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方针
在本章中将学习
- 直方图反投影
理论
这是由Michael J. Swain和Dana H. Ballard在他们的论文《 Indexing via color histograms》中提出的。
用简单的话来说，它用于图画切割或在图画中查找感兴趣的方针。简而言之，它创立的图画巨细与输入图画相同（但只有一个通道），其间每个像素对应于该像素归于物体的概率。用更简单的话来说，与其余部分比较，输出图画将在或许有方针的区域具有更多的白色值。直方图反投影与camshift算法等配合运用。
进程如下：
- 首要创立一个图画的直方图，其间包含感兴趣的方针。方针应尽或许填充图画以取得更好的效果。而且色彩直方图比灰度直方图更可取，由于对象的色彩对比灰度强度是定义方针的好办法
- 然后，将该直方图“反投影”到需求找到方针的测验图画上，换句话说，核算出归于背景的每个像素的概率并将其显示出来
- 在适当的阈值下发生的输出成果仅仅是取得背景
从这个进程能够看出，先求出原图画的直方图，再由直方图得到反向投影矩阵，由直方图到反向投影矩阵实际上便是一个反向的进程，所以叫反向。
Numpy中的算法
- 首要，需求核算要查找的方针（使其为
M）和要查找的图画（使其为I）的色彩直方图。
import cv2
import numpy as np
from matplotlib import pyplot as plt
# roi is the object or region of object we need to find
roi = cv2.imread('rose_red.png')
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
# target is the image we search in
target = cv2.imread('rose.png')
hsvt = cv2.cvtColor(target, cv2.COLOR_BGR2HSV)
# Find the histograms using calcHist. Can be done with np.histogram2d also
M = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] )
I = cv2.calcHist([hsvt],[0, 1], None, [180, 256], [0, 180, 0, 256] )
- 然后找到比例，R=MIR=\frac{M} {I}
即 运用R作为调色板，并运用每个像素创立新图画作为其方针的相应概率。
B(x,y) = R[h(x,y),s(x,y)], 其间h和s表明像素点(x,y)处的色调(h)，饱和度(s)。
h, s, v = cv2.split(hsvt)
B = R[h.ravel(), s.ravel()]
B = np.minimum(B,1)
B = B.reshape(hsvt.shape[:2])
- 现在用圆形核应用卷积， B = D ∗ B， 其间D是圆形卷积核
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
cv2.filter2D(B,-1, disc, B)
B = np.uint8(B)
cv2.normalize(B,B,0,255, cv2.NORM_MINMAX)
- 现在最大强度的方位给了物体的方位。如果期望图画中有一个区域，则对合适的值进行阈值处理将取得不错的成果。
ret, thresh = cv2.threshold(B,50,255,0)
OpenCV的反投影
OpenCV供给了一个内建的函数cv2.calcBackProject()。它的参数简直与cv2.calchist()函数相同。
dst = cv.calcBackProject( images, channels, hist, ranges, scale[, dst] )
- images Source arrays. They all should have the same depth, CV_8U, CV_16U or CV_32F , and the same size. Each of them can have an arbitrary number of channels.
- nimages Number of source images.
- channels The list of channels used to compute the back projection. The number of channels must match the histogram dimensionality. The first array channels are numerated from 0 to images[0].channels()-1 , the second array channels are counted from images[0].channels() to images[0].channels() + images[1].channels()-1, and so on.
- hist Input histogram that can be dense or sparse.
- backProject Destination back projection array that is a single-channel array of the same size and depth as images[0] .
- ranges Array of arrays of the histogram bin boundaries in each dimension. See calcHist .
- scale Optional scale factor for the output back projection.
- uniform Flag indicating whether the histogram is uniform or not (see above).
在传递给backproject函数之前，应该对方针直方图进行归一化。它返回概率图画。然后咱们用圆盘内核对图画进行卷积并应用阈值。下面是我的代码和成果:
import numpy as np
import cv2
from matplotlib import pyplot as plt
roi = cv2.imread('roi.png') # 感兴趣的图片 绿色草地
hsv = cv2.cvtColor(roi, cv2 .COLOR_BGR2HSV)
target = cv2.imread('messi.png') # 方针图片
hsvt = cv2.cvtColor(target, cv2.COLOR_BGR2HSV)
# calculating object histogram
roihist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256] )
# normalize histogram and apply backprojection
cv2.normalize(roihist,roihist,0,255,cv2.NORM_MINMAX)
dst = cv2.calcBackProject([hsvt], [0, 1], roihist, [0,180,0,256],1)
# Now convolute with circular disc
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
cv2.filter2D(dst,-1,disc,dst)
# threshold and binary AND
ret,thresh = cv2.threshold(dst, 50, 255, 0)
thresh = cv2.merge((thresh,thresh,thresh))
res = cv2.bitwise_and(target,thresh)
cv2.imshow('res', res)
cv2.waitKey()
cv2.destroyAllWindows()
以下是处理过的一个示例。将蓝色矩形内的区域用作示例方针，提取整个地上:
附加资源
- docs.opencv.org/4.1.2/dc/df…
- Indexing via color histograms”, Swain, Michael J. , Third international conference on computer vision,1990.
- docs.opencv.org/4.1.2/d6/dc…
- blog.csdn.net/shuiyixin/a…
- blog.csdn.net/jia20003/ar…
