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✨ Sharpen Your Vision: Super-Resolution of CCTV Images Using Hugging Face Diffusers ✨

📖 Table of Contents Sharpen Your Vision: Super-Resolution of CCTV Images Using Hugging Face Diffusers Configuring Your Development Environment Problem Statement How Does Super-Resolution Solve This? State-of-the-Art Approaches Generative Adversarial Networks (GANs) Diffusion Models Implementing Diffus...

🏷️ #ArtificialIntelligence #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #Tutorial

✨ Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques ✨

📖 Table of Contents Unlocking Image Clarity: A Comprehensive Guide to Super-Resolution Techniques Introduction Configuring Your Development Environment Need Help Configuring Your Development Environment? What Is Super-Resolution? Usual Problems with Low-Resolution Imagery Traditional Computer Vision A...

🏷️ #ArtificialIntelligence #ComputerVision #DeepLearning #ImageProcessing #MachineLearning #TechnologyApplications #Tutorial

✨ Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) ✨

📖 Table of Contents Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) Introduction to Advanced Adversarial Techniques in Machine Learning Harnessing NSL for Robust Model Training: Insights from Part 2 Deep Dive into…...

🏷️ #AdversarialLearning #DeepLearning #ImageProcessing #Keras #MachineLearning #NeuralNetworks #NeuralStructuredLearning #TensorFlow #Tutorial

✨ OpenCV Contour Approximation ✨

📖 In this tutorial, we’ll learn about a step-by-step implementation and utilization of OpenCV’s Contour Approximation. When I first chanced upon the concept of Contour Approximation, the first question that hit me was: Why? Throughout my journey in Machine Learning and…...

🏷️ #ImageProcessing #OpenCVTutorials #Tutorials

✨ Image Processing with Gemini Pro ✨

📖 Table of Contents Image Processing with Gemini Pro Getting Started with Gemini Pro: An Overview Gemini Pro Setup Integrating Google AI Python SDK with Gemini Pro Image Processing with Gemini Pro: Python Code Generation Comprehensive List of GenAI Models Compatible…...

🏷️ #ArtificialIntelligence #ChatGPT #DeepLearning #Gemini #GeminiPro #GenAI #GenerativeAI #GoogleCloud #ImageProcessing #Python #Transformers #Tutorial #VertexAI

# Real-World Case Study: E-commerce Product Pipeline
import boto3
from PIL import Image
import io

def process_product_image(s3_bucket, s3_key):
    # 1. Download from S3
    s3 = boto3.client('s3')
    response = s3.get_object(Bucket=s3_bucket, Key=s3_key)
    img = Image.open(io.BytesIO(response['Body'].read()))
    
    # 2. Standardize dimensions
    img = img.convert("RGB")
    img = img.resize((1200, 1200), Image.LANCZOS)
    
    # 3. Remove background (simplified)
    # In practice: use rembg or AWS Rekognition
    img = remove_background(img)
    
    # 4. Generate variants
    variants = {
        "web": img.resize((800, 800)),
        "mobile": img.resize((400, 400)),
        "thumbnail": img.resize((100, 100))
    }
    
    # 5. Upload to CDN
    for name, variant in variants.items():
        buffer = io.BytesIO()
        variant.save(buffer, "JPEG", quality=95)
        s3.upload_fileobj(
            buffer, 
            "cdn-bucket", 
            f"products/{s3_key.split('/')[-1].split('.')[0]}_{name}.jpg",
            ExtraArgs={'ContentType': 'image/jpeg', 'CacheControl': 'max-age=31536000'}
        )
    
    # 6. Generate WebP version
    webp_buffer = io.BytesIO()
    img.save(webp_buffer, "WEBP", quality=85)
    s3.upload_fileobj(webp_buffer, "cdn-bucket", f"products/{s3_key.split('/')[-1].split('.')[0]}.webp")

process_product_image("user-uploads", "products/summer_dress.jpg")

By: @DataScienceM 👁

#Python #ImageProcessing #ComputerVision #Pillow #OpenCV #MachineLearning #CodingInterview #DataScience #Programming #TechJobs #DeveloperTips #AI #DeepLearning #CloudComputing #Docker #BackendDevelopment #SoftwareEngineering #CareerGrowth #TechTips #Python3

Part 5: Training the Model

We train the model using the fit() method, providing our training data, batch size, number of epochs, and validation data to monitor performance on unseen data.

history = model.fit(x_train, y_train, 
                    epochs=15, 
                    batch_size=64,
                    validation_data=(x_test, y_test))

#Training #MachineLearning #ModelFit

---

Part 6: Evaluating and Discussing Results

After training, we evaluate the model's performance on the test set. We also plot the training history to visualize accuracy and loss curves. This helps us understand if the model is overfitting or underfitting.

# Evaluate the model on the test data
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(f'\nTest accuracy: {test_acc:.4f}')

# Plot training & validation accuracy values
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')

# Plot training & validation loss values
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')

plt.show()

Discussion:
The plots show how accuracy and loss change over epochs. Ideally, both training and validation accuracy should increase, while losses decrease. If the validation accuracy plateaus or decreases while training accuracy continues to rise, it's a sign of overfitting. Our simple model achieves a decent accuracy. To improve it, one could use techniques like Data Augmentation, Dropout layers, or a deeper architecture.

#Evaluation #Results #Accuracy #Overfitting

---

Part 7: Making Predictions on a Single Image

This is how you handle a single image file for prediction. The model expects a batch of images as input, so we must add an extra dimension to our single image before passing it to model.predict().

# Select a single image from the test set
img_index = 15
test_image = x_test[img_index]
true_label_index = np.argmax(y_test[img_index])

# Display the image
plt.imshow(test_image)
plt.title(f"Actual Label: {class_names[true_label_index]}")
plt.show()

# The model expects a batch of images, so we add a dimension
image_for_prediction = np.expand_dims(test_image, axis=0)
print("Image shape before prediction:", test_image.shape)
print("Image shape after adding batch dimension:", image_for_prediction.shape)

# Make a prediction
predictions = model.predict(image_for_prediction)
predicted_label_index = np.argmax(predictions[0])

# Print the result
print(f"\nPrediction Probabilities: {predictions[0]}")
print(f"Predicted Label: {class_names[predicted_label_index]}")
print(f"Actual Label: {class_names[true_label_index]}")

#Prediction #ImageProcessing #Inference

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By: @DataScienceM ✨