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Raspbot📖
1.First Trial and Installation
  • 1.1 Installation video
  • 1.2 First Trial
2.Remote control course
  • 2.1 APP control car
  • 2.2 IR control car
3.Preparation
  • 3.1 Preparation
  • 3.2 Run a code on JupyterLab
  • 3.5 TF card expand capacity
4.OpenCV Basic course
A.Getting started with OpenCV
  • 1.Introduction of OpenCV
  • 2.OpenCV image read and display
  • 3.OpenCV image write
  • 4.OpenCV image quality
  • 5.OpenCV pixel operation
B.OpenCV Geometric transformat
  • 1.Picture Zoom
  • 2.Picture Cut
  • 3.Picture Shift
  • 4.Picture Mirroring
  • 5.Affine Map
  • 6.Picture Rotation
  • 7.Perspective Transformation
C.OpenCV picture processing
  • 1.Grayscale processing
  • 2.Image Binarization
  • 3.Edge detection
  • 4.Drawing line segment
  • 5.Drawing rectangle circle
  • 6.Drawing text picture
D.OpenCV image beautification
  • 1.Histogram of color pictures
  • 2.Histogram equalization
  • 3.Image restoration
  • 4.Brightness enhancement
  • 5.Skin whitening
  • 6.Gaussian mean filtering
  • 7.Median filtering
E.OpenCV machine learning
  • 1.KNN recognition of handwritten numbers
  • 2.CNN recognition of handwritten numbers
  • 3.SVM recognition of handwritten numbers
5.Hardware Control course
  • 5.1 Drive buzzer
  • 5.2 Drive motor
  • 5.3 Drive servo
  • 5.4 Ultrasonic ranging
  • 5.5 Ultrasonic avoid
  • 5.6 Ultrasonic+IR avoid
  • 5.7 Tracking
  • 5.8 Restricting
  • 5.9 Drive LED
  • 5.10 IR control test
  • 5.11 IR control car
6.AI vision course
  • 6.1 Driver camera
  • 6.2 Color recognition
  • 6.3 HSV value test
  • 6.4 Camera color tracking
  • 6.5 Car color tracking
  • 6.6 Tensorflow object recognition
  • 6.7 QR code recognition
  • 6.8 QR code control
  • 6.9 Face detection
  • 6.10 Face tracking
  • 6.11 Autopilot
7.Battery and charging
  • Battery and charging
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  • Download image
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FAQ
  • 1、Raspbot-Raspberry Pi 4b version desktop connection WiFi method
  • 2、Can't connect your home WiFi
  • 3、How to check IIC function on Raspbot
  • 4、Gesture recognition cannot work normally.
  • 5、How to write image file into SD card

Welcome to Raspbot repository

5.Affine Map


Affine Transformation(Affine Map) is a linear transformation from two-dimensional coordinates (x, y) to two-dimensional coordinates (u, v). 

Its mathematical expression is as follows,
image.png
The corresponding homogeneous coordinate matrix mathematical expression is as follow, 
image.png

The affine transformation maintains the "straightness" of the two-dimensional graphics and "parallelism".

Straightness: The straight line will keep a straight line after affine transformation.

Parallelism: After the affine transformation, the parallel line will keep parallel line, and the position order of all points on the straight line will not be changed, and the relative positional relationship between the two straight lines will not be changed.

Non-collinear three pairs of corresponding points can determine an affine transformation.

Affine transformation = picture rotation + picture lift.

Affine transformation also requires an M matrix. Due to the complexity of the affine transformation, it is difficult to find this matrix. OpenCV can automatically solve the matrix M according to the correspondence between the three points before and after the transformation.

M = cv2.getAffineTransform (pos1, pos2), pos1 and pos2 are the corresponding positional relationship before and after the transformation.

The output is the affine matrix M.

Then, we can use the function cv2.warpAffine ().

Two methods of image transformation cv2.warpAffine and cv2.warpPerspective

Code path:

 /home/pi/Yahboom_Project/Raspbot/1.OpenCV_course/02Geometric_transformation/ 05_Affine_Map.ipynb

import cv2
import numpy as np
import matplotlib.pyplot as plt
 
img = cv2.imread('yahboom.jpg',1)
 
img_bgr2rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img_bgr2rgb)
plt.show()
# cv2.waitKey(0)

imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
#src 3->dst 3 (Upper left corner, Lower left corner, Upper right corner)
matSrc = np.float32([[0,0], [0,height-1], [width-1,0]])
matDst = np.float32([[50,50], [300,height-200], [width-300,100]])
# Combine
matAffine = cv2.getAffineTransform(matSrc,matDst)# mat 1 src 2 dst
dst = cv2.warpAffine(img,matAffine,(width,height))
img_bgr2rgb = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
plt.imshow(img_bgr2rgb)

After running the above program, we can see the picture shown below.

image.png image.png

     [Orginial picture]                  [Affine Transformation picture]



Picture Mirroring
Picture Rotation

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