Virtual Keyboard – Gaze controlled keyboard with Python and Opencv p.5
We’re going to see in this video how to draw a Virtual Keyboard on Opencv with Python. Source code:
Eye Gaze detection 2 – Gaze controlled keyboard with Python and Opencv p.4
In this fourth part of the serie “Gaze controlled keyboard” we’re going to detect the gaze of our eyes, especially if we’re looking to the left, center or right. Source code:
Eye Gaze detection 1 – Gaze controlled keyboard with Python and Opencv p.3
In this third part of the serie, we’re going to select the region where there is our eye and then put it on a new frame so that later on we can work with the eye only to detect the gaze. Source code:
Eye Blinking detection – Gaze controlled keyboard with Python and Opencv p.2
In this second part of the series “Gaze controlled keyboard” we’re going to detect when the Eye is blinking. We’re going to use this function later on to press the selected key on the virtual keyboard just blinking our eyes. Source Code:
Eye detection – Gaze controlled keyboard with Python and Opencv p.1
In this new video series “Gaze controlled keyboard” we’re going to create and app to control the keyboard trough our eyes. We’re going to develop the app in three steps: Detect the eyes (eyes blinking and eyes direction) Create a virtual keyboard on the screen…
Eye motion tracking – Opencv with Python
We’ll see in this video how to detect the pupil and it’s movement. Source code:
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import cv2 import numpy as np cap = cv2.VideoCapture("eye_recording.flv") while True: ret, frame = cap.read() if ret is False: break roi = frame[269: 795, 537: 1416] rows, cols, _ = roi.shape gray_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) gray_roi = cv2.GaussianBlur(gray_roi, (7, 7), 0) _, threshold = cv2.threshold(gray_roi, 3, 255, cv2.THRESH_BINARY_INV) _, contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True) for cnt in contours: (x, y, w, h) = cv2.boundingRect(cnt) #cv2.drawContours(roi, [cnt], -1, (0, 0, 255), 3) cv2.rectangle(roi, (x, y), (x + w, y + h), (255, 0, 0), 2) cv2.line(roi, (x + int(w/2), 0), (x + int(w/2), rows), (0, 255, 0), 2) cv2.line(roi, (0, y + int(h/2)), (cols, y + int(h/2)), (0, 255, 0), 2) break cv2.imshow("Threshold", threshold) cv2.imshow("gray roi", gray_roi) cv2.imshow("Roi", roi) key = cv2.waitKey(30) if key == 27: break cv2.destroyAllWindows() |
Files: eye_recording.flv
Real time shape detection – Opencv with Python 3
In this video tutorial we’re going to see how to detect shapes of geometric figures (like rectangles, triangles and circles) in a live video. Source code:
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import cv2 import numpy as np def nothing(x): # any operation pass cap = cv2.VideoCapture(1) cv2.namedWindow("Trackbars") cv2.createTrackbar("L-H", "Trackbars", 0, 180, nothing) cv2.createTrackbar("L-S", "Trackbars", 66, 255, nothing) cv2.createTrackbar("L-V", "Trackbars", 134, 255, nothing) cv2.createTrackbar("U-H", "Trackbars", 180, 180, nothing) cv2.createTrackbar("U-S", "Trackbars", 255, 255, nothing) cv2.createTrackbar("U-V", "Trackbars", 243, 255, nothing) font = cv2.FONT_HERSHEY_COMPLEX while True: _, frame = cap.read() hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) l_h = cv2.getTrackbarPos("L-H", "Trackbars") l_s = cv2.getTrackbarPos("L-S", "Trackbars") l_v = cv2.getTrackbarPos("L-V", "Trackbars") u_h = cv2.getTrackbarPos("U-H", "Trackbars") u_s = cv2.getTrackbarPos("U-S", "Trackbars") u_v = cv2.getTrackbarPos("U-V", "Trackbars") lower_red = np.array([l_h, l_s, l_v]) upper_red = np.array([u_h, u_s, u_v]) mask = cv2.inRange(hsv, lower_red, upper_red) kernel = np.ones((5, 5), np.uint8) mask = cv2.erode(mask, kernel) # Contours detection _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) for cnt in contours: area = cv2.contourArea(cnt) approx = cv2.approxPolyDP(cnt, 0.02*cv2.arcLength(cnt, True), True) x = approx.ravel()[0] y = approx.ravel()[1] if area > 400: cv2.drawContours(frame, [approx], 0, (0, 0, 0), 5) if len(approx) == 3: cv2.putText(frame, "Triangle", (x, y), font, 1, (0, 0, 0)) elif len(approx) == 4: cv2.putText(frame, "Rectangle", (x, y), font, 1, (0, 0, 0)) elif 10 < len(approx) < 20: cv2.putText(frame, "Circle", (x, y), font, 1, (0, 0, 0)) cv2.imshow("Frame", frame) cv2.imshow("Mask", mask) key = cv2.waitKey(1) if key == 27: break cap.release() cv2.destroyAllWindows() |
Raspberry Pi 3 and Opencv 3 Installation Tutorial
We’re going to see today how to install opencv on the raspberry pi 3 Model b+ (with camera). 1) Update os system:
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sudo apt-get update sudo apt-get upgrade |
2) Install dependencies:
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sudo apt-get install build-essential cmake pkg-config sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev sudo apt-get install libxvidcore-dev libx264-dev sudo apt-get install libgtk2.0-dev libgtk-3-dev sudo apt-get install libatlas-base-dev gfortran |
3) Install Python 3 and Pip3:
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sudo apt-get install python3-dev sudo apt-get install python3-pip |
4) Install Opencv:
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pip3 install opencv-python |
5) Extra depencies…
How to create a cartoon effect – Opencv with Python
We’re going to see in this video how to create a cartoon effect. A cartoon has 2 important charateristics: Really clear edges Homogeneus colours Starting from an original image taken with a camera we’re going to give to it a cartoon effect keeping in mind…
Face detection using Haar Cascades – OpenCV 3.4 with python 3 Tutorial 37
You will learn in this video how to detect Faces using the Haar Cascades object detection method. Source code:
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import cv2 import numpy as np def nothing(x): pass cap = cv2.VideoCapture(0) face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") cv2.namedWindow("Frame") cv2.createTrackbar("Neighbours", "Frame", 5, 20, nothing) while True: _, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) neighbours = cv2.getTrackbarPos("Neighbours", "Frame") faces = face_cascade.detectMultiScale(gray, 1.3, neighbours) for rect in faces: (x, y, w, h) = rect frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.imshow("Frame", frame) key = cv2.waitKey(1) if key == 27: break cap.release() cv2.destroyAllWindows() |
Files: haarcascades.zip