| import cv2 |
| import numpy as np |
| import os |
| import json |
|
|
| CONFIG_PATH = "config.json" |
|
|
| def load_config(): |
| if os.path.exists(CONFIG_PATH): |
| with open(CONFIG_PATH) as f: |
| return json.load(f) |
| return { |
| "preprocessing": { |
| "clahe_clip": 3.0, |
| "clahe_grid": 8, |
| "bilateral_d": 9, |
| "bilateral_sigma": 75, |
| "adaptive_blocksize": 21, |
| "adaptive_c": 8 |
| } |
| } |
|
|
|
|
| def correct_rotation(img): |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if len(img.shape) == 3 else img |
| edges = cv2.Canny(gray, 50, 150) |
| lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, minLineLength=30, maxLineGap=10) |
| if lines is None: |
| return img |
| angles = [np.degrees(np.arctan2(l[0][3]-l[0][1], l[0][2]-l[0][0])) for l in lines] |
| if abs(np.median(angles)) > 45: |
| img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) |
| return img |
|
|
|
|
| def preprocess_chassis(image_path, save_comparison=False, output_dir="results"): |
| config = load_config() |
| p = config["preprocessing"] |
|
|
| img = cv2.imread(image_path) |
| if img is None: |
| raise ValueError(f"Could not load image: {image_path}") |
|
|
| original = img.copy() |
| img = correct_rotation(img) |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
|
|
| clahe = cv2.createCLAHE( |
| clipLimit=p["clahe_clip"], |
| tileGridSize=(p["clahe_grid"], p["clahe_grid"]) |
| ) |
| enhanced = clahe.apply(gray) |
|
|
| v0 = cv2.cvtColor(enhanced, cv2.COLOR_GRAY2BGR) |
|
|
| filtered = cv2.bilateralFilter(enhanced, p["bilateral_d"], |
| p["bilateral_sigma"], p["bilateral_sigma"]) |
| v1 = cv2.cvtColor(filtered, cv2.COLOR_GRAY2BGR) |
|
|
| _, otsu = cv2.threshold(filtered, 0, 255, |
| cv2.THRESH_BINARY + cv2.THRESH_OTSU) |
| v2 = cv2.cvtColor(otsu, cv2.COLOR_GRAY2BGR) |
|
|
| bs = p["adaptive_blocksize"] |
| bs = bs if bs % 2 == 1 else bs + 1 |
| adaptive = cv2.adaptiveThreshold( |
| filtered, 255, |
| cv2.ADAPTIVE_THRESH_GAUSSIAN_C, |
| cv2.THRESH_BINARY, |
| blockSize=bs, C=p["adaptive_c"] |
| ) |
| v3 = cv2.cvtColor(adaptive, cv2.COLOR_GRAY2BGR) |
|
|
| pad = p.get("padding", 20) |
| def add_padding(im): |
| return cv2.copyMakeBorder(im, pad, pad, pad, pad, |
| cv2.BORDER_CONSTANT, value=(255, 255, 255)) |
|
|
| variations = [add_padding(v) for v in [v0, v1, v2, v3]] |
|
|
| if save_comparison: |
| os.makedirs(output_dir, exist_ok=True) |
| fname = os.path.splitext(os.path.basename(image_path))[0] |
| h = 200 |
|
|
| def resize_h(im, height): |
| r = height / im.shape[0] |
| return cv2.resize(im, (int(im.shape[1] * r), height)) |
|
|
| def add_label(im, label): |
| out = im.copy() if len(im.shape) == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) |
| cv2.putText(out, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) |
| return out |
|
|
| def to_bgr(p): |
| return p if len(p.shape) == 3 else cv2.cvtColor(p, cv2.COLOR_GRAY2BGR) |
|
|
| panels = [add_label(resize_h(cv2.cvtColor(original, cv2.COLOR_BGR2GRAY), h), "Original")] |
| labels = ["CLAHE", "Bilateral", "Otsu", "Adaptive"] |
| for i, var in enumerate(variations): |
| g = cv2.cvtColor(var, cv2.COLOR_BGR2GRAY) |
| panels.append(add_label(resize_h(g, h), labels[i])) |
|
|
| comparison = np.hstack([to_bgr(p) for p in panels]) |
| cv2.imwrite(os.path.join(output_dir, f"{fname}_comparison.jpg"), comparison) |
| print(f" Saved comparison -> {output_dir}/{fname}_comparison.jpg") |
|
|
| return variations |
|
|
|
|
| if __name__ == "__main__": |
| import sys |
| if len(sys.argv) < 2: |
| print("Usage: python preprocess.py <image_path>") |
| else: |
| variations = preprocess_chassis(sys.argv[1], save_comparison=True) |
| print(f"Generated {len(variations)} variations — check results/") |
