Gistogramma va kontrast: rasmni "o'qish"

asl = cv2.imread(str(RASM / "qurilish_kichik.jpg"), cv2.IMREAD_GRAYSCALE)
past = cv2.imread(str(RASM / "past_kontrast.jpg"), cv2.IMREAD_GRAYSCALE)
show(pd.DataFrame([{"rasm": n, "min": int(g.min()), "max": int(g.max()),
                    "oraliq": int(g.max()) - int(g.min()), "o'rtacha": round(float(g.mean()), 1),
                    "std (kontrast)": round(float(g.std()), 1)}
                   for n, g in [("asl", asl), ("past kontrast", past)]]))

plt.figure(figsize=(11, 3.2))
for i, (n, g) in enumerate([("asl", asl), ("past kontrast", past)], 1):
    plt.subplot(1, 2, i)
    plt.hist(g.ravel(), bins=256, range=(0, 256), color="#2c6fbb")
    plt.title(n); plt.xlim(0, 255); plt.xlabel("yorqinlik"); plt.ylabel("piksellar")
plt.tight_layout()
fig("gistogramma_asos.png", "Past kontrastli rasmda hamma piksel tor oraliqqa siqilgan — gistogramma ingichka ustun.")
ko_rsat(asl, past, nom="past_kontrast.png", sarlavhalar=["asl", "past kontrast"],
        izoh="Ikkalasida ham bir xil ma'lumot bor, lekin o'ngdagisida u tor yorqinlik oralig'iga siqilgan.")

gist = cv2.calcHist([asl], [0], None, [256], [0, 256]).ravel()
print(f"gistogramma uzunligi: {len(gist)} | yig'indi: {int(gist.sum())} = {asl.size} piksel")
print(f"eng ko'p uchraydigan yorqinlik: {int(gist.argmax())} ({int(gist.max())} piksel)")

rangli = cv2.imread(str(RASM / "qurilish_kichik.jpg"))
plt.figure(figsize=(7, 3.2))
for kanal, rang in enumerate(["b", "g", "r"]):
    h = cv2.calcHist([rangli], [kanal], None, [256], [0, 256]).ravel()
    plt.plot(h, color={"b": "#1f77b4", "g": "#2ca02c", "r": "#d62728"}[rang], label=rang.upper())
plt.legend(); plt.xlim(0, 255); plt.title("BGR kanallar gistogrammasi"); plt.tight_layout()
fig("bgr_gistogramma.png", "Uch kanal alohida: qaysi rang ustun ekanini ko'rsatadi.")

maska = np.zeros(asl.shape, np.uint8)
maska[250:, :] = 255                                        # faqat pastki qism
butun = cv2.calcHist([asl], [0], None, [256], [0, 256]).ravel()
pastki = cv2.calcHist([asl], [0], maska, [256], [0, 256]).ravel()
print(f"butun rasm o'rtachasi: {asl.mean():.1f} | pastki qism: {asl[250:].mean():.1f}")
plt.figure(figsize=(7, 3.2))
plt.plot(butun / butun.sum(), label="butun rasm")
plt.plot(pastki / pastki.sum(), label="pastki qism (maska)")
plt.legend(); plt.xlim(0, 255); plt.title("Maskali gistogramma"); plt.tight_layout()
fig("maskali_gistogramma.png", "Pastki qismning gistogrammasi o'ngga siljigan — u butun rasmdan yorug'roq.")

teng = cv2.equalizeHist(past)
def entropiya(g):
    h = cv2.calcHist([g], [0], None, [256], [0, 256]).ravel()
    p = h[h > 0] / h.sum()
    return round(float(-(p * np.log2(p)).sum()), 2)

show(pd.DataFrame([{"rasm": n, "min": int(g.min()), "max": int(g.max()),
                    "std": round(float(g.std()), 1), "entropiya (bit)": entropiya(g)}
                   for n, g in [("asl", asl), ("past kontrast", past), ("equalizeHist", teng)]]))
ko_rsat(past, teng, asl, nom="equalize.png", sarlavhalar=["past kontrast", "equalizeHist", "asl (solishtirish uchun)"],
        izoh="equalizeHist oraliqni to'liq ochdi, lekin natija aslga aynan teng emas.")

clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
c = clahe.apply(past)
show(pd.DataFrame([{"usul": n, "std": round(float(g.std()), 1), "entropiya": entropiya(g)}
                   for n, g in [("past kontrast", past), ("equalizeHist", teng), ("CLAHE 2.0", c),
                                ("asl", asl)]]))
ko_rsat(past, teng, c, nom="clahe.png", sarlavhalar=["past kontrast", "equalizeHist (global)", "CLAHE (mahalliy)"],
        izoh="CLAHE soyadagi tafsilotni ham ochadi va osmonni equalizeHist kabi 'yondirmaydi'.")

show(pd.DataFrame([{"clipLimit": cl, "std": round(float(cv2.createCLAHE(clipLimit=cl, tileGridSize=(8, 8)).apply(past).std()), 1),
                    "entropiya": entropiya(cv2.createCLAHE(clipLimit=cl, tileGridSize=(8, 8)).apply(past))}
                   for cl in (1.0, 2.0, 4.0, 10.0, 40.0)]))
ko_rsat(cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8)).apply(past),
        cv2.createCLAHE(clipLimit=40.0, tileGridSize=(8, 8)).apply(past), nom="clip_limit.png",
        sarlavhalar=["clipLimit 1.0 — ehtiyotkor", "clipLimit 40 — shovqin kuchaygan"],
        izoh="Chegara yo'q qilinsa CLAHE tekis joylardagi mayda shovqinni ham kontrastga aylantiradi.")

past_rangli = cv2.imread(str(RASM / "past_kontrast.jpg"))
notogri = cv2.merge([cv2.equalizeHist(k) for k in cv2.split(past_rangli)])       # BGR ga alohida — XATO
lab = cv2.cvtColor(past_rangli, cv2.COLOR_BGR2LAB)
L, A, B = cv2.split(lab)
togri = cv2.cvtColor(cv2.merge([clahe.apply(L), A, B]), cv2.COLOR_LAB2BGR)       # faqat L ga — TO'G'RI
def rang_ogishi(im):
    """Piksellarning o'rtacha rang to'yinganligi: qanchalik 'qichqiriq' rang."""
    return round(float(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)[:, :, 1].mean()), 1)
show(pd.DataFrame([{"usul": n, "to'yinganlik (S o'rtacha)": rang_ogishi(im)}
                   for n, im in [("past kontrast", past_rangli), ("BGR ga alohida equalizeHist", notogri),
                                 ("LAB ning L kanaliga CLAHE", togri),
                                 ("asl", cv2.imread(str(RASM / "qurilish_kichik.jpg")))]]))
ko_rsat(past_rangli, notogri, togri, nom="rangli_kontrast.png",
        sarlavhalar=["past kontrast", "BGR ga alohida — ranglar buzildi", "L kanaliga CLAHE — to'g'ri"],
        izoh="Har kanalni alohida tenglashtirsangiz kanallar nisbati o'zgaradi va rang siljiydi.")

def hsv_gist(im):
    h = cv2.calcHist([cv2.cvtColor(im, cv2.COLOR_BGR2HSV)], [0, 1], None, [50, 60], [0, 180, 0, 256])
    return cv2.normalize(h, h).flatten()

rasmlar = {"asl": cv2.imread(str(RASM / "qurilish_kichik.jpg")),
           "past kontrast": past_rangli,
           "tuzatilgan": togri,
           "shakllar (boshqa rasm)": cv2.imread(str(RASM / "shakllar.png"))}
baza = hsv_gist(rasmlar["asl"])
show(pd.DataFrame([{"rasm": n, "CORREL (1 = bir xil)": round(float(cv2.compareHist(baza, hsv_gist(im), cv2.HISTCMP_CORREL)), 3),
                    "BHATTACHARYYA (0 = bir xil)": round(float(cv2.compareHist(baza, hsv_gist(im), cv2.HISTCMP_BHATTACHARYYA)), 3)}
                   for n, im in rasmlar.items()]))