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1d64201 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # single_image_cleaning.py
import cv2
import numpy as np
import os
def preprocess_image(image_path):
"""
Preprocess a single floorplan image: denoising, CLAHE, edge enhancement.
"""
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
# Convert to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Apply Gaussian blur
denoisy_img = cv2.GaussianBlur(gray, (5, 5), 0)
# Apply CLAHE (Contrast Limited Adaptive Histogram Equalization)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
enhanced = clahe.apply(denoisy_img)
# Apply threshold
_, thresholded = cv2.threshold(enhanced, 150, 255, cv2.THRESH_BINARY)
# Detect edges
edges = cv2.Canny(thresholded, 100, 220, apertureSize=3)
# Detect lines and draw them
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)
# Blend the original image with line-enhanced version
blended_image = cv2.addWeighted(image, 0.7, output_img, 0.3, 0)
print(f"✅ Preprocessing complete for: {image_path}")
return blended_image
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