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Eye detection – Gaze controlled keyboard with Python and Opencv p.1

by Sergio Canu January 7, 2019

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…

Python, Tutorials 0

Eye motion tracking – Opencv with Python

by Sergio Canu January 4, 2019

We’ll see in this video how to detect the pupil and it’s movement. Source code:

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()

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

Python, Tutorials 0

Real time shape detection – Opencv with Python 3

by Sergio Canu December 29, 2018

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:

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()

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()

Python, Tutorials 3

Raspberry Pi 3 and Opencv 3 Installation Tutorial

by Sergio Canu October 31, 2018

We’re going to see today how to install opencv on the raspberry pi 3 Model b+ (with camera). 1) Update os system:

sudo apt-get update
sudo apt-get upgrade

1 2	sudo apt-get update sudo apt-get upgrade

2) Install dependencies:

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

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:

sudo apt-get install python3-dev
sudo apt-get install python3-pip

1 2	sudo apt-get install python3-dev sudo apt-get install python3-pip

4) Install Opencv:

pip3 install opencv-python

1	pip3 install opencv-python

5) Extra depencies…

Tutorials 1

How to create a cartoon effect – Opencv with Python

by Sergio Canu October 11, 2018

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…

Python, Tutorials 0

Face detection using Haar Cascades – OpenCV 3.4 with python 3 Tutorial 37

by Sergio Canu October 1, 2018

You will learn in this video how to detect Faces using the Haar Cascades object detection method. Source code:

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()

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

Python, Tutorials 0

Simple shape detection – Opencv with Python 3

by Sergio Canu September 25, 2018

We’ll se in this video how to perform a simple shape detection. Starting from an image with a few shapes, we’ll be able to detect exactly each shape (rectangle, circle, pentagon, etc.) and the position. Source code:

import cv2
import numpy as np

img = cv2.imread("shapes.jpg", cv2.IMREAD_GRAYSCALE)
_, threshold = cv2.threshold(img, 240, 255, cv2.THRESH_BINARY)
_, contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

font = cv2.FONT_HERSHEY_COMPLEX

for cnt in contours:
    approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt, True), True)
    cv2.drawContours(img, [approx], 0, (0), 5)
    x = approx.ravel()[0]
    y = approx.ravel()[1]

    if len(approx) == 3:
        cv2.putText(img, "Triangle", (x, y), font, 1, (0))
    elif len(approx) == 4:
        cv2.putText(img, "Rectangle", (x, y), font, 1, (0))
    elif len(approx) == 5:
        cv2.putText(img, "Pentagon", (x, y), font, 1, (0))
    elif 6 < len(approx) < 15:
        cv2.putText(img, "Ellipse", (x, y), font, 1, (0))
    else:
        cv2.putText(img, "Circle", (x, y), font, 1, (0))

cv2.imshow("shapes", img)
cv2.imshow("Threshold", threshold)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

import numpy as np

img = cv2.imread("shapes.jpg", cv2.IMREAD_GRAYSCALE)

_, threshold = cv2.threshold(img, 240, 255, cv2.THRESH_BINARY)

_, contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

font = cv2.FONT_HERSHEY_COMPLEX

for cnt in contours:

approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt, True), True)

cv2.drawContours(img, [approx], 0, (0), 5)

x = approx.ravel()[0]

y = approx.ravel()[1]

if len(approx) == 3:

cv2.putText(img, "Triangle", (x, y), font, 1, (0))

elif len(approx) == 4:

cv2.putText(img, "Rectangle", (x, y), font, 1, (0))

elif len(approx) == 5:

cv2.putText(img, "Pentagon", (x, y), font, 1, (0))

elif 6 < len(approx) < 15:

cv2.putText(img, "Ellipse", (x, y), font, 1, (0))

else:

cv2.putText(img, "Circle", (x, y), font, 1, (0))

cv2.imshow("shapes", img)

cv2.imshow("Threshold", threshold)

cv2.waitKey(0)

cv2.destroyAllWindows()

Files: shapes.jpg

Tutorials 0

Knn handwritten digits recognition – OpenCV 3.4 with python 3 Tutorial 36

by Sergio Canu August 26, 2018

In this video you will find an easy explanation of how the KNN algorythm works for handwritten digits recognition. We use a sample of 2500 digits (250 of each type 0 to 9) to train the algorythm and we have another small sample to test…

Tutorials 1

Fourier Transform – OpenCV 3.4 with python 3 Tutorial 35

by Sergio Canu August 4, 2018

Source code:

import cv2
import numpy as  np
import glob

list_images = glob.iglob("letters/*")

for image_title in list_images:
    img = cv2.imread(image_title, cv2.IMREAD_GRAYSCALE)
    f = np.fft.fft2(img)
    fshift = np.fft.fftshift(f)
    magnitude_spectrum = 20*np.log(np.abs(fshift))
    magnitude_spectrum = np.asarray(magnitude_spectrum, dtype=np.uint8)
    img_and_magnitude = np.concatenate((img, magnitude_spectrum), axis=1)

    cv2.imshow(image_title, img_and_magnitude)
    
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

import numpy as np

import glob

list_images = glob.iglob("letters/*")

for image_title in list_images:

img = cv2.imread(image_title, cv2.IMREAD_GRAYSCALE)

f = np.fft.fft2(img)

fshift = np.fft.fftshift(f)

magnitude_spectrum = 20*np.log(np.abs(fshift))

magnitude_spectrum = np.asarray(magnitude_spectrum, dtype=np.uint8)

img_and_magnitude = np.concatenate((img, magnitude_spectrum), axis=1)

cv2.imshow(image_title, img_and_magnitude)

cv2.waitKey(0)

cv2.destroyAllWindows()

Files: fourier_transform_files.zip

Python, Tutorials 0

Check if a set of images match the original one with Opencv and Python

by Sergio Canu July 27, 2018

Full source code: Files: images comparison.zip Import the libraries and load Sift and Flann objects From Line 1 to Line 3 we import the libraries. We added a new library glob, which we need to read all the files from a specific folder. It’s by…

Python, Tutorials 0

Home

Eye detection – Gaze controlled keyboard with Python and Opencv p.1

Eye motion tracking – Opencv with Python

Real time shape detection – Opencv with Python 3

Raspberry Pi 3 and Opencv 3 Installation Tutorial

How to create a cartoon effect – Opencv with Python

Face detection using Haar Cascades – OpenCV 3.4 with python 3 Tutorial 37

Simple shape detection – Opencv with Python 3

Knn handwritten digits recognition – OpenCV 3.4 with python 3 Tutorial 36

Fourier Transform – OpenCV 3.4 with python 3 Tutorial 35

Check if a set of images match the original one with Opencv and Python

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