rcnn_model/preprocessing/cleaning_single_image.py

#This file is a version of the original cleaning_images.py fiele, but it has been modified to only process a single image.
import cv2
import numpy as np

def preprocess_image(image_path):
    print(f" Preprocessing image: {image_path}")
    
    image = cv2.imread(image_path)
    if image is None:
        print(f"Error: Could not read image from {image_path}")
        return None
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    denoisy_img = cv2.GaussianBlur(gray, (5, 5), 0)
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    enhanced = clahe.apply(denoisy_img)
    _, thresholded = cv2.threshold(enhanced, 150, 255, cv2.THRESH_BINARY)
    edges = cv2.Canny(thresholded, 100, 220, apertureSize=3)
    output_img = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
    lines = cv2.HoughLinesP(edges, rho=1, theta=np.pi / 180, threshold=50,
                            minLineLength=35, maxLineGap=5)
    
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(output_img, (x1, y1), (x2, y2), (210, 210, 210), 1)

    blended_image = cv2.addWeighted(image, 0.7, output_img, 0.3, 0)

    print(f"Preprocessing complete for: {image_path}")
    return blended_image