問題描述

假設我們有如下圖片:

[![輸入][1]][1]

我想確定邊的數量，以及每邊的長度.

在圖像中，我們將三個邊緣作為直線，而上一個是彎曲的邊緣.我能夠使用 Canny 邊緣檢測找到三個直邊的長度.我們可以有四個頂點坐標，我們可以計算出三個直邊/線的長度，但無法找到彎曲邊的長度.

因此，預處理必須適應輸入圖像，并且超出了我的回答范圍！在下面的代碼中，預處理是針對給定的輸入圖像，而不是針對其他兩個示例.

import cv2將 matplotlib.pyplot 導入為 plt將 numpy 導入為 npdef extract_and_measure_edges(img_bin):# 檢測可能的角點，并提取候選者dst = cv2.cornerHarris(img_bin, 2, 3, 0.04)糖果= []對于 i, c in enumerate(np.argwhere(dst > 0.1 * np.max(dst)).tolist()):c = np.flip(np.array(c))如果 len(cand) == 0:cand.append(c)別的:添加=真對于 enumerate(cand) 中的 j，d:d = np.array(d)如果 np.linalg.norm(c - d) <5:添加 = 假休息如果添加:cand.append(c)# 獲取實際的、最近的匹配輪廓點的索引角=排序([np.argmin(np.linalg.norm(c-cnt.squeeze()，軸=1))對于 c 中的 cand])# 從輪廓中提取邊緣，并測量它們的長度輸出 = cv2.cvtColor(np.zeros_like(img_bin), cv2.COLOR_GRAY2BGR)對于 i_c, c in enumerate(corners):如果 i_c == len(corners) - 1:edge = np.vstack([cnt[c:, ...], cnt[0:corners[0], ...]])別的:邊緣 = cnt[c:corners[i_c + 1], ...]loc = tuple(np.mean(edge.squeeze(), axis=0, dtype=int).tolist())顏色 = 元組(np.random.randint(0, 255, 3).tolist())長度 = cv2.arcLength(邊，假)cv2.polylines(輸出，[邊緣]，假，顏色，2)cv2.putText(輸出，'{:.2f}'.format(長度)，loc，cv2.FONT_HERSHEY_COMPLEX，0.5，顏色，1)返回輸出# 讀取并預處理圖像，提取形狀輪廓# TODO:修改以適合您的輸入圖像img = cv2.imread('2B2m4.png')灰色 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)thr = cv2.threshold(灰色, 16, 255, cv2.THRESH_BINARY_INV)[1]cnts = cv2.findContours(thr, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)cnts = cnts[0] 如果 len(cnts) == 2 否則 cnts[1]cnt = max(cnts, key=cv2.contourArea)thr = cv2.drawContours(np.zeros_like(thr), [cnt], -1, 255, 1)# 提取和測量邊緣，并可視化輸出out = extract_and_measure_edges(thr)plt.figure(figsize=(18, 6))plt.subplot(1, 3, 1), plt.imshow(img), plt.title('原始輸入圖像')plt.subplot(1, 3, 2), plt.imshow(thr, cmap='gray'), plt.title('需要輪廓')plt.subplot(1, 3, 3), plt.imshow(out), plt.title('結果')plt.tight_layout(), plt.show()

這是輸出:

示例 #2:

輸出:

示例 #3:

輸出:

(我沒有注意正確的顏色順序...)

----------------------------------------系統信息--------------------------------------平臺:Windows-10-10.0.19041-SP0蟒蛇:3.9.1PyCharm:2021.1.1Matplotlib:3.4.2NumPy:1.19.5開放CV:4.5.2--------------------------------------

Suppose we have an image as below:

[![Input][1]][1]

I want to determine the number of sides, and the length of each side.

Here in the image, we have three edges as straight lines, and the upper one is a curved edge. I am able to find the length of the three straight edges using Canny edge detection. We can have four vertices coordinates, and we can calculate the length of the three straight edges/lines, but unable to find the length of the curved edge.

find number of sides and length of each side and number of vertices in image python This is a good answer for getting the number of edges in an image, and we get the coordinates of vertices through the code in the above link. Further to get the length of the sides using the coordinates, we can use below code to get the length, if the edges are straight lines:

for pt in zip(points,np.roll(points,-1,0)):
#     print( pt )
    x = pt[0][0]
    y = pt[1][0]
    d = math.sqrt((x[1]-y[1])*(x[1]-y[1]) + (x[0]-y[0])*(x[0]-y[0]))
    print('length between point:',x,'and', y,'is', d)

To simplify my question: I want to find, how many edges/sides are there for a shape in an image, and their respective lengths, if the image shape has any curved sides.

My idea would be to get the contour of the shape, try to detect "corners", e.g. using Harris corner detection, find matching points from the contour, and piecewise calculate the length of the edges using cv2.arcLength.

The input for the below extract_and_measure_edges method needs some binarized contour image like that one derived from your actual input image:

So, the pre-processing must be adapted to the input images, and is out of scope of my answer! In the below code, the pre-processing is for the given input image, not for the two other examples.

import cv2
import matplotlib.pyplot as plt
import numpy as np


def extract_and_measure_edges(img_bin):

    # Detect possible corners, and extract candidates
    dst = cv2.cornerHarris(img_bin, 2, 3, 0.04)
    cand = []
    for i, c in enumerate(np.argwhere(dst > 0.1 * np.max(dst)).tolist()):
        c = np.flip(np.array(c))
        if len(cand) == 0:
            cand.append(c)
        else:
            add = True
            for j, d in enumerate(cand):
                d = np.array(d)
                if np.linalg.norm(c - d) < 5:
                    add = False
                    break
            if add:
                cand.append(c)

    # Get indices of actual, nearest matching contour points
    corners = sorted([np.argmin(np.linalg.norm(c - cnt.squeeze(), axis=1))
                      for c in cand])

    # Extract edges from contour, and measure their lengths
    output = cv2.cvtColor(np.zeros_like(img_bin), cv2.COLOR_GRAY2BGR)
    for i_c, c in enumerate(corners):
        if i_c == len(corners) - 1:
            edge = np.vstack([cnt[c:, ...], cnt[0:corners[0], ...]])
        else:
            edge = cnt[c:corners[i_c + 1], ...]

        loc = tuple(np.mean(edge.squeeze(), axis=0, dtype=int).tolist())
        color = tuple(np.random.randint(0, 255, 3).tolist())
        length = cv2.arcLength(edge, False)
        cv2.polylines(output, [edge], False, color, 2)
        cv2.putText(output, '{:.2f}'.format(length), loc, cv2.FONT_HERSHEY_COMPLEX, 0.5, color, 1)
    return output


# Read and pre-process image, extract contour of shape
# TODO: MODIFY TO FIT YOUR INPUT IMAGES
img = cv2.imread('2B2m4.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thr = cv2.threshold(gray, 16, 255, cv2.THRESH_BINARY_INV)[1]
cnts = cv2.findContours(thr, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnt = max(cnts, key=cv2.contourArea)
thr = cv2.drawContours(np.zeros_like(thr), [cnt], -1, 255, 1)

# Extract and measure edges, and visualize output
out = extract_and_measure_edges(thr)
plt.figure(figsize=(18, 6))
plt.subplot(1, 3, 1), plt.imshow(img), plt.title('Original input image')
plt.subplot(1, 3, 2), plt.imshow(thr, cmap='gray'), plt.title('Contour needed')
plt.subplot(1, 3, 3), plt.imshow(out), plt.title('Results')
plt.tight_layout(), plt.show()

That's the output:

Example #2:

Output:

Example #3:

Output:

(I haven't paid attention to the correct color ordering...)

----------------------------------------
System information
----------------------------------------
Platform:      Windows-10-10.0.19041-SP0
Python:        3.9.1
PyCharm:       2021.1.1
Matplotlib:    3.4.2
NumPy:         1.19.5
OpenCV:        4.5.2
----------------------------------------

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