Konturlar: obyektni topish, o'lchash va tanib olish

img = cv2.imread(str(RASM / "shakllar.png"))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, binar = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY_INV)
konturlar, ierarxiya = cv2.findContours(binar, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print(f"topilgan konturlar: {len(konturlar)} ta (rasmda 6 ta shakl bor)")
chizilgan = img.copy()
cv2.drawContours(chizilgan, konturlar, -1, (0, 0, 255), 3)
ko_rsat(binar, chizilgan, nom="konturlar.png", sarlavhalar=["binar (obyekt oq)", "topilgan konturlar"],
        izoh="findContours binar rasmni oladi, drawContours esa natijani rangli rasm ustiga chizadi.")

HAQIQAT = {                                      # datasets/generate.py dagi chizish buyruqlaridan
    "to'rtburchak katta": 161 * 141,
    "to'rtburchak kichik": 91 * 91,
    "doira r=70": np.pi * 70 ** 2,
    "doira r=45": np.pi * 45 ** 2,
    "uchburchak": 8100.0,                        # uchlari: (300,400), (240,280), (380,290)
    "ellips 90x50": np.pi * 90 * 50,
}
olchangan = sorted((cv2.contourArea(k) for k in konturlar), reverse=True)
qatorlar = []
for (nom, haq), olch in zip(sorted(HAQIQAT.items(), key=lambda x: -x[1]), olchangan):
    qatorlar.append({"shakl": nom, "haqiqiy maydon": round(haq), "o'lchangan": round(olch),
                     "xato %": round(100 * abs(olch - haq) / haq, 2)})
show(pd.DataFrame(qatorlar))

qatorlar = []
for k in sorted(konturlar, key=cv2.contourArea, reverse=True):
    M = cv2.moments(k)
    cx, cy = int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])
    perim = cv2.arcLength(k, True)
    qatorlar.append({"maydon": round(cv2.contourArea(k)), "perimetr": round(perim),
                     "markaz (x, y)": f"({cx}, {cy})",
                     "yumaloqlik": round(4 * np.pi * cv2.contourArea(k) / perim ** 2, 3)})
show(pd.DataFrame(qatorlar))

def shakl_nomi(k):
    perim = cv2.arcLength(k, True)
    yaqin = cv2.approxPolyDP(k, 0.02 * perim, True)
    n = len(yaqin)
    if n == 3:
        return "uchburchak", n
    if n == 4:
        x, y, w, h = cv2.boundingRect(yaqin)
        return ("kvadrat" if 0.95 <= w / h <= 1.05 else "to'rtburchak"), n
    yumaloqlik = 4 * np.pi * cv2.contourArea(k) / perim ** 2
    return ("doira" if yumaloqlik > 0.9 else "ellips" if yumaloqlik > 0.7 else "ko'pburchak"), n

tartiblangan = sorted(konturlar, key=cv2.contourArea, reverse=True)
show(pd.DataFrame([{"topilgan shakl": shakl_nomi(k)[0], "burchaklar soni": shakl_nomi(k)[1],
                    "yumaloqlik": round(4 * np.pi * cv2.contourArea(k) / cv2.arcLength(k, True) ** 2, 3)}
                   for k in tartiblangan]))

def shakl_nomi_v2(k):
    perim = cv2.arcLength(k, True)
    n = len(cv2.approxPolyDP(k, 0.02 * perim, True))
    if n == 3:
        return "uchburchak", 1.0
    if n == 4:
        x, y, w, h = cv2.boundingRect(k)
        return ("kvadrat" if 0.95 <= w / h <= 1.05 else "to'rtburchak"), round(w / h, 2)
    (_, _), (a, b), _ = cv2.fitEllipse(k)
    nisbat = max(a, b) / min(a, b)
    return ("doira" if nisbat < 1.15 else "ellips"), round(nisbat, 2)

natija = img.copy()
qatorlar = []
for k in tartiblangan:
    nom, nisbat = shakl_nomi_v2(k)
    M = cv2.moments(k)
    cx, cy = int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])
    cv2.drawContours(natija, [k], -1, (0, 0, 255), 2)
    cv2.putText(natija, nom, (cx - 55, cy), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2, cv2.LINE_AA)
    qatorlar.append({"topilgan shakl": nom, "nisbat (w/h yoki o'qlar)": nisbat,
                     "eski usul": shakl_nomi(k)[0]})
show(pd.DataFrame(qatorlar))
ko_rsat(natija, nom="shakl_tanish.png", kenglik=9,
        izoh="Yumaloqlik o'rniga o'qlar nisbati: doira ham, ellips ham to'g'ri tanildi.")

uchburchak_k = min(konturlar, key=lambda k: 4 * np.pi * cv2.contourArea(k) / cv2.arcLength(k, True) ** 2)
doira_k = max(konturlar, key=lambda k: 4 * np.pi * cv2.contourArea(k) / cv2.arcLength(k, True) ** 2)
show(pd.DataFrame([{"epsilon": e,
                    "uchburchak: nuqtalar": len(cv2.approxPolyDP(uchburchak_k, e * cv2.arcLength(uchburchak_k, True), True)),
                    "doira: nuqtalar": len(cv2.approxPolyDP(doira_k, e * cv2.arcLength(doira_k, True), True))}
                   for e in (0.001, 0.005, 0.01, 0.02, 0.05, 0.1)]))

k = max(konturlar, key=cv2.contourArea)          # eng katta shakl
urab = img.copy()
x, y, w, h = cv2.boundingRect(k)
cv2.rectangle(urab, (x, y), (x + w, y + h), (0, 255, 0), 2)                  # yashil: to'g'ri to'rtburchak
qutib = cv2.boxPoints(cv2.minAreaRect(k)).astype(int)
cv2.drawContours(urab, [qutib], 0, (255, 0, 0), 2)                           # ko'k: burilgan to'rtburchak
(mx, my), r = cv2.minEnclosingCircle(k)
cv2.circle(urab, (int(mx), int(my)), int(r), (0, 0, 255), 2)                 # qizil: doira
qavariq = cv2.convexHull(k)
cv2.drawContours(urab, [qavariq], -1, (255, 0, 255), 2)                      # siyohrang: qavariq qobiq

qatorlar = []
for nom, c in [("eng katta shakl", k), ("uchburchak", uchburchak_k)]:
    maydon = cv2.contourArea(c)
    xx, yy, ww, hh = cv2.boundingRect(c)
    qatorlar.append({"shakl": nom, "kontur maydoni": round(maydon),
                     "boundingRect maydoni": ww * hh,
                     "to'ldirish (extent) %": round(100 * maydon / (ww * hh), 1),
                     "solidity %": round(100 * maydon / cv2.contourArea(cv2.convexHull(c)), 1)})
show(pd.DataFrame(qatorlar))
ko_rsat(urab, nom="urab_olish.png", kenglik=9,
        izoh="Yashil — boundingRect, ko'k — minAreaRect, qizil — minEnclosingCircle, siyohrang — convexHull.")

teshikli = np.full((300, 400, 3), 245, np.uint8)
cv2.rectangle(teshikli, (40, 40), (180, 260), (80, 80, 80), -1)
cv2.circle(teshikli, (110, 110), 30, (245, 245, 245), -1)                    # ichida teshik
cv2.circle(teshikli, (110, 200), 25, (245, 245, 245), -1)                    # yana bitta
cv2.circle(teshikli, (300, 150), 80, (80, 80, 80), -1)
g = cv2.cvtColor(teshikli, cv2.COLOR_BGR2GRAY)
_, b = cv2.threshold(g, 200, 255, cv2.THRESH_BINARY_INV)
tashqi, _ = cv2.findContours(b, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
hamma, ier = cv2.findContours(b, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print(f"RETR_EXTERNAL: {len(tashqi)} kontur | RETR_TREE: {len(hamma)} kontur")
show(pd.DataFrame([{"kontur": i, "maydon": round(cv2.contourArea(c)),
                    "ota-kontur": int(ier[0][i][3]), "daraja": "tashqi" if ier[0][i][3] == -1 else "teshik"}
                   for i, c in enumerate(hamma)]))
ko_rsat(teshikli, b, nom="ierarxiya.png", sarlavhalar=["ikkita obyekt, ikkita teshik", "binar"],
        izoh="RETR_TREE teshiklarni ham topadi; ierarxiyaning 4-ustuni ota-konturni ko'rsatadi.")

shovqinli = cv2.imread(str(RASM / "shovqinli.png"), cv2.IMREAD_GRAYSCALE)
_, sb = cv2.threshold(shovqinli, 200, 255, cv2.THRESH_BINARY_INV)
xom, _ = cv2.findContours(sb, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
yadro = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
tozalangan = cv2.morphologyEx(cv2.morphologyEx(sb, cv2.MORPH_OPEN, yadro), cv2.MORPH_CLOSE, yadro)
morf, _ = cv2.findContours(tozalangan, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
show(pd.DataFrame([
    {"usul": "xom binar", "konturlar": len(xom)},
    {"usul": "xom + maydon > 500", "konturlar": sum(1 for c in xom if cv2.contourArea(c) > 500)},
    {"usul": "morfologiyadan keyin", "konturlar": len(morf)},
    {"usul": "morfologiya + maydon > 500", "konturlar": sum(1 for c in morf if cv2.contourArea(c) > 500)},
]))

hujjat = cv2.imread(str(RASM / "hujjat.jpg"))
h_gray = cv2.cvtColor(hujjat, cv2.COLOR_BGR2GRAY)
chekka = cv2.Canny(cv2.GaussianBlur(h_gray, (5, 5), 0), 50, 150)
chekka = cv2.morphologyEx(chekka, cv2.MORPH_CLOSE, np.ones((5, 5), np.uint8))
h_kont, _ = cv2.findContours(chekka, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
h_kont = sorted(h_kont, key=cv2.contourArea, reverse=True)[:5]

varaq = None
for c in h_kont:
    yaqin = cv2.approxPolyDP(c, 0.02 * cv2.arcLength(c, True), True)
    if len(yaqin) == 4:
        varaq = yaqin
        break

KUTILGAN = np.array([[150, 90], [720, 170], [650, 940], [90, 830]])
topilgan = varaq.reshape(4, 2)
tartib = sorted(topilgan.tolist(), key=lambda p: (p[1], p[0]))    # burchaklarni bir xil tartibga solamiz
tartib = sorted(tartib[:2], key=lambda p: p[0]) + sorted(tartib[2:], key=lambda p: -p[0])
xato = np.linalg.norm(np.array(tartib) - KUTILGAN, axis=1)
show(pd.DataFrame([{"burchak": n, "topilgan": f"({t[0]}, {t[1]})", "haqiqiy": f"({k[0]}, {k[1]})",
                    "xato, piksel": round(float(e), 1)}
                   for n, t, k, e in zip(["yuqori-chap", "yuqori-o'ng", "past-o'ng", "past-chap"],
                                         tartib, KUTILGAN.tolist(), xato)]))
belgi = hujjat.copy()
cv2.drawContours(belgi, [varaq], -1, (0, 255, 0), 4)
for p in tartib:
    cv2.circle(belgi, tuple(p), 10, (0, 0, 255), -1)
ko_rsat(chekka, belgi, nom="varaq_burchak.png", sarlavhalar=["Canny + MORPH_CLOSE", "topilgan to'rtburchak"],
        izoh="Eng katta 4 burchakli kontur — varaq. Burchaklar 6-bobda qo'lda yozgan koordinatalarimizga mos.")