python-3.x - 如何在 cv2.findcontours 上获得准确的结果以在 BW/黑白图像中查找对象？

Question

这是一个完美的样本图像，包括一系列黑色物体。

这段代码假设找到所有黑色植物

import cv2 as cv
import numpy as np
img = cv.imread("12.jpg")
tresh_min= 200
tresh_max=255
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
blurred = cv.GaussianBlur(gray, (5, 5), 0)
_, threshold = cv.threshold(blurred, tresh_min, tresh_max, 0)
(contours, _)= cv.findContours(threshold, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)

print(f'Number of countours: {len(contours)}')
mask = np.ones(img.shape[:2], dtype="uint8") * 255


# Draw the contours on the mask
cv.drawContours(mask, contours=contours, contourIdx=-1, color=(0, 255, 255), thickness=2)

但结果却令人失望

这包括 118 个轮廓。请注意，我需要找到并取走 14 个对象。当轮廓实际上不正确时如何切割每株植物。或者至少将非常接近的那些加入到每个更大的轮廓中，以分别保存对象？谢谢

Answer 1

我们可以使用dilate而不是GaussianBlur，使用RETR_EXTERNAL而不是RETR_TREE，并且只保留大的轮廓。

• 反转阈值:

   _, threshold = cv.threshold(gray, tresh_min, tresh_max, cv.THRESH_BINARY_INV)
  

• 用柱核膨胀(假设植物又高又窄):

   dilate_threshold = cv.dilate(threshold, np.ones((15, 1), np.uint8))
  

• 遍历等高线列表，只保留面积大于 1000 的那些。

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代码示例:

import cv2 as cv
import numpy as np

img = cv.imread("12.jpg")
tresh_min= 200
tresh_max=255
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
#blurred = cv.GaussianBlur(gray, (5, 5), 0)
_, threshold = cv.threshold(gray, tresh_min, tresh_max, cv.THRESH_BINARY_INV)
dilate_threshold = cv.dilate(threshold, np.ones((15, 1), np.uint8))
(contours, _)= cv.findContours(dilate_threshold, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)  # Use RETR_EXTERNAL instead of RETR_TREE

print(f'Number of countours: {len(contours)}')
mask = np.ones(img.shape[:2], dtype="uint8") * 255


# Draw the contours on the mask
large_contours = []
for c in contours:
    area_tresh = 1000
    area = cv.contourArea(c)
    if area > area_tresh:
        cv.drawContours(mask, [c], contourIdx=-1, color=(0, 255, 255), thickness=1)
        large_contours.append(c)  # Append to list of "large contours".

print(f'Number of large countours: {len(large_contours)}')

# Show output for testing
cv.imshow('threshold', threshold)
cv.imshow('dilate_threshold', dilate_threshold)
cv.imshow('mask', mask)
cv.waitKey()
cv.destroyAllWindows()

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输出掩码: