img_array = np.asarray(cropped_image) img = Image.fromarray(img_array)

这段代码的作用是将一个被裁剪后的图像转换为 numpy 数组，然后再将该数组转换为 PIL（Python Imaging Library）中的 Image 对象。

首先，`cropped_image` 可能是一个 PIL Image 对象，也可能是一个 numpy 数组，但无论哪种类型，都可以使用 `np.asarray()` 函数将其转换为 numpy 数组。这个数组可以在 NumPy 中进行处理和操作，例如对像素值进行数学运算或平滑处理等。

然后，使用 `Image.fromarray()` 函数将 numpy 数组转换为 PIL Image 对象。这允许我们使用 PIL 库中的其他函数和方法，例如调整图像大小、应用滤镜或保存到磁盘等。

相关问题

from bm3d import bm3d_rgb from experiment_funcs1 import get_experiment_noise, get_psnr, get_cropped_psnr from PIL import Image import argparse import os import torch import numpy as np from torchvision.utils import save_image def main(): imagename = './test_image1/(1271).jpg' save_dir = 'test_result' save_path = 'noise' y = np.array(Image.open(imagename)) / 255 noise_type = 'g3' noise_var = 0.02 seed = 0 noise, psd, kernel = get_experiment_noise(noise_type, noise_var, seed, y.shape) z = np.atleast_3d(y) + np.atleast_3d(noise) y_est = bm3d_rgb(z, psd) psnr = get_psnr(y, y_est) print("PSNR:", psnr) y_est = np.minimum(np.maximum(y_est, 0), 1) z_rang = np.minimum(np.maximum(z, 0), 1) z_rang = torch.from_numpy(np.transpose(z_rang, (2, 0, 1))).float() y_est = torch.from_numpy(np.transpose(y_est, (2, 0, 1))).float() denoise_img_path = os.path.join(save_dir, 'denoised.jpg') save_image(y_est, denoise_img_path) noise_img_path = os.path.join(save_path, 'noise.jpg') save_image(z_rang, noise_img_path) if __name__ == '__main__': main()改为对灰度图处理

可以将代码中的`bm3d_rgb`函数改为`bm3d`函数，同时将读入图像的方式改为读入灰度图像即可。具体代码如下：

from bm3d import bm3d
from experiment_funcs1 import get_experiment_noise, get_psnr, get_cropped_psnr
from PIL import Image
import argparse
import os
import torch
import numpy as np
from torchvision.utils import save_image

def main():
    imagename = './test_image1/(1271).jpg'
    save_dir = 'test_result'
    save_path = 'noise'
    y = np.array(Image.open(imagename).convert('L')) / 255
    noise_type = 'g3'
    noise_var = 0.02
    seed = 0
    noise, psd, kernel = get_experiment_noise(noise_type, noise_var, seed, y.shape)
    z = y + noise
    y_est = bm3d(z, psd)
    psnr = get_psnr(y, y_est)
    print("PSNR:", psnr)
    y_est = np.minimum(np.maximum(y_est, 0), 1)
    z_rang = np.minimum(np.maximum(z, 0), 1)
    z_rang = torch.from_numpy(z_rang).unsqueeze(0).float()
    y_est = torch.from_numpy(y_est).unsqueeze(0).float()
    denoise_img_path = os.path.join(save_dir, 'denoised.jpg')
    save_image(y_est, denoise_img_path)
    noise_img_path = os.path.join(save_path, 'noise.jpg')
    save_image(z_rang, noise_img_path)

if __name__ == '__main__':
    main()

from PIL import Image, ImageDraw # 将图片平移并旋转 gray2 = Image.fromarray(src) width, height = gray2.size # 计算中心点和X轴角度 center = (max_point[0], max_point[1]) angle = np.arctan2(point2[1] - max_point[1], point2[0] - max_point[0]) * 180 / np.pi img_translated = gray2.transform((width, height), Image.AFFINE, (1, 0, center[0] - width/2, 0, 1, center[1] - height/2), resample=Image.BICUBIC) img_translated_rotated = img_translated.rotate(angle, resample=Image.BICUBIC, expand=True)from PIL import Image, ImageDraw #裁剪 img4 = Image.fromarray(src) width1, height1 = img4.size width2, height2 = img_translated_rotated.size left = (width2 - width1 )/2 top = (height2 - height1 )/2 right = (width2 - width1 )/2 + width1 bottom = (height2 - height1 )/2 + height1 cropped_image = img_translated_rotated.crop((left, top, right, bottom )) 现在已知在旋转前的图片gray2上有对应的点集合points，对点集points进行平移和旋转变换，使得其能在旋转后图片上完全重合的代码

可以参考以下代码：

from PIL import Image, ImageDraw
import numpy as np

# 原始图片
gray2 = Image.fromarray(src)

# 中心点和旋转角度
max_point = (100, 100)  # 假设最大点为(100, 100)
point2 = (200, 200)     # 假设另一个点为(200, 200)
center = (max_point[0], max_point[1])
angle = np.arctan2(point2[1] - max_point[1], point2[0] - max_point[0]) * 180 / np.pi

# 平移和旋转变换
width, height = gray2.size
img_translated = gray2.transform((width, height), Image.AFFINE, (1, 0, center[0] - width/2, 0, 1, center[1] - height/2), resample=Image.BICUBIC)
img_translated_rotated = img_translated.rotate(angle, resample=Image.BICUBIC, expand=True)

# 裁剪
width1, height1 = gray2.size
width2, height2 = img_translated_rotated.size
left = (width2 - width1 )/2
top = (height2 - height1 )/2
right = (width2 - width1 )/2 + width1
bottom = (height2 - height1 )/2 + height1
cropped_image = img_translated_rotated.crop((left, top, right, bottom))

其中，假设最大点为(100, 100)、另一个点为(200, 200)，可以根据实际情况修改。