package mainimport ("errors""fmt""strconv""strings"
)type point struct {x inty intvalue int
}type image struct {w inth intallPoint intpoints [][]*point
}func (i *image) newImage(m, n int) *image {img := make([][]*point, m)for i1 := 0; i1 < m; i1++ {img[i1] = make([]*point, n)for j := 0; j < n; j++ {itemPoint := &point{x: i1,y: j,value: 0,}img[i1][j] = itemPoint}}return &image{w: m,h: n,allPoint: m * n,points: img,}
}func (i *image) getPointValue(x, y int) (int, error) {for i1 := 0; i1 < i.w; i1++ {for j := 0; j < i.h; j++ {if i1 == x && j == y {return i.points[i1][j].value, nil}}}return 0, errors.New("not found")
}func (i *image) setPointValue(p1 *point) (*image, error) {for i1 := 0; i1 < i.w; i1++ {for j := 0; j < i.h; j++ {if i1 == p1.x && j == p1.y {i.points[i1][j].value = p1.valuereturn i, nil}}}return nil, errors.New("not found")
}func (i *image) setPointsValue(list []*point, value int) {for _, p := range list {p.value = valuei.setPointValue(p)}
}func (i *image) countWaitePoints() (count int) {for i1 := 0; i1 < i.w; i1++ {for j := 0; j < i.h; j++ {if i.points[i1][j].value == 1 {count++}}}return
}func (i *image) isAllWaite() bool {if i.countWaitePoints() == i.allPoint {return true}return false
}// 获取一个点周围的非白色像素点
func (i *image) listAroundNotWaitePoints(p1 *point) []*point {list := make([]*point, 0)centerX, centreY := p1.x, p1.yfor x := -1; x <= 1; x++ {for y := -1; y <= 1; y++ {if x != 0 && y != 0 {continue}itemX := centerX + xitemY := centreY + y//溢出if itemX*itemY < 0 {continue}if value, err := i.getPointValue(itemX, itemY); err == nil {if value != 1 {list = append(list, &point{x: itemX,y: itemY,value: i.points[itemX][itemY].value,})}}}}return list
}func (i *image) listWaitePoints() []*point {list := make([]*point, 0)for i1 := 0; i1 < i.w; i1++ {for j := 0; j < i.h; j++ {if i.points[i1][j].value == 1 {list = append(list, &point{x: i1,y: j,value: i.points[i1][j].value,})}}}return list
}func (i *image) timer(count int) int {if i.isAllWaite() {return count} else {count++waitePoints := i.listWaitePoints()for _, waitePoint := range waitePoints {//开始膨胀if notWaitePoints := i.listAroundNotWaitePoints(waitePoint); len(notWaitePoints) > 0 {i.setPointsValue(notWaitePoints, 1)}}return i.timer(count)}}func main() {var inputStr stringfmt.Scan(&inputStr)inputStrList := strings.Split(inputStr, ",")inputIntList := make([]int, len(inputStrList))for i, s2 := range inputStrList {inputIntList[i], _ = strconv.Atoi(s2)}var m, n intm = inputIntList[0]n = inputIntList[1]myImage := new(image)img := myImage.newImage(m, n)img.setPointValue(&point{x: inputIntList[2],y: inputIntList[3],value: 1,})img.setPointValue(&point{x: inputIntList[4],y: inputIntList[5],value: 1,})count := img.timer(0)fmt.Println(count)}
总结：矩阵扩散在图形图像中被称为膨胀，结合openCV中对图片和像素点的定义，在程序中我定义了两种struct: image 和point，并实现了膨胀系数为1的膨胀算法。在通过递归的方式不断执行膨胀操作，知道全部点都变白，则停止递归，每次膨胀时计数器timer加一，递归结束后返回timer.
