perspective.order_points

`perspective.order_points`是一个函数，用于对矩形框的四个顶点进行排序，以确保它们按照左上角、右上角、右下角和左下角的顺序排列。这在计算物体之间的距离时非常重要。

以下是`perspective.order_points`函数的示例代码：

def order_points(pts):
    # 初始化一个包含四个坐标点的数组
    rect = np.zeros((4, 2), dtype="float32")

    # 按照左上角、右上角、右下角和左下角的顺序计算矩形框的四个顶点
    s = pts.sum(axis=1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]

    diff = np.diff(pts, axis=1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]

    # 返回排序后的矩形框顶点坐标
    return rect

相关问题

调用cv2.findContours()函数实现轮廓检测，检测图像中物体的轮廓。 2、调用imutils.grab_contours()函数获取图像轮廓。 3、调用contours.sort_contours()函数将轮廓从左到右进行排序。 4、初始化比例系数pixelsPerMetric为空，用于后续真实长度计算。 5、初始化参照物宽度width为3，用于后续真实长度计算。python

非常好的问题！以下是代码示例：

import cv2
import imutils
from imutils import contours

# 读取图像
image = cv2.imread("example.jpg")

# 灰度化
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 边缘检测
edged = cv2.Canny(gray, 50, 100)

# 轮廓检测
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)

# 轮廓排序
(cnts, _) = contours.sort_contours(cnts)

# 初始化比例系数和参照物宽度
pixelsPerMetric = None
width = 3

# 真实长度计算
for c in cnts:
    if cv2.contourArea(c) < 100:
        continue
    orig = image.copy()
    box = cv2.minAreaRect(c)
    box = cv2.boxPoints(box)
    box = np.array(box, dtype="int")
    box = perspective.order_points(box)
    cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
    for (x, y) in box:
        cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)
    (tl, tr, br, bl) = box
    (tltrX, tltrY) = midpoint(tl, tr)
    (blbrX, blbrY) = midpoint(bl, br)
    (tlblX, tlblY) = midpoint(tl, bl)
    (trbrX, trbrY) = midpoint(tr, br)
    cv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
    cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
    dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
    dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
    if pixelsPerMetric is None:
        pixelsPerMetric = dB / width
    dimA = dA / pixelsPerMetric
    dimB = dB / pixelsPerMetric
    cv2.putText(orig, "{:.1f}in".format(dimA),
        (int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,
        0.65, (255, 255, 255), 2)
    cv2.putText(orig, "{:.1f}in".format(dimB),
        (int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,
        0.65, (255, 255, 255), 2)
    cv2.imshow("Image", orig)
    cv2.waitKey(0)

这段代码可以实现轮廓检测、轮廓排序和真实长度计算。

def four_point_transform(image, pts): # obtain a consistent order of the points and unpack them # individually rect = order_points(pts) (tl, tr, br, bl) = rect # compute the width of the new image, which will be the # maximum distance between bottom-right and bottom-left # x-coordiates or the top-right and top-left x-coordinates widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) # compute the height of the new image, which will be the # maximum distance between the top-right and bottom-right # y-coordinates or the top-left and bottom-left y-coordinates heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) # now that we have the dimensions of the new image, construct # the set of destination points to obtain a "birds eye view", # (i.e. top-down view) of the image, again specifying points # in the top-left, top-right, bottom-right, and bottom-left # order dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") # compute the perspective transform matrix and then apply it M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # return the warped image return warped

这是一个使用透视变换的函数，它可以将一个四边形区域从原图像中提取出来，并将其变换成一个矩形。主要步骤如下：

1. 对四个顶点进行排序，以确保它们按照左上、右上、右下、左下的顺序排列；
2. 计算新图像的宽度和高度，分别为原四边形区域的最大宽度和最大高度；
3. 构造目标顶点集合，将它们指定为矩形的左上、右上、右下、左下四个点；
4. 计算透视变换矩阵，并将其应用到原图像上，得到变换后的矩形图像；
5. 返回变换后的矩形图像。

这个函数可以用于许多应用，例如车牌识别、文档扫描等。