在python中使用openCV提高棋盤格的輪廓檢測精度-有解無憂

我想從下圖中檢測棋盤黑色方塊的輪廓。

在python中使用openCV提高棋盤格的輪廓檢測精度

以下代碼僅成功檢測到幾個黑色方塊，我們如何提高準確性？

import cv2
import numpy as np


imPath = r" "  # <----- image path


def imageResize(orgImage, resizeFact):
    dim = (int(orgImage.shape[1]*resizeFact),
           int(orgImage.shape[0]*resizeFact))  # w, h
    return cv2.resize(orgImage, dim, cv2.INTER_AREA)


img = imageResize(cv2.imread(imPath), 0.5)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

thresh = cv2.inRange(gray,  135, 155)  # to pick only black squares

# find contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

cntImg = img.copy()

minArea, maxArea = 3000, 3500 

valid_cnts = []
for c in cnts:
    area = cv2.contourArea(c)
    if area > minArea and area < maxArea:
        valid_cnts.append(c)

        # draw centers for troubleshooting
        M = cv2.moments(c)
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
        cv2.circle(cntImg, (cX, cY), 5, (0, 0, 255), -1)

cv2.drawContours(cntImg, valid_cnts, -1, (0, 255, 0), 2)


cv2.imshow('org', img)
cv2.imshow('threshold', thresh)
cv2.imshow('contour', cntImg)


cv2.waitKey(0)
cv2.destroyAllWindows()

給出閾值和輪廓 -

在python中使用openCV提高棋盤格的輪廓檢測精度

[0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 7.0, 7.5, 9.5, 3248.5, 3249.0, 6498.0]是獨特的cnts領域。所需黑色方塊的典型區域是3248.5, 3249.0，這是獲取獨特cnts區域的快速片段-

cntAreas = [cv2.contourArea(x) for x in cnts]
print(sorted(set(cntAreas)))

非常感謝任何幫助！！

uj5u.com熱心網友回復：

問題是由灰度影像中的噪聲引起的精明邊緣的間隙造成的。通過使用 dilate morph 操作，噪音被降低，現在可以提供連接良好的 canny 邊緣來制作封閉的輪廓。

完整代碼 -

import cv2
import numpy as np


imPath = r" "  # <----- image path


def imageResize(orgImage, resizeFact):
    dim = (int(orgImage.shape[1]*resizeFact),       
           int(orgImage.shape[0]*resizeFact))  # w, h
    return cv2.resize(orgImage, dim, cv2.INTER_AREA)


img = imageResize(cv2.imread(imPath), 0.5)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                  
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)) # <-----                                                                             
morphed = cv2.dilate(gray, kernel, iterations=1)

thresh = cv2.inRange(morphed,  135, 155)  # to pick only black squares

# find canny edge
edged_wide = cv2.Canny(thresh, 10, 200, apertureSize=3)
cv2.waitKey(0)

# find Contours
contours, hierarchy = cv2.findContours(
    thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)  # cv2.CHAIN_APPROX_NONE stores all coords unlike SIMPLE, cv2.RETR_EXTERNAL


cntImg = img.copy()

minArea, maxArea = 2000, 4000

valid_cnts = []
for c in contours:
    area = cv2.contourArea(c)
    if area > minArea and area < maxArea:
        valid_cnts.append(c)

        # draw centers 
        M = cv2.moments(c)
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
        cv2.circle(cntImg, (cX, cY), 5, (0, 0, 255), -1)


cv2.drawContours(cntImg, valid_cnts, -1, (0, 255, 0), 2)

cv2.imshow('threshold', thresh)
cv2.imshow('morphed', morphed)
cv2.imshow('canny edge', edged_wide)
cv2.imshow('contour', cntImg)
cv2.waitKey(0)

cv2.destroyAllWindows()

這是等高線圖 - 在python中使用openCV提高棋盤格的輪廓檢測精度

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標籤：Python opencv

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