如何在python中将另一个图像集中到一个零矩阵中？

Question

我是python图像处理方面的新手，我正在尝试做基于图像的细胞图像分割的遮罩。我做了阈值，使图像成为一个二进制掩模，但我需要的是中心图像的随机大小到一个36x36的掩模，我有图像以上和较小的这个大小。图像是这样的。我想做的是在一个36x36的零矩阵中心，但我不习惯做图像处理。你知道吗

原来是这样的：

Answer 1

您好，请尝试此代码，我为您制作一个图像（请理解：）

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

下面是一种使用numpy2d索引将一个图像插入另一个图像的方法。你知道吗

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)