339 lines
12 KiB
Python
339 lines
12 KiB
Python
#coding='utf-8'
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import cv2
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import numpy as np
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import random
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import math
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def draw_contours(imgColor,minArea=50):
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if len(imgColor.shape)>2:
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imgResult=cv2.cvtColor(imgColor,cv2.COLOR_BGR2GRAY)
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else:
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imgResult = imgColor.copy()
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ret, img_binary = cv2.threshold(imgResult, 125, 255, 0)
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contours0, hierarchy = cv2.findContours(img_binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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contours=list([])
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for cnt in contours0:
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rect = cv2.minAreaRect(cnt)
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if rect[1][0] * rect[1][1] < minArea: # 此矩形区域的面积小于10，则忽略
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continue
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contours.append(cnt)#contours=contours.append(cnt)会返回NoneType错误
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cv2.drawContours(imgResult, contours, -1, (0, 0, 255), 3)
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return imgResult
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def getDis(pointX, pointY, lineX1, lineY1, lineX2, lineY2):
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'''返回点线之间的距离
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'''
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a = lineY2 - lineY1
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b = lineX1 - lineX2
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c = lineX2 * lineY1 - lineX1 * lineY2
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dis = (math.fabs(a * pointX + b * pointY + c)) / (math.pow(a * a + b * b, 0.5))
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return dis
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def get_region_description(imgColor,minArea=50):
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if len(imgColor.shape)>2:
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imgResult=cv2.cvtColor(imgColor,cv2.COLOR_BGR2GRAY)
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else:
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imgResult = imgColor.copy()
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_, imgBinary = cv2.threshold(imgResult, 125, 255, 0)
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row,col=imgBinary.shape[:2]
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contours0, hierarchy = cv2.findContours(imgBinary, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
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# 去除小面积区域
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contours_=[]
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for cnt in contours0:
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rect = cv2.minAreaRect(cnt)
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if rect[1][0] * rect[1][1] < minArea: # 此矩形区域的面积小于10，则忽略
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continue
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contours_.append(cnt)#contours=contours.append(cnt)会返回NoneType错误
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cv2.drawContours(imgResult, contours_, -1, (0, 0, 255), 3)
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contours=[] #存放区域描述子，包括重心、周长、面积、圆形度、矩形度
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# 进行区域描述
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for cnt in contours_:
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oneContour = [] # 存放一个区域的描述子
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# minAreaRect返回值：（最小外接矩形的中心（x，y），（宽度，高度），旋转角度）
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#角度是【-90，0】，0度会写成-90度，方向不准确
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rect = cv2.minAreaRect(cnt)
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# maxRegion=min(rect[1][0],rect[1][1])/2
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# maxV=maxRegion*maxRegion #区域最大半径平方值
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points=cv2.boxPoints(rect)#可以返回四个点的值
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#获得重心
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cx,cy=int(rect[0][0]),int(rect[0][1])
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oneContour.append((cx,cy)) #添加重心
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# #获得区域方向，并绘制方向
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'''minV = math.inf #区域上的点到直线距离和的最小值
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min_i = 0
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min_j = 0
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for i in range(row):
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for j in range(col):
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sum = 0
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# 获得一个区域最大半径圆上的点，做点到质心的线
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if (i - cx) * (i - cx) + (j - cy) * (j - cy) == maxV: # 一个圆上的点
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for c0 in cnt[:][:]: # cnt中存放中区域轮廓上的点坐标
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sum += getDis(c0[0][0], c0[0][1], cx, cy, i, j)
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if sum < minV:
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minV = sum
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min_i = i
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min_j = j
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cv2.line(imgResult, (cx, cy), (min_i, min_j), (0, 0, 0), 3, 8)
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regionDirect = math.atan2(min_j-cy,min_i-cx)
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# regionDirect = rect[2]
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oneContour.append(regionDirect)'''
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minV = math.inf
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findPoint=(0,0)
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rectPoints=[]#获得区域最上边、下边、左边、右边线上的点,做点到中心的线
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minRow=int(min(points[i][0] for i in range(4))) #最小行坐标
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maxRow=int(max(points[i][0] for i in range(4)))
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minCol=int(min(points[i][1] for i in range(4)))
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maxCol=int(max(points[i][1] for i in range(4)))
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highPointNum=int(maxRow-minRow+1)
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widthPointNum=int(maxCol-minCol+1)
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fixI1=zip([minRow]*widthPointNum,range(minCol,maxCol+1,1))
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rectPoints.extend(fixI1)
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fixI2=zip([maxRow]*widthPointNum,range(minCol,maxCol+1,1))
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rectPoints.extend(fixI2)
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fixJ1=zip(range(minRow,maxRow+1,1),[minCol]*highPointNum)
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rectPoints.extend(fixJ1)
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fixJ2=zip(range(minRow,maxRow+1,1),[maxCol]*highPointNum)
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rectPoints.extend(fixJ2)
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for point in rectPoints:
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sum = 0
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for c0 in cnt[:][:]: # cnt中存放中区域轮廓上的点坐标
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sum += getDis(c0[0][0], c0[0][1], cx, cy, point[0],point[1])
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if sum < minV:
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minV = sum
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findPoint=point
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cv2.line(imgResult, (cx, cy), findPoint, (0, 0, 0), 3, 8)
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regionDirect =math.atan2(cy-findPoint[1],cx-findPoint[0])*180/3.14
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oneContour.append(regionDirect) # 添加重心
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# 计算轮廓面积和周长
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area = cv2.contourArea(cnt)
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perimeter = cv2.arcLength(cnt,False)
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circle = 4.0*np.pi*area/(perimeter*perimeter) # 计算圆形度
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rectangle = area/(rect[1][0]*rect[1][1]) # 计算矩形度
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oneContour.append(area)
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oneContour.append(perimeter)
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oneContour.append(circle)
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oneContour.append(rectangle)
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contours.append(oneContour)
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return imgResult,contours
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# Hu Moments 胡不变矩 wiki： https://en.wikipedia.org/wiki/Image_moment
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# 公式 https://docs.opencv.org/4.1.2/d3/dc0/group__imgproc__shape.html#gab001db45c1f1af6cbdbe64df04c4e944
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# 代码 https://github.com/opencv/opencv/blob/b6a58818bb6b30a1f9d982b3f3f53228ea5a13c1/modules/imgproc/src/moments.cpp
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# void cv::HuMoments( const Moments& m, double hu[7] )
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# {
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# CV_INSTRUMENT_REGION();
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#
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# double t0 = m.nu30 + m.nu12;
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# double t1 = m.nu21 + m.nu03;
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#
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# double q0 = t0 * t0, q1 = t1 * t1;
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#
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# double n4 = 4 * m.nu11;
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# double s = m.nu20 + m.nu02;
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# double d = m.nu20 - m.nu02;
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#
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# hu[0] = s;
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# hu[1] = d * d + n4 * m.nu11;
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# hu[3] = q0 + q1;
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# hu[5] = d * (q0 - q1) + n4 * t0 * t1;
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#
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# t0 *= q0 - 3 * q1;
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# t1 *= 3 * q0 - q1;
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#
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# q0 = m.nu30 - 3 * m.nu12;
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# q1 = 3 * m.nu21 - m.nu03;
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#
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# hu[2] = q0 * q0 + q1 * q1;
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# hu[4] = q0 * t0 + q1 * t1;
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# hu[6] = q1 * t0 - q0 * t1;
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# }
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#求图像的24个矩
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def get_moments(img,moments=None):
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# row == heigh == Point.y
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# col == width == Point.x
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# Mat::at(Point(x, y)) == Mat::at(y,x)
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# https://blog.csdn.net/puqian13/article/details/87937483
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mom = [0] * 10 #10个原点矩
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umom = [0] * 7 #7个中心矩
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numom = [0] * 7 #7个归一化中心矩
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rows, cols = img.shape
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for y in range(rows):
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# temp var
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x0 = 0
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x1 = 0
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x2 = 0
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x3 = 0
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for x in range(cols):
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p = img[y,x]
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xp = x * p
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xxp = x * xp
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x0 = x0 + p
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x1 = x1 + xp
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x2 = x2 + xxp
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x3 = x3 + xxp * x
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py = y * x0
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sy = y*y
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mom[9] += (py) * sy # m03
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mom[8] += (x1) * sy # m12
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mom[7] += (x2) * y # m21
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mom[6] += x3 # m30
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mom[5] += x0 * sy # m02
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mom[4] += x1 * y # m11
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mom[3] += x2 # m20
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mom[2] += py # m01
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mom[1] += x1 # m10
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mom[0] += x0 # m00
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x_a = mom[1] / mom[0]
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y_a = mom[2] / mom[0]
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for y in range(0,rows):
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x_a_0 = 0
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x_a_1 = 0
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x_a_2 = 0
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x_a_3 = 0
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for x in range(0,cols):
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p = img[y,x]
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x_a_0 = x_a_0 + p
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x_a_1 += p * (x - x_a )
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x_a_2 += x_a_1 * (x - x_a )
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x_a_3 += x_a_2 * (x - x_a )
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y_a_1 = (y - y_a)
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y_a_2 = y_a_1 * (y - y_a)
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y_a_3 = y_a_2 * (y - y_a)
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umom[0] += x_a_2
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umom[1] += x_a_1 * y_a_1
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umom[2] += x_a_0 * y_a_2
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umom[3] += x_a_3
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umom[4] += x_a_2 * y_a_1
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umom[5] += x_a_1 * y_a_2
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umom[6] += x_a_0 * y_a_3
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cx = mom[1] * 1.0 / mom[0]
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cy = mom[2] * 1.0 / mom[0]
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umom[0] = mom[3] - mom[1] * cx
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umom[1] = mom[4] - mom[1] * cy
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umom[2] = mom[5] - mom[2] * cy
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umom[3] = mom[6] - cx * (3 * umom[0] + cx * mom[1])
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umom[4] = mom[7] - cx * (2 * umom[1] + cx * mom[2]) - cy * umom[0]
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umom[5] = mom[8] - cy * (2 * umom[1] + cy * mom[1]) - cx * umom[2]
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umom[6] = mom[9] - cy * (3 * umom[2] + cy * mom[2])
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# nu
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inv_sqrt_m00 = np.sqrt(abs(1.0 / mom[0]))
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s2 = (1.0 / mom[0]) * (1.0 / mom[0])
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s3 = s2 * inv_sqrt_m00
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numom[0] = umom[0] * s2
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numom[1] = umom[1] * s2
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numom[2] = umom[2] * s2
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numom[3] = umom[3] * s3
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numom[4] = umom[4] * s3
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numom[5] = umom[5] * s3
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numom[6] = umom[6] * s3
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moments = mom + umom + numom
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return moments
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def get_hu_moments(numom):
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'''
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:param numom: 图像的原点矩
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:return: 返回7个不变矩
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'''
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hu = [0]*7
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t0 = numom[3] + numom[5]
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t1 = numom[4] + numom[6]
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q0 = t0 * t0
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q1 = t1 * t1
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n4 = 4 * numom[1];
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s = numom[0] + numom[2];
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d = numom[0] - numom[2];
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hu[0] = s
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hu[1] = d * d + n4 * numom[1]
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hu[3] = q0 + q1
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hu[5] = d * (q0 - q1) + n4 * t0 * t1
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t0 *= q0 - 3 * q1
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t1 *= 3 * q0 - q1
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q0 = numom[3] - 3 * numom[5]
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q1 = 3 * numom[4] - numom[6] ;
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hu[2] = q0 * q0 + q1 * q1
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hu[4] = q0 * t0 + q1 * t1
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hu[6] = q1 * t0 - q0 * t1
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return hu
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def moment_invariants(img):
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textLists=[] #用来存放几何变换名称
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imgLists=[] #用来存放几何变换图像
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# 旋转45度
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rotate_45_matrix = cv2.getRotationMatrix2D((img.shape[1] // 2, img.shape[0] // 2), -45, 1)
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rotate_45_image = cv2.warpAffine(img, rotate_45_matrix, dsize=(img.shape[1], img.shape[0]))
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textLists.append("rotate-45")
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imgLists.append(rotate_45_image)
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# 平移
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M = np.float32([[1, 0, 50], [0, 1, 50]])
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translation_img = cv2.warpAffine(img, M, dsize=(img.shape[1], img.shape[0]))
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textLists.append("translation")
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imgLists.append(translation_img)
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# 顺时针旋转90
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rotate_90_iamge = cv2.rotate(img, rotateCode=cv2.ROTATE_90_CLOCKWISE)
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textLists.append("rotate-90")
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imgLists.append(rotate_90_iamge)
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# 顺时针旋转180
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rotate_180_iamge = cv2.rotate(img, cv2.ROTATE_180)
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textLists.append("rotate-180")
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imgLists.append(rotate_180_iamge)
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# 缩小一半
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resize_0_5_img=np.zeros(img.shape)
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halfImg = cv2.resize(img, (img.shape[1] // 2, img.shape[0] // 2))
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textLists.append("shrink ")
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imgLists.append(halfImg)
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# 逆时针旋转90
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rotate_270_image = cv2.rotate(img, rotateCode=cv2.ROTATE_90_COUNTERCLOCKWISE)
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textLists.append("rotate-270")
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imgLists.append(rotate_270_image)
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# calc the moments of the image
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'''get 24 moments
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10 spatial moments 空间矩、原点矩
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m00 m10 m01 m20 m11 m02 m30 m21 m12 m03
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7 central moments 七个中心矩
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mu20 mu11 mu02 mu30 mu21 mu12 mu03
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7 central normalized moments个归一化中心矩
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nu20 nu11 nu02 nu30 nu21 nu12 nu03
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本来应该都是10个的 but
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mu00 = m00, nu00 = 1 nu10 = mu10 = mu01 = mu10 = 0, hence the values are not stored.
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原理 见论文
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论文 https://pdfs.semanticscholar.org/afc2/e9d5dfbd666bf4dd34adeb78a17393c8ee64.pdf?_ga=2.259665167.462545856.1577780532-1866022657.1577780532
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refrence https://docs.opencv.org/4.1.2/d8/d23/classcv_1_1Moments.html#a8b1b4917d1123abc3a3c16b007a7319b
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https://github.com/opencv/opencv/blob/b6a58818bb6b30a1f9d982b3f3f53228ea5a13c1/modules/imgproc/src/moments.cpp'''
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huMomentLists=[]
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for img in imgLists:
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m_=cv2.moments(img)
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hu = cv2.HuMoments(m_)
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huMomentLists.append(np.array(hu))
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# normal hu matrix
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huMomentLists = np.abs(huMomentLists)
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huMomentLists = np.log(huMomentLists)
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huMomentLists = np.abs(huMomentLists)
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return imgLists,textLists,huMomentLists
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if __name__=='__main__':
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img=cv2.imread(r'.\img\describe.png')
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# img=draw_contours(img)
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# cv2.imshow("contours",img)
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imgReturn,contours=get_region_description(img)
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i=0
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for cnt in contours:
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i+=1
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print("第%d个区域信息："%i)
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print("质心：",cnt[0])
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print("方向：%f度"%cnt[1])
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print("面积：", cnt[2])
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print("周长：", cnt[3])
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print("圆形度：", cnt[4])
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print("矩形度：", cnt[5])
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cv2.imshow("result",imgReturn)
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cv2.waitKey(0) |