Photos on Mac features an immersive, dynamic look that showcases your best photos. Find the shots you’re looking for with powerful search options. Organize your collection into albums, or keep your photos organized automatically with smart albums. Perfect your photos and videos with intuitive built-in editing tools, or use your favorite photo apps. And with iCloud Photos, you can keep all your photos and videos stored in iCloud and up to date on your Mac, Apple TV, iPhone, iPad, and even your PC.
A smarter way to find your favorites.
MTCNN is one of the most popular and most accurate face detection tools today. It consists of 3 neural networks connected in a cascade. It is an implementation of the MTCNN face detector for Keras. MTCNN is one of the most popular and most accurate face detection tools today. It consists of 3 neural networks connected in a cascade. It is an implementation of the MTCNN face detector for Keras. Overview This sample app presents a simple interface allowing you to choose between five augmented reality (AR) visualizations on devices with a TrueDepth front-facing camera. An overlay of x/y/z axes indicating the ARKit coordinate system tracking the face (and in iOS 12, the position and orientation of each eye).
Protect Your Mac With Facial Recognition Apps. If you are in the look out for a standalone face recognition system for protecting your Mac — MacBook Pro, MacBook Air, and iMac — against illegal access when you are out, then KeyLemon is worth giving a shot. The software detects your face, locks the computer when you are away, and unlocks the session with face detection. As one would expect. KeyLemon is a face recognition security system software for both laptop and desktop user, as the name implies, it automatically locks your computer when you move away and unlock it when you are back. With its patented webcam face recognition software technology, you can rest assure that your computer is safe from praying eyes.
This is a facial driven search engine based on similarity and distance. Just upload your picture from your mobile camera or gallery and the LogMe will detect and extract faces from the photos. The apps allows you to browse similar faces based on level of resemblance or distance of the upload. About dlib’s Face Recognition: Python provides facerecognition API which is built through dlib’s face recognition algorithms. This facerecognition API allows us to implement face detection, real-time face tracking and face recognition applications.
Photos intelligently declutters and curates your photos and videos — so you can easily see your best memories.
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Photos emphasizes the best shots in your library, hiding duplicates, receipts, and screenshots. Days, Months, and Years views organize your photos by when they were taken. Your best shots are highlighted with larger previews, and Live Photos and videos play automatically, bringing your library to life. Photos also highlights important moments like birthdays, anniversaries, and trips in the Months and Years views.
Your memories. Now playing.
Memories finds your best photos and videos and weaves them together into a memorable movie — complete with theme music, titles, and cinematic transitions — that you can personalize and share. So you can enjoy a curated collection of your trips, holidays, friends, family, pets, and more. And when you use iCloud Photos, edits you make to a Memory automatically sync to your other devices.
The moment you’re looking for, always at hand.
With Search, you can look for photos based on who’s in them or what’s in them — like strawberries or sunsets. Or combine search terms, like “beach 2017.” If you’re looking for photos you imported a couple of months ago, use the expanded import history to look back at each batch in chronological order. And in the Albums section, you’ll find your videos, selfies, panoramas, and other media types automatically organized into separate albums under Media Types.
Fill your library, not your device.
iCloud Photos can help you make the most of the space on your Mac. When you choose “Optimize Mac Storage,” all your full‑resolution photos and videos are stored in iCloud in their original formats, with storage-saving versions kept on your Mac as space is needed. You can also optimize storage on your iPhone, iPad, and iPod touch, so you can access more photos and videos than ever before. You get 5GB of free storage in iCloud — and as your library grows, you have the option to choose a plan for up to 2TB.
Make an edit here, see it there. With iCloud Photos, when you make changes on your Mac like editing a photo, making a Favorite, or adding to an album, they’re kept up to date on your iPhone, your iPad, and iCloud.com. And vice versa — any changes made on your iOS or iPadOS devices are automatically reflected on your Mac.
All your photos on all your devices. iCloud Photos gives you access to your entire Mac photo and video library from all your devices. If you shoot a snapshot, slo-mo, or selfie on your iPhone, it’s automatically added to iCloud Photos — so it appears on your Mac, iOS and iPadOS devices, Apple TV, iCloud.com, and your PC. Even the photos and videos imported from your DSLR, GoPro, or drone to your Mac appear on all your iCloud Photos–enabled devices. And since your collection is organized the same way across your Apple devices, navigating your library always feels familiar.
Resize. Crop. Collage. Zoom. Warp. GIF. And more.
Create standout photos with a comprehensive set of powerful but easy-to-use editing tools. Instantly transform photos taken in Portrait mode with five different studio-quality lighting effects. Choose Enhance to improve your photo with just a click. Then use a filter to give it a new look. Or use Smart Sliders to quickly edit like a pro even if you’re a beginner. Markup lets you add text, shapes, sketches, or a signature to your images. And you can turn Live Photos into fun, short video loops to share. You can also make edits to photos using third-party app extensions like Pixelmator, or edit a photo in an app like Photoshop and save your changes to your Photos library.
- LightBrilliance, a slider in Light, automatically brightens dark areas and pulls in highlights to reveal hidden details and make your photo look richer and more vibrant.
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- VignetteAdd shading to the edges of your photo to highlight a powerful moment.
- Editing ExtensionsDownload third-party editing extensions from the Mac App Store to add filters and texture effects, use retouching tools, reduce noise, and more.
- Reset AdjustmentsWhen you’ve made an edit, you can judge it against the original by clicking Compare. If you don’t like how it looks, you can reset your adjustments or revert to your original shot.
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With just a click, you can apply one of nine photo filters inspired by classic photography styles to your photos.
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Use the Share menu to easily share photos via Shared Albums and AirDrop. Or send photos to your favorite photo sharing destinations, such as Facebook and Twitter. You can also customize the menu and share directly to other compatible sites that offer sharing extensions.
Turn your pictures into projects.
Making high-quality projects and special gifts for loved ones is easier than ever with Photos. Create everything from gorgeous photo books to professionally framed gallery prints to stunning websites using third-party project extensions like Motif, Mimeo Photos, Shutterfly, ifolor, WhiteWall, Mpix, Fujifilm, and Wix.
Face Recognition with Python – Identify and recognize a person in the live real-time video.
In this deep learning project, we will learn how to recognize the human faces in live video with Python. We will build this project using python dlib’s facial recognition network. Dlib is a general-purpose software library. Using dlib toolkit, we can make real-world machine learning applications.
In this project, we will first understand the working of face recognizer. Then we will build face recognition with Python.
Face Recognition with Python, OpenCV & Deep Learning
About dlib’s Face Recognition:
Python provides face_recognition API which is built through dlib’s face recognition algorithms. This face_recognition API allows us to implement face detection, real-time face tracking and face recognition applications.
Project Prerequisites:
You need to install the dlib library and face_recognition API from PyPI:
Download the Source Code:
Steps to implement Face Recognition with Python:
We will build this python project in two parts. We will build two different python files for these two parts:
- embedding.py: In this step, we will take images of the person as input. We will make the face embeddings of these images.
- recognition.py: Now, we will recognize that particular person from the camera frame.
1. embedding.py:
First, create a file embedding.py in your working directory. In this file, we will create face embeddings of a particular human face. We make face embeddings using face_recognition.face_encodings method. These face embeddings are a 128 dimensional vector. In this vector space, different vectors of same person images are near to each other. After making face embedding, we will store them in a pickle file.
Paste the below code in this embedding.py file.
- Import necessary libraries:
- To identify the person in a pickle file, take its name and a unique id as input:
- Create a pickle file and dictionary to store face encodings:
- Open webcam and 5 photos of a person as input and create its embeddings:
Here, we will store the embeddings of a particular person in the embed_dictt dictionary. We have created embed_dictt in the previous state. In this dictionary, we will use ref_id of that person as the key.
To capture images, press ‘s’ five times. If you want to stop the camera press ‘q’:
- Update the pickle file with the face embedding.
Here we store the embed_dictt in a pickle file. Hence, to recognize that person in future we can directly load its embeddings from this file:
Now it’s time to execute the first part of python project.
Spy App Detection
Run the python file and take five image inputs with the person’s name and its ref_id:
2. recognition.py:
Here we will again create person’s embeddings from the camera frame. Then, we will match the new embeddings with stored embeddings from the pickle file. The new embeddings of same person will be close to its embeddings into the vector space. And hence we will be able to recognize the person.
Now, create a new python file recognition.py and paste below code:
- Import the libraries:
- Load the stored pickle files:
- Create two lists, one to store ref_id and other for respective embedding:
- Start the webcam to recognize the person:
Now run the second part of the project to recognize the person:
Summary:
Free Face Detection
This deep learning project teaches you how to develop human face recognition project with python libraries dlib and face_recognition APIs (of OpenCV).
Face Detection App Mac Pro
It also covers the introduction to face_recognition API. We have implemented this python project in two parts:
Face Detection App Mac Ios
- In the first part, we have seen how to store the information about human face structure, i.e face embedding. Then we learn how to store these embeddings.
- In the second part, we have seen how to recognize the person by comparing the new face embeddings with the stored one.