Https Www Snapchat Com Filters. This is a reasonably good model, so you can now move on to using this face-emotion classifier to determine which dog mask to apply to faces. Snapchat's dog face filter has emerged as an unlikely shorthand for flirty-sexy vibes. 738*800 Size:282 KB. If you smile, the filter will apply a corgi mask. such as race, gender, age, culture, first language, or other factors. In this section, you’ll build a second emotion classifier using neural networks instead of least squares. Inside the apply_mask function, replace pass with these two lines which extract the height and width of both images: Next, determine which dimension needs to be “shrunk more.” To be precise, we need the tighter of the two constraints. We need to ask two questions for each model that we consider. Open the step_4_dog_mask_simple.py file again and return to the apply_mask function: First, remove the line of code that writes the resized mask from the apply_mask function since you no longer need it: In its place, apply your knowledge of image representation from the start of this section to modify the image. Since that absolute path may vary, we’ll download our own copy instead and place it in the assets folder: The -O option specifies the destination as assets/haarcascade_frontalface_default.xml. I Ain T Doin It Snapchat Filter. How to use Snapchat filters? As it turns out, this objective yields a closed-form solution as well: Still using the featurized samples, retrain and reevaluate the model once more. With one click use it easily.
In this page you can download an image PNG (Portable Network Graphics) contains Snapchat Cute Dalmatian Dog Puppy Tongue Filter PNG isolated, no background with high quality, you will help you to not … To accomplish this, we featurize our inputs. First, extract image and label from the dataset loader and then wrap each in a PyTorch Variable. The Dog Filter is a special effect featured in Snapchat which allows users to place a dog’s nose, ears and tongue over their faces when taking a selfie. Hot Dog Snapchat Filter Costume. Baseline performance for least squares, with these extra enhancements, performs reasonably well. If save this matrix of values as an image. Scroll above to know the most popular of them. Hot To Make Snapchat Filter. Are you a fan of goats? The next objective is to link the computer’s camera to the face detector. Create an outputs folder for these annotated results. Now replace pass in the main function with this code which initializes a face classifier using the OpenCV parameters you downloaded to your assets folder: Next, add this line to load the image children.png. … Hub for Good Instead of detecting faces in a static image, you’ll detect all faces from your computer’s camera. 1080*1920 Size:110 KB. Once again reevaluating across a number of different d, we see a smaller gap between training and validation accuracies for ridge regression. Iterate over all detected objects and draw them on the image in green using cv2.rectangle: Finally, write the image with bounding boxes into a new file at outputs/children_detected.png: Your completed script should look like this: Save the file and exit your editor. To apply a dog mask, you will replace values in the child image with non-white dog mask pixels. The Standard Hollywood Filter That's Pretty Well-known - Hollywood Los Angeles Snapchat Filter. A neutral face or a frown will register as “sad” and yield the dalmation. Hot To Make Snapchat Filter. We’ll include these additional bells and whistles in a new script. Datasets necessary for this implementation can be downloaded from this link. Finally, release the capture and close all windows. For example, if you have your back to the sun, this process might not work very well. In this section you’ll create an emotion classifier to apply different masks based on displayed emotions. Change the value of d from 100 to 1000 as shown in the following code block: Then apply ridge regression using a regularization of lambda = 10^{10}. Start by making a copy of the child image. There can be an overwhelming magnitude of uncertainty in machine learning. See what the new dog lenses look like here. Snapchat Custom Filters - Illustration. Instead of writing an image to disk, you’ll display the annotated image back to the user’s screen: Also, add some code to watch for keyboard input so you can stop the program. Hot To Create A Snapchat Filter. Hot Dog Snapchat Filter Code. Computing (X^TX)^{-1} would take too long on commodity hardware, as X^TX is a 2304x2304 matrix with over four million values, so we’ll reduce this time by selecting only the first 100 features. We’ll create a script that accepts a single image and outputs an annotated image with the faces outlined with boxes. Instead, we’ll treat this as a black box that computes higher-order features for us. ... Snapchat Dog Png Filters is hand-picked png images from user's upload or the public platform. Eventually, this classifier will then determine which dog mask to apply. GOT Snapchat Filter? Right after cv2.imshow(...) add the following: The line cv2.waitkey(1) halts the program for 1 millisecond so that the captured image can be displayed back to the user. First, import the NumPy library at the top of the script: Then add the apply_mask function from your previous work into this new file above the main function: Second, locate this line in the main function: Add this code after that line to load the dog mask: Next, in the while loop, locate this line: Add this line after it to extract the image’s height and width: Next, delete the line in main that draws bounding boxes. The line of best fit, shown in the following image, is our model. For a three-way classification problem, 45.3% is reasonably above guessing, which is 33\%. Hot To Get A Snapchat Filter. To do these, implement PyTorch’s Dataset interface, which lets you load and use PyTorch’s built-in data pipeline for the face-emotion recognition dataset: Delete the pass placeholder in the Fer2013Dataset class. We want to center the dog image on the face, so compute the offset needed to center the dog image by adding this code to apply_mask: Copy all non-white pixels from the dog image into the child image. ... Kenneth The Dog Snapchat Lens. Making cool face filters for GOT Characters & yourself !! Your main function should now match the following: Insert the following line below the # apply mask line to select the appropriate mask by using the predictor: The completed file should look like this: Save and exit your editor. Hot Dog Snapchat Filter Code. Next, fit the dog mask to the child. Additionally, download the following dog mask. Here's how men ruined dog filter by calling it the 'hoe filter.' This means that for every (x, y) position in our image, we have three values (r, g, b). On December 24th, Snapchat released a new feature: Snapchat dog filters, designed specifically to get your pup in on the selfie action. If you are a makeup lover then you’d love this filter the most … This script contains the code above along with a command-line interface and an easy-to-import version of our code that will be used later. Snapchat QR codes) below directly through the Snapchat camera, however this method does not seem to work for all users. Specify and train the model: Set up a neural network using PyTorch layers. I Don T Feel So … Add this line to the apply_mask function: Then compute the new shape by adding this code to the function: Here we cast the numbers to integers, as the resize function needs integral dimensions. This Snapchat filter surrounds you in a sky full of stars. Where are my lenses on Snapchat? In other words, ridge regression was subject to less overfitting. In the following figure, we consider the performance of our new classifier as we vary d. Intuitively, as d increases, the accuracy should also increase, as we use more and more of our original data. Navigate to the data directory and unpack the data. You get paid; we donate to tech nonprofits. Add the following code to the end of your script to do that: Save the file and exit the editor once you’ve verified your code. Beauty Products Snapchat Lens. An image has its height and width expressed as h x w. In the current grayscale representation, each pixel is one value between 0 and 1. Https Www Snapchat Com Filters. To combat overfitting, we’ll regularize our model by penalizing complex models. OpenCV makes this easy by providing both. You’ll find this line in the for loop that iterates over detected faces: In its place, add this code which crops the frame. This gives us the following picture: We can use any value between 0 and 1, such as 0.1, 0.26, or 0.74391. Hot Dog Snapchat Filter Costume. You’ll see the following image that shows the faces outlined with boxes: At this point, you have a working face detector. To start, you will work with a single image. Now let’s use facial expression to determine the dog mask applied to a face. In other words, every (x, y) position in our image has just one value. We’ll detect all faces in the following image from Pexels (CC0, link to original image). final_frame= cv2.add(mask, dog_img) frame[up_center[1]: up_center[1] + dog_height,up_center[0]: up_center[0] + dog_width] = final_dog Output. We’ll set up a model and then load pre-trained parameters. Create your own Snapchat Filters and Lenses! Place this before your main function: To estimate labels, we take the inner product with each sample and get the indices of the maximum values using np.argmax. Then, launch this proof-of-concept training: You’ll see output similar to the following as the neural network trains: You can then augment this script using a number of other PyTorch utilities to save and load models, output training and validation accuracies, fine-tune a learning-rate schedule, etc. Your script should look like the following: This activates your camera and opens a window displaying your camera’s feed. Unlocking these hidden lenses and filters … Again, our goal is to produce a model that accepts faces as input and outputs an emotion. We can also flatten this box to become just a list of numbers. To introduce color, we need a way to encode more information. We won’t go into detail in this tutorial. We amend our ordinary least-squares objective function with a regularization term, giving us a new objective. By clicking below, you are giving us consent to use cookies. Wedding Snapchat Filter Dog Custom Portrait Snapchat Filter Dog Custom Weeding Dog Filter Unique Filter Personalized Dog Filter Customized MemoryTreasure. The return type is a list of tuples, where each tuple has four numbers denoting the minimum x, minimum y, width, and height of the rectangle in that order. To understand how to process our data and produce predictions, we’ll first briefly explore machine learning models. For data processing here, you will create the train and test datasets. Follow Me On Snapchat - Dog Filter Snapchat Code. This translates the dataset into an iterable to use later. We redefine our matrices, called design matrices, using this random featurization. If so, this is the Snapchat filter for … This field includes tasks such as object detection, image restoration (matrix completion), and optical flow. Fortunately, instead of writing our own face detection logic, we can use pre-trained models. Hacktoberfest Dancing HardBass Pianta Snapchat Lens. As you work through the tutorial, you’ll use OpenCV, a computer-vision library, numpy for linear algebra utilities, and matplotlib for plotting. Get the latest tutorials on SysAdmin and open source topics. Locate the following line, defining A_train and A_test: Directly above this definition for A_train and A_test, add a random feature matrix: Then replace the definitions for A_train and A_test. For example, imagine a job search engine where the models were trained with data about candidates. PyTorch is a particularly good place to start. Then for the train and validation sets, it wraps the dataset in a DataLoader. 1080*1920 Size:55 KB. The training and inference times, all together, take no more than 20 seconds for even the best results. Introduce a check in case the detected face is too close to the edge. As a sanity check, verify that the dataset utilities are functioning. Add the following to the end of your file: Verify that your completed script looks like this: This outputs the following pair of tensors. Get latest Snapchat Dog Filter news updates & stories. What shape do you see? I Don T Feel So … Our data pipeline outputs two samples and two labels. It will match the dog mask shown earlier in this section. Save the file and exit your editor. First, delete the last three lines you added in the previous iteration: In their place, define a PyTorch neural network that includes three convolutional layers, followed by three fully connected layers. Published: July 14, 2017 3:21 PM IST Howard The Alien Snapchat Filter With Music. Then run the script: Open outputs/children_detected.png. The original image tells us that position (0, 0) is red, (1, 0) is brown, and so on. Preprocess the data: As explained at the start of this section, our samples are vectors where each vector encodes an image of a face. Run the new script: Open the image at outputs/resized_dog.png to double-check the mask was resized correctly. I Ain T Doin It Snapchat Filter. Create the file step_1_face_detect using nano or your favorite text editor: Add the following code to the file. Preprocess the data: Apply one-hot encoding and then apply PyTorch abstractions. wget -O assets/model_best.pth https://github.com/alvinwan/emotion-based-dog-filter/raw/master/src/assets/model_best.pth, wget -O assets/dalmation.png https://assets.digitalocean.com/articles/python3_dogfilter/E9ax7PI.png # dalmation, wget -O assets/sheepdog.png https://assets.digitalocean.com/articles/python3_dogfilter/HveFdkg.png # sheepdog, cp step_4_dog_mask.py step_8_dog_emotion_mask.py. Import the necessary utilities and create a Python class that will hold your data. If you frown, it will apply a pug mask. Brown Ears Cute … Input: What information is the model given? Create a new file called step_4_dog_mask_simple.py which will hold the code for the script that applies the dog mask to faces: Add the following boilerplate for the Python script and import the OpenCV and numpy libraries: Replace pass in the main function with these two lines which load the original image and the dog mask into memory. Then add this code to convert the image to black and white, as the classifier was trained on black-and-white images. This is my implementation of a face keypoints detection algorithm, which predicts the keypoints of a face as below, and applies Snapchat-like Run a prediction using the model: Evaluate the neural network. Open step_6_ls_simple.py again in your editor: This time, increase the dimensionality of the new feature space to d=1000. Start with the following imports. Add this code: At this point, the dog image is, at most, as large as the child image. This snapcode links to a lens. When you open each link Snapchat will prompt you to "unlock" the filter for 48 hours. # frame = cv2.imread('assets/children.png') # DELETE ME, cv2.imwrite('outputs/children_detected.png', frame) # DELETE ME, cv2.imwrite('outputs/resized_dog.png', resized_mask) # delete this line, cv2.imwrite('outputs/child_with_dog_mask.png', face_with_mask), cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # DELETE ME, W = np.random.normal(size=(X_train.shape[1], d)), w = np.linalg.inv(A_train.T.dot(A_train) +, loader = torch.utils.data.DataLoader(trainset, batch_size=2, shuffle=False), mask = masks[predictor(frame[y:y+h, x: x+w])], https://github.com/do-community/emotion-based-dog-filter, How To Install and Set Up a Local Programming Environment for Python 3, approximation for the radial basis function (RBF) kernel, using a random Gaussian matrix, Equality of Opportunity in Machine Learning, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In this page you can download an image PNG (Portable Network Graphics) contains Snapchat Cute Dalmatian Dog Puppy Tongue Filter PNG isolated, no background with high quality, you will help you to not … To accomplish this, we featurize our inputs. First, extract image and label from the dataset loader and then wrap each in a PyTorch Variable. The Dog Filter is a special effect featured in Snapchat which allows users to place a dog’s nose, ears and tongue over their faces when taking a selfie. Hot Dog Snapchat Filter Costume. Baseline performance for least squares, with these extra enhancements, performs reasonably well. If save this matrix of values as an image. Scroll above to know the most popular of them. Hot To Make Snapchat Filter. Are you a fan of goats? The next objective is to link the computer’s camera to the face detector. Create an outputs folder for these annotated results. Now replace pass in the main function with this code which initializes a face classifier using the OpenCV parameters you downloaded to your assets folder: Next, add this line to load the image children.png. … Hub for Good Instead of detecting faces in a static image, you’ll detect all faces from your computer’s camera. 1080*1920 Size:110 KB. Once again reevaluating across a number of different d, we see a smaller gap between training and validation accuracies for ridge regression. Iterate over all detected objects and draw them on the image in green using cv2.rectangle: Finally, write the image with bounding boxes into a new file at outputs/children_detected.png: Your completed script should look like this: Save the file and exit your editor. To apply a dog mask, you will replace values in the child image with non-white dog mask pixels. The Standard Hollywood Filter That's Pretty Well-known - Hollywood Los Angeles Snapchat Filter. A neutral face or a frown will register as “sad” and yield the dalmation. Hot To Make Snapchat Filter. We’ll include these additional bells and whistles in a new script. Datasets necessary for this implementation can be downloaded from this link. Finally, release the capture and close all windows. For example, if you have your back to the sun, this process might not work very well. In this section you’ll create an emotion classifier to apply different masks based on displayed emotions. Change the value of d from 100 to 1000 as shown in the following code block: Then apply ridge regression using a regularization of lambda = 10^{10}. Start by making a copy of the child image. There can be an overwhelming magnitude of uncertainty in machine learning. See what the new dog lenses look like here. Snapchat Custom Filters - Illustration. Instead of writing an image to disk, you’ll display the annotated image back to the user’s screen: Also, add some code to watch for keyboard input so you can stop the program. Hot To Create A Snapchat Filter. Hot Dog Snapchat Filter Code. Computing (X^TX)^{-1} would take too long on commodity hardware, as X^TX is a 2304x2304 matrix with over four million values, so we’ll reduce this time by selecting only the first 100 features. We’ll create a script that accepts a single image and outputs an annotated image with the faces outlined with boxes. Instead, we’ll treat this as a black box that computes higher-order features for us. ... Snapchat Dog Png Filters is hand-picked png images from user's upload or the public platform. Eventually, this classifier will then determine which dog mask to apply. GOT Snapchat Filter? Right after cv2.imshow(...) add the following: The line cv2.waitkey(1) halts the program for 1 millisecond so that the captured image can be displayed back to the user. First, import the NumPy library at the top of the script: Then add the apply_mask function from your previous work into this new file above the main function: Second, locate this line in the main function: Add this code after that line to load the dog mask: Next, in the while loop, locate this line: Add this line after it to extract the image’s height and width: Next, delete the line in main that draws bounding boxes. The line of best fit, shown in the following image, is our model. For a three-way classification problem, 45.3% is reasonably above guessing, which is 33\%. Hot To Get A Snapchat Filter. To do these, implement PyTorch’s Dataset interface, which lets you load and use PyTorch’s built-in data pipeline for the face-emotion recognition dataset: Delete the pass placeholder in the Fer2013Dataset class. We want to center the dog image on the face, so compute the offset needed to center the dog image by adding this code to apply_mask: Copy all non-white pixels from the dog image into the child image. ... Kenneth The Dog Snapchat Lens. Making cool face filters for GOT Characters & yourself !! Your main function should now match the following: Insert the following line below the # apply mask line to select the appropriate mask by using the predictor: The completed file should look like this: Save and exit your editor. Hot Dog Snapchat Filter Code. Next, fit the dog mask to the child. Additionally, download the following dog mask. Here's how men ruined dog filter by calling it the 'hoe filter.' This means that for every (x, y) position in our image, we have three values (r, g, b). On December 24th, Snapchat released a new feature: Snapchat dog filters, designed specifically to get your pup in on the selfie action. If you are a makeup lover then you’d love this filter the most … This script contains the code above along with a command-line interface and an easy-to-import version of our code that will be used later. Snapchat QR codes) below directly through the Snapchat camera, however this method does not seem to work for all users. Specify and train the model: Set up a neural network using PyTorch layers. I Don T Feel So … Add this line to the apply_mask function: Then compute the new shape by adding this code to the function: Here we cast the numbers to integers, as the resize function needs integral dimensions. This Snapchat filter surrounds you in a sky full of stars. Where are my lenses on Snapchat? In other words, ridge regression was subject to less overfitting. In the following figure, we consider the performance of our new classifier as we vary d. Intuitively, as d increases, the accuracy should also increase, as we use more and more of our original data. Navigate to the data directory and unpack the data. You get paid; we donate to tech nonprofits. Add the following code to the end of your script to do that: Save the file and exit the editor once you’ve verified your code. Beauty Products Snapchat Lens. An image has its height and width expressed as h x w. In the current grayscale representation, each pixel is one value between 0 and 1. Https Www Snapchat Com Filters. To combat overfitting, we’ll regularize our model by penalizing complex models. OpenCV makes this easy by providing both. You’ll find this line in the for loop that iterates over detected faces: In its place, add this code which crops the frame. This gives us the following picture: We can use any value between 0 and 1, such as 0.1, 0.26, or 0.74391. Hot Dog Snapchat Filter Costume. You’ll see the following image that shows the faces outlined with boxes: At this point, you have a working face detector. To start, you will work with a single image. Now let’s use facial expression to determine the dog mask applied to a face. In other words, every (x, y) position in our image has just one value. We’ll detect all faces in the following image from Pexels (CC0, link to original image). final_frame= cv2.add(mask, dog_img) frame[up_center[1]: up_center[1] + dog_height,up_center[0]: up_center[0] + dog_width] = final_dog Output. We’ll set up a model and then load pre-trained parameters. Create your own Snapchat Filters and Lenses! Place this before your main function: To estimate labels, we take the inner product with each sample and get the indices of the maximum values using np.argmax. Then, launch this proof-of-concept training: You’ll see output similar to the following as the neural network trains: You can then augment this script using a number of other PyTorch utilities to save and load models, output training and validation accuracies, fine-tune a learning-rate schedule, etc. Your script should look like the following: This activates your camera and opens a window displaying your camera’s feed. Unlocking these hidden lenses and filters … Again, our goal is to produce a model that accepts faces as input and outputs an emotion. We can also flatten this box to become just a list of numbers. To introduce color, we need a way to encode more information. We won’t go into detail in this tutorial. We amend our ordinary least-squares objective function with a regularization term, giving us a new objective. By clicking below, you are giving us consent to use cookies. Wedding Snapchat Filter Dog Custom Portrait Snapchat Filter Dog Custom Weeding Dog Filter Unique Filter Personalized Dog Filter Customized MemoryTreasure. The return type is a list of tuples, where each tuple has four numbers denoting the minimum x, minimum y, width, and height of the rectangle in that order. To understand how to process our data and produce predictions, we’ll first briefly explore machine learning models. For data processing here, you will create the train and test datasets. Follow Me On Snapchat - Dog Filter Snapchat Code. This translates the dataset into an iterable to use later. We redefine our matrices, called design matrices, using this random featurization. If so, this is the Snapchat filter for … This field includes tasks such as object detection, image restoration (matrix completion), and optical flow. Fortunately, instead of writing our own face detection logic, we can use pre-trained models. Hacktoberfest Dancing HardBass Pianta Snapchat Lens. As you work through the tutorial, you’ll use OpenCV, a computer-vision library, numpy for linear algebra utilities, and matplotlib for plotting. Get the latest tutorials on SysAdmin and open source topics. Locate the following line, defining A_train and A_test: Directly above this definition for A_train and A_test, add a random feature matrix: Then replace the definitions for A_train and A_test. For example, imagine a job search engine where the models were trained with data about candidates. PyTorch is a particularly good place to start. Then for the train and validation sets, it wraps the dataset in a DataLoader. 1080*1920 Size:55 KB. The training and inference times, all together, take no more than 20 seconds for even the best results. Introduce a check in case the detected face is too close to the edge. As a sanity check, verify that the dataset utilities are functioning. Add the following to the end of your file: Verify that your completed script looks like this: This outputs the following pair of tensors. Get latest Snapchat Dog Filter news updates & stories. What shape do you see? I Don T Feel So … Our data pipeline outputs two samples and two labels. It will match the dog mask shown earlier in this section. Save the file and exit your editor. First, delete the last three lines you added in the previous iteration: In their place, define a PyTorch neural network that includes three convolutional layers, followed by three fully connected layers. Published: July 14, 2017 3:21 PM IST Howard The Alien Snapchat Filter With Music. Then run the script: Open outputs/children_detected.png. The original image tells us that position (0, 0) is red, (1, 0) is brown, and so on. Preprocess the data: As explained at the start of this section, our samples are vectors where each vector encodes an image of a face. Run the new script: Open the image at outputs/resized_dog.png to double-check the mask was resized correctly. I Ain T Doin It Snapchat Filter. Create the file step_1_face_detect using nano or your favorite text editor: Add the following code to the file. Preprocess the data: Apply one-hot encoding and then apply PyTorch abstractions. wget -O assets/model_best.pth https://github.com/alvinwan/emotion-based-dog-filter/raw/master/src/assets/model_best.pth, wget -O assets/dalmation.png https://assets.digitalocean.com/articles/python3_dogfilter/E9ax7PI.png # dalmation, wget -O assets/sheepdog.png https://assets.digitalocean.com/articles/python3_dogfilter/HveFdkg.png # sheepdog, cp step_4_dog_mask.py step_8_dog_emotion_mask.py. Import the necessary utilities and create a Python class that will hold your data. If you frown, it will apply a pug mask. Brown Ears Cute … Input: What information is the model given? Create a new file called step_4_dog_mask_simple.py which will hold the code for the script that applies the dog mask to faces: Add the following boilerplate for the Python script and import the OpenCV and numpy libraries: Replace pass in the main function with these two lines which load the original image and the dog mask into memory. Then add this code to convert the image to black and white, as the classifier was trained on black-and-white images. This is my implementation of a face keypoints detection algorithm, which predicts the keypoints of a face as below, and applies Snapchat-like Run a prediction using the model: Evaluate the neural network. Open step_6_ls_simple.py again in your editor: This time, increase the dimensionality of the new feature space to d=1000. Start with the following imports. Add this code: At this point, the dog image is, at most, as large as the child image. This snapcode links to a lens. When you open each link Snapchat will prompt you to "unlock" the filter for 48 hours. # frame = cv2.imread('assets/children.png') # DELETE ME, cv2.imwrite('outputs/children_detected.png', frame) # DELETE ME, cv2.imwrite('outputs/resized_dog.png', resized_mask) # delete this line, cv2.imwrite('outputs/child_with_dog_mask.png', face_with_mask), cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # DELETE ME, W = np.random.normal(size=(X_train.shape[1], d)), w = np.linalg.inv(A_train.T.dot(A_train) +, loader = torch.utils.data.DataLoader(trainset, batch_size=2, shuffle=False), mask = masks[predictor(frame[y:y+h, x: x+w])], https://github.com/do-community/emotion-based-dog-filter, How To Install and Set Up a Local Programming Environment for Python 3, approximation for the radial basis function (RBF) kernel, using a random Gaussian matrix, Equality of Opportunity in Machine Learning, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.