import colorsys from PIL import Image sharp_image = Image.open("red1.jpg") hsi_image = sharp_image.convert("HSV") pixels = hsi_image.load() for i in range(hsi_image.size[0]): for j in range(hsi_image.size[1]): h, s, v = pixels[i, j] s *= 1.5 v *= 1.5 pixels[i, j] = (h, s, v) rgb_image = hsi_image.convert("RGB") rgb_image.save("red2.jpg")报错TypeError: integer argument expected, got float

这个错误是因为在 HSV 颜色空间中，饱和度和亮度的值域是浮点数（0-1），但在转换为 RGB 颜色空间时，这些值应该是整数（0-255）。这意味着你需要将这些浮点数值乘以 255 并四舍五入取整。你可以尝试修改代码中的这一部分：

s *= 1.5
v *= 1.5

改为：

s = int(s * 255 * 1.5 + 0.5)
v = int(v * 255 * 1.5 + 0.5)

这将把浮点数值乘以 255 并四舍五入取整，然后再乘以 1.5。这样就能够正确地转换为 RGB 颜色空间了。

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 ImageFilter image = Image.open("red1.jpg") sharp_image = image.filter(ImageFilter.SHARPEN) sharp_image.save("red2.jpg")报错NameError: name 'Image' is not defined

这个错误是因为你没有正确导入所需的模块。你需要在代码的开头添加以下代码：

from PIL import Image, ImageFilter

这将导入 `Image` 和 `ImageFilter` 两个类，以便你可以使用它们来处理图像。