Introduction computer vision with python

Computers see digital images, a digital image is a representation of a two-dimensional image as a finite set of digital values, called pixels. Each pixel represents a small portion of the image. We have two types of pixel representation :

Pixels: The fundamental building blocks of digital images. Each pixel represents a single point in the image.
Grid of Pixels: An image is composed of a grid of pixels, where each pixel has a specific color value.

Let's code a simple python example here :

import numpy as np
import matplotlib.pyplot as plt

# Create a simple 5x5 image with different grayscale values
image = np.array([
    [0, 50, 100, 150, 200],
    [10, 60, 110, 160, 210],
    [20, 70, 120, 170, 220],
    [30, 80, 130, 180, 230],
    [40, 90, 140, 190, 240]
], dtype=np.uint8)

plt.imshow(image, cmap='gray')
plt.colorbar()
plt.show()

How a Computer Processes an Image

Image Acquisition: An image is captured using a digital camera or scanner.
Digitization: The image is converted into a grid of pixels, each with a specific color value.
Storage: The digital image is stored in a file using a specific format (e.g., PNG, JPEG).
Processing: Various algorithms are applied to enhance, transform, or analyze the image.
Display: The processed image is displayed on a monitor or screen.

Color Models

Then we have the Color Models, as you know now our images are not only in black and white, we discover colors lol. Below the famous colors models, do not worry we will be back at this soon with code :

Grayscale: Images represented in shades of gray.
RGB: Images represented using three color channels - Red, Green, and Blue.
Other Models: CMYK, YUV, etc.

More infos about colors models on geekforgeeks here

Image Storage Formats

Computers see images as a grid of pixels, each with a specific color or intensity value. These images are stored in various formats, processed using different algorithms, and displayed on screens. Understanding how computers store and manipulate images is fundamental, let's take a look on Bitmap and Vector images here :

Bitmap (Raster) Images

Definition: Images stored as a grid of pixels.
Formats: BMP, PNG, JPEG, GIF.
Characteristics: High quality, larger file sizes, not easily scalable.

Vector Images

Definition: Images stored as mathematical descriptions of shapes.
Formats: SVG, EPS, PDF.
Characteristics: Scalable without loss of quality, smaller file sizes for certain types of images.

Computer vision platforms

As the amount of image and video data continues to grow, computer vision has become a crucial technology in various industries, such as healthcare, automotive, retail and many others.

Let's examine the top computer vision platforms that are driving innovation in this field. These platforms offer a range of features, including image recognition, object detection, semantic segmentation and various others services.

Whether you're a data scientist, machine learning engineer, or developer looking to integrate computer vision into your projects, these platforms will provide you with the necessary tools and resources to build accurate and robust computer vision models.

End to end platform

Google Cloud Vision API: A cloud-based API that provides automated machine learning capabilities for computer vision tasks like object detection and segmentation.
IBM Watson Visual Recognition: A cloud-based API that provides image and video analysis capabilities, widely used for applications like object detection and facial recognition.
Microsoft Azure Vision Studio: A cloud-based API that provides image and video analysis capabilities, widely used for applications like object detection and facial recognition.
Amazon Rekognition: A cloud-based API that provides image and video analysis capabilities, widely used for applications like facial recognition and object detection.
Labellerr: A cloud-based data annotation tool that offers an easy-to-use interface and supports various video formats. It is one of the unique tools that support automated data labeling features that can help you fasten your model training process.
Roboflow: Used by over 500,000 engineers to create datasets, train models, and deploy to production (my favourite choice 🤭)

Using an end-to-end platform for your computer vision project can offer several advantages over using an open-source solution. One of the main benefits is that end-to-end platforms provide a complete solution, eliminating the need to integrate multiple open-source components. This can save time and reduce complexity, as you won't have to worry about manually integrating different components.

Note

Another advantage is that end-to-end platforms are designed to scale with your project's needs, providing automatic scaling and load balancing. This can be particularly useful if your project is expected to grow or change over time.

Additionally, end-to-end platforms typically provide pre-built deployment options, making it easier to deploy your model in production.

Open source solutions

TensorFlow: An open-source machine learning framework developed by Google, widely used for computer vision tasks like object detection, segmentation, and image classification.
PyTorch: An open-source machine learning framework developed by Facebook, widely used for computer vision tasks like object detection, segmentation, and image classification.
OpenCV: An open-source computer vision library that provides a wide range of functions for image and video processing, feature detection, and object recognition.
Keras: A high-level neural networks API that can run on top of TensorFlow, CNTK, or Theano, widely used for computer vision tasks like object detection, segmentation, and image classification.
Darknet: An open-source neural networks framework that provides a wide range of pre-trained models for computer vision tasks like object detection and segmentation.
YOLO (You Only Look Once): A real-time object detection system that detects objects in images and videos, widely used for applications like self-driving cars and surveillance systems.
SSD (Single Shot Detector): A real-time object detection system that detects objects in images and videos, widely used for applications like self-driving cars and surveillance systems.
Hugging Face Transformers: A library that provides pre-trained models for natural language processing and computer vision tasks like image captioning and visual question answering.

Not using an end-to-end platform allows for greater customization and control over the project, as you can choose the specific components and tools that best fit your needs. This approach can also be more cost-effective in the long run, as you only pay for the specific components and services you need.

Additionally, not using an end-to-end platform provides greater flexibility and adaptability, as you can choose the specific tools and components that best fit your project's specific needs. Furthermore, this approach can be a more efficient use of resources, as you can focus on the specific components and tools that are most relevant to your project.

The Plurality of Offers on the Market

In the computer vision market, there are numerous options available for companies and individuals looking to implement computer vision solutions. This plurality of offers can be overwhelming, especially for those who are new to the field or lack expertise in computer vision.

Characteristics

When evaluating the various options available, it's essential to consider the following characteristics:

Cost: The cost of the platform or solution can vary significantly depending on the provider, the complexity of the project, and the level of customization required.
Scalability: The ability to scale the solution to meet the needs of the project or business can be a critical factor in the decision-making process.
Customization: The level of customization available can vary greatly between providers, with some offering more flexibility than others.
Integration: The ease of integration with existing systems and infrastructure can be a significant consideration.
Support: The level of support provided by the provider, including training, documentation, and customer support, can impact the overall success of the project.
Security: The security measures in place to protect the data and intellectual property can be a critical consideration.
Flexibility: The flexibility of the solution to adapt to changing project requirements or business needs can be a significant factor.

In some cases, companies may opt for a tailor-made proprietary solution or a year-round license for the use of a platform. This approach can provide a high degree of customization and flexibility, but it may also come with a higher cost.

Today's challenges

Image recognition, also known as computer vision, is a rapidly evolving field with many real-world applications. Despite significant progress, there are still several challenges and problems that need to be addressed.

Here are some of the specific problems in image recognition today:

Domain Shifting: Models trained on one dataset may not generalize well to new, unseen data. This is due to differences in lighting, camera angles, or object appearances.
Class Imbalance: Many datasets are imbalanced, meaning some classes have significantly more instances than others. This can lead to biased models that perform poorly on minority classes.
Occlusion and Partial Occlusion: Objects may be partially or fully occluded, making it difficult for models to recognize them accurately.
Variability in Object Appearance: Objects can appear in different poses, scales, and orientations, making it challenging to recognize them.
Background Clutter: Background noise, such as cluttered scenes, can distract from the object of interest and make recognition more difficult.
Limited Data: Many datasets are limited in size, diversity, or quality, which can lead to overfitting and poor generalization.
Adversarial Attacks: Adversarial examples, designed to mislead models, can be used to compromise image recognition systems.
Lack of Diversity in Datasets: Many datasets are biased towards specific demographics, objects, or scenes, which can lead to biased models.
Evaluation Metrics: Choosing the right evaluation metrics is crucial, but some metrics may not accurately reflect the performance of a model.
Explainability and Transparency: It's often difficult to understand why a model makes a particular prediction, which can hinder trust and accountability.
Real-World Complexity: Real-world images often contain complex scenes, multiple objects, and varying lighting conditions, making recognition more challenging.
Domain Adaptation: Models trained on one domain may not generalize well to another domain, such as adapting from synthetic to real-world images.
Limited Attention Mechanisms: Current attention mechanisms may not be effective in handling complex scenes or multiple objects.
Lack of Robustness to Noise and Artifacts: Models may not be robust to noise, artifacts, or other forms of corruption in the input data.
Scalability and Computational Efficiency: Large-scale image recognition tasks require significant computational resources and may not be feasible on all devices.

Addressing these challenges is crucial for developing more accurate, robust, and trustworthy image recognition systems. Researchers and practitioners are actively working to overcome these challenges and improve the performance of image recognition models.

Which Model Should I Choose for My Project?

Choosing the right computer vision model for your project can be a daunting task, especially with the numerous options available. Here are some factors to consider when selecting a model:

Project goals: Determine the specific goals of your project, such as object detection, segmentation, or classification.
Data availability: Consider the availability and quality of your dataset, as well as the complexity of the data.
Model complexity: Choose a model that is suitable for your project's complexity, taking into account factors such as the number of classes, the complexity of the objects, and the level of noise in the data.
Computational resources: Consider the computational resources required to train and deploy the model, including the need for GPU acceleration or cloud computing.
Interpretability: Consider the level of interpretability required for your project, such as the need for feature importance or class activation maps.

Where to Start?

When starting a computer vision project, it's essential to begin with a clear understanding of the project goals and requirements. Here are some steps to follow:

Define the project goals: Clearly define the goals of your project, including the specific tasks and objectives.
Gather requirements: Gather requirements from stakeholders, including any specific constraints or limitations.
Conduct a feasibility study: Conduct a feasibility study to determine the feasibility of the project and identify potential risks.
Develop a project plan: Develop a project plan that outlines the scope, timeline, and resources required for the project.

Know How to Assess the Maturity of Your Project to Assess Its Cost

Assessing the maturity of your project is crucial to determining its cost. Here are some factors to consider:

Project complexity: Consider the complexity of your project, including the number of tasks, the level of automation, and the need for human intervention.
Data quality: Evaluate the quality of your data, including the availability, accuracy, and completeness of the data.
Model complexity: Consider the complexity of your model, including the number of parameters, the level of non-linearity, and the need for regularization.
Computational resources: Consider the computational resources required to train and deploy the model, including the need for GPU acceleration or cloud computing.

Evaluate the Return on Investment (ROI)

Evaluating the ROI of your project is essential to determine its cost-effectiveness. Here are some factors to consider:

Cost savings: Evaluate the potential cost savings of your project, including any reductions in labor costs, material costs, or other expenses.
Revenue growth: Evaluate the potential revenue growth of your project, including any increases in sales, market share, or other financial metrics.
Return on investment: Calculate the ROI of your project by dividing the net present value of the project by the initial investment.
Payback period: Evaluate the payback period of your project, including the time it takes for the project to generate a positive return on investment.

Creation of the Dataset

Creating a high-quality dataset is essential for training a computer vision model. Here are some best practices to follow:

Data collection: Collect high-quality data that is relevant to your project, including images, videos, or other data types.
Data preprocessing: Preprocess your data to ensure it is clean, consistent, and free of errors.
Data augmentation: Use data augmentation techniques to increase the size and diversity of your dataset.
Data validation: Validate your dataset to ensure it is accurate, complete, and free of errors.

Proof of Concept: Validation of Technical Feasibility and Provision of an Algorithm Trained on a Reduced Dataset

A proof of concept is a critical step in the computer vision project lifecycle. Here are some best practices to follow:

Validate technical feasibility: Validate the technical feasibility of your project by demonstrating the feasibility of the project.
Provision of an algorithm: Provide an algorithm trained on a reduced dataset to demonstrate the potential of the project.
Evaluation of the algorithm: Evaluate the performance of the algorithm on a reduced dataset to ensure it meets the project requirements.

Pilot : Training and Improvements of the Algorithm in Real Conditions

A pilot is a critical step in the computer vision project lifecycle. Here are some best practices to follow:

Training and improvements: Train and improve the algorithm in real conditions to ensure it meets the project requirements.
Evaluation of the algorithm: Evaluate the performance of the algorithm in real conditions to ensure it meets the project requirements.
Feedback and iteration: Gather feedback from stakeholders and iterate on the algorithm to ensure it meets the project requirements.

Below an example of Comet journalization for tracking metrics over your dataset :

Scale / Industrialization: Large-Scale Deployment

Scaling and industrializing a computer vision project is a critical step in the project lifecycle. Here are some best practices to follow:

Large-scale deployment: Deploy the algorithm on a large scale to ensure it meets the project requirements. Monitoring and maintenance: Monitor and maintain the algorithm to ensure it continues to meet the project requirements.
Continuous improvement: Continuously improve the algorithm to ensure it remains effective and efficient.

Project Management and Dashboarding

Effective project management is critical to the success of a computer vision project. Here are some best practices to follow:

Project dashboard: Create a project dashboard to track the progress of the project.
Task management: Manage tasks and responsibilities to ensure the project stays on track.
Communication: Communicate regularly with stakeholders to ensure everyone is informed and aligned.

Classic Pitfalls to Avoid

Here are some classic pitfalls to avoid when working on a computer vision project:

Insufficient data: Avoid insufficient data, which can lead to poor model performance.
Overfitting: Avoid overfitting, which can lead to poor model performance and high error rates.
Underfitting: Avoid underfitting, which can lead to poor model performance and high error rates.
Lack of interpretability: Avoid lack of interpretability, which can make it difficult to understand the model's behavior.
Lack of scalability: Avoid lack of scalability, which can make it difficult to deploy the model in real-world scenarios.

Chihuahua Or Muffin?

By following these best practices and avoiding common pitfalls, you can ensure the success of your computer vision project.

Best ressources 🥷🏼

Here is some ressources if you want to go dip and master computer vision in detail like a ninja

The github bible
Detail models list
Some very useful plug and play open source models