解释每行每列,X = np.array([[psnr1, ssim_score1], [psnr2, ssim_score2], [psnr3, ssim_score3]])
时间: 2024-06-05 08:07:58 浏览: 12
这段代码定义了一个2维的NumPy数组X,该数组包含3个元素(即3个一维数组),每个一维数组中包含2个数值,分别表示PSNR和SSIM评分。
具体来说,X的形状为(3,2),表示有3行2列,每行对应一个样本,每列对应一个特征(PSNR和SSIM评分)。其中,psnr1、ssim_score1、psnr2、ssim_score2、psnr3、ssim_score3是给定的变量,代表3个样本的PSNR和SSIM评分值。可以通过X[i,j]来访问第i行第j列的数据。例如,X[0,0]表示第1个样本的PSNR评分,X[2,1]表示第3个样本的SSIM评分。
相关问题
from PIL import Image import numpy as np import io # 读取原始图像和压缩后图像 original_img = Image.open('test.jpg') compressed_img = Image.open('test_compressed.jpg') # 将图像转换为 NumPy 数组 original_img_arr = np.array(original_img) compressed_img_arr = np.array(compressed_img) # 计算原始图像大小 original_size = original_img_arr.nbytes # 计算压缩后图像大小 compressed_size = compressed_img_arr.nbytes # 计算压缩率 compression_ratio = compressed_size / original_size # 计算峰值信噪比(PSNR) mse = np.mean((original_img_arr - compressed_img_arr) ** 2) psnr = 10 * np.log10(255**2 / mse) # 计算结构相似性指数(SSIM) from skimage.metrics import structural_similarity as ssim ssim_score = ssim(original_img_arr, compressed_img_arr, multichannel=True) # 计算峰值信噪比改进比(PSNR-HVS) from skimage.metrics import peak_signal_noise_ratio as psnr_hvs psnr_hvs_score = psnr_hvs(original_img_arr, compressed_img_arr, data_range=original_img_arr.max()) # 计算多样性信噪比(MS-SSIM) from skimage.metrics import multi_scale_ssim as ms_ssim ms_ssim_score = ms_ssim(original_img_arr, compressed_img_arr, data_range=original_img_arr.max(), win_size=11) # 计算复杂度压缩比(CPC) cpc = psnr / compression_ratio # 输出七种压缩率 print(f"Compression ratio: {compression_ratio:.4f}") print(f"Peak Signal-to-Noise Ratio (PSNR): {psnr:.2f}") print(f"Structural Similarity Index (SSIM): {ssim_score:.4f}") print(f"Peak Signal-to-Noise Ratio - HVS (PSNR-HVS): {psnr_hvs_score:.2f}") print(f"Multi-Scale Structural Similarity (MS-SSIM): {ms_ssim_score:.4f}") print(f"Complexity-Compression Ratio (CPC): {cpc:.2f}") print(f"Original size: {original_size:,}") print(f"Compressed size: {compressed_size:,}")ValueError: operands could not be broadcast together with shapes (417,556,3) (418,558,3)
这个错误通常是由于两个数组的形状不匹配导致的。在您的情况下,您正在尝试将形状为(417,556,3)和(418,558,3)的两个数组进行广播操作,但它们的形状不兼容。
根据您提供的代码,错误可能出现在以下几行中:
```python
original_img_arr = np.array(original_img)
compressed_img_arr = np.array(compressed_img)
```
这两行代码将 PIL.Image 对象转换为 NumPy 数组。如果两个图像的大小不同,则它们的数组形状也不同,这可能导致广播错误。
您可以尝试查看这两张图像的大小,看看它们是否匹配。或者,您可以尝试调整图像的大小,使它们具有相同的形状,然后再将它们转换为 NumPy 数组。
例如,您可以使用 PIL 库中的 resize() 函数来调整图像的大小:
```python
original_img = original_img.resize((width, height))
compressed_img = compressed_img.resize((width, height))
```
其中,width 和 height 是您想要的新图像的宽度和高度。然后,您可以将这两张图像转换为 NumPy 数组,并继续计算 PSNR、SSIM 等指标。
下面代码转化为paddle2.2.2代码 :from __future__ import division import os, time, scipy.io import torch import torch.nn as nn import torch.optim as optim import numpy as np import glob import cv2 import argparse from PIL import Image from skimage.measure import compare_psnr,compare_ssim from tensorboardX import SummaryWriter from models import RViDeNet from utils import * parser = argparse.ArgumentParser(description='Pretrain denoising model') parser.add_argument('--gpu_id', dest='gpu_id', type=int, default=0, help='gpu id') parser.add_argument('--num_epochs', dest='num_epochs', type=int, default=33, help='num_epochs') parser.add_argument('--patch_size', dest='patch_size', type=int, default=128, help='patch_size') parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='batch_size') args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id) save_dir = './pretrain_model' if not os.path.isdir(save_dir): os.makedirs(save_dir) gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/*.tiff') gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/*.tiff') gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-10_raw/*.tiff') gt_paths4 = glob.glob('./data/SRVD_data/raw_clean/MOT17-11_raw/*.tiff') gt_paths = gt_paths1 + gt_paths2 + gt_paths3 + gt_paths4 ps = args.patch_size # patch size for training batch_size = args.batch_size # batch size for training
把原始代码转换为PaddlePaddle 2.2.2 的代码如下:
```python
import os
import time
import argparse
import glob
import cv2
import numpy as np
from PIL import Image
from skimage.measure import compare_psnr, compare_ssim
from tensorboardX import SummaryWriter
from models import RViDeNet
from utils import *
import paddle
from paddle import nn
from paddle.optimizer import optim
paddle.set_device('gpu')
parser = argparse.ArgumentParser(description='Pretrain denoising model')
parser.add_argument('--gpu_id', dest='gpu_id', type=int, default=0, help='gpu id')
parser.add_argument('--num_epochs', dest='num_epochs', type=int, default=33, help='num_epochs')
parser.add_argument('--patch_size', dest='patch_size', type=int, default=128, help='patch_size')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='batch_size')
args = parser.parse_args()
save_dir = './pretrain_model'
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/*.tiff')
gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/*.tiff')
gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-10_raw/*.tiff')
gt_paths4 = glob.glob('./data/SRVD_data/raw_clean/MOT17-11_raw/*.tiff')
gt_paths = gt_paths1 + gt_paths2 + gt_paths3 + gt_paths4
ps = args.patch_size # patch size for training
batch_size = args.batch_size # batch size for training
num_epochs = args.num_epochs
train_dataset = DatasetDenoising(gt_paths, ps=ps)
train_loader = paddle.io.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
model = RViDeNet()
model.train()
optimizer = optim.Adam(learning_rate=1e-4, parameters=model.parameters())
writer = SummaryWriter()
for epoch in range(num_epochs):
epoch_start_time = time.time()
epoch_loss = 0
for i, (noisy_patches, gt_patches) in enumerate(train_loader()):
noisy_patches = paddle.to_tensor(noisy_patches)
gt_patches = paddle.to_tensor(gt_patches)
output = model(noisy_patches)
loss = nn.functional.mse_loss(output, gt_patches)
optimizer.clear_grad()
loss.backward()
optimizer.step()
epoch_loss += loss
epoch_time = time.time() - epoch_start_time
epoch_loss = epoch_loss / len(train_loader)
print("Epoch [{}/{}] Loss: {:.5f} [{:.2f}s]".format(epoch + 1, num_epochs, epoch_loss, epoch_time))
writer.add_scalar("Loss/train", epoch_loss, epoch + 1)
if (epoch + 1) % 10 == 0:
model_path = os.path.join(save_dir, 'RViDeNet_epoch{}.pdparams'.format(epoch + 1))
paddle.save(model.state_dict(), model_path)
print("Saving model to: {}".format(model_path))
writer.close()
```
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)