Ear-segmentation-ai

Quick Start Guide

Basic Usage

Command Line Interface

Process a single image

earsegmentationai process-image path/to/image.jpg --save-viz

Process a directory of images

earsegmentationai process-image path/to/directory --save-viz --save-mask

Real-time webcam processing

earsegmentationai process-camera --device-id 0 --save-video output.mp4

Python API

Basic example

from earsegmentationai import ImageProcessor

# Initialize processor
processor = ImageProcessor()

# Process single image
result = processor.process("path/to/image.jpg")
print(f"Number of ears detected: {result.num_ears}")

Process with visualization

# Process with visualization
result = processor.process(
    "path/to/image.jpg",
    return_visualization=True,
    save_results=True,
    output_dir="output"
)

# Access results
if result.success:
    print(f"Confidence: {result.confidence:.2f}")
    print(f"Processing time: {result.processing_time:.3f}s")

Batch processing

# Process multiple images
results = processor.process([
    "image1.jpg",
    "image2.jpg",
    "image3.jpg"
])

for idx, result in enumerate(results.results):
    print(f"Image {idx}: {result.num_ears} ears detected")

Common Use Cases

1. Save segmentation masks

earsegmentationai process-image image.jpg --save-mask --output masks/

2. Process with custom threshold

earsegmentationai process-image image.jpg --threshold 0.7

3. Use GPU acceleration

earsegmentationai process-image image.jpg --device cuda:0

4. Process video file

from earsegmentationai import VideoProcessor

processor = VideoProcessor()
processor.process("input_video.mp4", output_path="output_video.mp4")

Output Format

CLI Output

Processing: image.jpg
✓ Ear detected!
Area: 1.55% of image
Bounding box: x=54, y=144, w=76, h=65
Results saved to: output/

API Result Object

result.success          # bool: Processing successful
result.num_ears         # int: Number of ears detected
result.mask            # numpy array: Segmentation mask
result.confidence      # float: Detection confidence
result.processing_time # float: Time in seconds
result.visualization   # numpy array: Visualization image (optional)

Next Steps

• See Advanced Usage for more features
• Check API Reference for detailed documentation
• View Examples for more code samples

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