Chekka topish: Sobel, Laplacian va Canny

img = cv2.imread(str(RASM / "shakllar.png"), cv2.IMREAD_GRAYSCALE)
sx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3)          # x bo'yicha: vertikal chekkalar
sy = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=3)          # y bo'yicha: gorizontal chekkalar
kuch = cv2.magnitude(sx, sy)
print(f"Sobel x: min {sx.min():.0f}, max {sx.max():.0f}  (manfiy ham bor — shuning uchun CV_64F)")
ko_rsat(np.abs(sx).astype(np.uint8), np.abs(sy).astype(np.uint8),
        np.clip(kuch, 0, 255).astype(np.uint8), nom="sobel.png",
        sarlavhalar=["Sobel x (vertikal chekka)", "Sobel y (gorizontal chekka)", "kuch = sqrt(x² + y²)"],
        izoh="x bo'yicha hosila tik chiziqlarni, y bo'yicha yotiq chiziqlarni ko'rsatadi.")

lap = cv2.Laplacian(img, cv2.CV_64F, ksize=3)
lap_u8 = np.clip(np.abs(lap), 0, 255).astype(np.uint8)
shovqinli = cv2.imread(str(RASM / "shovqinli.png"), cv2.IMREAD_GRAYSCALE)
lap_shovqin = np.clip(np.abs(cv2.Laplacian(shovqinli, cv2.CV_64F)), 0, 255).astype(np.uint8)
ko_rsat(lap_u8, lap_shovqin, nom="laplacian.png",
        sarlavhalar=["toza rasmda Laplacian", "shovqinli rasmda Laplacian"],
        izoh="Ikkinchi hosila shovqinga juda sezgir: o'ngdagi rasm deyarli butunlay 'chekka' bo'lib ketdi.")
print("chekka deb belgilangan piksellar (>40): toza",
      f"{100 * (lap_u8 > 40).mean():.1f}%", "| shovqinli", f"{100 * (lap_shovqin > 40).mean():.1f}%")

qurilish = cv2.imread(str(RASM / "qurilish_kichik.jpg"), cv2.IMREAD_GRAYSCALE)
variantlar = {}
for past, yuqori in [(50, 150), (100, 200), (150, 300)]:
    variantlar[f"{past}/{yuqori}"] = cv2.Canny(qurilish, past, yuqori)
show(pd.DataFrame([{"chegaralar": k, "chekka piksellar %": round(100 * (v > 0).mean(), 2)}
                   for k, v in variantlar.items()]))
ko_rsat(*variantlar.values(), nom="canny_chegara.png", sarlavhalar=list(variantlar),
        izoh="Chegara past bo'lsa mayda tafsilot ham chekka bo'ladi; yuqori bo'lsa faqat eng kuchli chekkalar qoladi.")

def avto_canny(gray, sigma=0.33):
    med = float(np.median(gray))
    past = int(max(0, (1.0 - sigma) * med))
    yuqori = int(min(255, (1.0 + sigma) * med))
    return cv2.Canny(gray, past, yuqori), past, yuqori

for nom, rasm in [("qurilish", qurilish), ("shakllar", img), ("hujjat", cv2.imread(str(RASM / "hujjat.jpg"), cv2.IMREAD_GRAYSCALE))]:
    chekka, p, y = avto_canny(rasm)
    print(f"{nom:10} median {np.median(rasm):5.0f} -> chegaralar {p:3d}/{y:3d} | chekka piksellar {100 * (chekka > 0).mean():.2f}%")

qatorlar = []
for nom, rasm in [("shovqinli, filtrsiz", shovqinli),
                  ("shovqinli + Gauss 5x5", cv2.GaussianBlur(shovqinli, (5, 5), 0)),
                  ("shovqinli + Median 5", cv2.medianBlur(shovqinli, 5)),
                  ("toza rasm (haqiqat)", img)]:
    c = cv2.Canny(rasm, 100, 200)
    qatorlar.append({"holat": nom, "chekka piksellar %": round(100 * (c > 0).mean(), 2)})
show(pd.DataFrame(qatorlar))
ko_rsat(cv2.Canny(shovqinli, 100, 200), cv2.Canny(cv2.medianBlur(shovqinli, 5), 100, 200), cv2.Canny(img, 100, 200),
        nom="canny_shovqin.png", sarlavhalar=["filtrsiz — soxta chekkalar", "median dan keyin", "toza rasm"],
        izoh="Shovqinli rasmda Canny minglab soxta chekka topadi. Filtrdan keyin natija toza rasmnikiga yaqinlashadi.")

hujjat = cv2.imread(str(RASM / "hujjat.jpg"))
gray = cv2.cvtColor(hujjat, cv2.COLOR_BGR2GRAY)
silliq = cv2.GaussianBlur(gray, (5, 5), 0)
chekka = cv2.Canny(silliq, 50, 150)
yopiq = cv2.morphologyEx(chekka, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8))    # uzilgan chiziqlarni ulash
ko_rsat(chekka, yopiq, nom="hujjat_chekka.png", sarlavhalar=["Canny", "MORPH_CLOSE dan keyin"],
        izoh="Varaqning to'rt cheti aniq ko'rinadi. Keyingi qadam — shu chiziqlardan to'rtburchak yasash (10- va 17-boblar).")
print("chekka piksellar:", int((chekka > 0).sum()), "| yopilgandan keyin:", int((yopiq > 0).sum()))