【python】OpenCV—Sort the Point Set from Top Left to Bottom Right

在这里插入图片描述

文章目录

1、功能描述
2、代码实现
3、效果展示
4、更多例子
5、参考

1、功能描述

在这里插入图片描述

给出一张图片，里面含有各种图形，取各种图形的中心点，从左到右从上到下排序

例如

在这里插入图片描述

2、代码实现

import cv2
import numpy as np


def process_img(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_canny = cv2.Canny(img_gray, 100, 100)
    kernel = np.ones((2, 3))
    img_dilate = cv2.dilate(img_canny, kernel, iterations=1)
    img_erode = cv2.erode(img_dilate, kernel, iterations=1)
    return img_erode


def get_centeroid(cnt):
    length = len(cnt)
    sum_x = np.sum(cnt[..., 0])
    sum_y = np.sum(cnt[..., 1])
    return int(sum_x / length), int(sum_y / length)


def get_centers(img):
    contours, hierarchies = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    for cnt in contours:
        if cv2.contourArea(cnt) > 100:
            yield get_centeroid(cnt)


def get_rows(img, centers, row_amt, row_h):
    centers = np.array(centers)
    d = row_h / row_amt  # 每行的间距
    for i in range(row_amt):  # 遍历行数
        f = centers[:, 1] - d * i  # 行首纵坐标
        a = centers[(f < d) & (f > 0)]  # 一行内的 x
        yield a[a.argsort(0)[:, 0]]


img = cv2.imread("shape.png")
img_processed = process_img(img)

cv2.imwrite("shape_processed.png", img_processed)

centers = list(get_centers(img_processed))
print(centers)

"""
[(478, 466), (38, 454), (478, 432), (159, 442), (646, 436), (157, 403), (317, 430), (161, 369), (139, 368), (523, 385), 
(64, 381), (690, 355), (636, 366), (341, 325), (506, 329), (212, 319), (86, 283), (180, 275), (674, 283), (379, 322), 
(525, 256), (299, 311), (434, 254), (634, 212), (316, 203), (233, 204), (567, 172), (148, 228), (59, 199), (418, 158), 
(478, 171), (363, 109), (549, 88), (281, 89), (211, 58), (441, 50), (21, 75), (104, 62), (677, 86), (621, 39), 
(485, 32), (323, 24)]
"""
print(len(centers))  # 42

h, w, c = img.shape
count = 0

for row in get_rows(img, centers, 4, h):
    cv2.polylines(img, [row], False, (255, 0, 255), 2)  # 绘制每一行的点连成线
    for x, y in row:
        count += 1
        cv2.circle(img, (x, y), 10, (0, 0, 255), -1)  # 绘制每个点
        cv2.putText(img, str(count), (x - 10, y + 5), 1, cv2.FONT_HERSHEY_PLAIN, (0, 255, 255), 2)
        # 每个点上写上序号

cv2.imshow("Ordered", img)
cv2.waitKey(0)

process_img 后图像为

在这里插入图片描述

get_centers 函数找轮廓，面积较大的轮廓经过 get_centeroid 函数找轮廓中心

找轮廓中心的方法是遍历轮廓点，求横坐标和纵坐标的平均值

轮廓中心 centers 的长度为 42，可以看到我们找出来了图片中 42 个中心，也即找出来了 42 个轮廓

[(478, 466), (38, 454), (478, 432), (159, 442), (646, 436), (157, 403), (317, 430), (161, 369), (139, 368), (523, 385), 
(64, 381), (690, 355), (636, 366), (341, 325), (506, 329), (212, 319), (86, 283), (180, 275), (674, 283), (379, 322), 
(525, 256), (299, 311), (434, 254), (634, 212), (316, 203), (233, 204), (567, 172), (148, 228), (59, 199), (418, 158), 
(478, 171), (363, 109), (549, 88), (281, 89), (211, 58), (441, 50), (21, 75), (104, 62), (677, 86), (621, 39), 
(485, 32), (323, 24)]

遍历每行，排序纵坐标落在行内区间的轮廓中心

get_rows 中 f

array([466., 454., 432., 442., 436., 403., 430., 369., 368., 385., 381.,
       355., 366., 325., 329., 319., 283., 275., 283., 322., 256., 311.,
       254., 212., 203., 204., 172., 228., 199., 158., 171., 109.,  88.,
        89.,  58.,  50.,  75.,  62.,  86.,  39.,  32.,  24.])

get_rows 中 a

array([[363, 109],
       [549,  88],
       [281,  89],
       [211,  58],
       [441,  50],
       [ 21,  75],
       [104,  62],
       [677,  86],
       [621,  39],
       [485,  32],
       [323,  24]])

a.argsort(0) 按列排序

array([[ 5, 10],
       [ 6,  9],
       [ 3,  8],
       [ 2,  4],
       [10,  3],
       [ 0,  6],
       [ 4,  5],
       [ 9,  7],
       [ 1,  1],
       [ 8,  2],
       [ 7,  0]])

取出 x 的排序索引 a.argsort(0)[:, 0]

array([ 5,  6,  3,  2, 10,  0,  4,  9,  1,  8,  7])

得到排序后的结果 a[a.argsort(0)[:, 0]]

array([[ 21,  75],
       [104,  62],
       [211,  58],
       [281,  89],
       [323,  24],
       [363, 109],
       [441,  50],
       [485,  32],
       [549,  88],
       [621,  39],
       [677,  86]])