Python 如何使用OpenCv获取图像中新出现的对象列表？

我试图确定一张照片中新出现的物体列表。该计划是从原始图像中获得多个裁剪图像，并将其输入到用于目标检测的神经网络。现在，我在提取出现在帧中的对象时遇到问题

import cv2 as cv
import matplotlib.pyplot as plt

def mdisp(image):
    plt.imshow(image)
    plt.show()

im1 = cv.imread('images/litter-before.jpg')
mdisp(im1)
print(im1.shape)
im2 = cv.imread('images/litter-after.jpg')
mdisp(im2)
print(im2.shape)
backsub1=cv.createBackgroundSubtractorMOG2()
backsub2=cv.createBackgroundSubtractorKNN()
fgmask = backsub1.apply(im1)
fgmask = backsub1.apply(im2)
print(fgmask.shape)
mdisp(fgmask)
new_image = im2 * (fgmask[:,:,None].astype(im2.dtype))
mdisp(new_image)

理想情况下，我想得到一个红色圆圈内的项目裁剪图片。我如何使用OpenCv实现它

---

这里有一种方法，直接减去两帧。其思想是首先将图像转换为

灰度

，然后稍微模糊一点以忽略噪声

减去两个帧，
阈值
差值，然后查找大于某个区域阈值的最大的水滴

让我们看看：

import cv2
import numpy as np

# image path
path = "C:/opencvImages/"
fileName01 = "01.jpg"
fileName02 = "02.jpg"

# Read the2 images in default mode:
image01 = cv2.imread(path + fileName01)
image02 = cv2.imread(path + fileName02)

# Store a copy of the last frame for results drawing:
inputCopy = image02.copy()

# Convert RGB images to grayscale:
grayscaleImage01 = cv2.cvtColor(image01, cv2.COLOR_BGR2GRAY)
grayscaleImage02 = cv2.cvtColor(image02, cv2.COLOR_BGR2GRAY)

# Convert RGB images to grayscale:
filterSize = 5
imageMedian01 = cv2.medianBlur(grayscaleImage01, filterSize)
imageMedian02 = cv2.medianBlur(grayscaleImage02, filterSize)

现在有了灰度模糊的帧。接下来，我们需要计算这些帧之间的差异。我不想丢失数据，所以我必须小心这里的数据类型。请记住，这些是灰度，
uint8
矩阵，但这种差异可能会产生负值。让我们将矩阵转换为
float
s，取其差，然后将此矩阵转换为uint8：

# uint8 to float32 conversion:
imageMedian01 = imageMedian01.astype('float32')
imageMedian02 = imageMedian02.astype('float32')

# Take the difference and convert back to uint8
imageDifference = np.clip(imageMedian01 - imageMedian02, 0, 255)
imageDifference = imageDifference.astype('uint8')

这将为您提供帧差异：

让我们对其设置阈值以获得二值图像。我使用的阈值是
127
，因为它是8位范围的中心：

threshValue = 127
_, binaryImage = cv2.threshold(imageDifference, threshValue, 255, cv2.THRESH_BINARY)

这是二进制图像：

我们在这里寻找最大的斑点，让我们找到
斑点/轮廓
并过滤小斑点。让我们将最小面积设置为10像素：

# Perform an area filter on the binary blobs:
componentsNumber, labeledImage, componentStats, componentCentroids = \
    cv2.connectedComponentsWithStats(binaryImage, connectivity=4)

# Set the minimum pixels for the area filter:
minArea = 10

# Get the indices/labels of the remaining components based on the area stat
# (skip the background component at index 0)
remainingComponentLabels = [i for i in range(1, componentsNumber) if componentStats[i][4] >= minArea]

# Filter the labeled pixels based on the remaining labels,
# assign pixel intensity to 255 (uint8) for the remaining pixels
filteredImage = np.where(np.isin(labeledImage, remainingComponentLabels) == True, 255, 0).astype('uint8')

# Find the big contours/blobs on the filtered image:
contours, hierarchy = cv2.findContours(filteredImage, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)

contours_poly = [None] * len(contours)
boundRect = []

# Alright, just look for the outer bounding boxes:
for i, c in enumerate(contours):
    if hierarchy[0][i][3] == -1:
        contours_poly[i] = cv2.approxPolyDP(c, 3, True)
        boundRect.append(cv2.boundingRect(contours_poly[i]))


# Draw the bounding boxes on the (copied) input image:
for i in range(len(boundRect)):
    print(boundRect[i])
    color = (0, 255, 0)
    cv2.rectangle(inputCopy, (int(boundRect[i][0]), int(boundRect[i][1])), \
                  (int(boundRect[i][0] + boundRect[i][2]), int(boundRect[i][1] + boundRect[i][3])), color, 1)

查看结果：

请提供清晰的输入图像该图像是使用raspberry pi捕获的。我试图提高质量，但没有多大改善。我确实看到了与当前图像的区别，我们可以在其周围画一个矩形吗？提供文件
litter-before.jpg
和
litter-after.jpg
，以便其他人可以调试您的代码。上传的原始文件litter-before.jpg和litter-after.jpg是我使用的谢谢！！正是我想要的。我做了一些更改以增加框区域，并为find contours return
img，contours，hierarchy=cv2添加了一个变量。findContours
@Ravi没问题，我的朋友。很高兴我能帮忙。