AI & Machine Learning Solutions - Computer Vision

Oodles’ Computer Vision Development Services empowers your business to harness the untapped value of visual data with AI and ML-driven solutions that transform raw, unprocessed elements into actionable insights, and deliver impactful resolutions for real-world business challenges. Our custom solutions utilize technologies like OpenTorch, PyTorch, and Tesseract to deliver precise image recognition, object detection, and OCR capabilities, driving scalable innovation across diverse industries.

Computer Vision

Top Blog Posts

Face Recognition: A New Way Of Security

Many biometric techniques are used for identifying humans such as signature, fingerprint, speech, face, and hand geometric recognition. Of these, face recognition techniques is the simplest and most consistent. It is because facial recognition does not require active human cooperation. The basic functionality of face recognition goes through - verification, photography, identification, and result. While finding and knowing a comprehensive photo database is a daunting task, this biometric software application works as a reliable and robust security system. It is used in various fields such as driver's license system, ATMs, passport verification, rail booking, mobile platforms, and other monitoring and evaluation functions. Source: Shutter Stock WHAT IS FACE RECOGNITION? Face recognition is a technology that can identify or verify a topic with an image, video, or any visual object with its face. Typically, this identifier is used to access a program, program, or service. It is a biometric identification method that uses those physical steps, in this case, the face and head, to verify a person's identity with a pattern of biometric data and data. The technology collects a unique set of individual biometric data associated with face-to-face identification, verification, and/or authentication. In-depth Learning Programs Used for Face Recognition Currently, these are four well-known DL programs that work together DeepFace DeepID series of programs VGGFace FaceNet DeepFace- According to Deep convolutional neural networks, DeepFace is an in-depth face recognition program. Created by Facebook, it detects and determines the identity of a person's face through digital photography, which is reported to be 97.35% accurate. DeepID- It was first invented by Yi Sun in his paper Deep Learning Face Representation from predicting 10,000 classes, secret identity of the discovery of a common object, which is counted among the first models of in-depth face-to-face learning. DeepID has gained more accuracy than people in the project. VGGFace- By Omkar Parkhi, Andrea Vedaldi, and Andrew Zisserman of VGG (Visual Geometry Group) in Oxford for their paper “Deep Face Recognition.” This paper has contributed to understanding the construction of the enormous data needed to train CNN's modern face recognition systems. A set of available data is then used as the basis for CNN's deep development of visual functions. FaceNet- To achieve technical results in standard data sets, FaceNet uses a three-loss function to study vectors for better results in feature extraction and, consequently, authentication. FACE RECOGNITION SYSTEM Face Recognition / Biometric Face Technology has a wide variety of applications; for example, using the built-in camera, tablet, or computer, face recognition software can change the passwords of the device account and users' access passwords. In law enforcement, technology can assist in the identification of a suspect, while border controls can be used to make security operations more consistent. Another popular system for face recognition programs is to control access to a high-value area. In the commercial sector, retailers and retailers use technology as a means of collecting important personal information. The facial procedure can make two variations depending on when it is performed: That is, for the first time, a face-to-face recognition system to register and associate you with identity, in the sense that it is recorded in the system. This process is also known as digital onboarding with face recognition. The exception is where the user is verified, before registration. In this process, incoming data from the camera falls through the existing data in the database. If the face matches the registered ID, the user is given access to the system with his or her credentials. HOW DOES THIS WORKS? Face recognition systems capture incoming images from a camera device in a three- or three-dimensional way depending on the device's features. These compare the relevant details of the incoming image signal in real-time on a photo or video in a database, which is more reliable and secure than the information obtained from the still image. This biometric face recognition process requires an internet connection because the database cannot be accessed on the capture device as it is hosted on servers. In this face comparison, we statistically analyze the incoming image without the error limit and confirm that the biometric data is the same as the person who should use the service or request access to the program, program, or structure. Thanks to the use of artificial intelligence (AI) and machine learning technology, face recognition systems can operate with the highest standards of safety and reliability. Similarly, due to the combination of these algorithms and computer techniques, the process can be performed in real-time. BIOMETRIC FACIAL RECOGNITION Face recognition uses a focus on validation or validation. These technologies are used, for example, in situations such as: The second authentication feature is to add additional security to any login process. Access to mobile applications without a password. Access to pre-contracted Internet services (sign in to online platforms, for example). Access to Hotels, Building, Offices, etc. How to pay, both in physical stores and online. Access to a locked device. Access guest services (airports, hotels…). SMILEID, THE SOLUTION TO THE STANDARD BIOMETRIC FACIAL RUNNION At Electronic Identification SmileID is developed, faomet-based bio-metric recognition solution

Area Of Work:Computer Vision

Vikas Verma

29 Sep 2020

Understanding the CNN for Computer Vision Applications

Convolutional Neural Networks or CNN is a type of deep neural networks that are efficient at extracting meaningful information from visual imagery. As an experiential AI Development Company, Oodles AI decodes the underlying layers of CNN and how businesses can deploy CNN for computer vision applications. When it comes to us, humans, evolution has gifted us with very complex yet efficient techniques to view and detect several objects. Our brain keeps on learning continuously without our notice. There are several organs and parts of our brain involved in the process like eyes, receptors and visual cortex. In the era, with the resources and immense computational power, it would be pointless not to explore computer vision. With so many applications of computer vision services, we can take current generation technology to the next level. A great example is the upcoming Tesla's Robo-taxi which gives us a glimpse into the future. A very popular machine learning algorithm, especially for Object Detection, is Convolutional Neural Networks or CNN. CNN consists of four hidden layers such as- Convolutional layers Pooling layers fully connected layers, and Normalization layers. Convolutional Layers takes two input layers - a part of the image and an equally sized filter called the kernal. The output of this layer is the dot product of both inputs. The idea of Pooling is to down-sample data. The Pooling Layer takes the input (an image) and reduces its size in terms of a number of pixels. There are two ways to perform this - Max Pooling and Min Pooling. Max Pooling picks the maximum value from the selected region, whereas Min Pooling picks up the minimum value. Under Fully Connected Layers, as the name suggests, all the outputs from one layer are connected to the input of another layer. These layers are useful in the classification of the data. Normalization Layers are used to stabilize the neural networks. It performs normalization on the input data. CNN performs incredibly when it comes to analyzing a single image, but it lacks one essential quality - they only consider spatial features and visual data ignoring the temporal and time features i.e., how a frame is related to the previous frame. This is where Recurrent Neural Networks or RNN come into play. The term ‘recurrent' suggests that the neural network repeats the same tasks for every sequence. RNN can also be used in Natural Language Processing. Employing CNN for Computer Vision Applications with Oodles AI Oodles AI is a team of seasoned professionals working with artificial intelligence technologies including machine learning and deep learning to build next-gen solutions. We have hands-on expertise in deploying CNN and RNN models for applications such as the image caption generating model. In addition, our AI capabilities encompass- Predictive Analytics Machine learning Recommendation systems Natural Language Processing, and Chatbot Development Reach out to our AI team to know more about our artificial intelligence services.

Area Of Work:Computer Vision

Industry:Software Development

Asheesh Bhuria

28 Jan 2020

Learn Image Text Recognition Using Google Cloud Vision API

According to Google Cloud Vision API Documentation - Cloud Vision API enables developers to integrate Google CloudVision detection features within applications including face and landmark detection, image labeling, optical character recognition (OCR), and tagging of explicit content. Prerequisites: 1. Before we begin, we need to set up a project on Google Cloud Developers console (Link: https://console.cloud.google.com/) 2. Enable the Google Cloud Vision API under 'API and Services' 3. Copy the API key under Credentials which looks like this 'AIzaSyCwpab-fbRd6*******ne60NyTkA' 4. Android Studio(3+) with latest SDK Let's try our hands on implementation: Define Permissions in AndroidManifest.xml Create an Activity where request to Text recognition using Google CloudVision API will be processed Define Constants: private static final String CLOUD_VISION_API_KEY =API_KEY; public static final String FILE_NAME = "temp.jpg"; private static final String ANDROID_CERT_HEADER = "X-Android-Cert"; private static final String ANDROID_PACKAGE_HEADER = "X-Android-Package"; private static final int MAX_LABEL_RESULTS = 10; private static final int MAX_DIMENSION = 1200; private static final String TAG = MainActivity.class.getSimpleName(); private static final int GALLERY_PERMISSIONS_REQUEST = 0; private static final int GALLERY_IMAGE_REQUEST = 1; public static final int CAMERA_PERMISSIONS_REQUEST = 2; public static final int CAMERA_IMAGE_REQUEST = 3; Note: CLOUD_VISION_API_KEY is a variable where you'll have to define your API Key copied from Google Cloud developers console. Create a function to start Device Camera public void startCamera() { if (PermissionUtils.requestPermission( this, CAMERA_PERMISSIONS_REQUEST, Manifest.permission.READ_EXTERNAL_STORAGE, Manifest.permission.CAMERA)) { Intent intent = new Intent(MediaStore.ACTION_IMAGE_CAPTURE); Uri photoUri = FileProvider.getUriForFile(this, getApplicationContext().getPackageName() + ".provider", getCameraFile()); intent.putExtra(MediaStore.EXTRA_OUTPUT, photoUri); intent.addFlags(Intent.FLAG_GRANT_READ_URI_PERMISSION); startActivityForResult(intent, CAMERA_IMAGE_REQUEST); } } Read the image captured onActivityResult @Override protected void onActivityResult(int requestCode, int resultCode, Intent data) { super.onActivityResult(requestCode, resultCode, data); if (requestCode == CAMERA_IMAGE_REQUEST && resultCode == RESULT_OK) { Uri photoUri = FileProvider.getUriForFile(this, getApplicationContext().getPackageName() + ".provider", getCameraFile()); uploadImage(photoUri); } } Create functions to uploadImage to Google Cloud Storage for processing public void uploadImage(Uri uri) { if (uri != null) { try { // scale the image to save on bandwidth Bitmap bitmap = scaleBitmapDown( MediaStore.Images.Media.getBitmap(getContentResolver(), uri), MAX_DIMENSION); callCloudVision(bitmap); mMainImage.setImageBitmap(bitmap); } catch (IOException e) { Log.d(TAG, "Image picking failed because " + e.getMessage()); Toast.makeText(this, R.string.image_picker_error, Toast.LENGTH_LONG).show(); } } else { Log.d(TAG, "Image picker gave us a null image."); Toast.makeText(this, R.string.image_picker_error, Toast.LENGTH_LONG).show(); } } private void callCloudVision(final Bitmap bitmap) { // Switch text to loading mImageDetails.setText(R.string.loading_message); // Do the real work in an async task, because we need to use the network anyway try { AsyncTask textDetectionTask = new TextDetectionTask(this, prepareAnnotationRequest(bitmap)); labelDetectionTask.execute(); } catch (IOException e) { Log.d(TAG, "failed to make API request because of other IOException " + e.getMessage()); } } Create an AsyncTask that processes the image for text detection in background thread private Vision.Images.Annotate prepareAnnotationRequest(Bitmap bitmap) throws IOException { HttpTransport httpTransport = AndroidHttp.newCompatibleTransport(); JsonFactory jsonFactory = GsonFactory.getDefaultInstance(); VisionRequestInitializer requestInitializer = new VisionRequestInitializer(CLOUD_VISION_API_KEY) { /** * We override this so we can inject important identifying fields into the HTTP * headers. This enables use of a restricted cloud platform API key. */ @Override protected void initializeVisionRequest(VisionRequest visionRequest) throws IOException { super.initializeVisionRequest(visionRequest); String packageName = getPackageName(); visionRequest.getRequestHeaders().set(ANDROID_PACKAGE_HEADER, packageName); String sig = PackageManagerUtils.getSignature(getPackageManager(), packageName); visionRequest.getRequestHeaders().set(ANDROID_CERT_HEADER, sig); } }; Vision.Builder builder = new Vision.Builder(httpTransport, jsonFactory, null); builder.setVisionRequestInitializer(requestInitializer); Vision vision = builder.build(); BatchAnnotateImagesRequest batchAnnotateImagesRequest = new BatchAnnotateImagesRequest(); batchAnnotateImagesRequest.setRequests(new ArrayList() {{ AnnotateImageRequest annotateImageRequest = new AnnotateImageRequest(); // Add the image Image base64EncodedImage = new Image(); // Convert the bitmap to a JPEG // Just in case it's a format that Android understands but Cloud Vision ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream(); bitmap.compress(Bitmap.CompressFormat.JPEG, 90, byteArrayOutputStream); byte[] imageBytes = byteArrayOutputStream.toByteArray(); // Base64 encode the JPEG base64EncodedImage.encodeContent(imageBytes); annotateImageRequest.setImage(base64EncodedImage); // add the features we want annotateImageRequest.setFeatures(new ArrayList() {{ Feature labelDetection = new Feature(); textDetection.setType("DOCUMENT_TEXT_DETECTION"); add(labelDetection); }}); // Add the list of one thing to the request add(annotateImageRequest); }}); Vision.Images.Annotate annotateRequest = vision.images().annotate(batchAnnotateImagesRequest); // Due to a bug: requests to Vision API containing large images fail when GZipped. annotateRequest.setDisableGZipContent(true); Log.d(TAG, "created Cloud Vision request object, sending request"); return annotateRequest; } private static class TextDetectionTask extends AsyncTask { private final WeakReference mActivityWeakReference; private Vision.Images.Annotate mRequest; TextDetectionTask(MainActivity activity, Vision.Images.Annotate annotate) { mActivityWeakReference = new WeakReference<>(activity); mRequest = annotate; } @Override protected String doInBackground(Object... params) { try { Log.d(TAG, "created Cloud Vision request object, sending request"); BatchAnnotateImagesResponse response = mRequest.execute(); return convertResponseToString(response); } catch (GoogleJsonResponseException e) { Log.d(TAG, "failed to make API request because " + e.getContent()); } catch (IOException e) { Log.d(TAG, "failed to make API request because of other IOException " + e.getMessage()); } return "Cloud Vision API request failed. Check logs for details."; } protected void onPostExecute(String result) { MainActivity activity = mActivityWeakReference.get(); if (activity != null && !activity.isFinishing()) { TextView imageDetail = activity.findViewById(R.id.image_details); imageDetail.setText(result); } } } Create a function that will scale down the bitmap captured to make processing faster private Bitmap scaleBitmapDown(Bitmap bitmap, int maxDimension) { int originalWidth = bitmap.getWidth(); int originalHeight = bitmap.getHeight(); int resizedWidth = maxDimension; int resizedHeight = maxDimension; if (originalHeight > originalWidth) { resizedHeight = maxDimension; resizedWidth = (int) (resizedHeight * (float) originalWidth / (float) originalHeight); } else if (originalWidth > originalHeight) { resizedWidth = maxDimension; resizedHeight = (int) (resizedWidth * (float) originalHeight / (float) originalWidth); } else if (originalHeight == originalWidth) { resizedHeight = maxDimension; resizedWidth = maxDimension; } return Bitmap.createScaledBitmap(bitmap, resizedWidth, resizedHeight, false); } Finally, create a function to display the text extracted from image private static String convertResponseToString(BatchAnnotateImagesResponse response) { String result = ""; TextAnnotation fullTextAnnotation = response.getResponses().get(0).getFullTextAnnotation(); if (fullTextAnnotation != null) { result = fullTextAnnotation.get("text").toString(); } else { result = ""; } return result; } The variable result will contain the text extracted from image. Hope that helps :)

Area Of Work:Computer Vision