分割后如何去除前景中的阴影？

Question

我正在用 Python 开发一种算法，该算法旨在识别叶子上包含斑点的区域，以报告疾病的严重程度。

因此，为了实现目标，我需要在前景（叶子）和背景上分割图像。

在我的研究过程中，我发现了 LeafSnap（State of the Art），并按照论文使用 OpenCV Expectation Maximization 分割图像上的叶子，该模型使用 S 和 V 形式 HSV 颜色空间进行训练；然而，由于反射或阴影，它仍然会返回一些误报。

因此，我正在尝试找出一种方法来避免或减少误报的发生率。有什么提示吗？

原始图像

Answer 1

这是一种使用颜色分割来cv2.inRange()消除阴影的方法。这个想法是将图像转换为 HSV 格式并定义较低和较高的颜色范围。接下来，我们执行轮廓过滤以提取最大轮廓，将其绘制到新的空白掩模上，并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()

颜色分割 HSV 代码以确定颜色阈值范围的下限/上限

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()