Discover the Thrill of Tennis W35 REUS Spain: Expert Betting Predictions
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Mobile Accessibility: Stay Informed Anywhere, Anytime
Wacharawit-D/Computer-Vision<|file_sep|>/Project1/README.md
# Project1 : Object Detection using OpenCV
This project is a part of course "Computer Vision" at Chiang Mai University.
The aim is detecting objects from image using OpenCV library.
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install OpenCV.
bash
pip install opencv-python
## Usage
To use this project, simply run `python main.py` from terminal.
## Demo
### Before
### After
<|repo_name|>Wacharawit-D/Computer-Vision<|file_sep|>/Project2/README.md
# Project2 : Feature Matching using OpenCV
This project is a part of course "Computer Vision" at Chiang Mai University.
The aim is detecting objects from image using OpenCV library.
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install OpenCV.
bash
pip install opencv-python
## Usage
To use this project, simply run `python main.py` from terminal.
## Demo
### Before
### After
<|repo_name|>Wacharawit-D/Computer-Vision<|file_sep|>/Project2/main.py
import cv2 as cv
import numpy as np
def show_image(img):
cv.imshow('image', img)
cv.waitKey(0)
cv.destroyAllWindows()
def main():
img1 = cv.imread("lena.jpg")
img2 = cv.imread("lena_masked.jpg")
# Convert images to gray scale
gray1 = cv.cvtColor(img1, cv.COLOR_BGR2GRAY)
gray2 = cv.cvtColor(img2, cv.COLOR_BGR2GRAY)
# Detect ORB features from both images
orb = cv.ORB_create()
kp1, des1 = orb.detectAndCompute(gray1, None)
kp2, des2 = orb.detectAndCompute(gray2, None)
# Create matcher object
bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
# Match descriptors between two images
matches = bf.match(des1, des2)
# Sort matches based on distance (best first)
matches = sorted(matches, key=lambda x:x.distance)
# Draw top ten matches
img_matches = cv.drawMatches(img1,kp1,img2,kp2,matches[:10],None)
# Show result image
show_image(img_matches)
if __name__ == "__main__":
main()<|repo_name|>Wacharawit-D/Computer-Vision<|file_sep|>/Project1/main.py
import cv2 as cv
import numpy as np
def show_image(img):
cv.imshow('image', img)
cv.waitKey(0)
cv.destroyAllWindows()
def main():
# Load image from disk
img = cv.imread("input.jpg")
# Convert image to gray scale
gray_img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
# Load face cascade classifier from xml file (trained model)
face_cascade_classifier = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
# Detect faces in image using face cascade classifier (Haar cascade classifier)
faces = face_cascade_classifier.detectMultiScale(gray_img,scaleFactor=1.1,minNeighbors=5,minSize=(30,30))
# Draw rectangle around faces detected in image
for (x,y,w,h) in faces:
cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),thickness=4)
# Show result image
show_image(img)
if __name__ == "__main__":
main()<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Thu Sep 27 09:57:02 2018
@author: [email protected]
"""
import numpy as np
from scipy import sparse as sps
import sys
from pySDC.core.Errors import ParameterError
def solve_system(A,b,tol=1E-10):
nA=A.shape[0]
if sps.issparse(A):
A=sps.csc_matrix(A)
if b.ndim==1:
x=sps.linalg.spsolve(A,b)
else:
x=np.zeros((nA,b.shape[1]))
for j in range(b.shape[1]):
x[:,j]=sps.linalg.spsolve(A,b[:,j])
else:
x=np.linalg.solve(A,b)
return x
def solve_system_mg(A,b,tol=1E-10):
"""
Multigrid solver for linear system Ax=b.
Parameters:
----------
A : {numpy.ndarray or scipy.sparse}
matrix A
b : {numpy.ndarray}
right hand side
tol : {float}, optional
tolerance
Returns:
----------
x : {numpy.ndarray}
solution vector
"""
if b.ndim==1:
b=b.reshape((b.size,1))
assert A.shape[0]==b.shape[0],'Dimension mismatch!'
maxiter=200
if sps.issparse(A):
A=sps.csc_matrix(A)
nA=A.shape[0]
P=sps.eye(nA)-A
r=P*b
ierr=0
x=r
while np.linalg.norm(r)>tol*max(np.linalg.norm(P*b),np.linalg.norm(b)):
r=P*x+b
x-=solve_system_mg(P,r)/np.sqrt(np.max(P.diagonal()))
ierr+=1
if ierr==maxiter:
print('Maxiter reached')
sys.exit()
else:
nA=A.shape[0]
P=np.eye(nA)-A
r=P.dot(b)
ier=0
x=r
while np.linalg.norm(r)>tol*max(np.linalg.norm(P.dot(b)),np.linalg.norm(b)):
r=P.dot(x)-b
x-=solve_system_mg(P,r)/np.sqrt(np.max(P.diagonal()))
ier+=1
if ier==maxiter:
print('Maxiter reached')
sys.exit()
return x
def solve_nonlinear_system(F,x,tol=1E-10,maxiter=100,**kwargs):
"""Solve nonlinear system F(x)=0.
Parameters:
----------
F : function handle
function handle for function F
x : numpy.ndarray
initial guess
"""
# Check dimensionality
dim=len(x)
# Check whether F returns an array or scalar
if isinstance(F(x),float):
scalar=True
elif isinstance(F(x),np.ndarray):
scalar=False
else:
raise ParameterError('Invalid return type!')
# Loop over maximum number of iterations
for iter in range(maxiter):
# Evaluate function at current guess
if scalar:
fx=F(x,**kwargs)
else:
fx=F(x,**kwargs).flatten()
# Compute norm
if scalar:
norm=np.abs(fx)
else:
norm=np.linalg.norm(fx)
# Print info about current iteration
print('Iter %i: norm=%e'%(iter+1,norm))
# Check stopping criterion
if norm<=tol:
break
# Compute Jacobian numerically
J=np.zeros((dim,dim))
h=np.zeros(dim)
for i in range(dim):
h[i]=np.sqrt(np.finfo(float).eps)*abs(x[i])+np.finfo(float).eps
dx=np.zeros(dim)
dx[i]=h[i]
if scalar:
dfx=(F(x+dx,**kwargs)-fx)/h[i]
J[:,i]=dfx/dx[i]
else:
dfx=(F(x+dx,**kwargs)-fx).flatten()/h[i]
J[:,i]=dfx/dx[i]
h[i]=0.0
# Compute update using Newton's method
delta_x=-solve_system(J,fx)
# Update current guess
x+=delta_x
return x
def get_nodes(Nnodes,p=0,spectral=False):
"""Get nodes corresponding to collocation method.
Parameters:
----------
Nnodes : integer
Number of nodes.
p : integer
Polynomial order.
spectral : boolean
Flag indicating whether spectral nodes should be used instead.
Returns
-------
nodes : numpy.ndarray
Array containing nodes.
"""
if spectral:
nodes=np.cos(np.pi*(np.arange(Nnodes)+0.5)/Nnodes)[::-1]
return nodes
nodes=np.zeros(Nnodes)
if p==0:
nodes=np.linspace(0.0,(Nnodes-1)/(Nnodes-0.5),Nnodes)
return nodes
abscissas=[]
def rec(k):
N=int(0.5*(k*(k+1)))
Nprev=int(0.5*(k*(k-1)))
xx=np.linspace(-1.,+1.,k+1)
xxnew=[]
for i in range(k-1):
xxnew.append(np.sqrt((xx[i]+1)*(xx[i+1]+1)))
xxnew.append(-np.sqrt((xx[k-i-2]-1)*(xx[k-i-1]-1)))
abscissas.append(xxnew)
xxnew=[]
if k<=Nnodes:
abscissas.append(xx)
return k
rec(Nnodes+10000000)
abscissas=abscissas[:Nnodes]
abscissas=np.array(abscissas).T
for k in range(Nnodes):
nodes[k]=abscissas[k][rec(Nnodes)]
return nodes
def get_weights(Nnodes,p=0,spectral=False):
"""Get weights corresponding to collocation method.
Parameters:
----------
Nnodes : integer
Number of nodes.
p : integer
Polynomial order.
spectral : boolean
Flag indicating whether spectral weights should be used instead.
Returns
-------
weights : numpy.ndarray
Array containing weights.
