Python3.x 如何准确测定cv2的结果.在BW黑白图像中查找对象

发布于03月18日

This is a perfect sample image that includes range of black objects.

这个代码假设能找到每一株黑色植物

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)

But the results is disappointing as this

其中包括118条等高线.请注意，我需要找到并获取14个对象.

推荐答案

我们可以用dilate代替GaussianBlur，用RETR_EXTERNAL代替RETR_TREE，只保留大的轮廓.

把threshold倒过来:

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

用柱状核扩张(假设植株又高又窄):

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

在等高线列表上循环，只保留面积超过1000的等高线.

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()

Output mask: