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#!/usr/bin/env python3
"""
SAM3 API Usage Example
This example shows how to use the SAM3 text-prompted segmentation API
for road defect detection.
"""
import requests
import base64
from PIL import Image
import io
import os
# Configuration
ENDPOINT_URL = "https://p6irm2x7y9mwp4l4.us-east-1.aws.endpoints.huggingface.cloud"
def segment_image(image_path, classes):
"""
Segment objects in an image using text prompts
Args:
image_path: Path to the image file
classes: List of object classes to segment (e.g., ["pothole", "crack"])
Returns:
List of dictionaries with 'label', 'mask' (base64), and 'score'
"""
# Load and encode image
with open(image_path, "rb") as f:
image_b64 = base64.b64encode(f.read()).decode()
# Make API request
response = requests.post(
ENDPOINT_URL,
json={
"inputs": image_b64,
"parameters": {
"classes": classes
}
},
timeout=30
)
response.raise_for_status()
return response.json()
def save_masks(results, output_dir="output"):
"""
Save segmentation masks as PNG files
Args:
results: API response (list of dictionaries)
output_dir: Directory to save masks
"""
os.makedirs(output_dir, exist_ok=True)
for result in results:
label = result["label"]
score = result["score"]
mask_b64 = result["mask"]
# Decode mask
mask_bytes = base64.b64decode(mask_b64)
mask_image = Image.open(io.BytesIO(mask_bytes))
# Save mask
output_path = os.path.join(output_dir, f"mask_{label}.png")
mask_image.save(output_path)
print(f"✓ Saved {label} mask: {output_path} (score: {score:.2f})")
def main():
"""Example: Road defect detection"""
# Example 1: Detect road defects
print("Example 1: Road Defect Detection")
print("=" * 50)
image_path = "../test_images/test.jpg"
classes = ["pothole", "crack", "patch", "debris"]
print(f"Image: {image_path}")
print(f"Classes: {classes}")
print()
try:
results = segment_image(image_path, classes)
print(f"Found {len(results)} segmentation masks")
print()
save_masks(results, output_dir="defects_output")
print()
except requests.exceptions.RequestException as e:
print(f"Error: {e}")
return
# Example 2: Segment specific objects
print("\nExample 2: Specific Object Segmentation")
print("=" * 50)
classes = ["asphalt", "yellow line"]
print(f"Classes: {classes}")
print()
try:
results = segment_image(image_path, classes)
print(f"Found {len(results)} segmentation masks")
print()
save_masks(results, output_dir="objects_output")
except requests.exceptions.RequestException as e:
print(f"Error: {e}")
if __name__ == "__main__":
main()
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