Python从图像opencv中提取多个对象 [英] Python extract multiple objects from image opencv

问题描述

我正在尝试使用OpenCV使用颜色从图像中提取对象，我尝试通过反阈值和灰度与cv2.findContours()结合使用，但是无法递归使用它.此外，我无法弄清楚如何从原始图像中切出"匹配并将其保存到单个文件中.

I am trying to extract object from an image using the color using OpenCV, I have tried by inverse thresholding and grayscale combined with cv2.findContours() but I am unable to use it recursively. Furthermore I can't figure out how to "cut out" the match from the original image and save it to a single file.

~
import cv2
import numpy as np

# load the images
empty = cv2.imread("empty.jpg")
full = cv2.imread("test.jpg")

# save color copy for visualization
full_c = full.copy()

# convert to grayscale
empty_g = cv2.cvtColor(empty, cv2.COLOR_BGR2GRAY)
full_g = cv2.cvtColor(full, cv2.COLOR_BGR2GRAY)

empty_g = cv2.GaussianBlur(empty_g, (51, 51), 0)
full_g = cv2.GaussianBlur(full_g, (51, 51), 0)
diff = full_g - empty_g

#  thresholding

diff_th = 
cv2.adaptiveThreshold(full_g,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
cv2.THRESH_BINARY,11,2)

# combine the difference image and the inverse threshold
zone = cv2.bitwise_and(diff, diff_th, None)

# threshold to get the mask instead of gray pixels
_, zone = cv2.threshold(bag, 100, 255, 0)

# dilate to account for the blurring in the beginning
kernel = np.ones((15, 15), np.uint8)
bag = cv2.dilate(bag, kernel, iterations=1)

# find contours, sort and draw the biggest one
contours, _ = cv2.findContours(bag, cv2.RETR_TREE,
                              cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:3]
i = 0
while i < len(contours):
    x, y, width, height = cv2.boundingRect(contours[i])
    roi = full_c[y:y+height, x:x+width]
    cv2.imwrite("piece"+str(i)+".png", roi)
    i += 1

空白处是一张上面的一张，尺寸为1500 * 1000的白色图像，而测试是上面的一张.

Where empty is just a white image size 1500 * 1000 as the one above and test is the one above.

这是我想出的，只是缺点，我有第三张图像，而不是只有2张图像现在显示阴影区域...

This is what I came up with, only downside, I have a third image instead of only the 2 expected showing a shadow zone now...

推荐答案

这是一种简单的方法:

• 将图像转换为灰度和高斯模糊图像
• 执行Canny边缘检测
• 将图像放大以形成更大的轮廓
• 遍历轮廓并找到边界框
• 提取投资回报率并保存图像

Canny边缘检测

检测到的投资回报率

要提取ROI，您可以使用cv2.boundingRect()找到边界框坐标，裁剪所需区域，然后保存图像

To extract the ROI, you can find the bounding box coordinates using cv2.boundingRect(), crop the desired region, then save the image

x,y,w,h = cv2.boundingRect(c)
ROI = original[y:y+h, x:x+w]

第一个对象

第二个对象

import cv2
import numpy as np

image = cv2.imread('1.jpg')
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
canny = cv2.Canny(blurred, 120, 255, 1)
kernel = np.ones((5,5),np.uint8)
dilate = cv2.dilate(canny, kernel, iterations=1)

# Find contours
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

# Iterate thorugh contours and filter for ROI
image_number = 0
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
    ROI = original[y:y+h, x:x+w]
    cv2.imwrite("ROI_{}.png".format(image_number), ROI)
    image_number += 1

cv2.imshow('canny', canny)
cv2.imshow('image', image)
cv2.waitKey(0)

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