines the power of computer vision, deep learning, pattern recognition, image processing, and cloud computing to create intelligent and connected devices that can analyze and interpret visual data in real-time.
With the increasing use of IoT devices, there is a growing need for machine vision and machine learning to improve the performance and capabilities of these devices. By using deep learning algorithms, these devices can learn to recognize patterns and make predictions based on visual data. This can be used to improve the accuracy and speed of image and object detection, as well as other computer vision tasks.
One of the key advantages of using machine vision and machine learning with cloud for IoT devices is that it allows for the processing and analysis of large amounts of data in real-time. By using cloud computing, these devices can access powerful computing resources and store large amounts of data without the need for expensive hardware. This makes it possible to analyze and interpret visual data from multiple sources in real-time, which is essential for a wide range of IoT applications.
Overall, machine vision and machine learning with cloud for IoT devices is a powerful combination that can greatly improve the performance and capabilities of IoT devices and enable new and innovative applications in a variety of industries.
Introduction to Machine Vision and Machine Learning
Overview of Machine Vision and Machine Learning
Applications of Machine Vision and Machine Learning
Importance of Machine Vision and Machine Learning in Industry
Machine Vision Fundamentals
Image acquisition and processing
Feature extraction and representation
Image segmentation and object recognition
Machine Learning Fundamentals
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Deep Learning in Machine Vision
Overview of Deep Learning
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Generative Adversarial Networks (GANs)
Applications of Machine Vision and Machine Learning
Computer vision in Robotics
Computer vision in Autonomous vehicles
Computer vision in Healthcare
Computer vision in Agriculture
Ethics and Safety in Machine Vision and Machine Learning
Data privacy and security
Bias and fairness
Explainability and interpretability
Future of Machine Vision and Machine Learning
Advancements in hardware and software
Impact on society and industry
Challenges and opportunities
Conclusion and References
Summary of key points
Further reading and resources.
Write Paper, Patent, Article, Post, :
Best questions to write a paper: https://www.linkedin.com/posts/drasadnaveed_how-to-actually-use-chatgpt-for-academia-ugcPost-7028709603722887168-_wDP?utm_source=share&utm_medium=member_desktop
what are further areas of research in ....
generate topic ideas for me based on limitations of prior studies which highlight gaps
write an outline for the introduction section of my paper
write a cover letter for a research on ... a systematic review. Explain it's importance and that we found l... in 4 over 8 papers
ChatGPT, MacOS, Python
conda create --name chatgpt python=3.10
conda activate chatgpt
pip3 install torch torchvision
pip3 install transformers
code
from transformers import AutoModelWithLMHead, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large" , padding_side="left")
model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-large")
for step in range(5):
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
code
print("www.tiziran.com")
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model_name_or_path = 'microsoft/DialoGPT-medium'
tokenizer = GPT2Tokenizer.from_pretrained(model_name_or_path)
model = GPT2LMHeadModel.from_pretrained(model_name_or_path, timeout=3000)
input_text = "Hi, how are you?"
input_ids = tokenizer.encode(input_text, return_tensors='pt')
response = model.generate(input_ids)
output_text = tokenizer.decode(response[0], skip_special_tokens=True)
print(output_text)