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Taps and gestures · How to add a gesture recognizer to a view · How to read tap and double-tap gestures · How to force one gesture to recognize before another. A large queue of uploads will affect your download speed. Right-click the downloading torrent, go to Bandwidth Allocation. Notability is a handwriting note-taking app for the iPad, iPhone and Mac. It costs $/year and you access it on all your devices. TESCIOWIE ONLINE LEKTOR PL TORRENT Ben Stockton is under any of and an improperly. Read reviews, listen his content into. Many people said if the Purchase variable id that. After the initial occur for many reasons, one of.
If you want to get into self-driving cars, this project will be a good start. Lane detection works like this:. Computer vision is emerging in healthcare. The amount of data that pathologists analyze in a day can be too much to handle. As more images are entered and categorized into groups, the accuracy of these algorithms becomes better and better over time. It can detect various diseases in plants, animals, and humans.
For this application, the goal is to get datasets from Kaggle OCT and classify data into different sections. The dataset has around images. Optical coherence tomography OCT is an emerging medical technology for performing high-resolution cross-sectional imaging. Optical coherence tomography uses light waves to look inside a living human body. It can be used to evaluate thinning skin, broken blood vessels, heart diseases, and many other medical problems. One of the most used MNIST datasets was a database of handwritten images, which contains around 60, train and 10, test images of handwritten digits from 0 to 9.
These images are divided into two subsets, one with clothes similar to the fashion industry, and the other with clothes belonging to the general public. The dataset contains 1. Below are a few advanced-level fun projects you can work with if you have enough skills and knowledge. Image deblurring is an interesting technology with plenty of applications. Generative Adversarial Networks is a new deep-learning approach that has shown unprecedented success in various computer vision tasks, such as image super-resolution.
However, it remains an open problem how best to train these networks. A Generative Adversarial Network can be thought of as two networks competing with one another; just like humans compete against each other on game shows like Jeopardy or Survivor. There are 3 major steps involved in training for deblurring:. With this project, you can transform any image into different forms. For example, you can change a real image into a graphical one. This is kind of a creative and fun project to do.
The idea is that you train two competing neural networks against each other. The generator alters its parameters to try to fool the judge by producing more realistic samples. In this way, both networks improve with time and continue to improve indefinitely — this makes GANs an ongoing project rather than a one-off assignment. What Cycle Gan does is create a cycle of generating the input. Below is an example of how transforming images to artwork works.
When it comes to coloring black and white images, machines have never been able to do an adequate job. To overcome this issue, scientists from UC Berkeley, along with colleagues at Microsoft Research, developed a new algorithm that automatically colorizes photographs by using deep neural networks. Deep neural networks are a very promising technique for image classification because they can learn the composition of an image by looking at many pictures.
Densely connected convolutional neural networks CNN have been used to classify images in this study. They can be thought of as feature detectors that are applied to the original input image. Colourization is the process of adding color to a black and white photo. However, with the rapid advance of deep learning in recent years, a Convolutional Neural Network CNN can colorize black and white images by predicting what the colors should be on a per-pixel basis.
This project helps to colorize old photos. As you can see in the image below, it can even properly predict the color of coca-cola, because of the large number of datasets. Nowadays, many places are equipped with surveillance systems that combine AI with cameras, from government organizations to private facilities. These AI-based cameras help in many ways, and one of the main features is to count the number of vehicles.
It can be used to count the number of vehicles passing by or entering any particular place. This project can be used in many areas like crowd counting, traffic management, vehicle number plate, sports, and many more. The process is simple:. A vehicle license plate scanner in computer vision is a type of computer vision application that can be used to identify plates and read their numbers. This technology is used for a variety of purposes, including law enforcement, identifying stolen vehicles, and tracking down fugitives.
This project is very useful in many cases. The goal is to first detect the license plate and then scan the numbers and text written on it. Hope you liked the computer vision projects. Android Developer and Machine Learning enthusiast. I have a passion for developing mobile applications, making innovative products, and helping users. Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.
Pathology Classification. Advanced level Computer Vision projects. Image Deblurring using Generative Adversarial Networks. Image Transformation. Vehicle Counting and Classification. Vehicle license plate scanners. Reduce noise and smoothen image, Calculate the gradient, Non-maximum suppression, Double the threshold, Linking and edge detecting — hysteresis.
Capture and store the background frame just the background , Detect colors, Generate a mask, Generate the final output to create the invisible effect. Find face locations and encodings, Extract features using face embedding, Face recognition, compare those faces. Install the Pyautogui library — it helps to control the mouse and keyboard without any user interaction, Convert it into HSV, Find contours, Assign command at any value — below we used 5 from hand to jump.
Source: Kaggle Dataset. Github Kaggle Datasets Link. Create fake inputs based on noise using the generator, Train it with both real and fake sets, Train the whole model. Frame differencing, Image thresholding, Contour finding, Image dilation.
Capture image, Search for the number plate, Filter image, Line separate using row segmentation, OCR for the numbers and characters. Follow me on. In the beginning you talk about the neural network needed to create the embeddings. In my case i have a dataset of about people with around 10 images each.
As far as improving the accuracy of the system keep in mind that you are using just the produced face embeddings on images the network was not trained on. To fully improve the method you should train your own network using dlib on the faces you would like to recognize.
Its great tutorial. I follow ur tutorial from basic and now very comfortable in the code style you have. I would love to try this by my own and will give you the feedback soon. Hi Adrian, Thank you for wonderful tutorial! I have some problems. When but I run it real time, it is not correct all case. Do you have any solutions for this problem? You should know the faces in the images.
Your algorithm can make predictions on these testing images. And from there you can derive how many were correct i. The image size may be too small at only px. Try increasing it to see if you can find a nice balance between speed and accuracy. I just have quick question. They seem to have significant training and testing times between them with Hog being the faster of the two. HOG is faster but less accurate.
CNN is slower but more accurate. For real-time a GPU should be used. This has been eluding me for hours. I have managed to solve it by organising the cuDNN files in the appropriate cuda directories and downgrading apple clang version to 8. Is there a certain threshold you would use for knowing frame rate is too slow for good results? Is there a certain frame rate where the memory of the computer just cannot keep up and ultimately you will not get good results?
I am referring to video file analysis of a movie. The accuracy of the face recognition algorithm 2. If I have a trained algorithm with accuracy detecting in a real time, is there a certain frame rate where the algorithm will not detect very well because the video is choppy and it appears the computer is bogged down…? Not really referring to the algorithm accuracy itself but just the computer memory issues … Can my results be poor because of poor frame rates even tho the overall accuracy of the algorithm is good…?
The frame rate itself will not affect the accuracy of your system. The face recognition code will still see the same frames and the accuracy will be the same, but the throughput itself will be slower. Great Tutorial. I am trying to understand advantage of deep metric learning network here.
Why not take the output of the face detection box and feed directly through a common classification network to label them? Try it and see! And then compare your accuracy. By using triplet learning we can obtain embeddings that perform well. If we used standard feature extraction using the network instead of the embeddings we again would not obtain as high accuracy.
Thanks Adrian for your reply. I will definitely give it a try, it is the best suggestion to learn. I am always looking for more ways to improve accuracy. Your answer solidified the thoughts! Thanks a lot. I need live face detection difference to copy image. Thank you for this tutorial.
I would like to ask several questions regarding it. If so, could you please share the results? Take a look at FaceNet and DeepFace. Take a look at this post. Refer to 1. Thanks for the response. Basically I already get acquaintance of these publications. However it is not possible to conduct an experiment on own dataset, as both prototypes provide per-traned models and at least in publications there is no information regarding re-configuretion the model.
More over they are using Inception model, as you said in your previous comment, it is more used for object recognition. Looks promising! Just to clarify — the Inception architecture can be used for a variety of image recognition tasks.
The Inception network was originally trained on the ImageNet dataset for classification. However, it can be used as a base model for object detection and other tasks. Hi Adrian. Have been experimenting with Facenet for generating face embeddings. Checked it out, but I still need to check against a bigger corpus of data to see how well they do. How do you think they compare considering both the papers came out in a short span of couple of months.
A standard is the LFW dataset and all of those methods reportedly perform well on it. I love your blogs and have been following this since a few months. I have installed dlib successfully. I am running this on a windows 10 OS using anaconda and python 3. Please let me know how to fix this. Thank for the useful post.
I tried this code on custom images, most of the time it works. Just a little problem , sometimes for two peoples it recognise as same person. Do you suggest some debugging ideas. You might want to consider playing around with the minimum distance threshold when you compare the actual faces. Thanks a lot for your great effort. Could you please tell how I can do this?
Say a new face were to be introduced into the dataset. Is there a way to create an encoding for ONLY the new images in the dataset? Would this require some sort of comparison between the existing pickle files in which everything is ignored except the newly-introduced face? Your understanding is correct — the script would loop over all faces in your input dataset and recompute the embeddings for them.
The simple fix would be to just:. Store your new images in a separate directory from the old ones 2. Any advice? I would suggest posting on the official dlib GitHub page. I downloaded MySQLdb packages and used it in normal environment. But i cannot use it in virtual environment in which I installed opencv, dlib and face recognition packages. This StackOverflow thread should help you out. Great stuff Adrian. Question: Is it possible to run several distinct types of recognition on a video stream?
For example, I want to train some model to recognize several types of objects example: dog, cakes etc and I also want to use face recognition. Is it possible to run a script to detect all of them concurrently? Another example would be to use a license plate recognition script along with a facial recognition. So when it finds a specified license plate, it would pop a trigger or if it recognizes a specific person. Yes, this is possible. You would want to apply object detection to locate all objects in an image that you are interested in.
This means you need to train your model on examples of dogs, cake, faces, etc. After a given object is detected you can pass it on to another model for recognition. For such a project you would want to use two models.
Thank you. You may have entered a different email address and that is where the notifications are going. The dlib library is used under the hood which performs the NMS. What are your results and what would you ideally like to obtain?
How can I make it so that I do not encode the images every time I want to add a new face to the dataset? Can this be done with a database so as not to be coding each image? See my reply to Dauy. Thank you for your tutorial!!!
I learn so much when exploring your Python scripts and the dlib library. Hey Hami — I assume you are referring to my previous blog post on multiple cameras? Hello Adrian, I am new to opencv and I am currently enrolled in your 17 day course. I am currently doing a project where I would like to recognize the best photo, that is, if there are 6 photos and some of them come out of profile or some of them go out and remove them and only keep the one that is the best, that is, the front face and that photo another 5 discard them.
Do you have an example, or how could I do it? I use orientation. What makes one photo better than all the others? Hello Adrian! That sounds like it may be pretty subjective, but you can detect blur using this post and you can learn how to detect smiles by going through the Starter Bundle of Deep Learning for Computer Vision with Python. I hope that helps point you in the right direction! I want big project in face recognize thats way I want all topic which covere for project please tell and which topic have learn for fullfil the requirement of the face recognize project.
Have you considered working through the PyImageSearch Gurus course? Hey Adrian, thank you so much for this tutorial. I had a small doubt. In your face recognition video, there has been few instances where the lawyer is recognized as someone else instead of unknown. Is there any parameter that I could tweak to reduce occurrence of false positives? We used a simple variant of the k-NN classifier for simplicity, but you could take the d embeddings and then train a more advanced model on it, such as a Linear SVM, Logistic Regression, or non-linear model as well.
I cover the exact answer to that question in my face face recognition guide. So you are saying that using one of those methods instead k-nn will lead to a better face recognition, and using exactly the same steps presented here in the rest? No, it does not work What changes should I make to make two cameras for facial recognition in a raspberry pi? What specifically is not working, Hami?
Keep in mind that I can only provide suggestions or guidance if you can describe the problem. In a previous comment I already linked you to my tutorial on accessing multiple cameras. Or is this a bad idea because you are essentially changing the face? Run an object detector to detect a face 2. And then quantify that face using a network dedicated to facial recognition. Data augmentation can help a bit. Be sure to refer to the docs. Make sure you are correctly supplying the command line arguments to the script which you are not.
Make sure you read this link on how to use command line arguments. Please refer to my previous comment. Read up on command line arguments first. Keep in mind that basic knowledge of how the command line works is a requirement for more advanced tutorials such as this one. Hi Adrian, Thank you for the great post. Are they created in a particular way because the network has already been trained with over 3-million images?.
I mean, Is the result based on the prior training? I suppose during that prior training, the library we use deduces the way it will create distinctive features for the new images. By that token, perhaps we could even present here only one single face and the code will find out it. The result is based on prior training. If you had a lot of example faces of people you wanted to recognize you could also train the network from scratch or fine-tuning the existing model. It seems like a Python version issue.
Re-encode the faces which will generate a new LabelEncoder object. Once the new LabelEncoder is generated it should work perfectly. Create your dataset of images first. Then encode the faces. From there all other steps are the same. This is really a great, great piece of tutorial! Congratulations and thanks for sharing this goodness. I have two questions:. Yes, you can do that. You would loop over all images in the directory and then apply face recognition to each. Yes, you can write the results to disk.
I can run it on the laptop which is ideapads and when I run it on my desktop computer , it just stuck there. I am using cnn detection-method on both of them. I tried switching cnn to hog and it will work. Can you open up your activity monitor to verify that your CPU is being utilized by the Python process? Right now, face recognition only works as long as the subject is facing the camera. This method assumes you have the full frontal view of the face. Side profiles would be less accurate.
Maybe because I am asking a similar question with the other comments but I have read them already. I already answered your question — please make sure you review my answer to your previous comment. Hi Adrian, Thanks for the post. It seems like installation is just stuck there. Is there any other problems with the pi or libraries? Please Help. Have you installed all other dependencies? Try leaving your Pi on overnight. I am able to generate encodings, but when I run the recognition code, it restarts the runtime.
I am running it on google colab. I trained for first two folders only from the dataset and iam using example1. Sorry, I have not tested this code directly on Google Colab. Sorry, I do not have any experience with opencv. Thanks, I am a beginner and have benefited a lot. I have the used the tool pickel to generate the model file but the streaming speed from the webcam is extremely slow due to that the face recognition is also taking more time. I running the code on a CPU. The face recognition component is what is slowing your pipeline down.
Can you just remove the part of video storing so that recognizing becomes fast? Sure, absolutely. Feel free to modify the code as you see fit. The best way to learn is by doing. Give it a try. Maybe more than , or people? My general rule of thumb is once you get over people you should be training or fine-tuning the network. You would either need to train from scratch or fine-tuning an existing FaceNet, OpenFace, or dlib face recognition model.
Thank you for tutorial. Can you please tell me what is the best algorithm for detect facial key points. Refer to this post. Hey Adrian, thank u for ur awesome post. Can you verify that your GPU is being used for dlib? My guess is that your GPU is not being utilized. Have you tried training a more powerful model on top of the d face embeddings?
See this tutorial for more information. Hi Adrain, Is there any possibility of appending the encodings. Is it possible to identify different persons in a group photo? I actually answered this question in my reply to Dauy. Be sure to give it a read, I think it will help you. I got it to work, but its doing a frame every 20 seconds. I have installed correctly dlib for cuda. What is happening? I would go back and double-check. You can use simple array slicing. This tutorial shows you how to extract the face ROI.
You specify the image path via command line argument. Is there any way to run this on google collaboratory with GPU support, Can we remove the argparse and hardcode the path for dataset, encodings and the method. Can you please explain how the d encodings are generated? Like does the dataset that was used to train the NeuralNet in dlib have the images of the characters previously.
Hi, Adrian I finally get reached this Intuitive course to get familiar with computer vision, by the way, I have one question, does happen to have the possibility that making a bad recognition by the different resolution and quality of screen between encoding resources within dataset folder and example file within example folder. I am only afforded a single profile photo for every person stored in the dataset to encode. As a result, the encoding and face recognition results is not accurate.
No, you need more data. See this post for more information. Hi Adrian, I have been working on facial recognition for quite long and now i got this method for implementing. Can you tell what are the prerequisites for this code? I have openCV 2. I have downloaded the code however can you tell me how to start working with it. Your help will be highly appreciated. You will need to install dlib though so make sure you have dlib installed as well. OpenCV 2. Adding to the above info, the technique for facial recognition i am using is simple and not that appropriate in showing correct results, because if I create a dataset for even people it recognizes the faces wrong.
Within your code are you creating the dataset or you are keeping the sample images for every user and using them for the later real time recognition?? Thank you very much for the tutorial. Please clarify my doubt. We can detect and recognize a face appearing in front of webcam using python.
How can we ensure that the face appearing in front of the webcam is real or spoof. Actually detecting and recognizing a face is covered in this post. Hello Adrian.. I really appreciate your work! Hi Adrian, Great tutorial. I tried on my MacBook with no change with your repo. What should I do? What am I doing wrong? Could you please advise? Hey Hasan — it sounds like the script is working properly but your faces are not being properly recognized.
Hi Adrian Congratulations for the great tutorial. It is possible to use this tutorial in Android? This tutorial is not directly transferrable to Android. Hi Alvaro ,could you update with the results on porting dlib to android using beeware or kivy ,is it doable??
The less data there is to process, the faster your algorithms can run. Is there a way to improve this? This is causing the 2FPS. Unfortunately there is not a way to improve the throughput rate without using a GPU. You could try using a smaller, less deep model but then you may sacrifice accuracy.
You may also need to train the model yourself. I would highly encourage you to use the command line rather than PyCharm. I am running Geforce with 6GB of memory. I have dlib running GPU and all seems good until i hit an out of memory when executing. No, it just means that you cannot use the deep learning-based face detector.
Use HOG instead and the script will work for you. Hi Adrian, I am trying to make a project to identify faces from a webcam and display information stored in a text file or excel sheet like medicines that the person has to take after the face has been identified. What are the changes that has to be made to your code? If you want to compute a probability refer to my other face recognition tutorial.
Is there any chance of updating embeddings pickle file by adding encodings of only added images instead of running encodings for all the images from starting. Yes, absolutely. I provide the solution in my reply to duay. Be sure to refer to the other comments as well. I would like to do face recognition on lensed photos like snapchat B etc.
How can I approach this problem? Thank you so much Hasnat! Best of luck with your projects! Can i get some help? Your machine is simply running out of memory likely due to your input images being too large. Reduce the size of the images by resizing them. I would suggest using imutils. Do we need to provide our own hog detector?. No, the HOG face detector is provided for you with dlib. You could use a different face detector like I do in this tutorial. Thanks for the post.
Do you have a tutorial on how to fine-tune the network on the fly? I see others with similar problems, but they occur in the step after this. Take a look at this comment thread where the issue is discussed in more detail. Thank you for your work. Really helpful to me. I have a question about classification.
Is there a way to classify a person differently when an image of someone who has not been trained is entered? Result I expect is none of them A, B and C. How can I get what I want or expect? Best regards. I trained my model with my mates two men.
Be sure to refer to this tutorial where I discuss methods to improve your face recognition pipeline. I am ,already, C language programmer for Microcontrollers microchip with several years experienced and now I am learning Python too-. My idea is to mix electronics and this image recognition in a near future to control small experimental toy or a small trolley with wheels.
Deep Learning for Computer Vision with Python , which discusses how to train your own highly accurate, deep learning based object detectors, including detailing each detector and which ones are suitable for real-time detection. Additionally, you may be interested in the PyImageSearch Gurus course which will teach you more about computer vision and how to apply it to real-world applications.
Why the fascination with Command Line Args? You could certainly use a Jupyter notebook if you want. Keep in mind this is a computer vision blog and at least some basic knowledge of the command line is assumed. I also have same problem.
I have also install GPU version id dlib. Did you successfully compile dlib with CUDA support? The first problematic image was Alan Grant 24, with a size of x So I crafted a small script to resize all images with a height or width larger than px. You can find it here:. Thanks Manuel. Another option would be to simply resize the images via imutils. The only problem I encountered is the speed of the facial recognition process. But I do have multiple CPU cores, which the number is I have 48 cores.
Could I make good use of multiple CPU cores to speed up facial recognition? You have been doing great and your posts have helped me a lot as though I am a beginner. Could I ask you a favor? Anyway, thank you for your posts.
Hoping to see the improvements. Thanks for the suggestion, Jiss! Hi Adrian, I am doing a job to university and my teacher gave me your website to help. But I have a question, he wants us to use Scikit-learn for deep-leaning. It is possible do use it here? And how? I really appreciate your help.
The scikit-learn library is a machine learning library, not a deep learning library. I think you may have misunderstood your teacher so you should clarify with them. Yes, I missunderstood. Is there any chance that I can use the scikit-learn instead of dlib? Like replace it? It is very computationally expensive computing the face embeddings. To speedup the process you may want to use a GPU.
You can use grayscale images for neural networks provided you actually trained your network on grayscale images. Blank screen is coming instead of frame with rectangular boxes. How can I resolve this? It may be an issue with how your videos were recorded. Try with other videos from other pre-recorded sources. Hi Adrian, firstly greatly appreciate the tutorial as it is very helpful. Got it. Use the time Python module to grab the current timestamp.
I read your other page and still can not find the solution. If you refer to the guide you just linked to it will help you understand command line arguments and how they work. Most likely not. Hello Adrian, Great work there……. In identifying videos it takes frame by frame and detects and writes to disk again in video. How to just write to disk in frames i.
So that it would reduce time for identifying faces. You can use the cv2. This code is running too slow on AWS gpu. Could you please advise what can be done to make it it faster? Have you confirmed that your GPU is being utilized by dlib? Double-check and triple-check that dlib is accessing your GPU. Hi Adrian Rosebrock, according to you, data collection is about how many photos per person for accuracy to be acceptable.
Can you tell me how much time will it take to detect and recognize 60 different faces in single photograph? I would suggest running some benchmark tests with people on your own system and then using the timings to estimate how long it would take for 60 people.
But can you tell me an approximate time it will take for i7? I am asking this question because I am going to integrate this tool in my project. So, I want to confirm that whether its execution time is acceptable in my project. I want to know that every-time when a new person comes in-front of camera in case of video, do we have to execute this command every-time whenever a new person comes. The command is:. I would suggest having the face recognition model running along with your camera monitor.
Quantify each face and if the face is not recognized, add it to your database. Try using a GPU for faster recognition. You may also want to resize your images make them smaller. The smaller an image is, the less data there is to process, and therefore the faster the face recognition algorithm will run.
I have several questions. I need to find new face s from real-time stream in a database of persons. For better accuracy each person should have about 20 pics. This means that I need to perform up to 30 comparisons for each frame. Could you explain what is the hardest in terms of resources operation? To extract face from image and get embedding or to perform a comparison of 1-to-1 embeddings?
Does comparison of 1-to equals to ten times of 1-to-1 in terms of resources? What hardware resources do I need to accomplish such a task? Face detection is easy compared to face recognition. Both are different topics. Face verification is easier and could potentially scale well. Face recognition is significantly harder.
For 1,, people I would recommend you fine-tune or train a face recognition model. Using the d embeddings from a pre-trained network is not going to perform well. I want to identify unknown person in a stream against the database of known persons. Just like in your example. But in my case I have a database dataset about different persons. Could you suggest any tutorial or method of solving my task?
As I mentioned, you should look into fine-tuning or training from scratch a FaceNet network or equivalent. With 1,, images per person the pre-trained network here is not going to work. At the present time I do not have any tutorials on fine-tuning FaceNet. For Face verification, let say, for Hi Adrian so if I understand you correctly, d embeddings is a good choice for 20, to 30, employees but a pre-trained one is not a good option.
Is this what you meant by in case of huge dataset, we need to fine-tune the network again. I want FPS speed for video processing. Could you please suggest best configuration or any other method to get the required FPS? How large are your input dimensions in terms of width and height?
Try reducing your images to be as small as possible. What is the easiest way to extract all the unknown faces in a folder or as a list of embeddings and save it as a. I am having a hard time filtering just the unknowns. I want to know how to check the confidence for the face recognized. Any suggestion on how to do that. See this face recognition tutorial instead.
Hey Adrain,I want to recognize my own pet as you did with human faces. The technique you explained above can also applied to dogs? I just want my dog to be detected among all other dogs. That would require building your own dataset and having a good understanding of triplet loss.
Hi Adrian I have implemented the Facial recognition part and it is able to recognise faces with good accuracy. Now I would like to captire an image of the recognised photo and store it in a folder on my raspberry pi. Is that possible. It sounds like you may be new to computer vision and OpenCV. Thanks for the tutorial, the accuracy is good but it is taking 30 to 50 seconds to recognize an image is there any solution to overcome.
I tried you tutorial, it is great! I have another question that I want to ask you, now I am doing my own face recognition project. First, I trained my own face embedding extraction network, and it got Then I replaced the dlib face encoding model with my own one, however the performance is very pool even in some easy cases. Do you know the reason about it, or could you give me some advice to improve. Hope get your answer. Thanks advance Adrian Rosebrock! I have tested the sources on GPU using dataset with very large number of photos.
The performance is good. What will you suggest to improve the accuracy? You should read this tutorial to learn how to improve the face recognition accuracy. Has the script finished running? Or is it still processing the video file? Let it run to completion. Is it possible to change the extension of the output file to.
If yes, How and where in the code? That really depends on your OpenCV version and installed codecs. Hello sir your work is wonderful. Secondly command prompt does not stops even after the image window is closed? Thanks a lot for this great project. I have a question, is there a way this API can detect side face of anyone? Typically we discard side views and only try to perform face recognition on center views if at all possible. Thanks a lot for your reply. Another question please I want to run the service locally with automatic command, is there a document i can follow?
I want to automate the process without writing the commands myself. I have checked some codes but all use a method name and as i can see this code should read line by line there is no single method that do everything. Can you please advice. Take a look at shell scripts and crontab which will enable you to run multiple commands and even schedule them to run on reboot. Does this mean that the script is still running? Yes, it means the script is still running.
How do I change the data set? Rename the directory, fill the directory with example images of that person, then extract features from the faces. From there you can recognize the new faces. I am a beginner and I am currently doing a project at university based on facial recognition with python using OpenCV.
That is, I want to use this concept of face recognition inside my website. You can create an API that accepts an input image, performs face recognition, and then returns the result. That would be my suggestion. Hi Adrian, Thank you for this great application and your efforts. Stay tuned! Hi Adrain, Thanks for your great work. How to use trained caffe model with your application above? I have an trained caffe model with me. I thought it is possible to this caffe along with your application?
Sorry i am completely new this stuff. You mean use Caffe instead of OpenCV for the recognition process? Dear Dr. Adrian, I read the tutorial and found it so easy to follow and implement. Thank you very much for that!! For training it I used around images of one person. Still when i ran face detection on a couple of his videos, it recognised many other people also as the same person. So I went a step ahead for training just for experiment.
I re read the tutorial twice again just in case if I had missed anything but I am sure I have followed all the steps. Take a look at my other face recognition tutorial where I discuss reasons your face recognitions may be incorrect, including ways to improve on it.
Hi Dr. Adrian, Thanks for your posts particularly the facial recognition ones. Hi Adrian, Thanks for your brilliant blog. I have a question.. Means to say I want to recognize real live faces in real time, but this code even recognizes the faces if I show an image of mine from my phone on the webcam.
Can you please guide me that will be great favor from you. Refer to my tutorial on liveness detection. Hi we are using your code for monitoring the attendance of class but it is not able to detect the faces and if detected is giving incorrect output.
I managed to repeat everything in the article, but as a result the video is displayed very slowly, jerky, everything is fast in your demo, why? I am testing this algorithm for my research purposes, sometimes i see wrong faces are recognised Example: Face ID: A is Recognised as Face ID:B , Can you please share me your ideas to solve this problem. Refer to this tutorial where I share my suggestions on obtaining higher face recognition accuracy.
The previous post is to collect data which can be used here, however, one get around this by using other data set like we did here in this post. I am asking just to make sure that I am not missing something. You are correct. You can use whatever dataset you want with this code provided that you follow my directory structure. That pictures have largest number of pixels in dataset and when I removed them from dataset all working fine.
So I think there is some sort of memory leak issue. When I ran script whith those two pictures at the beginning of dataset there were no errors. They are just larger input images. In this case, we can have a better and not sensitive face recognition system. I get these names in this code each time I get a face, and then I write these names on the image.
The problem is that I want to get the first time I got these names then second time get the names and compare with the previous step and then do the puttext operation. Count the number of times the same label was consecutively predicted by the model. That will reduce sensitivity. So —yes has been removed. Do not give it to setup. I thinks this is coming because of the update in dlib library. But now which option should we use for installing dlib with GPU support is the question.
I use sd 32gb of SamSung. Follow this tutorial instead. No, you need to manually perform face alignment. This tutorial on face alignment will help you. Thank you for all the work you do and for providing such useful and inspiring code gratis. I have successfully installed and am using your facial recognition system on my laptop and would like to be able to use it on a remote cloud server with the user being able to use their local webcam as the video stream.
How can I feed my local camera to the script residing on the server and have the view returned to the local machine? Thank you again for the already existing code you have generously provided, and for any assistance you can provide on how to use local stream on remote server. Thank you for your reply. I see the book is upcoming soon and I will be sure to purchase a copy. Love your style man!! Here I was expecting to: 1- resize the converted frame not the original one?
Is this correct? Hi Ali and Adrian — I ran through the post and it worked great. I too had a question about those lines:. Otherwise it looks like we throw away the color conversion on line . A question about generating encodings for new added faces, how can we encode newly added faces without losing the previously encoded ones? Is this possible at all? This comment would be a good first start but make sure you give the others ones a read. Hey Adrian! I hope u doing good.
Can you guide me about updating an existing model with new data? Hi Adrian , is there a way to run these codes in colab? Im building a emotion detector for my university degree and I was wondering if I swap the data sets from the actors to the emotions would it work?? How can I improve it?
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