如何将另一个图像居中到 python 中的零矩阵？

Question

我是 python 图像处理方面的新手，我正在尝试根据图像为细胞图像分割做掩码。 我做了阈值将图像制作成二进制蒙版，但我需要将随机大小的图像居中放入 36x36 的蒙版中，并且我有高于和小于这个尺寸的图像。 图片是这样的。 我想要做的是以 36x36 的零矩阵为中心，但我不习惯进行图像处理。

原来是这样的：

Answer 1

您好，试试这段代码，我会为您制作一张图片供您理解:)

image = [[0, 0, 0, 0, 0],
         [0, 1, 0, 0, 0],
         [1, 1, 1, 0, 0], 
         [0, 1, 0, 0, 0],
         [0, 0, 0, 0, 0]]

image_width = 5
image_height = 5

lowest_x = -1
lowest_y = -1
bigest_x = -1
bigest_y = -1

# Get the square of the shape of your image (edge coordinate)
for y in range(len(image)):
    for x in range(len(image[y])):
        if image[y][x] != 0:
            if x < lowest_x or lowest_x == -1:
                lowest_x = x
            if y < lowest_y or lowest_y == -1:
                lowest_y = y
            if x > bigest_x or bigest_x == -1:
                bigest_x = x
            if y > bigest_y or bigest_y == -1:
                bigest_y = y

print ("Edge coordinate = " + str(lowest_y) + ":" + str(lowest_x) + "  -  " + str(bigest_y) + ":" + str(bigest_x))

chunk_width = bigest_x - lowest_x + 1
chunk_height = bigest_y - lowest_y + 1

print ("Chunk size = " + str(chunk_height) + " " + str(chunk_width))

y_delimiter = (image_height - chunk_height) / 2
x_delimiter = (image_width - chunk_width) / 2

print ("Start of new coord = " + str(y_delimiter) + " " + str(x_delimiter))

new_image = [[0 for i in range(image_height)] for j in range(image_width)]
for y in range(chunk_height):
    for x in range(chunk_width):
        new_image[y_delimiter + y][x + x_delimiter] = image[lowest_y + y][lowest_x + x]

print("")

for y in range(len(new_image)):
    print ' '.join(str(x) for x in new_image[y])

Answer 2

这是使用 numpy 2D 索引将一个图像插入另一个图像的一种方法。

Load the cell image as grayscale

Create a black image into which to recenter the cell data

Threshold the cell image using Otsu thresholding    

Get the contour(s) for the thresholded cell image

From each contour (presumably only one) get its bounding box and cut out the corresponding area of the gray image as roi

Compute the top left corner x and y offsets for centering the roi into the black image

Use numpy 2D array indexing to put the roi into the black image properly centered

输入：

import cv2
import numpy as np


# load image as grayscale
cell = cv2.imread('cell.png', cv2.IMREAD_GRAYSCALE)

# create 400x400 black image (larger than img) into which to  do the recentering
result = np.zeros((400,400), dtype=np.uint8)

# threshold input image with Otsu thresholding
ret, thresh = cv2.threshold(cell, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) 
cv2.imshow('THRESH', thresh)

# get contours --- presumably just one
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
for cntr in contours:
    x,y,w,h = cv2.boundingRect(cntr)
    print(x,y,w,h)
    roi=cell[y:y+h, x:x+w]
    # compute top left corner location to center roi in result image
    xoff = int((400 - w)/2)
    yoff = int((400 - h)/2)
    result[yoff:yoff+h, xoff:xoff+w] = roi
    # display result for each bounding box from contours
    cv2.imshow('CENTERED', result)

cv2.waitKey(0)
cv2.destroyAllWindows()

# save resulting centered image
cv2.imwrite('cell_centered.png', result)