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shivomsan / repository
Facial-Expression-Detection . Facial Expression or Facial Emotion Detector can be used to know whether a person is sad, happy, angry and so on only through his/her face. This Repository can be used to carry out such a task. It uses your WebCamera and then identifies your expression in Real Time. Yeah in real-time! PLAN This is a three step process. In the first, we load the XML file for detecting the presence of faces and then we retrain our network with our image on five diffrent categories. DEPENDENCIES Hit the following in CMD/Terminal if you don't have already them installed: pip install tensorflow pip install opencv-python That's it for now. So let's take a brief look at each step. STEP 1 - Implementation of OpenCV HAAR CASCADES I'm using the "Frontal Face Alt" Classifier for detecting the presence of Face in the WebCam. This file is included with this repository. You can find the other classifiers here. Next, we have the task to load this file, which can be found in the label.py program. E.g.: # We load the xml file classifier = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml') Now everything can be set with the emoji.py Program. So let's move to the next Step. STEP 2 - ReTraining the Network - Tensorflow Image Classifier We are going to create an Image classifier that identifies whether a person is sad, happy and so on and then show this text on the OpenCV Window. This step will consist of several sub steps: We need to first create a directory named images. In this directory, create five or six sub directories with names like Happy, Sad, Angry, Calm and Neutral. You can add more than this. Now fill these directories with respective images by downloading them from the Internet. E.g., In "Happy" directory, fill only those images of person who are happy. Now run the "train.py" program as suggested in the video Once you have only cleaned images, you are ready to retrain the network. For this purpose I'm using Mobilenet Model which is quite fast and accurate. To run the training, hit the got to the parent folder and open CMD/Terminal here and hit the following That's it for this Step. STEP 3 - Importing the ReTrained Model and Setting Everything Up Finally, I've put everything under the "train.py" file from where you can get everything. Now run the "emoji.py" program by typing the following in CMD/Terminal: python label.py It'll open a new window of OpenCV and then identifies your Facial Expression. We are done now! PLEASE DO STAR THIS REPO IF YOU FOUND SOMETHING INTERESTING. <3
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Facial-Expression-Detection . Facial Expression or Facial Emotion Detector can be used to know whether a person is sad, happy, angry and so on only through his/her face. This Repository can be used to carry out such a task. It uses your WebCamera and then identifies your expression in Real Time. Yeah in real-time! PLAN This is a three step process. In the first, we load the XML file for detecting the presence of faces and then we retrain our network with our image on five diffrent categories. DEPENDENCIES Hit the following in CMD/Terminal if you don't have already them installed: pip install tensorflow pip install opencv-python That's it for now. So let's take a brief look at each step.
STEP 1 - Implementation of OpenCV HAAR CASCADES I'm using the "Frontal Face Alt" Classifier for detecting the presence of Face in the WebCam. This file is included with this repository. You can find the other classifiers here. Next, we have the task to load this file, which can be found in the label.py program. E.g.: # We load the xml file classifier = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml') Now everything can be set with the emoji.py Program. So let's move to the next Step.
STEP 2 - ReTraining the Network - Tensorflow Image Classifier We are going to create an Image classifier that identifies whether a person is sad, happy and so on and then show this text on the OpenCV Window. This step will consist of several sub steps: We need to first create a directory named images. In this directory, create five or six sub directories with names like Happy, Sad, Angry, Calm and Neutral. You can add more than this. Now fill these directories with respective images by downloading them from the Internet. E.g., In "Happy" directory, fill only those images of person who are happy. Now run the "train.py" program as suggested in the video Once you have only cleaned images, you are ready to retrain the network. For this purpose I'm using Mobilenet Model which is quite fast and accurate. To run the training, hit the got to the parent folder and open CMD/Terminal here and hit the following That's it for this Step.
STEP 3 - Importing the ReTrained Model and Setting Everything Up Finally, I've put everything under the "train.py" file from where you can get everything. Now run the "emoji.py" program by typing the following in CMD/Terminal: python label.py It'll open a new window of OpenCV and then identifies your Facial Expression. We are done now! PLEASE DO STAR THIS REPO IF YOU FOUND SOMETHING INTERESTING. <3