opencv.py

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread("/home/wym/Downloads/google.png", 0)

cv2.imshow("google", img)
k = cv2.waitKey(0)
#for 64-bit machine, k = cv2.waitKey(0) & 0xFF
if k == 27:
    cv2.destroyAllWindows()
elif k == ord('s'):
    cv2.imwrite('googlegray.png', img)
    cv2.destroyAllWindows()


img = cv2.imread("/home/wym/Downloads/google.png", 0)
plt.imshow(img, cmap='gray', interpolation='bicubic')
plt.xticks([]), plt.yticks([])
plt.show()

#Color image loaded by OpenCV is in BGR mode. But Matplotlib displays in RGB mode. So color
#images will not be displayed correctly in Matplotlib if image is read with Opencv2.
#
#Solution:
#b,g,r = cv2.split(img)
#img = cv2.merge([r, g, b])

#Capture Video from Camera
cap = cv2.VideoCapture(0)
#To capture a video, you need to create a VideoCapture object. Its argument can be either the device index or the name
#of a video file. Device index is just the number to specify which camera.

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    cv2.imshow('frame', gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

#Playing Video from file
cap = cv2.VideoCapture('vtest.avi')
while(cap.isOpened()):
    ret, frame = cap.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    cv2.imshow('frame',gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()
#Make sure proper versions of ffmpeg or gstreamer is installed. Sometimes, it is a headache to work with Video
#Capture mostly due to wrong installation of ffmpeg/gstreamer.

#Saving a Video
cap = cv2.VideoCapture(0)

# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
#or fourcc = cv2.VideoWriter_fourcc('X', 'V', 'I', 'D')
out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))

while(cap.isOpened()):
    ret, frame = cap.read()
    if ret==True:
        frame = cv2.flip(frame,0)

        # write the flipped frame
        out.write(frame)

        cv2.imshow('frame',frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break

# Release everything if job is finished
cap.release()
out.release()
cv2.destroyAllWindows()

#1.2.3 Drawing Functions in OpenCV
#functions:cv2.line(), cv2.circle() , cv2.rectangle(), cv2.ellipse(), cv2.putText() etc.

#Drawing Line
#lineType;
#– LINE_8 (or omitted) - 8-connected line.
#– LINE_4 - 4-connected line.
#– LINE_AA - antialiased line.

# Create a black image
img = np.zeros((512,512,3), np.uint8)

# Draw a diagonal blue line with thickness of 5 px
img = cv2.line(img,(0,0),(511,511),(255,0,0),5)

#Drawing Rectangle
img = cv2.rectangle(img,(384,0),(510,128),(0,255,0),3)

#Drawing Circle
img = cv2.circle(img,(447,63), 63, (0,0,255), -1)

#Drawing Ellipse
img = cv2.ellipse(img,(256,256),(100,50),0,0,180,255,-1)

#Drawing Polygon
pts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32)
pts = pts.reshape((-1,1,2))
img = cv2.polylines(img,[pts],True,(0,255,255))
#cv2.polylines() can be used to draw multiple lines. Just create a list of all the lines you want to draw
#and pass it to the function. All lines will be drawn individually. It is more better and faster way to draw a group of
#lines than calling cv2.line() for each line.

#Adding Text to Images
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,cv2.LINE_AA)

#1.2.4 Mouse as a Paint-Brush

#Simple Demo

events = [i for i in dir(cv2) if 'EVENT' in i]
print(events)

# mouse callback function
def draw_circle(event,x,y,flags,param):
    if event == cv2.EVENT_LBUTTONDBLCLK:
        cv2.circle(img,(x,y),100,(255,0,0),-1)

# Create a black image, a window and bind the function to window
img = np.zeros((512,512,3), np.uint8)
cv2.namedWindow('image')
cv2.setMouseCallback('image',draw_circle)

while(1):
    cv2.imshow('image',img)
    if cv2.waitKey(20) & 0xFF == 27:
        break

cv2.destroyAllWindows()

#More Advanced Demo

drawing = False
# true if mouse is pressed

mode = True 
# if True, draw rectangle. Press 'm' to toggle to curve

ix,iy = -1,-1

# mouse callback function
def draw_circle(event,x,y,flags,param):
    global ix,iy,drawing,mode

    if event == cv2.EVENT_LBUTTONDOWN:
        drawing = True
        ix,iy = x,y

    elif event == cv2.EVENT_MOUSEMOVE:
        if drawing == True:
            if mode == True:
                cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),-1)
            else:
                cv2.circle(img,(x,y),5,(0,0,255),-1)

    elif event == cv2.EVENT_LBUTTONUP:
        drawing = False
        if mode == True:
            cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),-1)
        else:
            cv2.circle(img,(x,y),5,(0,0,255),-1)

img = np.zeros((512,512,3), np.uint8)
cv2.namedWindow('image')
cv2.setMouseCallback('image',draw_circle)

while(1):
    cv2.imshow('image',img)
    k = cv2.waitKey(1) & 0xFF
    if k == ord('m'):
        mode = not mode
    elif k == 27:
        break

cv2.destroyAllWindows()

#Trackbar as the Color Palette

def nothing(x): 
    pass

# Create a black image, a window 
img = np.zeros((300,512,3), np.uint8) 
cv2.namedWindow('image')

# create trackbars for color change 
cv2.createTrackbar('R','image',0,255,nothing) 
cv2.createTrackbar('G','image',0,255,nothing) 
cv2.createTrackbar('B','image',0,255,nothing)

# create switch for ON/OFF functionality 
switch = '0 : OFF \n1 : ON' 
cv2.createTrackbar(switch, 'image',0,1,nothing)

while(1): 
    cv2.imshow('image',img) 
    k = cv2.waitKey(1) & 0xFF 
    if k == 27: 
        break
    # get current positions of four trackbars 
    r = cv2.getTrackbarPos('R','image') 
    g = cv2.getTrackbarPos('G','image') 
    b = cv2.getTrackbarPos('B','image') 
    s = cv2.getTrackbarPos(switch,'image')
    if s == 0: 
        img[:] = 0 
    else: 
        img[:] = [b,g,r]

cv2.destroyAllWindows()

#Basic Operations on Images
import cv2
import numpy as np

img = cv2.imread("C:\\Users\\cole\\Desktop\\logo.png")

shape = img.shape

img.item(10, 10, 2)
img.itemset((10, 10, 2), 100)

print(img.size)
print(img.dtype)

#Image ROI
ball = img[280:340, 330:390]
img[273:333, 100:160] = ball

#Splitting and Merging Image Channels
b, g, r = cv2.split(img)
img = cv2.merge((b, g, r))

#or
b = img[:, :, 0]
img[:, :, 2] = 0
# cv2.split() is a costly operation (in terms of time), so only use it if necessary. Numpy indexing is much more efficient and should be used if possible.

#Making Borders for Images (Padding)
import cv2
import numpy as np
from matplotlib import pyplot as plt

BLUE = [255,0,0]

img1 = cv2.imread('C:\\Users\\cole\\Desktop\\logo.png')

replicate = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REPLICATE)
reflect = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT)
reflect101 = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_REFLECT_101)
wrap = cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_WRAP)
constant= cv2.copyMakeBorder(img1,10,10,10,10,cv2.BORDER_CONSTANT,value=BLUE)

plt.subplot(231),plt.imshow(img1,'gray'),plt.title('ORIGINAL')
plt.subplot(232),plt.imshow(replicate,'gray'),plt.title('REPLICATE')
plt.subplot(233),plt.imshow(reflect,'gray'),plt.title('REFLECT')
plt.subplot(234),plt.imshow(reflect101,'gray'),plt.title('REFLECT_101')
plt.subplot(235),plt.imshow(wrap,'gray'),plt.title('WRAP')
plt.subplot(236),plt.imshow(constant,'gray'),plt.title('CONSTANT')

plt.show()

#Arithmetic Operations on Images
#
#Image Addition
#There is a difference between OpenCV addition and Numpy addition. OpenCV addition is a saturated operation
#while Numpy addition is a modulo operation.
x, y = np.uint8([250]), np.uint8([10])

print(cv2.add(x, y))
print(x+y)

#Image Blending
#𝑑𝑠𝑡 = 𝛼 · 𝑖𝑚𝑔1 + 𝛽 · 𝑖𝑚𝑔2 + 𝛾
#dst(I) = saturate(src1(I) * alpha + src2(I) * beta + gamma)
img1 = cv2.imread('ml.png')
img2 = cv2.imread('opencv_logo.jpg')
dst = cv2.addWeighted(img1,0.7,img2,0.3,0)
cv2.imshow('dst',dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

#Bitwise Operations
# Load two images
img1 = cv2.imread('messi5.jpg')
img2 = cv2.imread('opencv_logo.png')

# I want to put logo on top-left corner, So I create a ROI
rows,cols,channels = img2.shape
roi = img1[0:rows, 0:cols ]

# Now create a mask of logo and create its inverse mask also
img2gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)

# Now black-out the area of logo in ROI
img1_bg = cv2.bitwise_and(roi,roi,mask = mask_inv)

# Take only region of logo from logo image.
img2_fg = cv2.bitwise_and(img2,img2,mask = mask)

# Put logo in ROI and modify the main image
dst = cv2.add(img1_bg,img2_fg)
img1[0:rows, 0:cols ] = dst
cv2.imshow('res',img1)
cv2.waitKey(0)
cv2.destroyAllWindows()

#Performance Measurement and Improvement Techniques
img1 = cv2.imread('messi5.jpg')

e1 = cv2.getTickCount()
for i in range(5,49,2):
    img1 = cv2.medianBlur(img1,i)
e2 = cv2.getTickCount()
t = (e2 - e1)/cv2.getTickFrequency()
print(t)

#Default Optimization in OpenCV
cv2.useOptimized()
%timeit res = cv2.medianBlur(img1,49)

cv2.setUseOptimized(False)
cv2.useOptimized()
%timeit res = cv2.medianBlur(img1,49)

cv2.setUseOptimized(True)

img = cv2.imread('opencv_logo.png')
#Measuring Performance in IPython
x = 5
%timeit y = x**2

%timeit y = x*x

z = np.uint8([5])
%timeit y = z*z

%timeit y = np.square(z)
#Python scalar operations are faster than Numpy scalar operations. So for operations including one or two
#elements, Python scalar is better than Numpy arrays. Numpy takes advantage when size of array is a little bit bigger.

%timeit z = cv2.countNonZero(img)

%timeit z = np.count_nonzero(img)
#Normally, OpenCV functions are faster than Numpy functions. So for same operation, OpenCV functions are
#preferred. But, there can be exceptions, especially when Numpy works with views instead of copies.

#There are several techniques and coding methods to exploit maximum performance of Python and Numpy. Only
#relevant ones are noted here and links are given to important sources. The main thing to be noted here is that, first try
#to implement the algorithm in a simple manner. Once it is working, profile it, find the bottlenecks and optimize them.
#
#1. Avoid using loops in Python as far as possible, especially double/triple loops etc. They are inherently slow.
#2. Vectorize the algorithm/code to the maximum possible extent because Numpy and OpenCV are optimized for
# vector operations.
#3. Exploit the cache coherence.
#4. Never make copies of array unless it is needed. Try to use views instead. Array copying is a costly operation.
#
#Even after doing all these operations, if your code is still slow, or use of large loops are inevitable, use additional
#libraries like Cython to make it faster.


#Image Processing in OpenCV
#Changing Colorspaces

import cv2
flags = [i for i in dir(cv2) if i.startswith('COLOR_')]
print(flags)

#Object Tracking
import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while(1):
    # Take each frame
    _, frame = cap.read()

    # Convert BGR to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # define range of blue color in HSV
    lower_blue = np.array([110,50,50])
    upper_blue = np.array([130,255,255])

    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower_blue, upper_blue)

    # Bitwise-AND mask and original image
    res = cv2.bitwise_and(frame,frame, mask= mask)
    cv2.imshow('frame',frame)
    cv2.imshow('mask',mask)
    cv2.imshow('res',res)

    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break

cv2.destroyAllWindows()


#How to find HSV values to track?
green = np.uint8([[[0,255,0]]])
hsv_green = cv2.cvtColor(green,cv2.COLOR_BGR2HSV)
print(hsv_green)
#Now you take [H-10, 100,100] and [H+10, 255, 255] as lower bound and upper bound respectively. Apart from this
#method, you can use any image editing tools like GIMP or any online converters to find these values, but don’t forget
#to adjust the HSV ranges.

#Image Thresholding
#Simple Thresholding
import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('gradient.png',0)

ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)
ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC)
ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO)
ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)

titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

for i in range(6):
    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])

plt.show()


#Adaptive Thresholding
import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('dave.jpg',0)
img = cv2.medianBlur(img,5)

ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)

titles = ['Original Image', 'Global Thresholding (v = 127)','Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']

images = [img, th1, th2, th3]

for i in range(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])

plt.show()

#Otsu’s Binarization
import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('noisy2.png',0)

# global thresholding
ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)

# Otsu's thresholding
ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(img,(5,5),0)
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# plot all the images and their histograms
images = [img, 0, th1, img, 0, th2, blur, 0, th3]
titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)', 'Original Noisy Image','Histogram',"Otsu's Thresholding", 'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]

for i in range(3):
    plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
    plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])
    plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
    plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([])
    plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')
    plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])

plt.show()


#How Otsu’s Binarization Works?
img = cv2.imread('noisy2.png',0)
blur = cv2.GaussianBlur(img,(5,5),0)

# find normalized_histogram, and its cumulative distribution function
hist = cv2.calcHist([blur],[0],None,[256],[0,256])
hist_norm = hist.ravel()/hist.max()
Q = hist_norm.cumsum()

bins = np.arange(256)

fn_min = np.inf
thresh = -1

for i in range(1,256):
    p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
    q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
    b1,b2 = np.hsplit(bins,[i]) # weights

    # finding means and variances
    m1,m2 = np.sum(p1*b1)/q1, np.sum(p2*b2)/q2
    v1,v2 = np.sum(((b1-m1)**2)*p1)/q1,np.sum(((b2-m2)**2)*p2)/q2

    # calculates the minimization function
    fn = v1*q1 + v2*q2
    if fn < fn_min:
        fn_min = fn
        thresh = i
        
# find otsu's threshold value with OpenCV function
ret, otsu = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
print(thresh,ret)

#Geometric Transformations of Images

# Histograms
import cv2 
import numpy as np 
from matplotlib import pyplot as plt
img = cv2.imread('data/home.jpg') 
color = ('b','g','r') 
for i,col in enumerate(color): 
	histr = cv2.calcHist([img],[i],None,[256],[0,256]) 
	plt.plot(histr,color = col) 
	plt.xlim([0,256]) 

plt.show()

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('data/wiki.jpg',0)
hist,bins = np.histogram(img.flatten(),256,[0,256])
cdf = hist.cumsum()
cdf_normalized = cdf * hist.max()/ cdf.max()
plt.plot(cdf_normalized, color = 'b')
plt.hist(img.flatten(),256,[0,256], color = 'r')
plt.xlim([0,256])
plt.legend(('cdf','histogram'), loc = 'upper left')
plt.show()

cdf_m = np.ma.masked_equal(cdf,0)
# not cdf_m*255/cdf_m.max(), avoid the situation that if input is zero, and the output is also zero
cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
cdf = np.ma.filled(cdf_m,0).astype('uint8')
img2 = cdf[img]
plt.imshow(img2, cmap='gray')

img = cv2.imread('data/wiki.jpg',0)
equ = cv2.equalizeHist(img)
res = np.hstack((img,equ)) #stacking images side-by-side
cv2.imwrite('data/wiki_equal.jpg',res)

import numpy as np
import cv2
img = cv2.imread('data/tsukuba_l.png',0)
# create a CLAHE object (Arguments are optional).
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
cl1 = clahe.apply(img)
cv2.imwrite('data/clahe_2.jpg',cl1)

import cv2
import numpy as np
img = cv2.imread('data/home.jpg')
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('data/home.jpg')
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
hist = cv2.calcHist( [hsv], [0, 1], None, [180, 256], [0, 180, 0, 256] )
plt.imshow(hist,interpolation = 'nearest')
plt.show()

import cv2
import numpy as np
from matplotlib import pyplot as plt
#roi is the object or region of object we need to find
roi = cv2.imread('data/rose_red.png')
hsv = cv2.cvtColor(roi,cv2.COLOR_BGR2HSV)
#target is the image we search in
target = cv2.imread('data/rose.png')
hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV)
# Find the histograms using calcHist. Can be done with np.histogram2d also
M = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] )
I = cv2.calcHist([hsvt],[0, 1], None, [180, 256], [0, 180, 0, 256] )

R = M/I

h,s,v = cv2.split(hsvt)
B = R[h.ravel(),s.ravel()]
B = np.minimum(B,1)
B = B.reshape(hsvt.shape[:2])

disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
cv2.filter2D(B,-1,disc,B)
B = np.uint8(B)
cv2.normalize(B,B,0,255,cv2.NORM_MINMAX)

ret,thresh = cv2.threshold(B,50,255,0)

import cv2
import numpy as np
roi = cv2.imread('data/rose_red.png')
hsv = cv2.cvtColor(roi,cv2.COLOR_BGR2HSV)
target = cv2.imread('data/rose.png')
hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV)
# calculating object histogram
roihist = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] )
# normalize histogram and apply backprojection
cv2.normalize(roihist,roihist,0,255,cv2.NORM_MINMAX)
dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],1)
# Now convolute with circular disc
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
cv2.filter2D(dst,-1,disc,dst)
# threshold and binary AND
ret,thresh = cv2.threshold(dst,50,255,0)
thresh = cv2.merge((thresh,thresh,thresh))
res = cv2.bitwise_and(target,thresh)
res = np.vstack((target,thresh,res))
cv2.imwrite('data/ros_res.jpg',res)

# add with closing operation

kernel = np.ones((5,5),np.uint8)

import cv2
import numpy as np
roi = cv2.imread('data/rose_red.png')
hsv = cv2.cvtColor(roi,cv2.COLOR_BGR2HSV)
target = cv2.imread('data/rose.png')
hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV)
# calculating object histogram
roihist = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] )
# normalize histogram and apply backprojection
cv2.normalize(roihist,roihist,0,255,cv2.NORM_MINMAX)
dst = cv2.calcBackProject([hsvt],[0,1],roihist,[0,180,0,256],1)
# Now convolute with circular disc
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
cv2.filter2D(dst,-1,disc,dst)
# threshold and binary AND
ret,thresh = cv2.threshold(dst,50,255,0)

thresh =  cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
thresh = cv2.merge((thresh,thresh,thresh))
res = cv2.bitwise_and(target,thresh)
res = np.vstack((target,thresh,res))
cv2.imwrite('data/ros_res.jpg',res)

# FFT
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('data/messi5.jpg',0)
f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)
magnitude_spectrum = 20*np.log(np.abs(fshift))

plt.subplot(121),plt.imshow(img, cmap = 'gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()

rows, cols = img.shape
crow,ccol = int(rows/2) , int(cols/2)
fshift[crow-30:crow+30, ccol-30:ccol+30] = 0
f_ishift = np.fft.ifftshift(fshift)
img_back = np.fft.ifft2(f_ishift)
img_back = np.abs(img_back)
plt.subplot(131),plt.imshow(img, cmap = 'gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(132),plt.imshow(img_back, cmap = 'gray')
plt.title('Image after HPF'), plt.xticks([]), plt.yticks([])
plt.subplot(133),plt.imshow(img_back)
plt.title('Result in JET'), plt.xticks([]), plt.yticks([])
plt.show()

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('data/messi5.jpg',0)
dft = cv2.dft(np.float32(img),flags = cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
magnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1]))
plt.subplot(121),plt.imshow(img, cmap = 'gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()

import cv2
import numpy as np
from matplotlib import pyplot as plt
# simple averaging filter without scaling parameter
mean_filter = np.ones((3,3))
# creating a guassian filter
x = cv2.getGaussianKernel(5,10)
gaussian = x*x.T
# different edge detecting filters
# scharr in x-direction
scharr = np.array([[-3, 0, 3],
[-10,0,10],
[-3, 0, 3]])
# sobel in x direction
sobel_x= np.array([[-1, 0, 1],
[-2, 0, 2],
[-1, 0, 1]])

# sobel in y direction
sobel_y= np.array([[-1,-2,-1],
[0, 0, 0],
[1, 2, 1]])
# laplacian
laplacian=np.array([[0, 1, 0],
[1,-4, 1],
[0, 1, 0]])
filters = [mean_filter, gaussian, laplacian, sobel_x, sobel_y, scharr]
filter_name = ['mean_filter', 'gaussian','laplacian', 'sobel_x', \
'sobel_y', 'scharr_x']
fft_filters = [np.fft.fft2(x) for x in filters]
fft_shift = [np.fft.fftshift(y) for y in fft_filters]
mag_spectrum = [np.log(np.abs(z)+1) for z in fft_shift]
for i in range(6):
    plt.subplot(2,3,i+1),plt.imshow(mag_spectrum[i],cmap = 'gray')
    plt.title(filter_name[i]), plt.xticks([]), plt.yticks([])
plt.show()

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('data/messi5.jpg',0)
img2 = img.copy()
template = cv2.imread('data/messi_face.jpg',0)

w, h = template.shape[::-1]
# All the 6 methods for comparison in a list
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
for meth in methods:
    img = img2.copy()
    method = eval(meth)
    # Apply template Matching
    res = cv2.matchTemplate(img,template,method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)
    cv2.rectangle(img,top_left, bottom_right, 255, 2)
    plt.subplot(121),plt.imshow(res,cmap = 'gray')
    plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
    plt.subplot(122),plt.imshow(img,cmap = 'gray')
    plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
    plt.suptitle(meth)
    plt.show()


import cv2
import numpy as np
from matplotlib import pyplot as plt
img_rgb = cv2.imread('data/mario.png')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('data/mario_coin.png',0)
w, h = template.shape[::-1]
res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
threshold = 0.8

loc = np.where( res >= threshold)
for pt in zip(*loc[::-1]):
    cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)
cv2.imwrite('data/mario_res.png',img_rgb)


import cv2
import numpy as np

img = cv2.imread('data/dave.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)
lines = cv2.HoughLines(edges,1,np.pi/180,110)
for rho,theta in lines[:, 0, :]:
	a = np.cos(theta)
	b = np.sin(theta)
	x0 = a*rho
	y0 = b*rho
	x1 = int(x0 + 1000*(-b))
	y1 = int(y0 + 1000*(a))
	x2 = int(x0 - 1000*(-b))
	y2 = int(y0 - 1000*(a))
	cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2)

cv2.imwrite('data/houghlines3.jpg',img)

import cv2
import numpy as np
img = cv2.imread('data/dave.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)
minLineLength = 100
maxLineGap = 10
lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength,maxLineGap)
for x1,y1,x2,y2 in lines[:, 0, :]:
    cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)
cv2.imwrite('data/houghlines5.jpg',img)

import cv2
import numpy as np
img = cv2.imread('data/opencv_logo.png',0)
img = cv2.medianBlur(img,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
param1=50,param2=30,minRadius=0,maxRadius=0)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
	# draw the outer circle
	cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
	# draw the center of the circle
	cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
cv2.imshow('detected circles',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('data/coins.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)

# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1
# Now, mark the region of unknown with zero
markers[unknown==255] = 0

markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('data/messi5.jpg')
mask = np.zeros(img.shape[:2],np.uint8)
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
rect = (50,50,450,290)
cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask2[:,:,np.newaxis]
plt.imshow(img),plt.colorbar(),plt.show()

# newmask is the mask image I manually labelled
newmask = cv2.imread('data/newmask.png',0)
# whereever it is marked white (sure foreground), change mask=1
# whereever it is marked black (sure background), change mask=0
mask[newmask == 0] = 0
mask[newmask == 255] = 1
mask, bgdModel, fgdModel = cv2.grabCut(img,mask,None,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK)
mask = np.where((mask==2)|(mask==0),0,1).astype('uint8')
img = img*mask[:,:,np.newaxis]
plt.imshow(img),plt.colorbar(),plt.show()

# feature detection
import cv2
import numpy as np
filename = 'data/chessboard.jpg'
img = cv2.imread(filename)

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)
#result is dilated for marking the corners, not important
dst = cv2.dilate(dst,None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst>0.01*dst.max()]=[0,0,255]
cv2.imshow('dst',img)
if cv2.waitKey(0) & 0xff == 27:
    cv2.destroyAllWindows()

import cv2
import numpy as np
filename = 'data/chessboard.jpg'
img = cv2.imread(filename)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# find Harris corners
gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)
dst = cv2.dilate(dst,None)
ret, dst = cv2.threshold(dst,0.01*dst.max(),255,0)
dst = np.uint8(dst)
# find centroids
ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)
# define the criteria to stop and refine the corners
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)
corners = cv2.cornerSubPix(gray,np.float32(centroids),(5,5),(-1,-1),criteria)
# Now draw them
res = np.hstack((centroids,corners))
res = np.int0(res)
img[res[:,1],res[:,0]]=[0,0,255]
img[res[:,3],res[:,2]] = [0,255,0]
cv2.imwrite('data/subpixel5.png',img)

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('data/simple.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
corners = cv2.goodFeaturesToTrack(gray,25,0.01,10)
corners = np.int0(corners)
for i in corners:
    x,y = i.ravel()
    cv2.circle(img,(x,y),3,255,-1)

plt.imshow(img),plt.show()

# sift
import cv2
import numpy as np
img = cv2.imread('data/home.jpg')

gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d.SIFT_create()
kp = sift.detect(gray,None)
cv2.drawKeypoints(gray, kp, img, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imwrite('data/sift_keypoints.jpg',img)

kp, des = sift.detectAndCompute(gray,None)

import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('data/simple.jpg',0)
# Initiate FAST object with default values
fast = cv2.FastFeatureDetector_create()
# find and draw the keypoints
kp = fast.detect(img,None)
img2 = cv2.drawKeypoints(img, kp, None, color=(255,0,0))
# Print all default params
print("Threshold: ", fast.getThreshold())
print("nonmaxSuppression: ", fast.getNonmaxSuppression())
print("neighborhood: ", fast.getType())
print("Total Keypoints with nonmaxSuppression: ", len(kp))
cv2.imwrite('data/fast_true.png',img2)
# Disable nonmaxSuppression
fast.setNonmaxSuppression(0)
kp = fast.detect(img,None)
print("Total Keypoints without nonmaxSuppression: ", len(kp))
img3 = cv2.drawKeypoints(img, kp, None, color=(255,0,0))
cv2.imwrite('data/fast_false.png',img3)

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('data/simple.jpg',0)
# Initiate FAST detector
star = cv.xfeatures2d.StarDetector_create()
# Initiate BRIEF extractor
brief = cv.xfeatures2d.BriefDescriptorExtractor_create()
# find the keypoints with STAR
kp = star.detect(img,None)
# compute the descriptors with BRIEF
kp, des = brief.compute(img, kp)
print( brief.descriptorSize() )
print( des.shape )

# ORB
import numpy as np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('data/simple.jpg',0)
# Initiate STAR detector
orb = cv2.ORB_create()
# find the keypoints with ORB
kp = orb.detect(img,None)
# compute the descriptors with ORB
kp, des = orb.compute(img, kp)
# draw only keypoints location,not size and orientation
img2 = cv2.drawKeypoints(img,kp,None, color=(0,255,0), flags=0)
plt.imshow(img2),plt.show()

import numpy as np
import cv2
from matplotlib import pyplot as plt
img1 = cv2.imread('data/book.png',0) # queryImage
img2 = cv2.imread('data/box_in_scene.png',0) # trainImage
# Initiate SIFT detector
orb = cv2.ORB_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)

# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
# Match descriptors.
matches = bf.match(des1,des2)
# Sort them in the order of their distance.
matches = sorted(matches, key = lambda x:x.distance)

# Draw first 10 matches.
img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10], None, flags=2)
plt.imshow(img3),plt.show()

import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
img1 = cv.imread('data/book.png',cv.IMREAD_GRAYSCALE)          # queryImage
img2 = cv.imread('data/box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
# Initiate SIFT detector
sift = cv.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
# BFMatcher with default params
bf = cv.BFMatcher()
matches = bf.knnMatch(des1,des2,k=2)
# Apply ratio test
good = []
for m,n in matches:
    if m.distance < 0.75*n.distance:
        good.append([m])
# cv.drawMatchesKnn expects list of lists as matches.
img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
plt.imshow(img3),plt.show()

import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
img1 = cv.imread('data/book.png',cv.IMREAD_GRAYSCALE)          # queryImage
img2 = cv.imread('data/box_in_scene.png',cv.IMREAD_GRAYSCALE) # trainImage
# Initiate SIFT detector
sift = cv.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
# FLANN parameters
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)   # or pass empty dictionary
flann = cv.FlannBasedMatcher(index_params,search_params)
matches = flann.knnMatch(des1,des2,k=2)
# Need to draw only good matches, so create a mask
matchesMask = [[0,0] for i in range(len(matches))]
# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
    if m.distance < 0.7*n.distance:
        matchesMask[i]=[1,0]
draw_params = dict(matchColor = (0,255,0),
                   singlePointColor = (255,0,0),
                   matchesMask = matchesMask,
                   flags = cv.DrawMatchesFlags_DEFAULT)
img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
plt.imshow(img3,),plt.show()


import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
MIN_MATCH_COUNT = 10
img1 = cv.imread('data/book.png',0)          # queryImage
img2 = cv.imread('data/box_in_scene.png',0) # trainImage
# Initiate SIFT detector
sift = cv.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1,des2,k=2)
# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)

if len(good)>MIN_MATCH_COUNT:
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
    M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0)
    matchesMask = mask.ravel().tolist()
    h,w = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
    dst = cv.perspectiveTransform(pts,M)
    img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA)
else:
    print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
    matchesMask = None

draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   matchesMask = matchesMask, # draw only inliers
                   flags = 2)
img3 = cv.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.imshow(img3, 'gray'),plt.show()

import numpy as np
import cv2 as cv
import argparse

parser = argparse.ArgumentParser(description='This sample demonstrates the meanshift algorithm. \
                                              The example file can be downloaded from: \
                                              https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
parser.add_argument('image', type=str, help='path to image file')
args = parser.parse_args()

cap = cv.VideoCapture(args.image)

# take first frame of the video
ret,frame = cap.read()

# setup initial location of window
x, y, w, h = 300, 200, 100, 50 # simply hardcoded the values
track_window = (x, y, w, h)

# set up the ROI for tracking
roi = frame[y:y+h, x:x+w]
hsv_roi =  cv.cvtColor(roi, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)

# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1 )

while(1):
    ret, frame = cap.read()

    if ret == True:
        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
        dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)

        # apply meanshift to get the new location
        ret, track_window = cv.meanShift(dst, track_window, term_crit)

        # Draw it on image
        x,y,w,h = track_window
        img2 = cv.rectangle(frame, (x,y), (x+w,y+h), 255,2)
        cv.imshow('img2',img2)

        k = cv.waitKey(30) & 0xff
        if k == 27:
            break
    else:
        break