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:

1. OpenCV: an open-source library with a wide range of functionality, including image processing, video analysis, and object detection.

2. TensorFlow: an open-source library developed by Google for machine learning and deep learning, which includes a module for computer vision tasks.

3. scikit-image: an image processing library for Python that is built on top of NumPy and SciPy.

4. PIL (Python Imaging Library) : It is also one of the most widely used library for image processing and manipulation.

5. MATLAB: a proprietary programming language and environment for numerical computation and visualization, which has a strong focus on image processing and computer vision.

6. 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:

1. Grayscale images: images with only one channel of intensity values, often used for tasks such as edge detection and image thresholding.

2. 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.

3. Binary images: images with only two intensity values, often used for tasks such as object segmentation and morphological operations.

4. 16-bit images: images with 16 bits per channel, often used for tasks such as high dynamic range imaging.

5. 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++:

1. Use the built-in OpenCV functions and avoid using for loops as much as possible, as they can be slow.

2. Use the appropriate data types and data structures for your image processing task.

3. Use the OpenCV parallel_for_ function to parallelize your code and take advantage of multi-core processors.

4. Use the OpenCV's GPU module to perform image processing on the GPU.

5. Avoid unnecessary memory allocation and deallocation by reusing memory where possible.

6. Use OpenCV's performance optimization techniques such as vectorization, unrolling loops, and using lookup tables.

7. Use profilers such as gprof, valgrind, or Visual Studio Profiler to identify and optimize performance bottlenecks in your code.

8. 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++:

1. Use the built-in OpenCV functions and avoid using for loops as much as possible, as they can be slow.

2. Use the appropriate data types and data structures for your image processing task.

3. Use the OpenCV parallel_for_ function to parallelize your code and take advantage of multi-core processors.

4. Use the OpenCV's GPU module to perform image processing on the GPU.

5. Avoid unnecessary memory allocation and deallocation by reusing memory where possible.

6. Use OpenCV's performance optimization techniques such as vectorization, unrolling loops, and using lookup tables.

7. Use profilers such as gprof, valgrind, or Visual Studio Profiler to identify and optimize performance bottlenecks in your code.

8. 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

1. Introduction to Image Processing

   • What is Image Processing

   • Digital Image Representation

   • Applications of Image Processing

2. Image Enhancement Techniques

   • Spatial Domain Techniques

     • Histogram Processing

     • Point Processing

   • Frequency Domain Techniques

     • Filtering in the Frequency Domain

     • Image Sharpening

3. Image Restoration Techniques

   • Degradation and Restoration Model

   • Noise Models

   • Restoration in the Spatial Domain

   • Restoration in the Frequency Domain

4. Image Compression Techniques

   • Lossless Compression

   • Lossy Compression

   • Image Compression Standards

5. Image Segmentation

   • Edge Detection

   • Thresholding

   • Region-based Segmentation

   • Clustering-based Segmentation

6. Object Recognition and Computer Vision

   • Feature Extraction

   • Object Representation

   • Object Classification

7. Applications of Image Processing

   • Medical Image Processing

   • Remote Sensing

   • Industrial Inspection

   • Computer Vision

8. 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

1. Introduction to Computer Vision for IoT and Cloud

• Overview of computer vision in IoT and cloud applications

• Importance of computer vision in modern technology

2. Fundamentals of Computer Vision

• Understanding image processing and analysis

• Key concepts and techniques in computer vision

3. Computer Vision in IoT Applications

• Smart cameras and surveillance systems

• Object detection and tracking in IoT devices

• Computer vision in drones and robots

4. 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

5. Challenges and Opportunities in Computer Vision for IoT and Cloud

• Security and privacy concerns

• Scalability and performance limitations

• Emerging trends and future developments

6. Case Studies and Applications

• Real-world examples of computer vision in IoT and cloud

• Industry use cases and potential applications

7. 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.

1. 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

2. Machine Vision Fundamentals

• Image acquisition and processing

• Feature extraction and representation

• Image segmentation and object recognition

3. Machine Learning Fundamentals

• Types of Machine Learning

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

4. Deep Learning in Machine Vision

• Overview of Deep Learning

• Convolutional Neural Networks (CNNs)

• Recurrent Neural Networks (RNNs)

• Generative Adversarial Networks (GANs)

5. Applications of Machine Vision and Machine Learning

• Computer vision in Robotics

• Computer vision in Autonomous vehicles

• Computer vision in Healthcare

• Computer vision in Agriculture

6. Ethics and Safety in Machine Vision and Machine Learning

• Data privacy and security

• Bias and fairness

• Explainability and interpretability

7. Future of Machine Vision and Machine Learning

• Advancements in hardware and software

• Impact on society and industry

• Challenges and opportunities

8. 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)