Emotion Detection

  Now, we reached the final phase of our project. Here, we will capture a fresh face on our camera and if this person had his face captured and trained before, our recognizer will make a “prediction” returning its id and an index, shown how confident the recognizer is with this match.

  On this second phase, we must take all user data from our dataset and “trainer” the OpenCV Recognizer. This is done directly by a specific OpenCV function. The result will be a .yml file that will be saved on a “trainer/” directory.

  First of all, I must thank Ramiz Raja for his great work on Face Recognition on photos: FACE RECOGNITION USING OPENCV AND PYTHON: A BEGINNER’S GUIDE and also Anirban Kar, that developed a very comprehensive tutorial using video: FACE RECOGNITION — 3 parts I really recommend that you take a look at both tutorials. Saying that, let’s start the first phase of our project. What we will do here, is starting from last step (Face Detecting), we will simply create a dataset, where we will store for each id, a group of photos in gray with the portion that was used for face detecting.

  You can also include classifiers for “eyes detection” or even “smile detection”. On those cases, you will include the classifier function and rectangle draw inside the face loop, because would be no sense to detect an eye or a smile outside of a face. Note that on a Pi, having several classifiers at same code will slow the processing, once this method of detection (HaarCascades) uses a great amount of computational power. On a desktop, it is easer to run it. Examples On my GitHub you will find other examples: Emotion_Detection.ipynb videotester.py tempCodeRunnerFile.py And in the picture, you can see the result. Happy Motion Detect Surprise Motion Detect

  This project was done with this fantastic “Open Source Computer Vision Library”, the  OpenCV . On this tutorial, we will be focusing on Raspberry Pi (so, Raspbian as OS) and Python, but I also tested the code on my Mac and it also works fine. "To run it on a Mac, there is a couple of changes that should be made on code. Do not worry, I will comment about it" -->  OpenCV was designed for computational efficiency and with a strong focus on real-time applications. So, it’s perfect for real-time face recognition using a camera.

  I am using a Raspberry Pi V3 updated to the last version of Raspbian (Stretch), so the best way to have OpenCV installed, is to follow the excellent tutorial developed by Adrian Rosebrock:   Raspbian Stretch: Install OpenCV 3 . I tried several different guides to install OpenCV on my Pi. Adrian’s tutorial is the best. I advise you to do the same, following his guideline step-by-step. Once you finished Adrian’s tutorial, you should have an OpenCV virtual environment ready to run our experiments on your Pi. Let’s go to our virtual environment and confirm that OpenCV 3 is correctly installed. Adrian recommends run the command “source” each time you open up a new terminal to ensure your system variables have been set up correctly. source ~/.profile Next, let’s enter on our virtual environment: workon cv If you see the text (cv) preceding your prompt, then you are in the  cv virtual environment: (cv) pi@raspberry:~$ Adrian calls the attention that the  cv Python virtual...

Once you have OpenCV installed in your RPi let’s test to confirm that your camera is working properly. I am assuming that you have a PiCam already installed and enabled on your Raspberry Pi. Enter the below Python code on your IDE: import numpy as np import cv2 cap = cv2.VideoCapture(0) cap.set(3,640) # set Width cap.set(4,480) # set Height while(True): ret, frame = cap.read() frame = cv2.flip(frame, -1) # Flip camera vertically gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('frame', frame) cv2.imshow('gray', gray) k = cv2.waitKey(30) & 0xff if k == 27: # press 'ESC' to quit break cap.release() cv2.destroyAllWindows() The above code will capture the video stream that will be generated by your PiCam, displaying both, in BGR color and Gray mode. Note that I rotated my camera vertically due the way it is assembled. If it is not your case, comment or delete the “flip” command line. You can alternatively down...

  To create a complete project on Face Recognition, we must work on 3 very distinct phases: Face Detection and Data Gathering Train the Recognizer Face Recognition The below block diagram resumes those phases:

  The most basic task on Face Recognition is of course, “Face Detecting”. Before anything, you must “capture” a face (Phase 1) in order to recognize it, when compared with a new face captured on future (Phase 3). The most common way to detect a face (or any objects), is using the “ Haar Cascade classifier ” Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features” in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. Here we will work with face detection. Initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier. Then we need to extract features from it. The good news is that OpenCV comes with a trainer as well...