Opencv 色彩追踪形态学处理|极客教程

本节介绍Opencv掩膜（色彩追踪（Color Tracking）+形态学处理）

在上节Opencv 掩膜文章中掩膜并不是十分精细，蝾螈的眼睛被去掉，背景也有些许残留。

因此，可以通过对掩膜图像应用N = 5闭运算和开运算，以使掩膜图像准确。

python实现：

import cv2
import numpy as np
import matplotlib.pyplot as plt

# BGR -> HSV
def BGR2HSV(_img):
    img = _img.copy() / 255.

    hsv = np.zeros_like(img, dtype=np.float32)

    # get max and min
    max_v = np.max(img, axis=2).copy()
    min_v = np.min(img, axis=2).copy()
    min_arg = np.argmin(img, axis=2)

    # H
    hsv[..., 0][np.where(max_v == min_v)]= 0
    ## if min == B
    ind = np.where(min_arg == 0)
    hsv[..., 0][ind] = 60 * (img[..., 1][ind] - img[..., 2][ind]) / (max_v[ind] - min_v[ind]) + 60
    ## if min == R
    ind = np.where(min_arg == 2)
    hsv[..., 0][ind] = 60 * (img[..., 0][ind] - img[..., 1][ind]) / (max_v[ind] - min_v[ind]) + 180
    ## if min == G
    ind = np.where(min_arg == 1)
    hsv[..., 0][ind] = 60 * (img[..., 2][ind] - img[..., 0][ind]) / (max_v[ind] - min_v[ind]) + 300

    # S
    hsv[..., 1] = max_v.copy() - min_v.copy()

    # V
    hsv[..., 2] = max_v.copy()

    return hsv

# make mask
def get_mask(hsv):
    mask = np.zeros_like(hsv[..., 0])
    #mask[np.where((hsv > 180) & (hsv[0] < 260))] = 255
    mask[np.logical_and((hsv[..., 0] > 180), (hsv[..., 0] < 260))] = 1
    return mask

# masking
def masking(img, mask):
    mask = 1 - mask
    out = img.copy()
    # mask [h, w] -> [h, w, channel]
    mask = np.tile(mask, [3, 1, 1]).transpose([1, 2, 0])
    out *= mask

    return out


# Erosion
def Erode(img, Erode_time=1):
    H, W = img.shape
    out = img.copy()

    # kernel
    MF = np.array(((0, 1, 0),
                (1, 0, 1),
                (0, 1, 0)), dtype=np.int)

    # each erode
    for i in range(Erode_time):
        tmp = np.pad(out, (1, 1), 'edge')
        # erode
        for y in range(1, H + 1):
            for x in range(1, W + 1):
                if np.sum(MF * tmp[y - 1 : y + 2 , x - 1 : x + 2]) < 1 * 4:
                    out[y - 1, x - 1] = 0

    return out


# Dilation
def Dilate(img, Dil_time=1):
    H, W = img.shape

    # kernel
    MF = np.array(((0, 1, 0),
                (1, 0, 1),
                (0, 1, 0)), dtype=np.int)

    # each dilate time
    out = img.copy()
    for i in range(Dil_time):
        tmp = np.pad(out, (1, 1), 'edge')
        for y in range(1, H + 1):
            for x in range(1, W + 1):
                if np.sum(MF * tmp[y - 1 : y + 2, x - 1 : x + 2]) >= 1:
                    out[y - 1, x - 1] = 1

    return out


# Opening morphology
def Morphology_Opening(img, time=1):
    out = Erode(img, Erode_time=time)
    out = Dilate(out, Dil_time=time)
    return out

# Closing morphology
def Morphology_Closing(img, time=1):
    out = Dilate(img, Dil_time=time)
    out = Erode(out, Erode_time=time)
    return out


# Read image
img = cv2.imread("imori.jpg").astype(np.float32)

# RGB > HSV
hsv = BGR2HSV(img / 255.)

# color tracking
mask = get_mask(hsv)

# closing
mask = Morphology_Closing(mask, time=5)

# opening
mask = Morphology_Opening(mask, time=5)

# masking
out = masking(img, mask)

out = out.astype(np.uint8)

# Save result
cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()

输入(imori.jpg)：

Opencv 掩膜