![]() If not for them, the program would crash if you were to blinked. Notice the if not None conditions, they are here for cases when nothing was detected. Let’s define a main() function that’ll start video recording and process every frame using our functions. All that’s left is setting up camera capture and passing its every frame to our functions. We’ll cut the image in two by introducing the width variable: def detect_eyes(img, classifier): gray_frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) eyes = tectMultiScale(gray_frame, 1.3, 5) # detect eyes width = np.size(image, 1) # get face frame width height = np.size(image, 0) # get face frame height for (x, y, w, h) in eyes: if y > height / 2: pass eyecenter = x + w / 2 # get the eye center if eyecenter 1: biggest = (0, 0, 0, 0) for i in coords: if i > biggest: biggest = i biggest = np.array(, np.int32) elif len(coords) = 1: biggest = coords else: return None for (x, y, w, h) in biggest: frame = img return frameĪlso notice how we once again detect everything on a gray picture, but work with the colored one.īelieve it or not, that’s basically all. Now we can display the result by adding the following lines at the very end of our file: cv2.imshow('my image',img) cv2.waitKey(0) cv2.destroyAllWindows() Those lines draw rectangles on our image with (255, 255, 0) color in RGB space and contour thickness of 2 pixels. To see if it works for us, we’ll draw a rectangle at (X, Y) of width and height size: for (x,y,w,h) in faces: cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2) It would mean that there are two faces on the image. For example, it might be something like this: ] They are X, Y, width and height of the detected face. gray_picture = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#make picture gray faces = face_tectMultiScale(gray_picture, 1.3, 5)įaces object is just an array with small sub arrays consisting of four numbers. To detect faces on a picture, we first need to make it gray. ![]() Also it saves us from potential false detections. It saves a lot of computational power and makes the process much faster. Then you proceed to eyes, pupils and so on. Meaning you don’t start with detecting eyes on a picture, you start with detecting faces. In object detection, there’s a simple rule: from big to small. Once it’s in your working directory, add the following line to your code: img = cv2.imread(“your_image_name.jpg”) Make sure they are in your working directory. To download them, right click “Raw” => “Save link as”. There are available face and eyes classifiers(haar cascades) that come with the OpenCV library, you can download them from their official github repository: Eye Classifier, Face Classifier ![]() For example, whether a picture has a face on it or not, and where the face is if it does. In general, “detection” processes are machine-learning based classifications that classify between object or non-object images. We specify the 3.4 version because if we don’t, it’ll install a 4.x version, and all of them are either buggy or lack in functionality. To start, we need to install packages that we will be using:Įven though it’s only one line, since OpenCV is a large library that uses additional instruments, it will install some dependencies like NumPy. It might sound complex and difficult at first, but if we divide the whole process into subcategories, it becomes quite simple. It’s a step-by-step guide with detailed explanations, so even newbies can follow along. ![]() This article is an in-depth tutorial for detecting and tracking your pupils’ movements with Python using the OpenCV library.
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