""" Polygon seyreltme karşılaştırma testi. Kullanım: python test_simplify.py python test_simplify.py --epsilon 3.0 --max-points 30 """ import argparse import json import random import sys from pathlib import Path import cv2 import matplotlib.patches as mpatches import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Polygon as MplPolygon from scipy.ndimage import gaussian_filter1d from scipy.signal import find_peaks BASE = Path(__file__).parent / "SICAPv2" MASKS_DIR = BASE / "masks" IMGS_DIR = BASE / "images" GRADE_RANGES = [(30, 80, "G3"), (80, 125, "G4"), (125, 256, "G5")] GRADE_COLORS = {"G3": "#00cc44", "G4": "#ff8800", "G5": "#ff2222"} MERGE_DIST = 20 MIN_AREA = 100 # --------------------------------------------------------------------------- # Kalibrasyon (extract_polygons'tan kopyalandı) # --------------------------------------------------------------------------- def grade_for_value(val): for lo, hi, grade in GRADE_RANGES: if lo <= val < hi: return grade return None def calibrate(gray): hist = np.bincount(gray.ravel(), minlength=256).astype(float) smooth = gaussian_filter1d(hist[8:], sigma=2) min_h = max(smooth.max() * 0.02, 5) idxs, _ = find_peaks(smooth, height=min_h, distance=10, prominence=min_h * 0.3) peaks = sorted([(int(i + 8), int(hist[i + 8])) for i in idxs]) merged = [] for val, cnt in peaks: if merged and val - merged[-1][0] <= MERGE_DIST: merged[-1] = (val, cnt) if cnt > merged[-1][1] else merged[-1] else: merged.append((val, cnt)) grade_map = {} for val, _ in merged: g = grade_for_value(val) if g and g not in grade_map.values(): grade_map[val] = g return grade_map def quantize(gray, centers): q = np.zeros_like(gray, dtype=np.int32) all_c = sorted([0] + list(centers)) for i in range(1, len(all_c)): c = all_c[i] lo = (all_c[i - 1] + c) // 2 hi = (all_c[i + 1] + c) // 2 if i + 1 < len(all_c) else 256 q[(gray >= lo) & (gray < hi)] = c return q def remove_small(binary): n, labels, stats, _ = cv2.connectedComponentsWithStats(binary, connectivity=8) clean = np.zeros_like(binary) for lid in range(1, n): if stats[lid, cv2.CC_STAT_AREA] >= MIN_AREA: clean[labels == lid] = 1 return clean # --------------------------------------------------------------------------- # İki farklı polygon çıkarma yöntemi # --------------------------------------------------------------------------- def extract_original(cnt): """Mevcut yöntem: epsilon=0.5""" approx = cv2.approxPolyDP(cnt, 0.5, closed=True) if len(approx) < 3: return None pts = [[int(p[0][0]), int(p[0][1])] for p in approx] pts.append(pts[0]) return pts def extract_simplified(cnt, epsilon_start=1.0, max_points=80): """Yeni yöntem: başlangıç epsilon yüksek + adaptif seyreltme.""" approx = cv2.approxPolyDP(cnt, epsilon_start, closed=True) arr = approx.astype(np.float32) eps = epsilon_start while len(arr) > max_points and eps <= 20: eps *= 1.5 arr = cv2.approxPolyDP(arr, eps, closed=True).astype(np.float32) if len(arr) < 3: return None pts = [[int(p[0][0]), int(p[0][1])] for p in arr] pts.append(pts[0]) return pts # --------------------------------------------------------------------------- # Tek patch işleme # --------------------------------------------------------------------------- def process_mask(mask_path, epsilon_start, max_points): gray = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE) if gray is None: return None, None gray_clean = cv2.medianBlur(gray, 5) grade_map = calibrate(gray_clean) if not grade_map: return None, None q = quantize(gray_clean, list(grade_map.keys())) orig_polys, simp_polys = [], [] for center, label in grade_map.items(): binary = (q == center).astype(np.uint8) binary = remove_small(binary) if binary.sum() == 0: continue cnts, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for cnt in cnts: if cv2.contourArea(cnt) < MIN_AREA: continue op = extract_original(cnt) sp = extract_simplified(cnt, epsilon_start, max_points) if op: orig_polys.append((label, op)) if sp: simp_polys.append((label, sp)) return orig_polys, simp_polys # --------------------------------------------------------------------------- # Çizim # --------------------------------------------------------------------------- def draw_polys(ax, image, polys, title, alpha=0.35): ax.imshow(image) ax.set_title(title, fontsize=9) ax.axis("off") legend = {} for label, pts in polys: color = GRADE_COLORS.get(label, "#ffffff") arr = np.array(pts[:-1]) patch = MplPolygon(arr, closed=True, facecolor=color, alpha=alpha, edgecolor=color, linewidth=1.5) ax.add_patch(patch) if label not in legend: legend[label] = mpatches.Patch(color=color, label=label) if legend: ax.legend(handles=list(legend.values()), loc="upper right", fontsize=8, framealpha=0.7) def compare_patch(mask_path, epsilon_start, max_points): stem = mask_path.stem img_path = IMGS_DIR / mask_path.name image = cv2.cvtColor(cv2.imread(str(img_path)), cv2.COLOR_BGR2RGB) \ if img_path.exists() else np.zeros((512, 512, 3), dtype=np.uint8) mask_rgb = cv2.cvtColor(cv2.imread(str(mask_path)), cv2.COLOR_BGR2RGB) orig_polys, simp_polys = process_mask(mask_path, epsilon_start, max_points) if orig_polys is None: print(f" Kalibrasyon başarısız: {stem}") return orig_pts = sum(len(p) - 1 for _, p in orig_polys) simp_pts = sum(len(p) - 1 for _, p in simp_polys) reduction = (1 - simp_pts / orig_pts) * 100 if orig_pts else 0 fig, axes = plt.subplots(1, 4, figsize=(20, 5)) fig.suptitle(stem, fontsize=8, y=1.01) axes[0].imshow(mask_rgb) axes[0].set_title("Mask (ham)") axes[0].axis("off") axes[1].imshow(image) axes[1].set_title("Orijinal görüntü") axes[1].axis("off") draw_polys(axes[2], image, orig_polys, f"Mevcut (eps=0.5)\n{len(orig_polys)} poly {orig_pts} nokta") draw_polys(axes[3], image, simp_polys, f"Seyrelmiş (eps={epsilon_start}, max={max_points})\n" f"{len(simp_polys)} poly {simp_pts} nokta (-%{reduction:.0f})") plt.tight_layout() plt.savefig(f"simplify_test_{stem[:40]}.png", dpi=120, bbox_inches="tight") plt.show() print(f" {stem}") print(f" Mevcut : {len(orig_polys):3d} polygon {orig_pts:5d} nokta") print(f" Seyrelmiş: {len(simp_polys):3d} polygon {simp_pts:5d} nokta (-%{reduction:.1f})") # --------------------------------------------------------------------------- # Main # --------------------------------------------------------------------------- def main(): parser = argparse.ArgumentParser() parser.add_argument("--epsilon", type=float, default=1.0) parser.add_argument("--max-points", type=int, default=50) parser.add_argument("--n", type=int, default=3, help="Kaç patch test edilsin") parser.add_argument("--seed", type=int, default=42) args = parser.parse_args() masks = sorted(MASKS_DIR.glob("*.jpg")) if not masks: sys.exit(f"Mask bulunamadı: {MASKS_DIR}") # Karmaşık polygon içerme ihtimali yüksek maskleri seç # (dosya boyutu büyük olanlar genellikle daha fazla içerik barındırır) masks_by_size = sorted(masks, key=lambda p: p.stat().st_size, reverse=True) candidates = masks_by_size[:50] random.seed(args.seed) selected = random.sample(candidates, min(args.n, len(candidates))) print(f"Test parametreleri: epsilon={args.epsilon} max_points={args.max_points}") print(f"Seçilen {len(selected)} patch:\n") for mp in selected: compare_patch(mp, args.epsilon, args.max_points) print("\nGörüntüler simplify_test_*.png olarak kaydedildi.") if __name__ == "__main__": main()