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import os
import uuid
import logging
import numpy as np
from PIL import Image, ImageEnhance, ImageFilter
import cv2
def process_image(image_path, output_folder):
"""
Process the input image for geospatial analysis:
- Convert to grayscale
- Apply threshold to highlight features
- Apply noise reduction
- Apply edge detection
Args:
image_path (str): Path to the input image
output_folder (str): Directory to save processed images
Returns:
str: Path to the processed image
"""
try:
logging.info(f"Processing image: {image_path}")
# Open the image
img = Image.open(image_path)
# Convert to RGB if it's not already
if img.mode != 'RGB':
img = img.convert('RGB')
# Convert to numpy array for OpenCV processing
img_array = np.array(img)
# Convert to grayscale
gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
# Apply Gaussian blur for noise reduction
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# Apply adaptive thresholding
thresh = cv2.adaptiveThreshold(
blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV, 11, 2
)
# Edge detection using Canny algorithm
edges = cv2.Canny(thresh, 50, 150)
# Morphological operations to clean up the result
kernel = np.ones((3, 3), np.uint8)
cleaned = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
# Convert back to PIL Image
processed_img = Image.fromarray(cleaned)
# Save the processed image
processed_filename = f"{uuid.uuid4().hex}_processed.png"
output_path = os.path.join(output_folder, processed_filename)
processed_img.save(output_path)
logging.info(f"Image processing complete: {output_path}")
return output_path
except Exception as e:
logging.error(f"Error in image processing: {str(e)}")
raise Exception(f"Image processing failed: {str(e)}")
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