我正在尝试计算一组蟋蟀（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时。这张照片是从智能手机上拍摄的。不幸的是，结果不如预期。由于大多数蟋蟀相互重叠，我的代码无法将它们分开，导致计数不正确。
我应该应用什么方法来解决这个问题？我的代码有问题吗？
蟋蟀图片
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，我正在使用闭合形态学和轮廓层次结构进行椭圆形状方法。
这里有一个选项，使用自适应阈值，进行侵蚀/扩张和高斯模糊，然后进行轮廓，然后通过大小和长宽比对它们进行过滤，最后找到每个过滤后轮廓的质心。
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()
另一种选择是类似于这里所做的：
这些图像是经过处理的阈值（不是昆虫是白色的）和带有中心（红点）的轮廓。现实中不是很好，但我能想到的最好的。看起来工作量很大，但解决误报（在空白中）的方法可能是第二次通过取出质心不暗的轮廓。
