在不可预测的背景下检测到叶子

Question

我从事了一段时间的项目是无监督的叶分割。叶子被捕获在白色或彩色纸上，其中一些带有阴影。

我希望能够对叶子进行阈值处理并去除阴影（同时保留叶子的详细信息）；但是由于疾病改变了叶子的颜色，我无法使用固定的阈值。

然后，我开始研究并找出Horprasert等人的建议。等（1999年）在“一种用​​于实时鲁棒背景扣除和阴影检测的统计方法”中，该方法使用色度失真度量将图像中的区域与已知背景的颜色进行比较。此措施考虑到以下事实：对于不饱和的颜色，色相不是一个相关的措施。

基于此，我能够实现以下结果：

但是，在白纸上捕获的叶子需要更改Mask V cv2.bitwise_not（）才能得到以下结果：

我在想，我忘记了获得完整遮罩的步骤，该遮罩适用于我的全部或大部分叶子。可以找到示例here。

我的代码：

import numpy as np
import cv2
import matplotlib.pyplot as plot
import scipy.ndimage as ndimage

def brightness_distortion(I, mu, sigma):
    return np.sum(I*mu/sigma**2, axis=-1) / np.sum((mu/sigma)**2, axis=-1)


def chromacity_distortion(I, mu, sigma):
    alpha = brightness_distortion(I, mu, sigma)[...,None]
    return np.sqrt(np.sum(((I - alpha * mu)/sigma)**2, axis=-1))

def bwareafilt ( image ):
    image = image.astype(np.uint8)
    nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)
    sizes = stats[:, -1]

    max_label = 1
    max_size = sizes[1]
    for i in range(2, nb_components):
        if sizes[i] > max_size:
            max_label = i
            max_size = sizes[i]

    img2 = np.zeros(output.shape)
    img2[output == max_label] = 255

    return img2

img = cv2.imread("Amostra03.jpeg")
sat = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)[:,:,1]
val = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)[:,:,2]
sat = cv2.medianBlur(sat, 11)
val = cv2.medianBlur(val, 11)
thresh_S = cv2.adaptiveThreshold(sat , 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 401, 10);
thresh_V = cv2.adaptiveThreshold(val , 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 401, 10);

mean_S, stdev_S = cv2.meanStdDev(img, mask = 255 - thresh_S)
mean_S = mean_S.ravel().flatten()
stdev_S = stdev_S.ravel()
chrom_S = chromacity_distortion(img, mean_S, stdev_S)
chrom255_S = cv2.normalize(chrom_S, chrom_S, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX).astype(np.uint8)[:,:,None]

mean_V, stdev_V = cv2.meanStdDev(img, mask = 255 - thresh_V)
mean_V = mean_V.ravel().flatten()
stdev_V = stdev_V.ravel()
chrom_V = chromacity_distortion(img, mean_V, stdev_V)
chrom255_V = cv2.normalize(chrom_V, chrom_V, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX).astype(np.uint8)[:,:,None]

thresh2_S = cv2.adaptiveThreshold(chrom255_S , 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 401, 10);
thresh2_V = cv2.adaptiveThreshold(chrom255_V , 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 401, 10);

images = [img, thresh_S, thresh_V, cv2.bitwise_and(thresh2_S, cv2.bitwise_not(thresh2_V))]
titles = ['Original Image', 'Mask S', 'Mask V', 'S + V']
for i in range(4):
    plot.subplot(2,2,i+1),
    if i == 0 :
        plot.imshow(images[i])
    else :
        plot.imshow(images[i], cmap='gray')
    plot.title(titles[i])
    plot.xticks([]),plot.yticks([])
plot.show()

有解决这个问题的主意吗？

Answer 1

请尝试...我正在使用openCV库中的“ grabCut”。这并不完美，但这可能是一个好的开始。

tarfile.open(tgz_path)

寻址外部掩码。这是一个HDBSCAN群集的示例...我将不赘述...您可以查找文档并进行更改或按原样使用。

import cv2
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
#%matplotlib inline #uncomment if in notebook

def mask_leaf(im_name, external_mask=None):

    im = cv2.imread(im_name)
    im = cv2.blur(im, (5,5))

    height, width = im.shape[:2]

    mask = np.ones(im.shape[:2], dtype=np.uint8) * 2 #start all possible background
    '''
    #from docs:
    0 GC_BGD defines an obvious background pixels.
    1 GC_FGD defines an obvious foreground (object) pixel.
    2 GC_PR_BGD defines a possible background pixel.
    3 GC_PR_FGD defines a possible foreground pixel.
    '''

    #2 circles are "drawn" on mask. a smaller centered one I assume all pixels are definite foreground. a bigger circle, probably foreground.
    r = 100
    cv2.circle(mask, (int(width/2.), int(height/2.)), 2*r, 3, -3) #possible fg
    #next 2 are greens...dark and bright to increase the number of fg pixels.
    mask[(im[:,:,0] < 45) & (im[:,:,1] > 55) & (im[:,:,2] < 55)] = 1  #dark green
    mask[(im[:,:,0] < 190) & (im[:,:,1] > 190) & (im[:,:,2] < 200)] = 1  #bright green
    mask[(im[:,:,0] > 200) & (im[:,:,1] > 200) & (im[:,:,2] > 200) & (mask != 1)] = 0 #pretty white

    cv2.circle(mask, (int(width/2.), int(height/2.)), r, 1, -3) #fg

    #if you pass in an external mask derived from some other operation it is factored in here.
    if external_mask is not None:
        mask[external_mask == 1] = 1

    bgdmodel = np.zeros((1,65), np.float64)
    fgdmodel = np.zeros((1,65), np.float64)
    cv2.grabCut(im, mask, None, bgdmodel, fgdmodel, 1, cv2.GC_INIT_WITH_MASK)

    #show mask
    plt.figure(figsize=(10,10))
    plt.imshow(mask)
    plt.show()

    #mask image
    mask2 = np.where((mask==1) + (mask==3), 255, 0).astype('uint8')
    output = cv2.bitwise_and(im, im, mask=mask2)
    plt.figure(figsize=(10,10))
    plt.imshow(output)
    plt.show()

mask_leaf('leaf1.jpg', external_mask=None)
mask_leaf('leaf2.jpg', external_mask=None)

然后将初始函数与新掩码一起使用（禁止输出）：

import hdbscan
from collections import Counter


def hdbscan_mask(im_name):

    im = cv2.imread(im_name)
    im = cv2.blur(im, (5,5))

    indices = np.dstack(np.indices(im.shape[:2]))
    data = np.concatenate((indices, im), axis=-1)
    data = data[:,2:]

    data = imb.reshape(im.shape[0]*im.shape[1], 3)
    clusterer = hdbscan.HDBSCAN(min_cluster_size=1000, min_samples=20)
    clusterer.fit(data)

    plt.figure(figsize=(10,10))
    plt.imshow(clusterer.labels_.reshape(im.shape[0:2]))
    plt.show()

    height, width = im.shape[:2]

    mask = np.ones(im.shape[:2], dtype=np.uint8) * 2 #start all possible background
    cv2.circle(mask, (int(width/2.), int(height/2.)), 100, 1, -3) #possible fg

    #grab cluster number for circle
    vals_im = clusterer.labels_.reshape(im.shape[0:2])

    vals = vals_im[mask == 1]
    commonvals = []
    cnts = Counter(vals)
    for v, count in cnts.most_common(20):
    #print '%i: %7d' % (v, count)
    if v == -1:
        continue
    commonvals.append(v)

    tst = np.in1d(vals_im, np.array(commonvals))
    tst = tst.reshape(vals_im.shape)

    hmask = tst.astype(np.uint8)

    plt.figure(figsize=(10,10))
    plt.imshow(hmask)
    plt.show()

    return hmask

hmask = hdbscan_mask('leaf1.jpg')

这些都是从头开始在笔记本中完成的，因此希望没有错误的变量在其他地方运行时会阻塞它。 （注意：抱歉，我没有将BGR交换为RGB进行ptt显示）