OpenCV在昆虫计数中的对象重叠问题

svmlkihl 于 2023-04-07 发布在其他

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bounty明天到期，回答此问题可获得+50声望奖励，Jose R希望引起更多关注此问题：这是个有趣的问题，我很想知道答案。

我试图数一组蟋蟀（昆虫）的数量。我将使用图像处理方法，通过openCV库。这将提供更多的准确性实践，当农民出售他们的蟋蟀。照片是从智能手机拍摄的。不幸的是，结果并不像预期的那样。因为，大多数蟋蟀彼此重叠，我的代码无法将它们分离成个体，导致计数错误。
我应该用什么方法来解决这个问题？我的代码有什么问题吗？
蟋蟀意象

这是我的代码。

import cv2
import numpy as np

img = cv2.imread("c1.jpg",1)

roi=img[0:1500,0:1100]  
gray = cv2.cvtColor(roi,cv2.COLOR_BGR2GRAY)
gray_blur=cv2.GaussianBlur(gray,(15,15),0)
thresh = cv2.adaptiveThreshold(gray_blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,11,1)
kernel=np.ones((1,1),np.uint8)
closing=cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel,iterations=10)

result_img=closing.copy()
contours,hierachy=cv2.findContours(result_img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
counter=0
for cnt in contours:
    area = cv2.contourArea(cnt)
    if area <  150  :
    #if area<  300 :
        continue
    counter+=1
    ellipse = cv2.fitEllipse(cnt)
    cv2.ellipse(roi,ellipse,(0,255,0),1)
        
  
cv2.putText(roi,"Crickets="+str(counter),(100,70),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,0),1,cv2.LINE_AA)
cv2.imshow('ImageOfCrickets',roi)
#cv2.imshow('ImageOfGray',gray)
#cv2.imshow('ImageOfGray_blur',gray_blur)
#cv2.imshow('ImageOfThreshold',thresh)
#cv2.imshow('ImageOfMorphology',closing)

print('Crickets = '+ str(counter))     

cv2.waitKey(0)
cv2.destroyAllWindows()

现在，我使用封闭形态学和轮廓层次的椭圆形状的方法。

opencv

来源：https://stackoverflow.com/questions/68473889/overlapping-objects-issue-in-counting-insects-with-opencv

1条答案

按热度按时间

wwwo4jvm1#

这里有一个选项。使用自适应阈值，执行侵 eclipse /扩张和高斯模糊，然后是轮廓，然后按大小和纵横比过滤它们，最后找到每个过滤轮廓的质心。

import cv2

# Load the image
img = cv2.imread('insects.jpg')

# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray,(3,3),2)
# Threshold the grayscale image
thresh = cv2.adaptiveThreshold(gray,300,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,85,-21)

# # Perform morphological operations
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
thresh = cv2.erode(thresh, kernel, iterations=2)
thresh = cv2.dilate(thresh, kernel, iterations=1)
thresh = cv2.GaussianBlur(thresh, (3,3), 1)

# Find contours
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

print(contours)

# Filter contours by area and aspect ratio
min_area = 500  # minimum area of contour
max_area = 10000  # maximum area of contour
min_aspect_ratio = 0.3  # minimum aspect ratio of contour
max_aspect_ratio = 3  # maximum aspect ratio of contour

filtered_contours = []
for contour in contours:
    area = cv2.contourArea(contour)
    x, y, w, h = cv2.boundingRect(contour)
    aspect_ratio = float(w) / h if h != 0 else 0
    if area >= min_area and area <= max_area and aspect_ratio >= min_aspect_ratio and aspect_ratio <= max_aspect_ratio:
        filtered_contours.append(contour)
        print(contour)

# Compute centers of mass and draw circles for filtered contours
for contour in filtered_contours:
    # Compute moments of the contour
    M = cv2.moments(contour)
    if M['m00'] != 0:
        # Compute center of mass
        cx = int(M['m10'] / M['m00'])
        cy = int(M['m01'] / M['m00'])
        # Draw circle at center of mass
        cv2.circle(img, (cx, cy), 5, (0, 255, 0), -1)

# Show the original image with filtered contours
cv2.drawContours(img, filtered_contours, -1, (0, 0, 255), 2)
cv2.imwrite('Image_contours.jpg', img)
cv2.imshow('Gray', gray)
cv2.imwrite('image_thresholded_preprocessed.jpg', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

另一个选择，将是类似于这里所做的事情：