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python - 从图像opencv python中删除背景颜色

问题描述

我有许多具有无法控制的背景颜色的标本图像。其中一些具有黑色背景。其中一些具有白色背景。其中一些具有绿色背景等。

我想删除给定图像的这些背景颜色，其中图像中的对象只是一个样本。我尝试了这段代码，但它不像我预期的那样工作。

def get_holes(image, thresh):
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

        im_bw = cv2.threshold(gray, thresh, 255, cv2.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)

这些是一些示例图像

样品 1

样品 2

我可以有你的建议吗？

标签： pythonimageopencvimage-processingcomputer-vision

解决方案

这是一种简单的方法，假设每张图像只有一个样本。

  1. Kmeans 颜色量化。我们加载图像，然后执行 Kmeans 颜色量化，将图像分割成指定的颜色簇。例如，使用clusters=4，图像将被标记为四种颜色。

  2. 获取二值图像。转换为灰度、高斯模糊、自适应阈值。

  3. 在面具上画出最大的封闭圆圈。查找轮廓，使用轮廓区域过滤对最大轮廓进行排序，然后使用cv2.minEnclosingCircle.

  4. 按位与。由于我们已经隔离了要提取的所需部分，因此我们只需按位-掩码和输入图像

输入图像->Kmeans->二进制图像

检测到最大的封闭圆->掩码->结果

这是第二张图片的输出

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

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()

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