zoo*_*ter 11 python matplotlib
如何使用matlib函数plt.imshow(图像)显示多个图像?
例如我的代码如下:
for file in images:
process(file)
def process(filename):
image = mpimg.imread(filename)
<something gets done here>
plt.imshow(image)
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我的结果显示只有最后处理的图像才能有效地覆盖其他图像
Aad*_*att 26
要显示多个图像,请使用 subplot()
plt.figure()
#subplot(r,c) provide the no. of rows and columns
f, axarr = plt.subplots(4,1)
# use the created array to output your multiple images. In this case I have stacked 4 images vertically
axarr[0].imshow(v_slice[0])
axarr[1].imshow(v_slice[1])
axarr[2].imshow(v_slice[2])
axarr[3].imshow(v_slice[3])
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dat*_*ler 13
您可以使用以下内容设置框架以显示多个图像:
for file in images:
process(file)
def process(filename):
image = mpimg.imread(filename)
<something gets done here>
plt.figure()
plt.imshow(image)
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这将垂直堆叠图像
首先,将文件中的图像加载到 numpy 矩阵中
from typing import Union,List
import numpy
import cv2
import os
def load_image(image: Union[str, numpy.ndarray]) -> numpy.ndarray:
# Image provided ad string, loading from file ..
if isinstance(image, str):
# Checking if the file exist
if not os.path.isfile(image):
print("File {} does not exist!".format(imageA))
return None
# Reading image as numpy matrix in gray scale (image, color_param)
return cv2.imread(image, 0)
# Image alredy loaded
elif isinstance(image, numpy.ndarray):
return image
# Format not recognized
else:
print("Unrecognized format: {}".format(type(image)))
print("Unrecognized format: {}".format(image))
return None
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然后您可以使用以下方法绘制多个图像:
import matplotlib.pyplot as plt
def show_images(images: List[numpy.ndarray]) -> None:
n: int = len(images)
f = plt.figure()
for i in range(n):
# Debug, plot figure
f.add_subplot(1, n, i + 1)
plt.imshow(images[i])
plt.show(block=True)
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该show_images
方法接收输入的图像列表,您可以使用该load_image
方法迭代读取这些图像。