I'm 4 months into programming, so I'll often find myself asking beginners' questions on forums, trying to understand/unpack/reverse engineer something I'm working on. I came across this website today:
https://denigma.app/#demo
I'm pretty sceptical about peoples' claims about AI, so I tested it with some javascript code snippets.
It doesn't work as effectively as a human being, of course, but it did help me understand the structure of some functions. I think it would work quite well if you're using code that's from a well-known library or language.
How does it work? Paste a code snippet into the left column, you can select 'line-by-line' mode which gives a more exhaustive explanation, and get a humanized description of what the snippet may be doing.
A great tool for beginners IMO, and hopefully it'll keep improving.
Seems pretty good, I tried it with sections of a long bit of javascript for validating input. There was a lot of regex in it, although I'm still not sure I understand it! lol
The code starts by importing the necessary libraries.
The code then imports a dataset called ImageListDataset which is used to create images for the model to learn from.
Next, it imports Coach and uses this class as an example of how to use the make_dataset function in order to create a new dataset with data about coaches.
This dataset will be used later on when training the model.
The next line creates a list of images that are stored in json format using tqdm library's .tojson method.
This list is then passed into save_tuned_G function which saves tuned G values for each image in this list based on its size and location within the image (top left corner).
These tuned G values are saved as JSON objects with keys being "size" and "location".
Next, it creates an ImageListDataset object from scratch using make_dataset function so that we can load these images into our model later on when training it.
Finally, some helper functions are created such as get_image , get_images , etc...
The code is a simple example of how to import the necessary libraries for this project.
This code imports the necessary libraries needed for this project.
The code starts by importing the paths_config and global_config modules.
These are used to define the folder where images will be saved, as well as the name of each image in that folder.
The next line imports hyperparameters from utils.alignment .
This is a module for calculating alignment coefficients between two images using OpenCV's calcCoeff function.
The crop_faces function is also imported so that it can be used later on in this program to crop faces out of an image if necessary.
Next, there is a function called save_image which takes an Image object (which has been converted into RGB), an output folder location, and a filename for saving the file with its index number appended onto it (e.g., "face1-001" would become "face1-001.jpg").
It then creates a new directory inside of that output folder with the same name as the input filename but with ".jpg" appended onto it (e.g., "face1-001".jpg).
Finally, it saves this newly created file inside of this directory using os .
path .
join() , which joins all three parameters together into one string value ("/home/user/output/folder
The code will save the image on the output folder with a name that is composed of the image index and the file extension.
The purpose of this code is to save an image on a specific output folder.
The code takes a Tensor and returns an Image.
The code takes the tensor, which is a 3D vector of numbers, and converts it into a 2D image.
The code then applies perspective to the image so that it appears three-dimensional on screen.
The code creates an empty mask for the image by setting all pixels to 0 in both dimensions.
The code will take a tensor and convert it into an image.
def to_pil_image(tensor: torch.Tensor) -> Image.Image: img = torch.from_numpy(torch.zeros((1, 1), dtype=torch.float32)) # img is the input tensor # img is the output tensor # img is the result of converting the input tensor into an image projected = img.convert('RGBA').transform(orig_image.size, Image.PERSPECTIVE, coeffs, Image.BILINEAR) pasted_image = orig_
The code starts by importing the click package.
It then defines a function that takes two arguments: input_folder and output_folder.
The first argument is required, and the second one is optional.
The next line of code creates an instance of Click class named analysis with these two arguments set to their default values (the current working directory).
Then it sets up some options for this analysis including -i which specifies the path to where you have your images saved in your computer, -o which specifies the name of the folder where you want to save all your processed images, and --input_folder which specifies what folder should be used as input for this analysis.
Next, we define a list called data that will hold our training data (images) along with their labels or tags associated with them.
This list will be initialized using np.random .
We then create an image from each label in our list by calling Image() on it followed by converting it into a numpy array using numpy's astype method so that we can use NumPy's built-in functions like np.sum , np.max , etc., on our arrays without having to convert them back into Python lists first before doing any calculations on them again later when they
The code is a Python code that computes the color value of an image from its pixel values.
The code above has the following arguments: -i, --input_folder, type=str, help='Path to (unaligned) images folder', required=True -o, --output_folder, type=str, help='Path to output folder', required=True
The code starts by importing the click library.
The next line sets up a class called "Analyzer" that will be used to analyze frames of video.
The next line creates an instance of the Analyzer class and assigns it to a variable called analyzer.
This is done so that we can use this object later on in our code when we want to run some analysis on the frame data.
Next, there are two variables set up: start_frame and end_frame which represent where in the video file we want to start and stop analyzing for frames respectively.
These values are then assigned default values if they aren't specified otherwise (0).
Then, there's an option called -r which stands for --run_name, which specifies what name you would like your output file saved as after running through all of its frames with this program.
If you specify any other value than '--run_name', then it will save it under that name instead (e.g., if you specify '-r myvideo', then it will save your output as myvideo).
Finally, there's another option called --use_fa/--use_dlib which determines whether or not you would like to use Fast Artificial
The code will run the name of the file that is being clicked on.
The code will use Dlib's face detection library and scale it by 1.0
The code starts by importing the numpy library.
The code then defines a function called "fit_model" which takes in two parameters: num_pti_steps and l2_lambda.
These are both integers, so they must be passed as ints to the function.
The next line of code sets up some variables that will be used later on in the program: center_sigma, xy_sigma, and pti_learning rate.
The last line of code is where we start our main loop for this program; it starts with an if statement that checks whether or not there are any command-line arguments left over from when we ran python train.py --help earlier on in this file (if there were no command-line arguments left over, then execution would continue at the end of this block).
If there are still command-line arguments left over from when we ran python train.py --help earlier on in this file, then those remaining command-line arguments will be passed into fit model as additional input values for num pti steps and l2 lambda (these extra input values will overwrite what was set previously).
The code will train a neural network for 300 steps with an L2-lambda of 10.0 and a center-sigma of 1.0, and with a learning rate of 3e-5.
The code will train a neural network for 300 steps with an L2-lambda of 10.0 and a center-sigma of 1.0, and with a learning rate of 3e-5
The code starts by importing the necessary libraries.
It then creates a global variable called config that will be used to store all of the hyperparameters and other settings for this program.
Next, it sets up some variables that are needed later on in the code: start_frame, end_frame, run_name, scale, num_pti_steps, l2_lambda, center_sigma, xy_sigma.
The main function is where all of the work happens.
The first thing it does is create an input folder and output folder for storing images from training and testing data respectively.
Then it starts running with a name specified as well as starting frame (start frame) and ending frame (end frame).
After those two things are set up it runs through each step in order using a loop until there are no more steps left to run or stop condition has been met such as when max number of steps have been reached or if use wandb flag was set to true which would mean we want to use WandB instead of Adam's beta1 model since WandB uses locality regularization instead of Adam's beta1 model which doesn't use locality regularization but rather L2 Regularization .
The code is meant to be run as a function in the main() function.
The input_folder, output_folder, start_frame, end_frame, run_name and scale are all parameters passed into the function.
The config parameter is used to store global configuration values that can be accessed by other functions within this module.
The hyperparameters are stored in a dictionary with keys being the names of the hyperparameters and values being their respective values.
The code starts by importing the necessary packages.
It then creates a new instance of the WandB class and sets it to reinit=True, which means that this instance will be initialized with default parameters.
The next line is where we create our dataset from input_folder, which contains all of the images in our project folder.
We then iterate through each image in files and assign them to an ImageListDataset object called ds.
This object has two transforms applied: ToTensor() and Normalize().
These transforms are used for scaling up or down the size of each image so that they can be aligned properly on a single face (quad).
The next line is where we initialize our Coach class with our newly created dataset ds and set use_wandb=True, meaning that this coach will use WandB's alignment algorithm instead of its own built-in one.
The code is a snippet of code that will align the images.
The first line of the code above creates an instance of ImageListDataset, which is used to store all the crops and transformations.
The second line creates an instance of Coach, which is used to run WandB on each crop image.
The code starts by creating a new coach object.
Then it creates a new image, which is the size of the screen, and then it sets up an input function that will take in images from the user.
The next line starts to create a list of quads for each joint angle (0-360).
It does this by using calc_alignment_coefficients() with quad + 0.5 as its first argument and [[0, 0], [0, image_size], [image_size, image_size], [image Size, 0]] as its second argument.
This tells the program to calculate coefficients for all four angles at once because they are all equal to one another when you go from zero degrees to 360 degrees.
The next line calculates inverse transforms for each quad: inverse = [] for i in range(len(quads)): inverse = calc_alignment_coefficients(quad + 1/4 * i - 1/2 * len(quads), [[1 / 4 * i - 1 / 2 * len(quads), 0 ], [-1 / 4 * i - 1 / 2 * len(quords) , image_size ]
[*] The code will create a folder in the current working directory called output_folder.
[*] The code will then run the following command: save_tuned_G(coach.G, ws, quads, global_config.run_name) This command will save the tuned G to the output folder and call it "coach.G".
[*] The code will then run the following commands: inverse_transforms = [calc_alignment_coefficients(quad + 0.5, [[0, 0], [0, image_size], [image_size, image_size], [image Size, 0]]) for quad in quads] This command will calculate
[*] The code starts by importing the necessary packages.
[*] Then it creates a list of all the transforms that are needed to be inverted, in this case, there is only one transform so it will just create a list with one element.
[*] Next, it creates a list of crops and an image for each crop.
[*] It then iterates through the lists of images and projects them onto their corresponding crops using torch's no_grad() function.
[*] The code starts by creating a new folder called "projected" which will hold all the projected images from this point on out.
[*] The next line opens up an output file for writing json data into named "opts.json".
[*] This file is used later on when saving these projected images to make sure they have been saved properly before moving on to save them elsewhere in our project directory structure as well as uploading them online via FTP or something similar (this step can be skipped if you want).
[*] Next we use tqdm() to iterate over every transform that needs projecting onto its corresponding crop and then saves those projections into another file called "inverse_transforms".
[*] We also keep track of how many times we've done this process so far with len(ws) .
[*] Finally, we paste our original image back
[*] The code will iterate through the list of transforms, crops, and original images.
[*] It will then take the inverse transform of each one and paste it into a new image.
[*] This is done by using the save_image() function with an output folder as well as a name for the projected image.
[*] This code also uses torch.no_grad() to make sure that no gradients are applied to this projective transformation.
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