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Real_Time_Image_Animation | real time application in opencv using first order | Machine Learning library

 by   anandpawara Python Version: Current License: GPL-3.0

 by   anandpawara Python Version: Current License: GPL-3.0

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kandi X-RAY | Real_Time_Image_Animation Summary

Real_Time_Image_Animation is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, OpenCV applications. Real_Time_Image_Animation has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has medium support. However Real_Time_Image_Animation has 1 bugs. You can download it from GitHub.
The Project is real time application in opencv using first order model
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kandi-support Support

  • Real_Time_Image_Animation has a medium active ecosystem.
  • It has 2771 star(s) with 424 fork(s). There are 95 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 12 open issues and 5 have been closed. There are 12 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Real_Time_Image_Animation is current.
This Library - Support
Best in #Machine Learning
Average in #Machine Learning
This Library - Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • Real_Time_Image_Animation has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 12 code smells.
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • Real_Time_Image_Animation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • Real_Time_Image_Animation code analysis shows 0 unresolved vulnerabilities.
  • There are 4 security hotspots that need review.
This Library - Security
Best in #Machine Learning
Average in #Machine Learning
This Library - Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • Real_Time_Image_Animation is licensed under the GPL-3.0 License. This license is Strong Copyleft.
  • Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
This Library - License
Best in #Machine Learning
Average in #Machine Learning
This Library - License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • Real_Time_Image_Animation releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • Real_Time_Image_Animation saves you 706 person hours of effort in developing the same functionality from scratch.
  • It has 1633 lines of code, 122 functions and 17 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed Real_Time_Image_Animation and discovered the below as its top functions. This is intended to give you an instant insight into Real_Time_Image_Animation implemented functionality, and help decide if they suit your requirements.

  • Calculate the loss function
    • Transform a frame
    • Compute the Jacobian of the Jacobian
    • Warp coordinates
  • Compute the heatmap for a source image
    • Create deformed source image
    • Compute heatmap for source image
    • Convert a kp to a gaussian distribution
  • Animation function
    • R Normalize kp
    • Load a checkpoint
    • Visualize the driver
  • Forward computation
    • Run the worker
    • Return the result of the condition
  • Forward transformation to the source image
    • Transform input into a grid
  • Load data from a config file
    • Find the best frame for a given source
      • Log a single epoch
        • Compute the feature map
          • R Normalize a curve
            • Calculate the mean standard deviation of a list
              • Make an animation
                • Patch the replication callbacks
                  • Compute the heatmap for a given feature

                    Get all kandi verified functions for this library.

                    Get all kandi verified functions for this library.

                    Real_Time_Image_Animation Key Features

                    The Project is real time application in opencv using first order model

                    Step 4 : Download cascade file ,weights and model and save in folder named extract

                    copy iconCopydownload iconDownload
                    The file is also availible via direct link on Google's Drive:
                    https://drive.google.com/uc?id=1wCzJP1XJNB04vEORZvPjNz6drkXm5AUK
                    
                    **On Linux machine** : ```unzip checkpoints.zip```
                    
                    If on windows platfrom unzip checkpoints.zip using unzipping software like 7zip.
                    
                    **Delete zip file** : ```rm checkpoints.zip```
                    
                    ## Step 5 : Run the project
                    
                    **Run application from live camera** : ```python image_animation.py -i path_to_input_file -c path_to_checkpoint```
                    
                    **Example** : ```python .\image_animation.py -i .\Inputs\Monalisa.png -c .\checkpoints\vox-cpk.pth.tar```
                    
                    **Run application from video file** : ```python image_animation.py -i path_to_input_file -c path_to_checkpoint -v path_to_video_file```
                    
                    **Example** : ```python .\image_animation.py -i .\Inputs\Monalisa.png -c .\checkpoints\vox-cpk.pth.tar -v .\video_input\test1.mp4 ```
                    
                    ![test demo](animate.gif)
                    
                    ### TODO:
                    Tkinter version
                    
                    Need work on face alignments
                    
                    Future plans adding deepfake voice and merging with video
                    
                    Credits
                    =======

                    Community Discussions

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                    Trending Discussions on Machine Learning

                    QUESTION

                    Using RNN Trained Model without pytorch installed

                    Asked 2022-Feb-28 at 20:17

                    I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

                    I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

                    I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

                    import torch
                    import torch.nn as nn
                    import torch.nn.functional as F
                    import torch.optim as optim
                    import random
                    
                    torch.manual_seed(1)
                    random.seed(1)
                    device = torch.device('cpu')
                    
                    class RNN(nn.Module):
                      def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
                        super(RNN, self).__init__()
                        self.input_size = input_size
                        self.hidden_size = hidden_size
                        self.output_size = output_size
                        self.num_layers = num_layers
                        self.batch_size = batch_size
                        self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
                        self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
                        self.hidden2out = nn.Linear(hidden_size, output_size)
                        self.hidden = self.init_hidden()
                      def forward(self, feature_list):
                        feature_list=torch.tensor(feature_list)
                        
                        if self.matching_in_out:
                          lstm_out, _ = self.lstm( feature_list.view(len( feature_list), 1, -1))
                          output_space = self.hidden2out(lstm_out.view(len( feature_list), -1))
                          output_scores = torch.sigmoid(output_space) #we'll need to check if we need this sigmoid
                          return output_scores #output_scores
                        else:
                          for i in range(len(feature_list)):
                            cur_ft_tensor=feature_list[i]#.view([1,1,self.input_size])
                            cur_ft_tensor=cur_ft_tensor.view([1,1,self.input_size])
                            lstm_out, self.hidden = self.lstm(cur_ft_tensor, self.hidden)
                            outs=self.hidden2out(lstm_out)
                          return outs
                      def init_hidden(self):
                        #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
                        return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device),
                                torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device))
                    

                    I am aware of this question, but I'm willing to go as low level as possible. I can work with numpy array instead of tensors, and reshape instead of view, and I don't need a device setting.

                    Based on the class definition above, what I can see here is that I only need the following components from torch to get an output from the forward function:

                    • nn.LSTM
                    • nn.Linear
                    • torch.sigmoid

                    I think I can easily implement the sigmoid function using numpy. However, can I have some implementation for the nn.LSTM and nn.Linear using something not involving pytorch? Also, how will I use the weights from the state dict into the new class?

                    So, the question is, how can I "translate" this RNN definition into a class that doesn't need pytorch, and how to use the state dict weights for it? Alternatively, is there a "light" version of pytorch, that I can use just to run the model and yield a result?

                    EDIT

                    I think it might be useful to include the numpy/scipy equivalent for both nn.LSTM and nn.linear. It would help us compare the numpy output to torch output for the same code, and give us some modular code/functions to use. Specifically, a numpy equivalent for the following would be great:

                    rnn = nn.LSTM(10, 20, 2)
                    input = torch.randn(5, 3, 10)
                    h0 = torch.randn(2, 3, 20)
                    c0 = torch.randn(2, 3, 20)
                    output, (hn, cn) = rnn(input, (h0, c0))
                    

                    and also for linear:

                    m = nn.Linear(20, 30)
                    input = torch.randn(128, 20)
                    output = m(input)
                    

                    ANSWER

                    Answered 2022-Feb-17 at 10:47

                    You should try to export the model using torch.onnx. The page gives you an example that you can start with.

                    An alternative is to use TorchScript, but that requires torch libraries.

                    Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

                    ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

                    A running example

                    Just modifying a little your example to go over the errors I found

                    Notice that via tracing any if/elif/else, for, while will be unrolled

                    import torch
                    import torch.nn as nn
                    import torch.nn.functional as F
                    import torch.optim as optim
                    import random
                    
                    torch.manual_seed(1)
                    random.seed(1)
                    device = torch.device('cpu')
                    
                    class RNN(nn.Module):
                      def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
                        super(RNN, self).__init__()
                        self.input_size = input_size
                        self.hidden_size = hidden_size
                        self.output_size = output_size
                        self.num_layers = num_layers
                        self.batch_size = batch_size
                        self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
                        self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
                        self.hidden2out = nn.Linear(hidden_size, output_size)
                      def forward(self, x, h0, c0):
                        lstm_out, (hidden_a, hidden_b) = self.lstm(x, (h0, c0))
                        outs=self.hidden2out(lstm_out)
                        return outs, (hidden_a, hidden_b)
                      def init_hidden(self):
                        #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
                        return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach(),
                                torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach())
                    
                    # convert the arguments passed during onnx.export call
                    class MWrapper(nn.Module):
                        def __init__(self, model):
                            super(MWrapper, self).__init__()
                            self.model = model;
                        def forward(self, kwargs):
                            return self.model(**kwargs)
                    

                    Run an example

                    rnn = RNN(10, 10, 10, 3)
                    X = torch.randn(3,1,10)
                    h0,c0  = rnn.init_hidden()
                    print(rnn(X, h0, c0)[0])
                    

                    Use the same input to trace the model and export an onnx file

                    
                    torch.onnx.export(MWrapper(rnn), {'x':X,'h0':h0,'c0':c0}, 'rnn.onnx', 
                                      dynamic_axes={'x':{1:'N'},
                                                   'c0':{1: 'N'},
                                                   'h0':{1: 'N'}
                                                   },
                                      input_names=['x', 'h0', 'c0'],
                                      output_names=['y', 'hn', 'cn']
                                     )
                    

                    Notice that you can use symbolic values for the dimensions of some axes of some inputs. Unspecified dimensions will be fixed with the values from the traced inputs. By default LSTM uses dimension 1 as batch.

                    Next we load the ONNX model and pass the same inputs

                    import onnxruntime
                    ort_model = onnxruntime.InferenceSession('rnn.onnx')
                    print(ort_model.run(['y'], {'x':X.numpy(), 'c0':c0.numpy(), 'h0':h0.numpy()}))
                    

                    Source https://stackoverflow.com/questions/71146140

                    Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                    Vulnerabilities

                    No vulnerabilities reported

                    Install Real_Time_Image_Animation

                    You can download it from GitHub.
                    You can use Real_Time_Image_Animation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

                    Support

                    For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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