from skimage.morphology import binary_dilation, diskfrom skimage import img_as_floatim = img_as_float(imread('../images/tagore.png'))im = 1 - im[...,3]im[im <= 0.5] = 0im[im > 0.5] = 1pylab.gray()pylab.figure(figsize=(18,9))pylab.subplot(131)pylab.imshow(im)pylab.title('...
1、unit8转float fromskimageimportdata,img_as_float img=data.chelsea()print(img.dtype.name) dst=img_as_float(img)print(dst.dtype.name) 输出: uint8 float64 2、float转uint8 fromskimageimportimg_as_ubyteimportnumpy as np img= np.array([0, 0.5, 1], dtype=float)print(img.dtype.name) dst...
fromPILimportImageimportnumpyasnp# 加载图像img=Image.open('path_to_image.jpg')# 将PIL图像转换为numpy数组img_array=np.array(img)# 检查数据类型print("原始数据类型:",img_array.dtype)# 如果数据类型是整数,转换为浮点数ifimg_array.dtype==np.uint8:img_array_float=img_array.astype(np.float32)pr...
img=data.chelsea()print(img.dtype.name) 在上面的表中,特别注意的是float类型,它的范围是[-1,1]或[0,1]之间。一张彩色图片转换为灰度图后,它的类型就由unit8变成了float 1、unit8转float fromskimageimportdata,img_as_float img=data.chelsea()print(img.dtype.name) dst=img_as_float(img)print(dst...
import PIL.ImageStat as stat from skimage.io import imread, imsave, imshow, show, imread_collection, imshow_collection from skimage import color, viewer, exposure, img_as_float, data from skimage.transform import SimilarityTransform, warp, swirl ...
img=data.lena() io.imshow(img) 1. 2. 3. 4. 以上内容来自于: 本文重点学习了1.调整图像对比度,2.旋转图片,3. 图片缩放,4.直方图均衡化等操作。 1.调整图片对比度 # 1.调整图片对比度 from skimage import io,data, exposure, img_as_float ...
import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import Rectangle from skimage.io import imread, imshow import skimage.io as skio from skimage import img_as_ubyte, img_as_float 现在我们看看正在处理的图像。 overcast = imread("image_overcast.PNG") plt.figure(num=None...
#encoding:utf-8importcv2importnumpyasnpimportmatplotlib.pyplotasplt #读取图片 img=cv2.imread('test3.jpg')image=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)#图像平移矩阵M=np.float32([[1,0,80],[0,1,30]])rows,cols=image.shape[:2]img1=cv2.warpAffine(image,M,(cols,rows))#图像缩小 ...
gray2 = img2.mean(axis=2).astype('float64') # 计算SSIM ssim_value = ssim(gray1, gray2, data_range=gray1.max() - gray1.min(), multichannel=False) print(f'SSIM: {ssim_value}') 2. 视觉信息保真度(VIF) VIF是衡量图像信息保真度的指标,它基于自然场景统计模型,能够评估图像融合过程中信息...
matplotlib.pyplotasplt#读取图片img = cv2.imread('lena.bmp')#灰度转换gray = cv2.cvtColor(img, ...