我正在用Python开发一种算法,该算法应该确定包含报告疾病严重程度的斑点的叶子区域。
为了能够实现该目标,我需要在前景(叶子)和背景上分割图像。
[在研究过程中,我了解了LeafSnap(最新技术),并按照本文进行了研究,使用OpenCV Expectation Maximization(在S和V形式的HSV颜色空间中进行训练)对叶子进行了分割;但是,由于反射或阴影,它仍会返回一些误报。
因此,我正在尝试找出一种避免或减少误报率的方法。有任何提示吗?
原始图像
这是在cv2.inRange()
中使用颜色分割来去除阴影的方法。这个想法是将图像转换成HSV格式并定义一个较低和较高的颜色范围。接下来,我们执行轮廓滤波以提取最大轮廓,将其绘制到新的空白蒙版上,然后执行按位与运算以获得结果。
使用这些截图的分割图像作为输入(左),这是结果(右)
代码
import numpy as np
import cv2
image = cv2.imread('1.png')
blank_mask = np.zeros(image.shape, dtype=np.uint8)
original = image.copy()
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([18, 42, 69])
upper = np.array([179, 255, 255])
mask = cv2.inRange(hsv, lower, upper)
cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
for c in cnts:
cv2.drawContours(blank_mask,[c], -1, (255,255,255), -1)
break
result = cv2.bitwise_and(original,blank_mask)
cv2.imshow('result', result)
cv2.waitKey()
通过颜色分割HSV代码确定上下颜色阈值范围
import cv2
import sys
import numpy as np
def nothing(x):
pass
# Create a window
cv2.namedWindow('image')
# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)
# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)
# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0
img = cv2.imread('1.png')
output = img
waitTime = 33
while(1):
# get current positions of all trackbars
hMin = cv2.getTrackbarPos('HMin','image')
sMin = cv2.getTrackbarPos('SMin','image')
vMin = cv2.getTrackbarPos('VMin','image')
hMax = cv2.getTrackbarPos('HMax','image')
sMax = cv2.getTrackbarPos('SMax','image')
vMax = cv2.getTrackbarPos('VMax','image')
# Set minimum and max HSV values to display
lower = np.array([hMin, sMin, vMin])
upper = np.array([hMax, sMax, vMax])
# Create HSV Image and threshold into a range.
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower, upper)
output = cv2.bitwise_and(img,img, mask= mask)
# Print if there is a change in HSV value
if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
phMin = hMin
psMin = sMin
pvMin = vMin
phMax = hMax
psMax = sMax
pvMax = vMax
# Display output image
cv2.imshow('image',output)
# Wait longer to prevent freeze for videos.
if cv2.waitKey(waitTime) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()