問(wèn)題描述

我有許多樣本圖像，其背景顏色無(wú)法控制.其中一些具有黑色背景.其中一些具有白色背景.其中一些有綠色背景等.

I have many images of specimen which have uncontrollable background color. Some of them have black background. Some of them have white background. Some of them have green background, etc.

我想刪除給定圖像的這些背景顏色，其中圖像中的對(duì)象只是一個(gè)樣本.我嘗試了這段代碼，但它沒(méi)有按我的預(yù)期工作.

I would like to remove these background color of a given image where the object in the image is just only one specimen. I try this code but it does not work as i expect.

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)

這些是一些示例圖像

我可以給你一些建議嗎?

May i have your suggestions?

推薦答案

這是一個(gè)簡(jiǎn)單的方法，假設(shè)每個(gè)圖像只有一個(gè)樣本.

Here's a simple approach with the assumption that there is only one specimen per image.

1. Kmeans 顏色量化. 我們加載圖像，然后執(zhí)行 Kmeans 顏色量化，將圖像分割成指定的顏色簇.例如，使用 clusters=4，圖像將被標(biāo)記為四種顏色.

1. Kmeans color quantization. We load the image then perform Kmeans color quantization to segment the image into a specified cluster of colors. For instance with clusters=4, the image will be labeled into four colors.

獲取二值圖像.轉(zhuǎn)換為灰度、高斯模糊、自適應(yīng)閾值.

Obtain binary image. Convert to grayscale, Gaussian blur, adaptive threshold.

在遮罩上繪制最大的封閉圓. 查找輪廓，使用輪廓區(qū)域過(guò)濾對(duì)最大輪廓進(jìn)行排序，然后使用 cv2.minEnclosureCircle.

Draw largest enclosing circle onto mask. Find contours, sort for largest contour using contour area filtering then draw the largest enclosing circle onto a mask using cv2.minEnclosingCircle.

按位與.由于我們已經(jīng)隔離了要提取的所需部分，因此我們只需按位與掩碼和輸入圖像

Bitwise-and. Since we have isolated the desired sections to extract, we simply bitwise-and the mask and input image

<小時(shí)>

輸入圖像-> Kmeans ->二值圖像

Input image -> Kmeans -> Binary image

檢測(cè)到最大包圍圈->掩碼->結(jié)果

Detected largest enclosing circle -> Mask -> Result

這是第二張圖片的輸出

輸入圖像-> Kmeans ->二值圖像

Input image -> Kmeans -> Binary image

檢測(cè)到最大包圍圈->掩碼->結(jié)果

Detected largest enclosing circle -> Mask -> Result

代碼

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()

這篇關(guān)于從圖像opencv python中刪除背景顏色的文章就介紹到這了，希望我們推薦的答案對(duì)大家有所幫助，也希望大家多多支持html5模板網(wǎng)！