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

article 2024/10/22 6:38:30

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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

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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

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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

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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]])