从图像opencv python中删除背景色
我有很多标本的图像,它们的背景颜色无法控制。其中一些有黑色背景。其中一些有白色背景。其中一些有绿色背景等 我想删除给定图像的这些背景色,其中图像中的对象只是一个样本。我尝试了这段代码,但它没有像我期望的那样工作从图像opencv python中删除背景色,python,image,opencv,image-processing,computer-vision,Python,Image,Opencv,Image Processing,Computer Vision,我有很多标本的图像,它们的背景颜色无法控制。其中一些有黑色背景。其中一些有白色背景。其中一些有绿色背景等 我想删除给定图像的这些背景色,其中图像中的对象只是一个样本。我尝试了这段代码,但它没有像我期望的那样工作 def get_holes(image, thresh): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) im_bw = cv2.threshold(gray, thresh, 255, cv2.THRES
def get_holes(image, thresh):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
im_bw = cv2.threshold(gray, thresh, 255, cv2.THRESH_BINARY)[1]
im_bw_inv = cv2.bitwise_not(im_bw)
_, contour, _ = cv2.findContours(im_bw_inv, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contour:
cv2.drawContours(im_bw_inv, [cnt], 0, 255, -1)
nt = cv2.bitwise_not(im_bw)
im_bw_inv = cv2.bitwise_or(im_bw_inv, nt)
return im_bw_inv
def remove_background(image, thresh, scale_factor=.25, kernel_range=range(1, 15), border=None):
border = border or kernel_range[-1]
holes = get_holes(image, thresh)
small = cv2.resize(holes, None, fx=scale_factor, fy=scale_factor)
bordered = cv2.copyMakeBorder(small, border, border, border, border, cv2.BORDER_CONSTANT)
for i in kernel_range:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2*i+1, 2*i+1))
bordered = cv2.morphologyEx(bordered, cv2.MORPH_CLOSE, kernel)
unbordered = bordered[border: -border, border: -border]
mask = cv2.resize(unbordered, (image.shape[1], image.shape[0]))
fg = cv2.bitwise_and(image, image, mask=mask)
return fg
file = your_file_location
img = cv2.imread(file)
nb_img = dm.remove_background(img, 255)
以下是一些示例图像
我可以听听你的建议吗?这里有一个简单的方法,假设每张图像只有一个样本
clusters=4
,图像将被标记为四种颜色输入图像
->
Kmeans->
二进制图像
检测到最大的封闭圆->
掩码->
结果
这是第二幅图像的输出
输入图像->
Kmeans->
二进制图像
检测到最大的封闭圆->
掩码->
结果
代码
如果我的样本不是圆形而是椭圆形呢。我想我应该把cv2.MineConcloseingCircle(c)这行修改成其他的东西。我可以听听你的建议吗?你可以尝试另一种方法。如果它不是一个完美的圆,但椭圆不是使用一些圆/椭圆拟合函数来获得遮罩,你可以简单地使用
cv2来查找轮廓。查找轮廓然后使用cv2来绘制最大轮廓。在遮罩上绘制轮廓。它应该适合任何方向
import cv2
import numpy as np
# Kmeans color segmentation
def kmeans_color_quantization(image, clusters=8, rounds=1):
h, w = image.shape[:2]
samples = np.zeros([h*w,3], dtype=np.float32)
count = 0
for x in range(h):
for y in range(w):
samples[count] = image[x][y]
count += 1
compactness, labels, centers = cv2.kmeans(samples,
clusters,
None,
(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10000, 0.0001),
rounds,
cv2.KMEANS_RANDOM_CENTERS)
centers = np.uint8(centers)
res = centers[labels.flatten()]
return res.reshape((image.shape))
# Load image and perform kmeans
image = cv2.imread('2.jpg')
original = image.copy()
kmeans = kmeans_color_quantization(image, clusters=4)
# Convert to grayscale, Gaussian blur, adaptive threshold
gray = cv2.cvtColor(kmeans, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.adaptiveThreshold(blur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,21,2)
# Draw largest enclosing circle onto a mask
mask = np.zeros(original.shape[:2], dtype=np.uint8)
cnts = cv2.findContours(thresh, 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:
((x, y), r) = cv2.minEnclosingCircle(c)
cv2.circle(image, (int(x), int(y)), int(r), (36, 255, 12), 2)
cv2.circle(mask, (int(x), int(y)), int(r), 255, -1)
break
# Bitwise-and for result
result = cv2.bitwise_and(original, original, mask=mask)
result[mask==0] = (255,255,255)
cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.imshow('mask', mask)
cv2.imshow('kmeans', kmeans)
cv2.imshow('image', image)
cv2.waitKey()