Shpargalka: tez ma'lumotnoma

img = cv2.imread("fayl.jpg")                    # BGR, None qaytarishi mumkin!
img = cv2.imread("fayl.jpg", cv2.IMREAD_GRAYSCALE)
if img is None:
    raise SystemExit("fayl o'qilmadi")          # HAR DOIM tekshiring
cv2.imwrite("chiqish.png", img)
cv2.imwrite("chiqish.jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 92])

cv2.imshow("oyna", img); cv2.waitKey(0); cv2.destroyAllWindows()   # oynali muhitda
plt.imshow(img[:, :, ::-1])                     # Jupyter da: BGR -> RGB

h, w = img.shape[:2]                            # balandlik BIRINCHI
kanallar = img.shape[2] if img.ndim == 3 else 1
img.dtype                                       # uint8 (0-255)
img.size                                        # jami elementlar soni

bolak = img[y1:y2, x1:x2]                       # KO'RINISH (view), nusxa emas!
bolak = img[y1:y2, x1:x2].copy()                # haqiqiy nusxa
b, g, r = cv2.split(img)
img = cv2.merge([b, g, r])
yonma_yon = np.hstack([a, b])
ustma_ust = np.vstack([a, b])

kichik = cv2.resize(img, (640, 360))                      # (kenglik, balandlik)
kichik = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
# kichraytirish -> INTER_AREA | kattalashtirish -> INTER_CUBIC yoki INTER_LINEAR

M = cv2.getRotationMatrix2D((w / 2, h / 2), 30, 1.0)      # markaz, burchak, masshtab
burilgan = cv2.warpAffine(img, M, (w, h))
akslangan = cv2.flip(img, 1)                              # 1 gorizontal, 0 vertikal, -1 ikkalasi

P = cv2.getPerspectiveTransform(manba4, nishon4)          # np.float32, 4x2
togri = cv2.warpPerspective(img, P, (w, h))

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hsv  = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)               # H: 0-179, S: 0-255, V: 0-255
lab  = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
rgb  = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

maska = cv2.inRange(hsv, (18, 90, 90), (35, 255, 255))    # sariq
# QIZIL uchun IKKI oraliq: (0,90,70)-(8,255,255) va (170,90,70)-(179,255,255)

cv2.blur(img, (5, 5))                           # eng tez, eng qo'pol
cv2.GaussianBlur(img, (5, 5), 0)                # umumiy shovqin — standart tanlov
cv2.medianBlur(img, 5)                          # TUZ-QALAMPIR uchun eng yaxshi
cv2.bilateralFilter(img, 9, 75, 75)             # chekkani saqlaydi, SEKIN

_, b = cv2.threshold(gray, 140, 255, cv2.THRESH_BINARY)
ch, b = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)   # chegarani o'zi topadi
b = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                          cv2.THRESH_BINARY, 31, 10)     # blockSize TOQ

yadro = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
cv2.erode(b, yadro)                             # oq kichrayadi
cv2.dilate(b, yadro)                            # oq kattalashadi
cv2.morphologyEx(b, cv2.MORPH_OPEN, yadro)      # yakka nuqtalarni o'chiradi
cv2.morphologyEx(b, cv2.MORPH_CLOSE, yadro)     # teshiklarni to'ldiradi
cv2.morphologyEx(b, cv2.MORPH_GRADIENT, yadro)  # kontur chizig'i
cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, k31) # notekis fondagi qorong'i matn

sx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)  # CV_8U EMAS — manfiy qiymatlar yo'qoladi
lap = cv2.Laplacian(gray, cv2.CV_64F)
keskinlik = lap.var()                            # xira rasmni aniqlash

chekka = cv2.Canny(gray, 50, 150)                # yuqori ≈ 2-3 × past
med = np.median(gray)                            # avtomatik chegaralar
chekka = cv2.Canny(gray, int(0.67 * med), int(1.33 * med))

kont, ier = cv2.findContours(b, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, kont, -1, (0, 255, 0), 2)

cv2.contourArea(c)
cv2.arcLength(c, True)
M = cv2.moments(c); cx, cy = M["m10"] / M["m00"], M["m01"] / M["m00"]
yaqin = cv2.approxPolyDP(c, 0.02 * cv2.arcLength(c, True), True)   # burchaklar soni
x, y, w, h = cv2.boundingRect(c)
quti = cv2.boxPoints(cv2.minAreaRect(c))         # burilgan to'rtburchak
(mx, my), r = cv2.minEnclosingCircle(c)
qobiq = cv2.convexHull(c)
(mx, my), (a, b_), burchak = cv2.fitEllipse(c)   # kamida 5 nuqta kerak

gist = cv2.calcHist([gray], [0], None, [256], [0, 256])   # hammasi ro'yxat ichida
teng = cv2.equalizeHist(gray)                              # global
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
c = clahe.apply(gray)                                      # mahalliy — odatda yaxshiroq

# RANGLI rasmda: BGR kanallariga ALOHIDA qilmang!
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
L, A, B = cv2.split(lab)
natija = cv2.cvtColor(cv2.merge([clahe.apply(L), A, B]), cv2.COLOR_LAB2BGR)

r = cv2.matchTemplate(gray, shablon, cv2.TM_CCOEFF_NORMED)
mn, mx, mn_joy, mx_joy = cv2.minMaxLoc(r)
joy = mn_joy if usul in (cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED) else mx_joy

orb = cv2.ORB_create(nfeatures=1000)
n, d = orb.detectAndCompute(gray, None)

bf = cv2.BFMatcher(cv2.NORM_HAMMING)             # SIFT uchun cv2.NORM_L2
juftlar = bf.knnMatch(d1, d2, k=2)
yaxshi = [a for a, b in juftlar if a.distance < 0.75 * b.distance]   # Lowe ratio

src = np.float32([n1[m.queryIdx].pt for m in yaxshi]).reshape(-1, 1, 2)
dst = np.float32([n2[m.trainIdx].pt for m in yaxshi]).reshape(-1, 1, 2)
H, maska = cv2.findHomography(src, dst, cv2.RANSAC, 5.0)
ichkilar = int(maska.sum())                      # 10 dan kam bo'lsa ishonmang

cap = cv2.VideoCapture("video.mp4")              # yoki 0 — kamera
print(cap.get(cv2.CAP_PROP_FPS), cap.get(cv2.CAP_PROP_FRAME_COUNT))
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)           # kamera uchun — dekodlashni tejaydi
while True:
    ok, kadr = cap.read()
    if not ok:                                   # HAR DOIM tekshiring
        break
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break
cap.release()

yoz = cv2.VideoWriter("chiq.mp4", cv2.VideoWriter_fourcc(*"mp4v"), 30.0, (640, 360))
assert yoz.isOpened()                            # jim ravishda ishlamay qo'yadi!
yoz.write(kadr)                                  # o'lcham VA 3 kanal aniq mos kelsin
yoz.release()

fon = cv2.createBackgroundSubtractorMOG2(history=200, varThreshold=25, detectShadows=False)
maska = fon.apply(kadr)                          # soya = 127, kerak bo'lmasa detectShadows=False

oqim = cv2.calcOpticalFlowFarneback(g1, g2, None, 0.5, 3, 15, 3, 5, 1.2, 0)
kuch, burchak = cv2.cartToPolar(oqim[..., 0], oqim[..., 1])

tr = cv2.TrackerMIL_create()                     # CSRT/KCF: opencv-contrib-python kerak
tr.init(kadr, (x, y, w, h))
ok, quti = tr.update(kadr)

haar = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
yuzlar = haar.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(20, 20))

yunet = cv2.FaceDetectorYN.create("yunet.onnx", "", (320, 320), 0.7, 0.3, 5000)
yunet.setInputSize((w, h))                       # HAR rasm o'lchami uchun qayta chaqiring
_, F = yunet.detect(img)                         # har qator: quti(4) + 5 nuqta(10) + ishonch(1)

cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)     # -1 = to'ldirish
cv2.circle(img, (cx, cy), r, (0, 0, 255), -1)
cv2.putText(img, "matn", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 2, cv2.LINE_AA)
cv2.polylines(img, [nuqtalar], True, (255, 0, 0), 2)