SmartImageLab

Sugere um laboratório para experimentação com imagens de forma inteligente. modelo de software em Python que utilize a API da OpenAI e a infraestrutura da Oracle Cloud para tratamento, classificação e identificação de imagens. O código será estruturado em classes para facilitar a organização e a reutilização.

AI Image Processing Project with OpenAI API and Oracle Cloud Overview This project aims to develop a software application for image processing, classification, and identification using the OpenAI API and Oracle Cloud infrastructure. The application will leverage advanced machine learning techniques and will be structured in classes for better organization and reuse.

Key Features Image Processing: Use the OpenAI API to process images effectively. Image Classification: Classify images based on predefined categories. Image Identification: Identify specific objects or patterns within images. Cloud Integration: Utilize Oracle Cloud for storage and processing capabilities. Robust Error Handling: Implement comprehensive error handling to ensure application stability.

Infrastructure Setup The infrastructure will be provisioned using Terraform on Oracle Cloud Infrastructure (OCI).

Terraform Configuration Create a Directory:

bash mkdir ai-image-processing cd ai-image-processing Create Terraform Files:

main.tf: Define your infrastructure components (compute instances, networking, etc.). variables.tf: Specify variables for flexibility and customization. outputs.tf: Define outputs for easier access to resource information. Terraform Script Example:

hcl

provider "oci" { region = var.region }

resource "oci_core_instance" "instance" { availability_domain = var.availability_domain compartment_id = var.compartment_id display_name = "AI-Image-Processing-Instance" shape = var.shape

create_vnic_details { subnet_id = var.subnet_id }

source_details { source_type = "image" source_id = var.image_id } } Create terraform.tfvars:

hcl

region = "us-phoenix-1" availability_domain = "Phx2" compartment_id = "ocid1.compartment.oc1..example" shape = "VM.Standard2.1" subnet_id = "ocid1.subnet.oc1.phx.example" image_id = "ocid1.image.oc1.phx.example" Steps to Set Up the Infrastructure Install Terraform: Installation Instructions.

Configure OCI: Ensure your ~/.oci/config file is correctly set up.

Navigate to the Terraform Directory:

bash cd terraform Initialize Terraform:

bash terraform init Plan the Infrastructure:

bash terraform plan Apply the Configuration:

bash terraform apply Software Usage Install Dependencies:

bash pip install -r requirements.txt Run the Application:

bash python src/main.py License This project is licensed under the MIT License. See the LICENSE file for more information.

Final Considerations Dependencies: Ensure to create a requirements.txt file with necessary dependencies, such as requests, torch, Pillow, torchvision, etc. Models: Paths for the models (if using pre-trained models) should be adjusted based on your implementation. Testing: It is recommended to test each component of the system in isolation before integrating them.

###############################This README is a starting point. Feel free to expand it as necessary.#####################################################################

Estrutura do Diretório do Projeto

ImageProcessingOpenAI/ │ ├── terraform/ │ ├── main.tf │ ├── variables.tf │ ├── outputs.tf │ ├── terraform.tfvars │ ├── src/ │ ├── init.py │ ├── main.py │ ├── image_processor.py │ ├── image_classifier.py │ ├── image_identifier.py │ ├── image_storage.py │ └── README.md

1. Infraestrutura: Terraform main.tf hcl provider "oci" {

Your OCI config file path

config_file = "~/.oci/config" profile = "DEFAULT" # Change to your profile name if necessary }

Create a VCN

resource "oci_core_virtual_cloud_network" "vcn" { compartment_id = var.compartment_id display_name = "ImageProcessingVCN" cidr_block = "10.0.0.0/16" }

Create Subnets

resource "oci_core_subnet" "public_subnet" { compartment_id = var.compartment_id vcn_id = oci_core_virtual_cloud_network.vcn.id display_name = "PublicSubnet" cidr_block = "10.0.1.0/24" dhcp_options_id = oci_core_virtual_cloud_network.vcn.default_dhcp_options_id availability_domain = var.availability_domain prohibit_public_ip_on_vnic = false }

resource "oci_core_subnet" "private_subnet" { compartment_id = var.compartment_id vcn_id = oci_core_virtual_cloud_network.vcn.id display_name = "PrivateSubnet" cidr_block = "10.0.2.0/24" dhcp_options_id = oci_core_virtual_cloud_network.vcn.default_dhcp_options_id availability_domain = var.availability_domain prohibit_public_ip_on_vnic = true }

Create a compute instance

resource "oci_core_instance" "app_instance" { availability_domain = var.availability_domain compartment_id = var.compartment_id display_name = "ImageProcessingInstance" shape = "VM.Standard2.1" # Choose the shape based on your needs

create_vnic_details { subnet_id = oci_core_subnet.public_subnet.id assign_public_ip = true }

metadata = { ssh_authorized_keys = file(var.ssh_public_key_path) } }

Outputs

output "public_ip" { value = oci_core_instance.app_instance.public_ip }

output "instance_id" { value = oci_core_instance.app_instance.id }

variables.tf hcl variable "compartment_id" { description = "The OCID of the compartment" }

variable "availability_domain" { description = "The availability domain to create resources in" }

variable "ssh_public_key_path" { description = "Path to your SSH public key" }

outputs.tf hcl output "public_ip" { description = "Public IP of the instance" value = oci_core_instance.app_instance.public_ip }

output "instance_id" { description = "ID of the instance" value = oci_core_instance.app_instance.id }

terraform.tfvars hcl compartment_id = "ocid_do_seu_compartimento" availability_domain = "AD-1" # Substitua pelo seu AD ssh_public_key_path = "~/.ssh/id_rsa.pub" # O caminho para a sua chave SSH pública

2. Código do Aplicativo em Python src/init.py python C

Este arquivo pode ser deixado vazio ou usado para inicializar o pacote.

src/image_processor.py python import requests from PIL import Image import io

class ImageProcessor: def init(self, openai_api_key): self.openai_api_key = openai_api_key

def generate_image(self, prompt):
    url = "https://api.openai.com/v1/images/generations"
    headers = {
        "Authorization": f"Bearer {self.openai_api_key}",
        "Content-Type": "application/json",
    }
    data = {
        "prompt": prompt,
        "n": 1,
        "size": "1024x1024",
    }
    
    response = requests.post(url, headers=headers, json=data)
    image_url = response.json()['data'][0]['url']
    return self.download_image(image_url)

def download_image(self, url):
    response = requests.get(url)
    image = Image.open(io.BytesIO(response.content))
    return image

def preprocess_image(self, image, size=(224, 224)):
    return image.resize(size)

src/image_classifier.py python import torch from torchvision import models, transforms

class ImageClassifier: def init(self, model_path): self.model = self.load_model(model_path)

def load_model(self, model_path):
    model = models.resnet50(pretrained=True)
    model.eval()
    return model

def classify(self, image):
    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    input_tensor = preprocess(image)
    input_batch = input_tensor.unsqueeze(0)  # Cria um mini-batch como esperado pela rede

    with torch.no_grad():
        output = self.model(input_batch)

    return output.argmax().item()  # Retorna a classe com maior pontuação

src/image_identifier.py python import torch

class ImageIdentifier: def init(self, model_path): self.model = self.load_model(model_path)

def load_model(self, model_path):
    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
    model.eval()
    return model

def identify(self, image):
    results = self.model(image)
    return results.xyxy[0]  # Retorna resultados com coordenadas das caixas delimitadoras

src/image_storage.py python from oracleai import Cloud import io

class ImageStorage: def init(self, oracle_cloud_credentials): self.cloud = Cloud(oracle_cloud_credentials)

def upload_image(self, image, image_name):
    # Assume que 'image' é um objeto PIL Image
    with io.BytesIO() as output:
        image.save(output, format='JPEG')
        image_data = output.getvalue()
    
    # Código para upload para OCI
    self.cloud.upload(image_name, image_data)

def download_image(self, image_id):
    # Código para download da imagem do OCI usando o image_id
    return self.cloud.download(image_id)

src/main.py python from image_processor import ImageProcessor from image_classifier import ImageClassifier from image_identifier import ImageIdentifier from image_storage import ImageStorage

if name == "main": OPENAI_API_KEY = 'sua-chave-da-api'(a minha nao passo!!) ORACLE_CLOUD_CREDENTIALS = 'suas-credenciais'

processor = ImageProcessor(OPENAI_API_KEY)
classifier = ImageClassifier('model_path')
identifier = ImageIdentifier('model_path')
storage = ImageStorage(ORACLE_CLOUD_CREDENTIALS)

# Gerar e processar uma imagem
generated_image = processor.generate_image("A beautiful landscape")
processed_image = processor.preprocess_image(generated_image)

# Classificar a imagem
class_id = classifier.classify(processed_image)
print(f'Class ID: {class_id}')

# Identificar objetos na imagem
detections = identifier.identify(processed_image)
print(f'Detections: {detections}')

# Fazer upload da imagem para Oracle Cloud
storage.upload_image(generated_image, 'landscape_image.jpg')

3.Condieracoes Finais

Image Processing with OpenAI and Oracle Cloud

Este projeto é um software de inteligência artificial que utiliza a API da OpenAI para gerar, classificar e identificar imagens. Ele é projetado para ser hospedado na Oracle Cloud usando Terraform para a infraestrutura.

Estrutura do Projeto (relembrando deve ficar assim!)

ImageProcessingOpenAI/ │ ├── terraform/ │ ├── main.tf │ ├── variables.tf │ ├── outputs.tf │ ├── terraform.tfvars │ ├── src/ │ ├── init.py │ ├── main.py │ ├── image_processor.py │ ├── image_classifier.py │ ├── image_identifier.py │ ├── image_storage.py │ └── README.md

Infraestrutura

A infraestrutura é provisionada usando Terraform. Antes de executar o Terraform, configure seu arquivo de configuração da Oracle Cloud.

Passos para a Infraestrutura

1. Instalar o Terraform: Instruções de instalação.

2. Configurar OCI: Certifique-se de que seu arquivo ~/.oci/config está corretamente configurado.

3. Navegar até o diretório do Terraform:

   cd terraform

Inicializar o Terraform:

bash terraform init Preencher as variáveis: Crie um arquivo terraform.tfvars com os valores apropriados.

Planejar a infraestrutura:

bash terraform plan Aplicar a configuração:

bash terraform apply Uso do Software Instalar Dependências:

bash pip install -r requirements.txt Executar o Aplicativo:

bash python src/main.py Licença Este projeto está licenciado sob a MIT License. Veja o arquivo LICENSE para mais informações.

Considerações Finais

1. Dependências: Certifique-se de criar um arquivo requirements.txt com as dependências necessárias, como requests, torch, Pillow, torchvision, etc.

2. Modelos: Os caminhos para os modelos (se estiver usando modelos pré-treinados) devem ser ajustados conforme sua implementação.

3. Testes: É recomendável testar cada componente do sistema de forma isolada antes de integrá-los.

4. Documentação: Este README é um ponto de partida. Sinta-se à vontade para expandi-lo conforme necessário.

Esse guia fornece uma visão completa de como montar o projeto e a infraestrutura necessária para