Pysource - Python / Php / Opencv Development

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 0

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 0

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

Detect how similar two images are with Opencv and Python

by Sergio Canu July 20, 2018

Code up to this point: Calculate percentage of how similar two images are: In the code below from Line 35 to Line 46 we detect how similar two images are. Considering that high quality images (high quality in this case it means high number of…

Python, Tutorials 0

Find similarities between two images with Opencv and Python

by Sergio Canu July 20, 2018

In this article you will learn how to compare and find similarities between two images when they’re similar but not exactly identical. For example you can take an Image and compare it with the same image that has different filters applied on. I took as…

Python, Tutorials 4

Check if two images are equal with Opencv and Python

by Sergio Canu July 19, 2018

Finding if two images are equal with Opencv, is a quite simple operation. There are 2 fundamental elements to consider: The images have both the same size and channels Each pixel has the same value We’re going first to load the images. First we load…

Python, Tutorials 0

Home

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

Detect how similar two images are with Opencv and Python

Find similarities between two images with Opencv and Python

Check if two images are equal with Opencv and Python

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