获取图像最大轮廓、最小外接矩形、最大内接矩形

1.图像最大轮廓及最小外接矩形的获取

img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.blur(gray, (9, 9))
_, thresh = cv2.threshold(blurred, 155, 255, cv2.THRESH_BINARY)
_, cnts, _ = cv2.findContours( thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
rect = cv2.minAreaRect(c)
#box即为最小外接矩形坐标
box = np.int0(cv2.boxPoints(rect))
cv2.drawContours(img, [box], -1, (0, 255, 0), 3)
cv2.imshow("Image", img)
cv2.imwrite("pic.jpg", img)
cv2.waitKey(0)

2.最大内接矩形

从轮廓中所有坐标中获取其中4个坐标即可，过程如下：

def order_points(pts):
    # pts为轮廓坐标
    # 列表中存储元素分别为左上角，右上角，右下角和左下角
    rect = np.zeros((4, 2), dtype = "float32")
    # 左上角的点具有最小的和，而右下角的点具有最大的和
    s = pts.sum(axis = 1)
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    # 计算点之间的差值
    # 右上角的点具有最小的差值,
    # 左下角的点具有最大的差值
    diff = np.diff(pts, axis = 1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    # 返回排序坐标(依次为左上右上右下左下)
    return rect

img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.blur(gray, (9, 9))
_, thresh = cv2.threshold(blurred, 155, 255, cv2.THRESH_BINARY)
_, cnts, _ = cv2.findContours( thresh.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]<br>先找出轮廓点rect = order_points(c.reshape(c.shape[0], 2))
print(rect)
xs = [i[0] for i in rect]
ys = [i[1] for i in rect]
xs.sort()
ys.sort()
#内接矩形的坐标为
print(xs[1],xs[2],ys[1],ys[2])