寻找给定点的最小面积矩形？

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MBR <- function(p) {
  # Analyze the convex hull edges     
  a <- chull(p)                                   # Indexes of extremal points
  a <- c(a, a[1])                                 # Close the loop
  e <- p[a[-1],] - p[a[-length(a)], ]             # Edge directions
  norms <- sqrt(rowSums(e^2))                     # Edge lengths
  v <- e / norms                                  # Unit edge directions
  w <- cbind(-v[,2], v[,1])                       # Normal directions to the edges

  # Find the MBR
  vertices <- p[a, ]                              # Convex hull vertices
  x <- apply(vertices %*% t(v), 2, range)         # Extremes along edges
  y <- apply(vertices %*% t(w), 2, range)         # Extremes normal to edges
  areas <- (y[1,]-y[2,])*(x[1,]-x[2,])            # Areas
  k <- which.min(areas)                           # Index of the best edge (smallest area)

  # Form a rectangle from the extremes of the best edge
  cbind(x[c(1,2,2,1,1),k], y[c(1,1,2,2,1),k]) %*% rbind(v[k,], w[k,])
}

# Create sample data
set.seed(23)
p <- matrix(rnorm(20*2), ncol=2)                 # Random (normally distributed) points
mbr <- MBR(p)

# Plot the hull, the MBR, and the points
limits <- apply(mbr, 2, range) # Plotting limits
plot(p[(function(x) c(x, x[1]))(chull(p)), ], 
     type="l", asp=1, bty="n", xaxt="n", yaxt="n",
     col="Gray", pch=20, 
     xlab="", ylab="",
     xlim=limits[,1], ylim=limits[,2])                # The hull
lines(mbr, col="Blue", lwd=3)                         # The MBR
points(p, pch=19)                                     # The points

#3 楼

import numpy as np
from scipy.spatial import ConvexHull

def minimum_bounding_rectangle(points):
    """
    Find the smallest bounding rectangle for a set of points.
    Returns a set of points representing the corners of the bounding box.

    :param points: an nx2 matrix of coordinates
    :rval: an nx2 matrix of coordinates
    """
    from scipy.ndimage.interpolation import rotate
    pi2 = np.pi/2.

    # get the convex hull for the points
    hull_points = points[ConvexHull(points).vertices]

    # calculate edge angles
    edges = np.zeros((len(hull_points)-1, 2))
    edges = hull_points[1:] - hull_points[:-1]

    angles = np.zeros((len(edges)))
    angles = np.arctan2(edges[:, 1], edges[:, 0])

    angles = np.abs(np.mod(angles, pi2))
    angles = np.unique(angles)

    # find rotation matrices
    # XXX both work
    rotations = np.vstack([
        np.cos(angles),
        np.cos(angles-pi2),
        np.cos(angles+pi2),
        np.cos(angles)]).T
#     rotations = np.vstack([
#         np.cos(angles),
#         -np.sin(angles),
#         np.sin(angles),
#         np.cos(angles)]).T
    rotations = rotations.reshape((-1, 2, 2))

    # apply rotations to the hull
    rot_points = np.dot(rotations, hull_points.T)

    # find the bounding points
    min_x = np.nanmin(rot_points[:, 0], axis=1)
    max_x = np.nanmax(rot_points[:, 0], axis=1)
    min_y = np.nanmin(rot_points[:, 1], axis=1)
    max_y = np.nanmax(rot_points[:, 1], axis=1)

    # find the box with the best area
    areas = (max_x - min_x) * (max_y - min_y)
    best_idx = np.argmin(areas)

    # return the best box
    x1 = max_x[best_idx]
    x2 = min_x[best_idx]
    y1 = max_y[best_idx]
    y2 = min_y[best_idx]
    r = rotations[best_idx]

    rval = np.zeros((4, 2))
    rval[0] = np.dot([x1, y2], r)
    rval[1] = np.dot([x2, y2], r)
    rval[2] = np.dot([x2, y1], r)
    rval[3] = np.dot([x1, y1], r)

    return rval

>>> %timeit minimum_bounding_rectangle(a)
1000 loops, best of 3: 245 µs per loop

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library(alphahull)                                  # Exposes ashape()
MBR <- function(points) {
    # Analyze the convex hull edges                       
    a <- ashape(points, alpha=1000)                 # One way to get a convex hull...
    e <- a$edges[, 5:6] - a$edges[, 3:4]            # Edge directions
    norms <- apply(e, 1, function(x) sqrt(x %*% x)) # Edge lengths
    v <- diag(1/norms) %*% e                        # Unit edge directions
    w <- cbind(-v[,2], v[,1])                       # Normal directions to the edges

    # Find the MBR
    vertices <- (points) [a$alpha.extremes, 1:2]    # Convex hull vertices
    minmax <- function(x) c(min(x), max(x))         # Computes min and max
    x <- apply(vertices %*% t(v), 2, minmax)        # Extremes along edges
    y <- apply(vertices %*% t(w), 2, minmax)        # Extremes normal to edges
    areas <- (y[1,]-y[2,])*(x[1,]-x[2,])            # Areas
    k <- which.min(areas)                           # Index of the best edge (smallest area)

    # Form a rectangle from the extremes of the best edge
    cbind(x[c(1,2,2,1,1),k], y[c(1,1,2,2,1),k]) %*% rbind(v[k,], w[k,])
}

MBR2 <- function(points) {
    tryCatch({
        a2 <- geometry::convhulln(points, options = 'FA')

        e <- points[a2$hull[,2],] - points[a2$hull[,1],]            # Edge directions
        norms <- apply(e, 1, function(x) sqrt(x %*% x)) # Edge lengths

        v <- diag(1/norms) %*% as.matrix(e)                        # Unit edge directions


        w <- cbind(-v[,2], v[,1])                       # Normal directions to the edges

        # Find the MBR
        vertices <- as.matrix((points) [a2$hull, 1:2])    # Convex hull vertices
        minmax <- function(x) c(min(x), max(x))         # Computes min and max
        x <- apply(vertices %*% t(v), 2, minmax)        # Extremes along edges
        y <- apply(vertices %*% t(w), 2, minmax)        # Extremes normal to edges
        areas <- (y[1,]-y[2,])*(x[1,]-x[2,])            # Areas
        k <- which.min(areas)                           # Index of the best edge (smallest area)

        # Form a rectangle from the extremes of the best edge
        as.data.frame(cbind(x[c(1,2,2,1,1),k], y[c(1,1,2,2,1),k]) %*% rbind(v[k,], w[k,]))
    }, error = function(e) {
        assign('points', points, .GlobalEnv)
        stop(e)  
    })
}


# Create sample data
#set.seed(23)
points <- matrix(rnorm(200000*2), ncol=2)                 # Random (normally distributed) points
system.time(mbr <- MBR(points))
system.time(mmbr2 <- MBR2(points))


# Plot the hull, the MBR, and the points
limits <- apply(mbr, 2, function(x) c(min(x),max(x))) # Plotting limits
plot(ashape(points, alpha=1000), col="Gray", pch=20, 
     xlim=limits[,1], ylim=limits[,2])                # The hull
lines(mbr, col="Blue", lwd=10)                         # The MBR
lines(mbr2, col="red", lwd=3)                         # The MBR2
points(points, pch=19)   

#7 楼