super-resolution | Tensorflow 2.x based implementation | Computer Vision library
kandi X-RAY | super-resolution Summary
kandi X-RAY | super-resolution Summary
You can find an introduction to single-image super-resolution in this article. It also demonstrates how EDSR and WDSR models can be fine-tuned with SRGAN (see also this section).
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super-resolution Key Features
super-resolution Examples and Code Snippets
Lines of Code : 36License : Permissive (MIT)
Copy# For PCARN (L1)
python pcarn/main.py \
--model pcarn \
--ckpt_dir ./checkpoints/ \
--batch_size 64 --patch_size 48 \
--scale 0 --max_steps 600000 --decay 400000 \
--memo
# For PCARN-M (L1)
python pcarn/main.py \
--model pca 
License : No LicenseCopydef pixelShuffler(inputs, scale=2):
size = tf.shape(inputs)
batch_size = size[0]
h = size[1]
w = size[2]
c = inputs.get_shape().as_list()[-1]
# Get the target channel size
channel_target = c // (scale * scale)
channel # take L1 Loss as example
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import pytorch_ssim
class Loss(nn.modules.loss._Loss):
def __init__(self, spl_alpha, spl_beta, spl_maxVal):
super(Loss, self).__init__() ../GAN/GAN.py Community Discussions
Trending Discussions on super-resolution
QUESTION
I installed OpenCV version 4.5.2.52 to try and use super-resolution on pictures that I want to read but are too blurry.
To do so, I am following the code from this site: https://programmer.group/opencv-advanced-super-resolution-based-on-opencv.html
There are different codes on this page that I can copy, I wanted to try this one :
...ANSWER
Answered 2022-Feb-22 at 15:25Do not install both opencv-python and opencv-contrib-python at the same time. Install only one of both.
I can access cv2.dnn_superres in opencv-contrib-python version 4.5.5 (currently the latest).
QUESTION
I am working on a program that works on hyperspectral image super-resolution by using Neural Networks, Now in here the Mains directory of the program contains multiple parsers. The parsers and subparsers seem to have been defined correctly
...ANSWER
Answered 2022-Jan-31 at 19:06All you need to do is:
QUESTION
I'm having a problem implementing a super-resolution model
...ANSWER
Answered 2021-Nov-12 at 03:38I have found a solution
First of all, I'm using checkpoints to save model weight when training
during the implementation and testing of the model, I have changed some of the layers so the input size is changed too, but my weight still remember the input size from the previous checkpoint
so I delete the checkpoints folder and then everything works again
QUESTION
I am trying to implement GANs for super-resolution enhancement using Tensorflow. Here is the link of my collab code https://colab.research.google.com/drive/1F376Y6T5ehoE-mk2q7LGBmLq-OkWxqtr?usp=sharing
First image is the output image
Second is the Expected output
Can someone help me figure out what is wrong in the image. Any help would be appreciated. Thank you
...ANSWER
Answered 2021-Jun-07 at 17:59Actually, your generator looks quite simple. Try adding some more convolutional layers Conv2D after upsampling the inputs. There are also many techniques & architectures (such as Pix2Pix) that handle very well high quality images.
This repository contains quite a few GANs that might help you improve your model + some tricks You can do to improve the overall quality: https://github.com/kochlisGit/Keras-GAN
QUESTION
I have implemented and trained the model from the following website, and using the author's source code:
I am now running an image through the trained network and want to get the network output (feature maps etc.) at every stage.
My ApproachTo that end, I have tried making sub-models from groups of layers from the full model (called sizedModel in my code) and checking their output.
I have done that for the first L1(Conv2D)
ANSWER
Answered 2021-May-02 at 03:16If I understand your question properly, you want to get output feature maps of each layer of a model. Normally, as we mentioned in the comment box, a model with one (or multiple) inputs and one (or multiple) outputs. But in order to inspect the activation feature maps of inside layers, we can adopt some strategies. Some possible scenarios: (1). Want to get output feature maps of each layer in run-time or training time. (2). Want to get output feature maps of each layer in the inference time or after training. And as you quoted:
I am now running an image through the trained network and want to get the network output (feature maps etc.) at every stage.
That goes to number 2, get the feature maps in inference time. Below is a simple possible workaround to do this. First, we build a model, and then after training, we will modify the trained model to get feature maps of each layer within it (technically creating the same model with some modification).
QUESTION
I am trying to merge two trained neural networks. I have two trained Keras model files A and B.
Model A is for image super-resolution and model B is for image colorization.
I am trying to merge two trained networks so that I can inference SR+colorization faster. (I am not willing to use a single network to accomplish both SR and colorization tasks. I need to use two different networks for SR and colorization tasks.)
Any tips on how to merge two Keras neural networks?
...ANSWER
Answered 2021-Jan-04 at 10:37As long a the shape of the output of the network A is compatible with the shape of the input of the model B, it is possible.
As a tf.keras.models.Model inherits from tf.keras.layers.Layer, you can use a Model as you would use a Layer when creating your keras model.
A simple example :
Lets first create 2 simple networks, A and B, with the constraints that the input of B has the same shape as the output of A.
QUESTION
i am Python beginner.
I succeeded in using "image enhancing(from tensorflow lib)" running on colab and I wanted to run it on jupyter notebook, so I moved it.
...ANSWER
Answered 2020-Dec-15 at 20:31wget isn't a Python module, so you can't import it. urllib is an importable Python module and can be used to retrieve internet resources such as images.
You will find it helpful to read the urllib HOWTO and other relevant Python docs: https://docs.python.org/3/howto/urllib2.html
QUESTION
Testing ONNX model for super resolution model, I have error running this sample program.
My ONNX version is 1.5.0 with onnxruntime 1.4.0. Onnxruntime was installed using pip. Pytorch version is 1.6.0
Error is at ort_session = onnxruntime.InferenceSession('/home/itc/pytorch/sub_pixel_cnn_2016/model/super-resolution-10.onnx')
The error is in loading onnx model.
...ANSWER
Answered 2020-Oct-12 at 23:33super-resolution-10.onnx seems to load OK for me.
I downloaded the file from https://github.com/onnx/models/blob/master/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.onnx
QUESTION
I recently finished the Image super-resolution using Autoencoders in Coursera and when I try to run the same code on my laptop using Spyder and Jupyter notebook, I keep getting this error. I am using Nvidia GeForce 1650Ti along with Tensorflow-gpu=2.3.0, CUDA=10.1, cuDNN=7.6.5 and python=3.8.5. I have used the same configurations for running many deep neural network problems and none of them gave this error.
Code:
...ANSWER
Answered 2020-Sep-19 at 07:26The conv2d op raised an error message:
Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
Looking above, we see
Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3891 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1650 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
failed to allocate 3.80G (4080218880 bytes) from device:
CUDA_ERROR_OUT_OF_MEMORY: out of memory
failed to allocate 3.42G (3672196864 bytes) from device:
CUDA_ERROR_OUT_OF_MEMORY: out of memory
So this graph would need more memory than there is available on your GeForce GTX 1650 Ti (3891 MB). Try using a smaller input image size and/or a smaller batch size.
QUESTION
https://towardsdatascience.com/deep-learning-based-super-resolution-with-opencv-4fd736678066
this is the code copied out of the article;
import cv2 from cv2 import dnn_superres
sr = dnn_superres.DnnSuperResImpl_create()
image = cv2.imread('./input.png')
path = "EDSR_x3.pb" sr.readModel(path)
sr.setModel("edsr", 3)
result = sr.upsample(image)
cv2.imwrite("./upscaled.png", result)
i also tried opencv Super Resolution Tutorial;https://docs.opencv.org/master/d5/d29/tutorial_dnn_superres_upscale_image_single.html
import cv2 from cv2 import dnn_superres
sr = dnn_superres.DnnSuperResImpl_create()
image = cv2.imread('./image.png')
path = "EDSR_x4.pb" sr.readModel(path)
sr.setModel("edsr", 4)
result = sr.upsample(image)
cv2.imwrite("./upscaled.png", result)
My enviroment is anaconda3 opencv 4.3.0. I either get the error from the title or i get "killed" when i run the opencv example.
*My file directory is all on the same level of the sample codes. I would just change my image file names. I did try to compile opencv and opencv_contrib from cmake but, i didn't know how to have python refer to opencv and opencv_contrib from source. ifollow this documentation to install opencv from source; https://d*ocs.opencv.org/3.4/d2/de6/tutorial_py_setup_in_ubuntu.html
I opted to use anaconda wrapping of opencv 4.3.0 because i ran into too many dependency and wrongly installed package problems.
My friend from a meetup managed to apply the code from the article just as the article depicted while i tried to follow exactly what he did, using an anaconda enviorment. Would my problem stem from my virtual enviorment or opencv package version or the code itself? i did have another colleague run my code from my github branch and he had my exact same problems. How should i apprach the bugs im having and apply the super resolution examples i found?
...ANSWER
Answered 2020-Jun-30 at 13:35The error 'Model not specified' comes from the fact that the Net is empty. You have to actually download the model and then provide the path to the 'sr.readModel()' function.
If you did that and it still does not work, you could try these two things:
Try a smaller image (in .png format).
Build OpenCV from source. Do not forget the contrib modules (this is where the dnn_superres module resides). You said you had a problem linking python. I would suggest to use this tutorial. After following that tutorial, do the following command (after you already did
sudo make install), to link the python libraries:
sudo ldconfig
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Install super-resolution
Examples in this section require following pre-trained weights for running (see also example notebooks):.
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