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6. **Activate the Layer Comps feature (located in the Edit>Layers window).**
7. **Open the four Photoshop templates that are provided.**
8. **Add new layers, duplicate them, hide layers, and create layer groups.**
A layer is simply an object that you can add to the Photoshop document window to help you create an image. You can add text to a layer or create your own style guides and guides. You can also create paths, which are one of the best tools you can use to create precise geometric shapes. Layers enable you to combine objects in the background, foreground, and between one layer to another. All these basic elements are the building blocks of everything you create.
9. **Add objects to the canvas.**
10. **Create a new frame and resize it with the Move tool.**
When you’re adding objects to the canvas, instead of dragging and dropping, you can sometimes use the Move tool (the smaller, triangle symbol) to drag and drop any object to the canvas.
Figure 15-1 illustrates how to create a basic frame using the Move tool.
11. **When you’re ready, save your work.**
Photoshop automatically saves the document in your default format after you save the file.
A few tips for using the Move tool:
* **Move:** Click and drag to move and resize the canvas. To reposition the object, click and drag the control point (the small circle beneath the object that enables you to move and resize the object). To scale the object, hold down Alt and drag the control point.
* **Pan/zoom:** To zoom in on an object in the browser window, hold down Ctrl and click and drag the icon in the upper-left corner of the canvas window, as shown in Figure 15-2. To zoom out, hold down Ctrl and click and drag the icon in the lower-right corner of the canvas window. If you don’t want to use the mouse to zoom in and out on an image, you can use the keyboard shortcut Ctrl+0 (press and hold it until the space bar appears, and then press the number keys to specify a zoom magnification). This shortcut works for both the Windows or Mac operating systems.
* **Zoom:** Right-click and choose Zoom In or Zoom Out from the shortcut menu. These commands are also available by pressing
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Photoshop has several different features and functions. They include:
Zoom into images
Rotate, move, and scale images
Adjust the brightness, contrast and saturation of images
Split and merge images
Rotate, crop, flip, and crop images
Lay out the images
Apply text or graphics
Edit paths
Add a variety of text effects (i.e. fonts, outlines, etc.)
Create graphics
Create videos
Render and output images
Image Comparison Software
Adobe Photoshop as a Virtual Machine
Those that want a fully functional copy of Photoshop on their computer with all features are in luck. Although it can still be a time-consuming process to install, there are two ways to get a Photoshop licence: As a virtual machine on Windows, Mac, or Linux; or legally on DVD, through Adobe’s website.
As a virtual machine
The first method of obtaining Photoshop is through the use of a virtual machine.
On a Windows computer, these are software emulators such as VirtualBox, Parallels Desktop or VMware Player.
On a Mac, virtual machines can be downloaded from the Mac App Store.
On a Linux computer, a third-party software can be used to create a virtual machine.
Cost for a.pdf guide with instructions to run Photoshop on a Windows or Mac with VirtualBox can be found on Adobe’s website.
Legal download
The second method of obtaining Photoshop is through Adobe’s website.
A full download can cost up to $2,000.
Adobe also sells a software license on DVD for $1,000.
Structure of Photoshop Elements
Step 1 – Download the Photoshop Elements software and install it on your computer.
For your computer’s hard drive, you should download and install a file called Photoshop Elements. Right click on the link and save it to your hard drive.
For Windows users
In the control panel, right click and “run as administrator”.
Click on “Windows” > “Command Prompt”.
You should now have access to the “Administrator” command prompt.
Type in “@e D:\photoshop\elements.exe /s” (pointing to the folder where you saved the installer file) and press enter.
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Q:
why am I getting ‘Cannot convert value of type ‘object[]’ to expected argument type ‘String?
I am getting ‘Cannot convert value of type object to expected argument type ‘String’ error when I call
“combinedArray[3]”
I can get it to printout when I remove the id to call it, but just not when I am trying to get it to work. How can I fix this?
var combinedArray: [String] = []
combinedArray.append(“id”)
combinedArray.append(“name”)
combinedArray.append(“phone”)
if let mainDict = [“id”: id, “name”: name, “phone”: phone] as? [[String: Any]] {
combinedArray.append(mainDict)
}
let selectedValue = combinedArray[3]
let final = selectedValue as! String
print(“final: \(final)”)
A:
You can fix it by adding the following line, which casts the array to its corresponding type:
selectedValue as! [String: Any]
In Swift2 the code would look like this:
selectedValue as! [String : Any]
And for Swift 3
selectedValue as! [String : Any]
UPDATE:
In Swift 2 you could also do this:
if let selectedValue = combinedArray[3] as? [String: Any] {
let final = selectedValue[“id”] as! String
print(“final: \(final)”)
}
UPDATE2:
If you want to use something like:
if let final = selectedValue.values.first {
print(“final: \(final)”)
}
It might look like this:
if let final = selectedValue.values.first as? String {
print(“final: \(final)”)
}
But that would only print final if the value from the dictionary is of type String
[String: Any] or [String: AnyObject]
EDIT:
Like @Zhenya_H mentioned in the comment below, you could use computed property values as well:
if let final = selectedValue.values.first {
print(“final: \(final)”)
}
[
What’s New in the?
Transfer Learning with Edge Augmentation
Thanks to Danilo C. Batista for the collaboration that lead us to this tutorial.
Deep Learning approach based on Edge Augmentation for Image Classification. Transfer Learning or fine-tuning allow us to learn the parameters of a model based on a sample dataset. This learning is usually useful for models where the knowledge was acquired with domain-specific data. In our case we will use a model trained on the ImageNet dataset.
Our model will learn the classification of images, such as our cat, or animals in general. These images were taken from the internet.
More example images.
All the images were resized to 300 x 300 pixels, normalize the image and put in Matlab 64x64x3x3 image data format.
In the first steps we will train the image classifier with the training dataset from the ImageNet dataset. Our model will learn from this dataset, resulting the model is called pretrained or frozen model.
Pretrained model:
We will use the code of Object detection with Convolutional Neural Networks (Inception-V3) available in the TensorFlow Hub repository. This code was trained on the ImageNet dataset for the task of object detection. Our model (pretrained model) will use the pretrained weights of the model and we will train the model with our test dataset. In this tutorial, we will retrain only the last fully connected layer of the pretrained model.
After training we will see in the plot the accuracy of the trained model. The 1, 2 and 3 are the models weights. The last fully connected layer contains the fully connected weights of the network, so the weights of this layer will be very relevant for the task of learning.
Most of the times, when using image classifiers, the last fully connected layer is the one which has the most complex filters and the smallest output size.
The ImageNet dataset has a training set of 50 thousand images and a test set of 20 thousand images. Our test data is much smaller than the dataset used in the pretrained model, so we will need to train our model on a smaller dataset.
We will use the ImageNet data only for the initialization of our model, the performance of our model will be based only in the test dataset.
In the first case we are using a very small dataset to initialize the model.
To apply transfer learning on a dataset like Image
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