(0) # 255也可以改为256 def tensor_to_np(tensor):img = tensor.mul(255).byte() img = img.cpu().numpy().squeeze(0).transpose((1, 2, 0)) return img def show_from_cv(img, title=None): img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) plt.figure() plt.imshow(img) if title is ...
但是这样就多了一个数据转换,所以不想这么干,简介的步骤就是将opencv的numpy格式的数据直接转为tensor,然后进行resize。 基于以上需求,在这个过程中,主要问题花在数据对其上,就是需要对比以下转换的数据是否一致 import cv2 import torch from PIL import Image from torchvision import transforms #pil加载的图像默认是...
fromtorchvision.modelsimportresnet34importtorch.nn.functional as Fimporttorch.nn as nnimporttorchimportcv2#读取一张图片,并转换成[1,3,224,224]的float张量并归一化image = cv2.imread("flower.jpg") image= cv2.resize(image,(224,224)) input_tensor= torch.tensor(image).permute(2,0,1).unsqueeze(...
[I] [Parser]: The input tensor(s) is/are: in0_0,in1_0,in2_0,in3_0,in4_0 [I] [Parser]: Input in0 from cfg is shown as tensor in0_0 in IR! [I] [Parser]: Input in1 from cfg is shown as tensor in1_0 in IR! [I] [Parser]: Input in2 from cfg is shown as tens...
import cv2 import numpy as np import torch # 读取图像文件 image = cv2.imread('path/to/image.jpg') #将BGR转换为RGB image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 将图像转换为PyTorch张量 image_tensor = torch.from_numpy(image).permute(2, 0, 1).float() / 255.0 # 打印图像张量的形...
import torchimport torchvisionimport torchvision.transformsastransformsimport matplotlib.pyplotaspltimport numpyasnptransform = transforms.Compose([# resize 32×32transforms.Resize(32),# center-crop裁剪变换transforms.CenterCrop(32),#to-tensortransforms.ToTensor(),# normalize 标准化transforms.Normalize([0.5, ...
image = cv2.resize(image, (200,64)) image = image.transpose(2,0,1) img_input = image.astype(np.float32) img_input = torch.from_numpy(img_input) img_input = img_input.unsqueeze(0) img_input = img_input.to(device) # 运行模型 ...
model.to('cpu') self.model.eval() # 切换为 eval 模式,不计算梯度 self.detect_layer = self.model.model[-1] # 得到最后的检测层 self.model.traced = True # False 修改为 True # 随机制造一个 bs=1 输入tensor rand_example = torch.rand(1, 3, img_size, img_size) traced_script_module ...
2.1 `Image` 、`Tensor` 与 `ndarray` 之间的相互转化 2.1.1 ToTensor() 2.1.2 PILToTensor() 2.1.3 ToPILImage() 2.2 常见的图像操作 2.2.1 TF.adjust_brightness() 2.2.2 TF.adjust_contrast() 2.2.3 TF.adjust_saturation() 2.2.4 TF.adjust_sharpness() ...
('model.pth',map_location=torch.device('cuda:0')))self.model.eval()def preprocess(self,batch):"""预处理输入数据"""images=[img.convert('RGB')forimginbatch]images=[img.resize((224,224))forimginimages]images=[torch.tensor(np.array(img)).permute(2,0,1).float()forimginimages]images=...