# Convert image to tensor

Access all courses and lessons, gain confidence and expertise, and learn how things work and how to use them. We import NumPy as np. We can print this multidimensional array and see that it is two arrays with three rows and four columns each. What we want to do is use PyTorch from NumPy functionality to import this multi-dimensional array and make it a PyTorch tensor. Then we can print our converted tensor and see that it is a PyTorch FloatTensor of size 2x3x4 which matches the NumPy multi-dimensional array shape, and we see that we have the exact same numbers.

AI Workbox. Up next. Transcript: We import NumPy as np. Next, I paste in a NumPy array. We can look at the shape which is a 2x3x4 multi-dimensional array. Here, the first row of this PyTorch tensor, we see that it is 1, 2, 3, 4. Again, it is a FloatTensor which was the exact NumPy data type that we passed in float PyTorch by default uses a float 32 for the FloatTensor. Want to hear when new videos are released? Email Address. You might also enjoy these deep learning videos:.

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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Sparse Tensors for example are returned as SparseTensorValue:.

To convert back from tensor to numpy array you can simply run. Eager Execution is enabled by default, so just call. Numpy array may share memory with the Tensor object. Any changes to one may be reflected in the other. If Eager Execution is disabled, you can build a graph and then run it through tf. Session :. See also TF 2. This worked for me. You can try it in a ipython notebook.

Just don't forget to add the following line:.

## 执行tf.convert_to_tensor()时，究竟发生了什么？

It seems that tensor. Please note that the placeholder name is x in my case, but I suppose you should find out the right name for the input placeholder. Learn more. How can I convert a tensor into a numpy array in TensorFlow? Ask Question. Asked 4 years, 4 months ago. Active 2 months ago. Viewed k times. How to convert a tensor into a numpy array when using Tensorflow with Python bindings? If you are using tensorflow 2.

Active Oldest Votes. Any tensor returned by Session.Transforms are common image transformations. They can be chained together using Compose. Additionally, there is the torchvision. Functional transforms give fine-grained control over the transformations. This is useful if you have to build a more complex transformation pipeline e. If size is an int instead of sequence like h, wa square crop size, size is made.

Should be non negative numbers. This transform returns a tuple of images and there may be a mismatch in the number of inputs and targets your Dataset returns. See below for an example of how to deal with this.

If size is an int instead of sequence like h, wa square crop of size size, size is made. Grayscale version of the input.

If a single int is provided this is used to pad all borders. If a tuple of length 4 is provided this is the padding for the left, top, right and bottom borders respectively. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.

Should be: constant, edge, reflect or symmetric. Default is constant. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2]. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3]. Set to 0 to deactivate rotations. Will not translate by default.

Will keep original scale by default. Else if shear is a tuple or list of 2 values a shear parallel to the x axis in the range shear[0], shear[1] will be applied. Else if shear is a tuple or list of 4 values, a x-axis shear in shear[0], shear[1] and y-axis shear in shear[2], shear[3] will be applied.

Will not apply shear by default. See filters for more information. Default is None, i. If a sequence of length 4 is provided, it is used to pad left, top, right, bottom borders respectively. Since cropping is done after padding, the padding seems to be done at a random offset. Grayscale version of the input image with probability p and unchanged with probability 1-p.

Default value is 0. A crop of random size default: of 0. This crop is finally resized to given size. This is popularly used to train the Inception networks. If true, expands the output to make it large enough to hold the entire rotated image. If false or omitted, make the output image the same size as the input image. Note that the expand flag assumes rotation around the center and no translation.

Origin is the upper left corner. Default is the center of the image.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time.

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### tf.convert_to_tensor

I want to capture frames from a video with python and opencv and then classify the captured Mat images with tensorflow. This is how i am doing now with tensorflow loading the image from file :. For feeding into inception v3, you need to use the Mult:0 Tensor as entry point, this expects a 4 dimensional Tensor that has the layout: [Batch index,Width,Height,Channel] The last three are perfectly fine from a cv::Mat, the first one just needs to be 0, as you do not want to feed a batch of images, but a single image.

The code looks like:. Edit: I just noticed, that the inception network wants intensity values normalized as floats to [ If so, this tensor expects a scalar string containing the bytes for a JPEG image. You have a couple of options, one is to look further down the network for the node where the JPEG is converted to a matrix. If you can get your image in that format you can replace the DecodeJpeg Once you have the data as a numpy array you can pass it to tensor flow through a feeding mechanism as in the link that thesonyman referenced:.

In my case i had to read an image from file, do some processing and then inject into inception to obtain the return from a features layer, called last layer. My solution is short but effective. Learn more. Convert python opencv mat image to tensorflow image data Ask Question. Asked 3 years, 5 months ago. Active 1 month ago. Viewed 12k times. FastGFile imagePath, 'rb'. Txeif Txeif 1 1 gold badge 1 1 silver badge 7 7 bronze badges.

**Pytorch convert torch tensor to numpy ndarray and numpy array to tensor**

Active Oldest Votes. Load the OpenCV image using imread, then convert it to a numpy array. Kind regards, Chris Edit: I just noticed, that the inception network wants intensity values normalized as floats to [See Stable See Nightly. Converts the given value to a Tensor. This function converts Python objects of various types to Tensor objects.

It accepts Tensor objects, numpy arrays, Python lists, and Python scalars. For example:. All standard Python op constructors apply this function to each of their Tensor-valued inputs, which allows those ops to accept numpy arrays, Python lists, and scalars in addition to Tensor objects.

A Tensor based on value. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see the Google Developers Site Policies. Install Learn Introduction. TensorFlow Lite for mobile and embedded devices. TensorFlow Extended for end-to-end ML components. API r2. API r1 r1. Pre-trained models and datasets built by Google and the community. Ecosystem of tools to help you use TensorFlow. Libraries and extensions built on TensorFlow.

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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Sparse Tensors for example are returned as SparseTensorValue:. To convert back from tensor to numpy array you can simply run.

Eager Execution is enabled by default, so just call. Numpy array may share memory with the Tensor object. Any changes to one may be reflected in the other. If Eager Execution is disabled, you can build a graph and then run it through tf.

Session :. See also TF 2. This worked for me. You can try it in a ipython notebook. Just don't forget to add the following line:. It seems that tensor. Please note that the placeholder name is x in my case, but I suppose you should find out the right name for the input placeholder.

Learn more. How can I convert a tensor into a numpy array in TensorFlow? Ask Question. Asked 4 years, 4 months ago. Active 2 months ago. Viewed k times. How to convert a tensor into a numpy array when using Tensorflow with Python bindings?

If you are using tensorflow 2. Active Oldest Votes. Any tensor returned by Session. Lenar Hoyt Lenar Hoyt 4, 5 5 gold badges 40 40 silver badges 50 50 bronze badges. I get ValueError: Cannot evaluate tensor using 'eval ': No default session is registered.

Use 'with sess. EduardoPignatelli It works for me in Theano with no extra work. Not sure about tf. EduardoPignatelli you need to run the. Session ; with sess. TensorFlow 2.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have image data in a compressed numpy format like the Item data below.

I would like to transform it to a tensor like the desired output below. Learn more. Convert Image to Tensor Ask Question. Asked 1 year, 9 months ago.

Active 20 days ago. Viewed 1k times. This has nothing to do with TensorFlow. It's just about reading an image using NumPy. Have a look at cv2. Active Oldest Votes. Maybe it is not decoded correctly. Try with this before the output instruction: item. Asier Alcaide Asier Alcaide 3 1 1 bronze badge. I added an update to the original post with code that creates the correct tensor from the image.

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