Sample image tensor dimensions: (channels=3, height=100, width=100)
2024-01-15
langspythonpytorchChatGPTimport cv2
import numpy as np
import torch
# Sample image tensor dimensions: (channels=3, height=100, width=100)
image_tensor = torch.rand(3, 100, 100) # Replace with your actual tensor
# Convert PyTorch tensor to NumPy array
image_np = image_tensor.permute(1, 2, 0).cpu().numpy() # Channels last for OpenCV
# Define the target size
target_height = 64
target_width = 64
# Resize the image using OpenCV
resized_image_np = cv2.resize(image_np, (target_width, target_height))
# Convert NumPy array back to PyTorch tensor
resized_image_tensor = torch.from_numpy(resized_image_np).permute(2, 0, 1).float()
# Print the shapes to verify
print("Original tensor shape:", image_tensor.shape)
print("Resized tensor shape:", resized_image_tensor.shape)import torch
import torch.nn.functional as F
# 假设你有一个480x480的RGB图片，可以表示为一个3维的torch.Tensor
# 假设img是你的原始图像，大小为(1, 3, 480, 480)
# (1, 3, 480, 480)表示(batch_size, channels, height, width)
# 生成一个480x480的假图片，这里用随机数代替
img = torch.rand(1, 3, 480, 480)
# 缩放成128x128的图像
scaled_img = F.interpolate(img, size=(128, 128), mode='bilinear', align_corners=False)
# 输出缩放后图像的大小
print("缩放后图像大小:", scaled_img.size()) # 输出: torch.Size([1, 3, 128, 128]