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Kodak Zi8 1080p camcorder with image stabilizer

Kodak have announced their latest point-and-shoot pocket camcorder, the HD-capable Zi8.  Intended to rival Flip’s Ultra HD, the Kodak Zi8 packed 1080p recording, image stabilization and face tracking. 

As well as video, the Kodak Zi8 captures 5-megapixel 16:9 stills, and the company are claiming improved low-light performance too.  It will land – in aqua, raspberry and black – from September 2009, priced at $179.95.

Press Release:

KODAK Zi8 Pocket Video Camera brings sleek style and high-performance to pocket video

Easily shoot and share high-quality 1080p HD video

Rochester, NY, July 29, 2009 – Eastman Kodak Company (NYSE:EK) today announced an exciting new addition to its popular line of Digital Video Cameras – the KODAK Zi8 Pocket Video Camera, featuring a sleek design, high-quality full 1080p High Definition video capture, and built-in electronic image stabilisation.

“Images and video have tremendous power to help us stay connected to family and friends,” says Phil Scott, Worldwide Director of Marketing, Digital Capture and Devices and Vice President, Consumer Digital Group. “The KODAK Zi8 Pocket Video Camera makes it easy to spontaneously capture HD video – of heartwarming moments, of ‘can you believe that?’ moments, and of just plain laugh-out-loud moments – and then quickly and easily share them.”

• Full HD 1080p video capture wherever you go;

• Built-in electronic image stabilisation for sharper videos and reduced blurring;

• Vivid 2.5″ colour LCD;

• Flexible swing-out USB arm for fast uploading, sharing, and charging;

• 5 MP 16:9 widescreen HD still pictures;

• Easy upload to Facebook and YouTube;

• Compatible with PC and Mac operating systems;

• Record up to 10 hours of HD video* with the expandable SD/SDHC card slot that can hold up to 32 GB;

• Capture family and friends in their best light with smart face tracking technology;

• See more details and accurate colours in low light;

• External microphone jack;

• In-box HDMI cable;

• Record from a distance or playback on your TV conveniently with optional KODAK Pocket Video remote control;

• Grab attention and define your style with the ultra compact design, stunning looks, and a range of colours.

Uploading to Facebook and YouTube

The KODAK Zi8 Pocket Video Camera provides one-button upload to Facebook, the premiere social networking and sharing website. Content can also be quickly and easily uploaded to YouTube, the world’s most popular online video community. The built-in software on the camera allows seamless upload of your video and pictures from the same desktop interface used for video editing and movie creation.

Accessories

A range of accessories are available for the KODAK Zi8 Pocket Video Camera, including:

• KODAK SDHC Memory Cards, available in 4, 8, and 16GB capacities customised for optimal video capture;

• KODAK Pocket Video Remote control;

• KODAK KLIC-7004 Lithium Ion batteries;

• KODAK Flexi-tripod;

• KODAK Adventure Mount for helmet, handlebars and more;

• KODAK cases, camera bags and neck straps.

Pricing and Availability

The KODAK Zi8 Pocket Video Camera will be available in aqua, raspberry and black** from September, 2009, and retail for US$179.95 MSRP.

*Record approximately 20 minutes per 1GB at HD 30fps.

** colour availability may vary.

[via Gizmodo Australia]

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Improving Image Tone With Levels In Photoshop

Improving Image Tone With Levels In Photoshop

Written by Steve Patterson.

In this photo editing tutorial, we’ll learn how to quickly correct overall tonal problems in an image using the Levels adjustment in Photoshop. In a previous tutorial, we looked at how to fix both tone and color cast problems at once using the Levels command, but a more common first step in a good photo editing workflow is to simply correct any tonal problems, brightening highlights, darkening shadows and adjusting the midtones, leaving any needed color corrections for later steps.

As we’ll see, the Levels adjustment makes tonal correction so fast and easy, you’ll be turning dull, lifeless images into ones that seem to pop right off the screen in a matter of seconds. And unlike the Brightness/Contrast adjustment in Photoshop CS3 and higher which doesn’t give you a great deal of control and relies mainly on your own personal opinion of what looks good, the Levels adjustment is what the pros use for accurate, professional quality results.

Here’s an image I have open on my screen:

The original photo.

The histogram shows why the image is looking rather dull. Notice how the edges of the histogram do not extend all the way to the far left or right. This tells us that there is currently nothing in our image that’s pure black or pure white, which means our shadow areas are not as dark as they could be and our highlights are not as bright as they could be, resulting in the image’s flat appearance (be sure to check out our How To Read A Histogram tutorial for a more detailed explanation of how histograms work):

The Histogram palette showing that the shadows and highlights could both use a boost.

This brings up the Levels dialog box, with its most noticeable feature being the histogram in the center. The histogram found in the Levels command is the exact same histogram we saw a moment ago in the Histogram palette. The difference is that with the Histogram palette, all we can do is look at the histogram to see where the problems are. With Levels, not only can we see the problems, we can do something about them!

First, let’s take a closer look at the problems, since they’re easier to understand in the Levels dialog box. Below the histogram in Levels is a horizontal gradient going from pure black on the left to pure white on the right. The brightness levels in the histogram match up perfectly with the brightness levels in the gradient below it. If we draw lines from the left and right edges of the histogram straight down to where the edges line up with the gradient, we can see more clearly where the current tonal range of our image falls. Notice that there’s still quite a bit of room between the left edge of the histogram and pure black on the far left of the gradient, and between the right edge of the histogram and pure white on the far right of the gradient. This means that our blacks in the image are currently not pure black. They’re a dark shade of gray, and our whites are not pure white but a light shade of gray:

The arrows show where the left and right edges of the histogram line up with the gradient.

If you look directly below the histogram, you’ll see three small sliders, one on each end and one in the middle. The slider on the far left is the black point slider. It’s easy to remember because the slider itself is black. The black point slider allows us to darken the shadow areas in the image by setting a new black point. The slider on the right is the white point slider. Again, it’s easy to remember because the slider itself is white. With it, we can brighten the highlights by setting a new white point (this will all make sense in a moment). The slider in the middle is the midtone slider. It appears gray because it allows us to brighten or darken the brightness levels in between black and white:

The three sliders below the histogram allow us to adjust the black point (left slider), white point (right slider) and midtones (middle slider) in the image.

Drag the black point slider to the left edge of the histogram to set a new black point.

As you drag the slider towards the right, you’ll see the dark areas of your image becoming progressively darker. By dragging the slider to the left edge of the histogram, those pixels in the image that were just a dark shade of gray a moment ago are forced to pure black, which causes all of the shadow areas in the image to become darker as well. Here’s my photo after adjusting the black point. We can already see an improvement in image contrast:

The shadow areas in the image now appear darker, improving image contrast.

The Histogram palette updates to show the changes we made in the Levels dialog box.

The left edge of the histogram now extends all the way to the left, letting us know that we now have deep, dark shadows in our image thanks to our new black point. But notice also that the histogram suddenly seems to be missing sections, creating a comb-like effect. That’s because we only have a set amount of image information in the photo to work with and by darkening the shadows, we’ve essentially spread out and stretched the image information like an accordion or a slinky. Those missing sections mean we no longer have any image detail at those brightness levels, but there’s no need to worry because we haven’t lost enough detail yet to make it noticeable. The unfortunate reality with photo editing is that with every edit we make to an image, we damage it in some way. All we can do is hope that the “damaged” version we end up with looks better to us than the original “undamaged” version did.

We still have a problem with the highlights, so we’ll fix that next.

Drag the white point slider to the right edge of the histogram to set a new white point.

As you drag the slider, you’ll see the bright areas in the image becoming gradually brighter. With the white point slider moved to the right edge of the histogram, the pixels that were a light shade of gray a moment ago are forced to pure white, causing all of the light areas in the image to become lighter in the process. Here’s my image after setting the new white point. The highlights are now nice and bright, and the overall image contrast has been greatly improved from how it looked originally:

Both the shadows and highlights in the image have now been corrected.

Once again, if we look to the Histogram palette, we can see the effects of the changes we’ve made. The right side of the histogram now extends all the way to the right edge, telling us that our highlights are now nice and bright. And by forcing the highlights to white, we’ve stretched out our image information even further, losing more detail at various brightness levels and creating even more of a comb-like effect in the histogram:

The histogram now stretches from left to right, although some brightness levels have been lost.

As a side note, if you’ve been wondering why my histogram is showing a tall spike near the right edge, it’s because this particular photo that I’m working with consists mainly of a light blue lake and a light blue sky. In other words, it’s made up mostly of light blue, which means the majority of the pixels in the image have a similar brightness value. Since the histogram shows us a comparison of the various brightness levels in the image, having so many pixels sharing a similar brightness value is causing that level to tower over the others. All photos are different, and if you’re following along with your own image, your histogram will undoubtedly look different from mine.

At this point, we’ve successfully lightened our shadows and brightened our highlights and the image is looking much better. However, one problem you may run into after adjusting the black and white levels is that the overall image can still appear either too bright or too dark. To fix that, we simply need to adjust the midtone slider. Dragging the midtone slider towards the left will brighten the image in the midtones, while dragging it towards the right will darken the midtones. It’s important to note that the midtone slider does not affect the black or white points. Only the brightness levels between black and white are affected.

Drag the midtone slider towards the left to lighten the midtones or the right to darken them.

Let’s check out the Histogram palette one last time. If you look closely, you’ll notice that the left side of the histogram now seems to be missing fewer brightness levels than the right side does. That’s because by darkening the midtones, we’ve taken image information from the lighter tonal values and pushed it into the darker values. This filled up some of the missing shadow areas but stretched out the lighter areas even further:

The histogram now shows less information remaining in the highlights than in the shadows after darkening the midtones.

The final tone-corrected result.

And there we have it! That’s how easy it is to correct overall tonal problems in an image with Levels in Photoshop! Check out our Photo Retouching section for more Photoshop image editing tutorials!

Ml Kit Image Labeling: Determine An Image’s Content With Machine Learning

What is Image Labeling?

On device, or in the cloud?

There’s several benefits to using the on-device model:

It’s free – No matter how many requests your app submits, you won’t be charged for performing Image Labeling on-device.

It doesn’t require an Internet connection – By using the local Image Labeling model, you can ensure your app’s ML Kit features remain functional, even when the device doesn’t have an active Internet connection. In addition, if you suspect your users might need to process a large number of images, or process high-resolution images, then you can help preserve their mobile data by opting for on-device image analysis.

It’s faster – Since everything happens on-device, local image processing will typically return results quicker than the cloud equivalent.

Which are we using, and will I need to enter my credit card details?

In our app, we’ll be implementing both the on-device and cloud Image Labeling models, so by the end of this article you’ll know how to harness the full power of ML Kit’s cloud-based processing, and how to benefit from the real-time capabilities of the on-device model.

Although the cloud model is a premium feature, there’s a free quota in place. At the time of writing, you can perform Image Labeling on up to 1,000 images per month for free. This free quota should be more than enough to complete this tutorial, but you will need to enter your payment details into the Firebase Console.

If you don’t want to hand over your credit card information, just skip this article’s cloud sections — you’ll still end up with a complete app.

Create your project and connect to Firebase

To start, create a new Android project with the settings of your choice.

Since ML Kit is a Firebase service, we need to create a connection between your Android Studio project, and a corresponding Firebase project:

In your web browser, head over to the Firebase Console.

Select “Add project” and give your project a name.

Read the terms and conditions, and then select “I accept…” followed by “Create project.”

Select “Add Firebase to your Android app.”

Select “Download google-services.json.” This file contains all the necessary Firebase metadata.

In Android Studio, drag and drop the chúng tôi file into your project’s “app” directory.

Next, open your project-level build.gradle file and add Google Services:

Code

classpath 'com.google.gms:google-services:4.0.1'

Open your app-level build.gradle file, and apply the Google services plugin, plus the dependencies for ML Kit, which allows you to integrate the ML Kit SDK into your app:

Code

apply plugin: 'com.google.gms.google-services' … … … dependencies { implementation fileTree(dir: 'libs', include: ['*.jar']) implementation 'com.google.firebase:firebase-core:16.0.5' implementation 'com.google.firebase:firebase-ml-vision:18.0.1' implementation 'com.google.firebase:firebase-ml-vision-image-label-model:17.0.2'

To make sure all these dependencies are available to your app, sync your project when prompted.

Next, let the Firebase Console know you’ve successfully installed Firebase. Run your application on either a physical Android smartphone or tablet, or an Android Virtual Device (AVD).

Back in the Firebase Console, select “Run app to verify installation.”

Firebase will now check that everything is working correctly. Once Firebase has successfully detected your app, it’ll display a “Congratulations” message. Select “Continue to the console.”

On-device Image Labeling: Downloading Google’s pre-trained models

<application android:allowBackup=”true” android:icon=”@mipmap/ic_launcher” android:label=”@string/app_name” android:roundIcon=”@mipmap/ic_launcher_round” android:supportsRtl=”true” <meta-data android:name=”com.google.firebase.ml.vision.DEPENDENCIES”

Building our Image Labeling layout

I want my layout to consist of the following:

An ImageView – Initially, this will display a placeholder, but it’ll update once the user selects an image from their device’s gallery.

A “Device” button – This is how the user will submit their image to the local Image Labeling model.

A “Cloud” button – This is how the user will submit their image to the cloud-based Image Labeling model.

A TextView – This is where we’ll display the retrieved labels and their corresponding confidence scores.

A ScrollView – Since there’s no guarantee the image and all of the labels will fit neatly on-screen, I’m going to display this content inside a ScrollView.

Here’s my completed activity_main.xml file:

android:layout_width=”match_parent” android:layout_height=”match_parent” android:padding=”20dp” <ScrollView android:layout_width=”match_parent” android:layout_height=”match_parent” android:layout_alignParentTop=”true” <LinearLayout android:layout_width=”match_parent” android:layout_height=”wrap_content” <ImageView android:id=”@+id/imageView” android:layout_width=”match_parent” android:layout_height=”wrap_content” android:adjustViewBounds=”true” <Button android:id=”@+id/btn_device” android:layout_width=”match_parent” android:layout_height=”wrap_content” <TextView android:id=”@+id/textView” android:layout_width=”match_parent” android:layout_height=”wrap_content” android:layout_marginTop=”20dp” <Button android:id=”@+id/btn_cloud” android:layout_width=”match_parent” android:layout_height=”wrap_content”

Open the “Icon Type” dropdown and select “Action Bar and Tab Icons.”

Make sure the “Clip Art” radio button is selected.

Select the image that you want to use as your placeholder; I’m using “Add to photos.”

In the “Name” field, enter “ic_placeholder.”

Action bar icons: Choosing an image

Next, we need to create an action bar item, which will launch the user’s gallery, ready for them to select an image.

You define action bar icons inside a menu resource file, which lives inside a “res/menu” directory. If your project doesn’t already contain a “menu” directory, then you’ll need to create one:

Open the “Resource type” dropdown and select “menu.”

The “Directory name” should update to “menu” automatically, but if it doesn’t then you’ll need to rename it manually.

Next, create the menu resource file:

Name this file “my_menu.”

Open the “my_menu.xml” file, and add the following:

<item android:id=”@+id/action_gallery” android:orderInCategory=”102″ android:title=”@string/action_gallery” android:icon=”@drawable/ic_gallery”

Set the “Icon Type” dropdown to “Action Bar and Tab Icons.”

Choose a drawable; I’m using “image.”

To ensure this icon is clearly visible in your app’s action bar, open the “Theme” dropdown and select “HOLO_DARK.”

Name this icon “ic_gallery.”

Name this class “BaseActivity.”

Open BaseActivity and add the following:

Code

import android.Manifest; import android.content.Intent; import android.content.pm.PackageManager; import android.os.Bundle; import android.provider.MediaStore; import android.support.annotation.NonNull; import android.support.annotation.Nullable; import android.support.v4.app.ActivityCompat; import android.support.v7.app.ActionBar; import android.support.v7.app.AppCompatActivity; import android.view.Menu; import android.view.MenuItem; import java.io.File; public class BaseActivity extends AppCompatActivity { public static final int RC_STORAGE_PERMS1 = 101; public static final int RC_SELECT_PICTURE = 103; public static final String ACTION_BAR_TITLE = "action_bar_title"; public File imageFile; @Override protected void onCreate(@Nullable Bundle savedInstanceState) { super.onCreate(savedInstanceState); ActionBar actionBar = getSupportActionBar(); if (actionBar != null) { actionBar.setDisplayHomeAsUpEnabled(true); actionBar.setTitle(getIntent().getStringExtra(ACTION_BAR_TITLE)); } } @Override public boolean onCreateOptionsMenu(Menu menu) { getMenuInflater().inflate(R.menu.my_menu, menu); return true; } @Override public boolean onOptionsItemSelected(MenuItem item) { switch (item.getItemId()) { case R.id.action_gallery: checkStoragePermission(RC_STORAGE_PERMS1); break; } return super.onOptionsItemSelected(item); } @Override public void onRequestPermissionsResult(int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) { super.onRequestPermissionsResult(requestCode, permissions, grantResults); switch (requestCode) { case RC_STORAGE_PERMS1: selectPicture(); } else { MyHelper.needPermission(this, requestCode, R.string.permission_request); } break; } } public void checkStoragePermission(int requestCode) { switch (requestCode) { case RC_STORAGE_PERMS1: int hasWriteExternalStoragePermission = ActivityCompat.checkSelfPermission(this, Manifest.permission.WRITE_EXTERNAL_STORAGE); if (hasWriteExternalStoragePermission == PackageManager.PERMISSION_GRANTED) { selectPicture(); } else { ActivityCompat.requestPermissions(this, new String[]{Manifest.permission.WRITE_EXTERNAL_STORAGE}, requestCode); } break; } } private void selectPicture() { imageFile = MyHelper.createTempFile(imageFile); Intent intent = new Intent(Intent.ACTION_PICK, MediaStore.Images.Media.EXTERNAL_CONTENT_URI); startActivityForResult(intent, RC_SELECT_PICTURE); } } Don’t waste time processing large images!

Next, create a new “MyHelper” class, where we’ll resize the user’s chosen image. By scaling the image down before passing it to ML Kit’s detectors, we can accelerate the image processing tasks.

Code

import android.app.Activity; import android.app.Dialog; import android.content.Context; import android.content.DialogInterface; import android.content.Intent; import android.database.Cursor; import android.graphics.Bitmap; import android.graphics.BitmapFactory; import android.net.Uri; import android.os.Environment; import android.provider.MediaStore; import android.provider.Settings; import android.support.v7.app.AlertDialog; import android.widget.ImageView; import android.widget.LinearLayout; import android.widget.ProgressBar; import java.io.File; import java.io.FileNotFoundException; import java.io.FileOutputStream; import java.io.IOException; import static android.graphics.BitmapFactory.decodeFile; import static android.graphics.BitmapFactory.decodeStream; public class MyHelper { private static Dialog mDialog; public static String getPath(Context context, Uri uri) { String path = ""; String[] projection = {MediaStore.Images.Media.DATA}; Cursor cursor = context.getContentResolver().query(uri, projection, null, null, null); int column_index; if (cursor != null) { column_index = cursor.getColumnIndexOrThrow(MediaStore.Images.Media.DATA); cursor.moveToFirst(); path = cursor.getString(column_index); cursor.close(); } return path; } public static File createTempFile(File file) { File dir = new File(Environment.getExternalStorageDirectory().getPath() + "/com.example.mlkit"); dir.mkdirs(); } if (file == null) { file = new File(dir, "original.jpg"); } return file; } public static void showDialog(Context context) { mDialog = new Dialog(context); mDialog.addContentView( new ProgressBar(context), new LinearLayout.LayoutParams(LinearLayout.LayoutParams.WRAP_CONTENT, LinearLayout.LayoutParams.WRAP_CONTENT) ); mDialog.setCancelable(false); if (!mDialog.isShowing()) { mDialog.show(); } } public static void dismissDialog() { if (mDialog != null && mDialog.isShowing()) { mDialog.dismiss(); } } public static void needPermission(final Activity activity, final int requestCode, int msg) { AlertDialog.Builder alert = new AlertDialog.Builder(activity); alert.setMessage(msg); @Override dialogInterface.dismiss(); Intent intent = new Intent(Settings.ACTION_APPLICATION_DETAILS_SETTINGS); intent.setData(Uri.parse("package:" + activity.getPackageName())); activity.startActivityForResult(intent, requestCode); } }); @Override dialogInterface.dismiss(); } }); alert.setCancelable(false); alert.show(); } public static Bitmap resizeImage(File imageFile, Context context, Uri uri, ImageView view) { BitmapFactory.Options options = new BitmapFactory.Options(); try { decodeStream(context.getContentResolver().openInputStream(uri), null, options); int photoW = options.outWidth; int photoH = options.outHeight; options.inSampleSize = Math.min(photoW / view.getWidth(), photoH / view.getHeight()); return compressImage(imageFile, BitmapFactory.decodeStream(context.getContentResolver().openInputStream(uri), null, options)); } catch (FileNotFoundException e) { e.printStackTrace(); return null; } } public static Bitmap resizeImage(File imageFile, String path, ImageView view) { BitmapFactory.Options options = new BitmapFactory.Options(); options.inJustDecodeBounds = true; decodeFile(path, options); int photoW = options.outWidth; int photoH = options.outHeight; options.inJustDecodeBounds = false; options.inSampleSize = Math.min(photoW / view.getWidth(), photoH / view.getHeight()); return compressImage(imageFile, BitmapFactory.decodeFile(path, options)); } private static Bitmap compressImage(File imageFile, Bitmap bmp) { try { FileOutputStream fos = new FileOutputStream(imageFile); fos.close(); } catch (IOException e) { e.printStackTrace(); } return bmp; } } Displaying the user’s chosen image

Next, we need to grab the image the user selected from their gallery, and display it as part of our ImageView.

Code

import android.content.Intent; import android.graphics.Bitmap; import android.net.Uri; import android.os.Bundle; import android.view.View; import android.widget.ImageView; import android.widget.TextView; private Bitmap mBitmap; private ImageView mImageView; private TextView mTextView; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); mTextView = findViewById(R.id.textView); mImageView = findViewById(R.id.imageView); } @Override protected void onActivityResult(int requestCode, int resultCode, Intent data) { super.onActivityResult(requestCode, resultCode, data); if (resultCode == RESULT_OK) { switch (requestCode) { case RC_STORAGE_PERMS1: checkStoragePermission(requestCode); break; case RC_SELECT_PICTURE: Uri dataUri = data.getData(); String path = MyHelper.getPath(this, dataUri); if (path == null) { mBitmap = MyHelper.resizeImage(imageFile, this, dataUri, mImageView); } else { mBitmap = MyHelper.resizeImage(imageFile, path, mImageView); } if (mBitmap != null) { mTextView.setText(null); mImageView.setImageBitmap(mBitmap); } break; } } } @Override } } Teaching an app to label images on-device

We’ve laid the groundwork, so we’re ready to start labeling some images!

Customize the image labeler

While you could use ML Kit’s image labeler out of the box, you can also customize it by creating a FirebaseVisionLabelDetectorOptions object, and applying your own settings.

I’m going to create a FirebaseVisionLabelDetectorOptions object, and use it to tweak the confidence threshold. By default, ML Kit only returns labels with a confidence threshold of 0.5 or higher. I’m going to raise the bar, and enforce a confidence threshold of 0.7.

Code

FirebaseVisionLabelDetectorOptions options = new FirebaseVisionLabelDetectorOptions.Builder() .setConfidenceThreshold(0.7f) .build(); Create a FirebaseVisionImage object

ML Kit can only process images when they’re in the FirebaseVisionImage format, so our next task is converting the user’s chosen image into a FirebaseVisionImage object.

Since we’re working with Bitmaps, we need to call the fromBitmap() utility method of the FirebaseVisionImage class, and pass it our Bitmap:

Code

FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(mBitmap); Instantiate the FirebaseVisionLabelDetector

ML Kit has different detector classes for each of its image recognition operations. Since we’re working with the Image Labeling API, we need to create an instance of FirebaseVisionLabelDetector.

If we were using the detector’s default settings, then we could instantiate the FirebaseVisionLabelDetector using getVisionLabelDetector(). However, since we’ve made some changes to the detector’s default settings, we instead need to pass the FirebaseVisionLabelDetectorOptions object during instantiation:

Code

FirebaseVisionLabelDetector detector = FirebaseVision.getInstance().getVisionLabelDetector(options); The detectInImage() method

Next, we need to pass the FirebaseVisionImage object to the FirebaseVisionLabelDetector’s detectInImage method, so it can scan and label the image’s content. We also need to register onSuccessListener and onFailureListener listeners, so we’re notified whenever results become available, and implement the related onSuccess and onFailure callbacks.

} } }).addOnFailureListener(new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { } }); } } }

Retrieving the labels and confidence scores

Assuming the image labeling operation is a success, an array of FirebaseVisionLabels will pass to our app’s OnSuccessListener. Each FirebaseVisionLabel object contains the label plus its associated confidence score, so the next step is retrieving this information and displaying it as part of our TextView:

Code

@Override for (FirebaseVisionLabel label : labels) { mTextView.append(label.getLabel() + "n"); mTextView.append(label.getConfidence() + "nn"); } }

At this point, your MainActivity should look something like this:

Code

import android.content.Intent; import android.graphics.Bitmap; import android.net.Uri; import android.os.Bundle; import android.support.annotation.NonNull; import android.view.View; import android.widget.ImageView; import android.widget.TextView; import com.google.android.gms.tasks.OnFailureListener; import com.google.android.gms.tasks.OnSuccessListener; import com.google.firebase.ml.vision.FirebaseVision; import com.google.firebase.ml.vision.label.FirebaseVisionLabel; import com.google.firebase.ml.vision.label.FirebaseVisionLabelDetector; import com.google.firebase.ml.vision.label.FirebaseVisionLabelDetectorOptions; import java.util.List; private Bitmap mBitmap; private ImageView mImageView; private TextView mTextView; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); mTextView = findViewById(R.id.textView); mImageView = findViewById(R.id.imageView); } @Override mTextView.setText(null); switch (view.getId()) { case R.id.btn_device: if (mBitmap != null) { FirebaseVisionLabelDetectorOptions options = new FirebaseVisionLabelDetectorOptions.Builder() .setConfidenceThreshold(0.7f) .build(); FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(mBitmap); FirebaseVisionLabelDetector detector = FirebaseVision.getInstance().getVisionLabelDetector(options); @Override for (FirebaseVisionLabel label : labels) { mTextView.append(label.getLabel() + "n"); mTextView.append(label.getConfidence() + "nn"); } } }).addOnFailureListener(new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { mTextView.setText(e.getMessage()); } }); } } } @Override protected void onActivityResult(int requestCode, int resultCode, Intent data) { super.onActivityResult(requestCode, resultCode, data); if (resultCode == RESULT_OK) { switch (requestCode) { case RC_STORAGE_PERMS1: checkStoragePermission(requestCode); break; case RC_SELECT_PICTURE: Uri dataUri = data.getData(); String path = MyHelper.getPath(this, dataUri); if (path == null) { mBitmap = MyHelper.resizeImage(imageFile, this, dataUri, mImageView); } else { mBitmap = MyHelper.resizeImage(imageFile, path, mImageView); } if (mBitmap != null) { mTextView.setText(null); mImageView.setImageBitmap(mBitmap); } break; } } } } Analyze an image with ML Kit

At this point, our app can download ML Kit’s Image Labeling model, process an image on device, and then display the labels and corresponding confidence scores for that image. It’s time to put our application to the test:

Install this project on your Android device, or AVD.

Tap the action bar icon to launch your device’s Gallery.

Select the image that you want to process.

Give the “Device” button a tap.

Analyzing images in the cloud

Code

FirebaseVisionCloudDetectorOptions options = new FirebaseVisionCloudDetectorOptions.Builder() .setModelType(FirebaseVisionCloudDetectorOptions.LATEST_MODEL) .setMaxResults(5) .build();

Next, you need to run the image labeler by creating a FirebaseVisionImage object from the Bitmap, and passing it to the FirebaseCloudVisionLabelDetector’s detectInImage method:

Code

FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(mBitmap);

Then we need to get an instance of FirebaseVisionCloudLabelDetector:

Code

FirebaseVisionCloudLabelDetector detector = FirebaseVision.getInstance().getVisionCloudLabelDetector(options);

Finally, we pass the image to the detectInImage method, and implement our onSuccess and onFailure listeners:

@Override } } }).addOnFailureListener(new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { } }); }

If the image labeling operation is a success, a list of FirebaseVisionCloudLabel objects will be passed to our app’s success listener. We can then retrieve each label and its accompanying confidence score, and display it as part of our TextView:

Code

@Override MyHelper.dismissDialog(); for (FirebaseVisionCloudLabel label : labels) { mTextView.append(label.getLabel() + ": " + label.getConfidence() + "nn"); mTextView.append(label.getEntityId() + "n"); } }

At this point, your MainActivity should look something like this:

Code

import android.content.Intent; import android.graphics.Bitmap; import android.net.Uri; import android.os.Bundle; import android.support.annotation.NonNull; import android.view.View; import android.widget.ImageView; import android.widget.TextView; import com.google.android.gms.tasks.OnFailureListener; import com.google.android.gms.tasks.OnSuccessListener; import com.google.firebase.ml.vision.FirebaseVision; import com.google.firebase.ml.vision.cloud.FirebaseVisionCloudDetectorOptions; import com.google.firebase.ml.vision.cloud.label.FirebaseVisionCloudLabel; import com.google.firebase.ml.vision.cloud.label.FirebaseVisionCloudLabelDetector; import com.google.firebase.ml.vision.label.FirebaseVisionLabel; import com.google.firebase.ml.vision.label.FirebaseVisionLabelDetector; import com.google.firebase.ml.vision.label.FirebaseVisionLabelDetectorOptions; import java.util.List; private Bitmap mBitmap; private ImageView mImageView; private TextView mTextView; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); mTextView = findViewById(R.id.textView); mImageView = findViewById(R.id.imageView); } @Override mTextView.setText(null); switch (view.getId()) { case R.id.btn_device: if (mBitmap != null) { FirebaseVisionLabelDetectorOptions options = new FirebaseVisionLabelDetectorOptions.Builder() .setConfidenceThreshold(0.7f) .build(); FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(mBitmap); FirebaseVisionLabelDetector detector = FirebaseVision.getInstance().getVisionLabelDetector(options); @Override for (FirebaseVisionLabel label : labels) { mTextView.append(label.getLabel() + "n"); mTextView.append(label.getConfidence() + "nn"); } } }).addOnFailureListener(new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { mTextView.setText(e.getMessage()); } }); } break; case R.id.btn_cloud: if (mBitmap != null) { MyHelper.showDialog(this); FirebaseVisionCloudDetectorOptions options = new FirebaseVisionCloudDetectorOptions.Builder() .setModelType(FirebaseVisionCloudDetectorOptions.LATEST_MODEL) .setMaxResults(5) .build(); FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(mBitmap); FirebaseVisionCloudLabelDetector detector = FirebaseVision.getInstance().getVisionCloudLabelDetector(options); @Override MyHelper.dismissDialog(); for (FirebaseVisionCloudLabel label : labels) { mTextView.append(label.getLabel() + ": " + label.getConfidence() + "nn"); mTextView.append(label.getEntityId() + "n"); } } }).addOnFailureListener(new OnFailureListener() { @Override public void onFailure(@NonNull Exception e) { MyHelper.dismissDialog(); mTextView.setText(e.getMessage()); } }); } break; } } @Override protected void onActivityResult(int requestCode, int resultCode, Intent data) { super.onActivityResult(requestCode, resultCode, data); if (resultCode == RESULT_OK) { switch (requestCode) { case RC_STORAGE_PERMS1: checkStoragePermission(requestCode); break; case RC_SELECT_PICTURE: Uri dataUri = data.getData(); String path = MyHelper.getPath(this, dataUri); if (path == null) { mBitmap = MyHelper.resizeImage(imageFile, this, dataUri, mImageView); } else { mBitmap = MyHelper.resizeImage(imageFile, path, mImageView); } if (mBitmap != null) { mTextView.setText(null); mImageView.setImageBitmap(mBitmap); } } } } } Activating Google’s cloud-based APIs

ML Kit’s cloud-based APIs are all premium services, so you’ll need to upgrade your Firebase project to a Blaze plan before your cloud-based code actually returns any image labels.

Although you’ll need to enter your payment details and commit to a pay-as-you-go Blaze plan, at the time of writing you can upgrade, experiment with the ML Kit features within the 1,000 free quota limit, and switch back to the free Spark plan without being charged. However, there’s no guarantee the terms and conditions won’t change at some point, so before upgrading your Firebase project always read all the available information, particularly the AI & Machine Learning Products and Firebase pricing pages.

If you’ve scoured the fine print, here’s how to upgrade to Firebase Blaze:

Head over to the Firebase Console.

A popup should now guide you through the payment process. Make sure you read all the information carefully, and you’re happy with the terms and conditions before you upgrade.

You can now enable ML Kit’s cloud-based APIs:

In the Firebase Console’s left-hand menu, select “ML Kit.”

Push the “Enable Cloud-based APIs” slider into the “On” position.

Testing your completed machine learning app

That’s it! Your app can now process images on-device and in the cloud. Here’s how to put this app to the test:

Install the updated project on your Android device, or AVD.

Make sure you have an active internet connection.

Choose an image from your device’s Gallery.

Give the “Cloud” button a tap.

Keep an eye on your spending

Since the cloud API is a pay-as-you-go service, you should monitor how your app uses it. The Google Cloud Platform has a dashboard where you can view the number of requests your application processes, so you don’t get hit by any unexpected bills!

You can also downgrade your project from Blaze back to the free Spark plan at any time:

Head over to the Firebase Console.

Select the free Spark plan.

You should receive an email confirming that your project has been downgraded successfully.

Wrapping up

You’ve now built your own machine learning-powered application, capable of recognizing entities in an image using both on-device and in-the-cloud machine learning models.

Have you used any of the ML Kit APIs we’ve covered on this site?

Clipping Masks And Type – Placing An Image In Text With Photoshop

As with the previous tutorial, I’ll be using Photoshop CS6 here but everything we’ll cover applies to any recent version of Photoshop.

As we’ll see in this tutorial, Type layers in Photoshop are different from pixel-based layers in that there are no actual “transparent” areas on a Type layer. The type itself simply becomes the layer’s contents. When we use a clipping mask with a Type layer, any part of the image on the layer above that sits directly over top of the text remains visible in the document, while areas of the image that fall outside the text are hidden. This creates the illusion that the image is actually inside the text! Let’s see how it works.

In that tutorial, we focused mainly on using clipping masks with pixel-based layers , but another common use for them is with type . Specifically, they can be used to easily place a photo inside of text !

We learned that clipping masks use the content and transparent areas of the bottom layer to determine which parts of the layer above it remain visible, and as a real world example, we used a clipping mask to place one image into a photo frame that was inside a second image.

In a previous tutorial, we learned the basics and essentials of using clipping masks in Photoshop to hide unwanted parts of a layer from view in our designs and documents.

Using Clipping Masks With Type

Here’s a document I have open containing two images. The first photo on the bottom Background layer will be used as the main image for the project (friends enjoying snowfall photo from Shutterstock):

The main image that will be used as the background.

We see the image I’m going to be placing inside of some text (abstract winter background from Shutterstock):

The image that will be going inside the text.

Step 1: Add Your Text

Selecting the top layer.

With the top layer selected, I’ll add my text. If you’re looking for more information on working with type in Photoshop, be sure to check out our full Photoshop Type Essentials tutorial, the first of several tutorials covering everything you need to know. Here, I’ll start by grabbing the Type Tool from the Tools panel:

Selecting the Type Tool.

With the Type Tool selected, I’ll choose my font up in the Options Bar along the top of the screen. When you know you’re going to be placing an image inside your text, you’ll usually want to choose a font with thick letters so you’ll be able to see more of the image. I’ll choose Impact since it’s a nice thick font, and I’ll set the initial size of my font to 24pt. Don’t worry about choosing a color for the text because the color won’t be visible once we’ve added the image:

Selecting the font options in the Options Bar.

Adding the type to the document.

The Layers panel showing the new Type layer.

Step 2: Resize The Text With Free Transform

Unfortunately, the font size I chose in the Options Bar was too small for my design, but that’s okay because there’s an easy way to resize the text. We’ll just use Photoshop’s Free Transform command. I’ll select it by going up to the Edit menu in the Menu Bar along the top of the screen and choosing Free Transform. Or, I could press Ctrl+T (Win) / Command+T (Mac) on my keyboard to select Free Transform with the shortcut. Either way is fine:

Holding Shift and dragging the corner handles to resize the text.

Step 3: Create A Clipping Mask

Now that the type is the size we need, let’s go ahead and add our clipping mask to place the image inside the text. The image I want to place inside my text is on Layer 1, but Layer 1 is currently sitting below my Type layer and as we learned in the Clipping Masks Essentials tutorial, we need the layer that’s going to serve as the clipping mask (in this case, the Type layer) to be below the layer that’s going to be “clipped” (Layer 1). This means I’ll first need to move my Type layer below Layer 1.

Dragging the Type layer below Layer 1.

When the highlight bar appears, I’ll release my mouse button and the Type layer is moved right where I need it directly below Layer 1:

Layer 1 now sits above the Type layer.

Next, we need to make sure we have the layer that’s going to be “clipped” by the clipping mask selected, so I’ll select Layer 1:

Selecting the image layer above the Type layer.

With the Type layer now directly below the image and the image layer selected, I’ll add the clipping mask by going up to the Layer menu at the top of the screen and choosing Create Clipping Mask:

If we look again in the Layers panel, we see that Layer 1 is now indented to the right, with a small arrow to the left of its preview thumbnail pointing down at the Type layer below it. This tells us that Layer 1 is now being clipped by the Type layer:

The Layers panel showing the clipping mask.

And if we look in the document window, we see that the image on Layer 1 now appears to be inside the text! It’s not really inside the text. It only looks that way because any part of the image that is not sitting directly above the type is being hidden from view thanks to the clipping mask:

Photoshop is now hiding any part of the image that is not sitting directly above the type.

Step 4: Reposition The Text

Of course, I picked a pretty bad spot to place my text. It’s blocking the faces of the two people in the photo so I’ll need to move the text into position. First, I’ll select the Type layer in the Layers panel:

Then I’ll grab Photoshop’s Move Tool from the top of the Tools panel:

Selecting the Move Tool.

Use the Move Tool to move the text, or the image inside the text (depending on which layer is selected in the Layers panel).

Warping And Reshaping The Type

Also since the type is still type, that means you can even warp it into different shapes! First make sure you have the Type layer selected in the Layers panel, then go up to the Edit menu at the top of the screen, choose Transform, and then choose Warp:

With the Warp command selected, look up near the far left of the Options Bar at the top of the screen and you’ll see a Warp option that by default is set to None:

The Warp option in the Options Bar.

Choosing Wave from the list of preset warp styles.

This instantly warps the text into a fun “wave” shape, yet the clipping mask remains active with the image still appearing inside the text. Anything you can normally do with type in Photoshop, you can do with it even when it’s being used as a clipping mask:

The text after applying the Warp command.

Adding Layer Styles

We also learned in the Clipping Masks Essentials tutorial that we can add layer styles to clipping masks, and that’s true even when using type. To quickly finish things off, I’ll add a layer style to the text to help it blend in better with the main photo behind it. First, I’ll select the Type layer in the Layers panel:

Selecting the Type layer.

I’ll choose Outer Glow from the list of layer styles that appears:

Choosing an Outer Glow style.

The Outer Glow options.

The Outer Glow style appears below the Type layer.

And with that, we’re done! Here’s my final result with the Outer Glow added to the text (I also used to Move Tool to move the type down just a bit so it appears more centered between the two girls and the top of the image):

The final “image in text” result.

Hp Omen 25I Review: Speedy 165Hz 1080P Gaming Monitor

Best Prices Today: HP Omen 25i

Retailer

Price

299.99

View Deal

The HP Omen 25i is a budget-friendly display that will appeal to casual gamers. HP keeps the price low by doing away with any special gaming design and foregoing some equipment details.

Note: This review is part of our ongoing roundup of the best gaming monitors. Go there to learn about competing products, what to look for in a gaming monitor, and buying recommendations.

HP Omen 25i: The specs

At first glance, the HP Omen 25i looks more like an elegantly designed business model than a gaming monitor. Nevertheless, the 1080p screen is a full-fledged gaming display that offers 1920×1080 resolution and a maximum refresh rate of 165Hz with its IPS panel.

Display size 24.5-inchNative resolution1920 x 1080Panel typeIPS / 16:9Refresh rate165HzAdaptive syncCompatible with FreeSync and Nvidia’s G-SyncPorts1 DisplayPort, 1 HDMI, 2 USB, 1 analog audio jackStand adjustmentNoneVESA mountYes, 100x100mmSpeakersNoHDRYes, HDR10Price$350

In addition, the gaming monitor’s high frame rate not only synchronizes with AMD graphics cards via FreeSync, but you can also use it with Nvidia cards since the monitor is on the manufacturer’s compatibility list.

HP Omen 25i: Image quality

The HP Omen 25i delivers good image quality, bolstered by its high and balanced brightness distribution across the entire panel. In addition, the 24.5-inch model delivers strong contrast and neutral color reproduction with 99 percent of the sRGB color range.

Even when gaming, the HP Omen 25i stands up. At the maximum refresh rate, it shows smooth image reproduction, which benefits from the fast response time of 1 millisecond (GtG) and shows no input lag. The gaming monitor also handles fast, responsive games without noticeable errors.

HP Omen 25i: inexpensive and good gaming performance.

PC Welt

HP Omen 25i: Ports

Here you have to accept the first compromise due to the low price of the HP Omen 25i. The 24.5-inch display only has one, instead of customary two HDMI ports, and only two instead of the usual four USB ports.

Also, only one video cable is included in the box—a DisplayPort cable that mercifully supplies the 165Hz refresh rate. There are also no internal stereo speakers, but headphones and external speakers can be connected via audio output.

HP Omen 25i: The 24.5-inch display only has one HDMI and one DP port.

PC Welt

HP Omen 25i: Features and menu

You have to put up with some serious limitations regarding the ergonomic adjustment options. The HP Omen 25i cannot be adjusted in height, which is particularly important for prolonged, fatigue-free gaming. Nor can it be rotated on its base. This is unfortunate considering how common these features have become even in the cheapest monitors.

HP Omen 25i: Convenient control of the OSD via a five-way joystick.

PC Welt

HP Omen 25i: Power consumption

At maximum brightness, the HP Omen 25i draws around 22 watts, which isn’t much for an 1080p gaming monitor. The consumption in standby mode is also low at 0.4 watts.

Should you buy the HP Omen 25i?

The HP Omen 25i monitor delivers decent performance when it comes to gaming, allowing you to enjoy it in most online environments. The plethora of control sets allowing you to adjust onscreen options is also a nice touch. However, the lack of height adjustment and limited ports are disappointing and may leave you looking for other monitors with better options.

The image quality is average, not outstanding, and you can likely find better options out there if that is your main concern. However, for a 1080p monitor with a fast refresh rate, the HP Omen 25i is worth consideration—especially if you can find it on sale.

This review originally appeared on PC-Welt, PCWorld’s German sister site.

How Image Compression Works: The Basics

Methods, Approaches, Algorithms Galore.

It’s naive to think that there’s just one way to compress an image. There are different methods, each with a unique approach to a common problem, and each approach being used in different algorithms to reach a similar conclusion. Each algorithm is represented by a file format (PNG, JPG, GIF, etc.). For now, we’re going to talk about the methods that are generally used to compress images, which will explain why some of them take up so much less space.

Lossless Compression

When you think of the word “lossless” in the context of image compression, you probably think about a method that tries its hardest to preserve quality while still maintaining a relatively small image size. That’s very close to the truth. As a method, lossless compression minimizes distortion as much as possible, preserving image clarity. It does this by building an index of all the pixels and grouping same-colored pixels together. It’s kind of like how file compression works, except we’re dealing with smaller units of data.

DEFLATE is among the most common algorithms for this kind of job. It’s based on two other algorithms (Huffman and LZ77, if you’re a bookworm) and it has a very tried-and-true way of grouping data found within images. Instead of just running through the length of the data and storing multiple instances of a pixel with the same color into a single data unit (known as run-length encoding), it grabs duplicate strings found within the entire code and sets a “pointer” for each duplicate found. Wherever a particular string of data (pixels) is used frequently, it replaces all of those pixels with a weighted symbol that further compresses everything.

Notice how with run-length encoding and DEFLATE, none of the pixels are actually eaten up or forced to change color. Using this method purely results in an image that is identical to the raw original. The only difference between the two lies in how much space is actually taken up on your hard drive!

Lossy Compression

As the name implies, lossy compression makes an image lose some of its content. When taken too far, it can actually make the image unrecognizable. But lossy doesn’t imply that you’re eliminating pixels. There are actually two algorithms commonly used to compress images this way: transform encoding and chroma subsampling. The former is more common in images and the latter in video.

Chroma subsampling takes another approach. Instead of averaging small blocks of color, which also may affect the brightness of an image, it carefully attempts to keep brightness the same on all areas. This tricks your eyes into not readily noticing any dip in quality. It’s actually great for the compression of animations, which is why it is used more in video streams. That’s not to say that images don’t also use this algorithm.

But wait, there’s more! Google also took a shot at a new lossy algorithm, known as WebP. Instead of averaging color information, it predicts the color of a pixel by looking at the fragments surrounding it. The data that’s actually written into the resulting compressed image is the difference between the predicted color and the actual color. In the end, many of the predictions will be accurate, resulting in a zero. And instead of printing a whole bunch of zeroes, it just compresses all of them into one symbol that represents them. Image accuracy is improved and the compression reduces image size by an average of 25 percent compared to other lossy algorithms, according to Google.

It’s Time For Questions And Discussion!

Miguel Leiva-Gomez

Miguel has been a business growth and technology expert for more than a decade and has written software for even longer. From his little castle in Romania, he presents cold and analytical perspectives to things that affect the tech world.

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