Loading images – OpenCV 3.4 with python 3 Tutorial 1
Load and show images with Opencv is a really simple operation.
On Line 1 we import the opencv library.
On Line 3 we load the image into a variable. You can put just the title of the image and the format (example .jpg) if the image is in the same folder as the python file, otherwise you need to insert the full path, if the image is on another folder.
For example if you have it on the desktop, it would look something like this cv2.imread(“C:\Users\myusername\Desktop\red_panda.jpg”)
On Line 3 we convert it into a black and white image, or better to say gray scale image as there will be different gradients of black and of white.
Finally on Line 6 and Line 7 we show both images, the one with original color and the one in gray scale format.
On Line 8 we have the function that keeps the images open. cv2.waitKey(0) is literally waiting that you press a key. If you press for example “ESC”, it will close all the windows.
On Line 9 it destroy all the windows. It won’t make any difference in this simple code, as the windows will be destroied anyway because the code will finish its execution, but for example if you have a bigger program and later there are some other lines of code running, then the windows might still be open.
So it’s always a good practice to put that line at the end.
import cv2 image = cv2.imread("red_panda.jpg") gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imshow("Gray panda", gray_image) cv2.imshow("Red panda", image) cv2.waitKey(0) cv2.destroyAllWindows()
Save images is also a really simple operation.
After we load the image, on Line 6 we are going to save it.
You need to insert two parameters. The first one is the title, followed by the format of the image “gray_panda.jpg” if you want to save it in jpg format or “gray_panda.png” if you want png format.
Second parameter is the image that you want to save, in this case we’re saving the gray image.
import cv2 image = cv2.imread("red_panda.jpg") gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) cv2.imwrite("gray_panda.jpg", gray_image)
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Opencv Beginner Tutorial
- 2) Loading Video and Webcam
- 3) Drawing and writing on images
- 4) Basic operations on images
- 5) Add images and Threshold
- 6) Blending images
- 7) Bitwise Operators
- 8) Trackbars
- 9) Object detection using HSV Color space
- 10) Basic Thresholding
- 11) Histograms
- 12) Basic geometric transformations
- 13) Perspective transformation
- 14) Affine transformation
- 15) Adaptive thresholding5
- 16) Smoothing images
- 17) Morphological transformation
- 18) Edge detection
- 19) Find and Draw Contours
- 20) Template matching
- 21) Lines detection with Hough Transform
- 22) Corners detection
- 23) Image Pyramids
- 24) Image Pyramids (Blending and reconstruction)
- 25) Feature detection (SIFT, SURF, ORB)
- 26) Feature Matching (Brute-Force)
- 27) Mouse Events
- 28) Histogram and Back Projection
- 29) Object tracking with Mean-shift
- 30) Object tracking with Camshift
- 31) Optical Flow with Lucas-Kanade method
- 32) Background Subtraction
- 33) k-Nearest Neighbour classification
- 34) Object tracking using Homography
- 35) Fourier Transform
- 36) Knn handwritten digits recognition
- 37) Face detection using Haar Cascades
- Find and Draw Contours – OpenCV 3.4 with python 3 Tutorial 19
- Simple shape detection – Opencv with Python 3
- Feature detection (SIFT, SURF, OBR) – OpenCV 3.4 with python 3 Tutorial 25
- Check if two images are equal with Opencv and Python
- Lines detection with Hough Transform – OpenCV 3.4 with python 3 Tutorial 21