Real-World Applications of AI in Image Processing

In this tutorial, we will explore how Artificial Intelligence (AI) can simplify and enhance image processing tasks. The case studies covered include:

• Creating Image Watermarks
• Automating Image Processing Workflows
• Batch Resizing and Format Conversion

Case Study 1: Creating Image Watermarks

Adding watermarks to images is a common technique to protect intellectual property. Here’s how AI can streamline this process:

Steps:

1. Input Preparation: Collect the images and watermark design (logo or text).
2. AI Integration: Use an AI tool or script to overlay the watermark on images in a consistent location and style.
3. Batch Processing: Apply the watermark to multiple images simultaneously.

Example:

Here’s a Python snippet using the Pillow library:

from PIL import Image, ImageDraw, ImageFont
 
def add_watermark(input_image, output_image, watermark_text):
    image = Image.open(input_image).convert("RGBA")
    watermark = Image.new("RGBA", image.size)
    draw = ImageDraw.Draw(watermark)
    font = ImageFont.truetype("arial.ttf", 36)
    text_position = (image.size[0] - 200, image.size[1] - 50)
    draw.text(text_position, watermark_text, fill=(255, 255, 255, 128), font=font)
    combined = Image.alpha_composite(image, watermark)
    combined.save(output_image)
 
add_watermark("input.jpg", "output.jpg", "© YourName")

Case Study 2: Automating Image Processing Workflows

AI can be used to automate repetitive tasks like filtering, categorizing, and tagging images, saving time and effort.

Example Workflow:

1. Define Tasks: Identify the processing steps (e.g., cropping, resizing, and tagging).
2. Set Rules: Use AI to analyze and apply rules to images (e.g., detect objects and crop around them).
3. Automate: Run the workflow on a batch of images.

Tools:

• Open-source libraries like OpenCV
• Cloud-based AI platforms such as AWS Rekognition

Case Study 3: Batch Resizing and Format Conversion

Batch resizing and converting images to different formats is a common requirement in web development and graphic design.

Steps:

1. Input Specification: List all images to be resized and the desired dimensions.
2. AI Implementation: Use AI tools or scripts to resize images intelligently without compromising quality.
3. Format Conversion: Convert images to formats like JPEG, PNG, or WebP as needed.

Example:

Here’s a Python snippet for batch resizing:

from PIL import Image
import os
 
def batch_resize(input_folder, output_folder, size):
    os.makedirs(output_folder, exist_ok=True)
    for filename in os.listdir(input_folder):
        if filename.endswith(('.jpg', '.png', '.jpeg')):
            image = Image.open(os.path.join(input_folder, filename))
            image = image.resize(size)
            image.save(os.path.join(output_folder, filename))
 
batch_resize("input_images", "output_images", (800, 600))

Activities:

1. Add Your Watermark:

   • Use the provided script to add a custom watermark to your images.

2. Automate a Workflow:

   • Define a sequence of tasks for your image processing needs and automate them using Python or a cloud-based tool.

3. Batch Resize Challenge:

   • Resize a folder of images to 400x400 pixels and convert them to WebP format.

By the end of this tutorial, you’ll have hands-on experience with AI-powered image processing and its practical applications.