decode(image_data) # run on GPU assert image_tensor.is_cuda # 使用 torchvision.transform 的图像预处理算子 import torchvision transform = torchvision.transform.Resize((224, 224)) resized_tensor = transform(image_tensor.permute((2, 0, 1))) # run on GPU 同时支持 batch decode 和 多线程 ...
image_string = tf.read_file(filename) image_decoded = tf.image.decode_image(image_string) image_resized = tf.image.resize_images(image_decoded, [28, 28]) return image_resized, label # 图片文件的列表 filenames = tf.constant(["/var/data/image1.jpg", "/var/data/image2.jpg", ...]...
方法四:TensorFlow方法: with tf.Session() as sess: img = tf.read_file(imgfile) #读取图片, img_data = tf.image.decode_jpeg(img, channels=3) #解码 #img_data = sess.run(tf.image.decode_jpeg(img, channels=3)) img_data = sess.run(tf.image.rgb_to_grayscale(img_data)) #灰度化print...
save_image(pic, './conv_autoencoder/decode_image_{}.png'.format(t + 1)) save_image(img, './conv_autoencoder/raw_image_{}.png'.format(t + 1)) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28....
int32]) return x, y def _parse_path_function(image_path): image = tf.io.read_file(image_path) image = tf.image.decode_jpeg(image, channels=3) image = tf.image.resize(image, [224, 224]) image /= 255.0 filename=image_path.numpy() filename=str(filename, "utf-8") filename=...
c. save_decode_image() 用于存储自编码器重构的图片。 #utility functionsdefget_device():iftorch.cuda.is_available(): device='cuda:0'else: device='cpu'returndevicedefmake_dir(): image_dir='FashionMNIST_Images'ifnotos.path.exists(image_dir): ...
# tensor_image is a CxHxW uint8 Tensor tensor_image=read_image('path_to_image.jpeg')# or equivalently from torchvision.ioimportread_file,decode_image # raw_data is a 1d uint8 Tensorwiththe raw bytes raw_data=read_file('path_to_image.jpeg')tensor_image=decode_image(raw_data)# all op...
代码运行次数:0 运行 AI代码解释 rgb=decode_segmap(om)plt.imshow(rgb);plt.show() 3.2.6.最终结果 接下来,让我们把所有操作放入一个函数下,并测试更多的图像! 代码语言:javascript 代码运行次数:0 运行 AI代码解释 defsegment(net,path):img=Image...
files = [{'image': image_name, 'label': label_name} for image_name, label_name in zip(image_files, label_files)] 为了可视化带有相应标签的图像,可以使用TorchIO,这是一个Python库,用于深度学习中多维医学图像的加载、预处理、增强和采样。
image_filename = root_dir +'/Oasis_Data_Processed/OAS1_0001_MR1_mpr_n4_anon_111_t88_masked_gfc.nii'label_filename = root_dir +'/Oasis_Labels_Processed/OAS1_0001_MR1_mpr_n4_anon_111_t88_masked_gfc_fseg.nii'...