Opencv learning notes 3

import cv2 #opencv The reading format is BGR
import numpy as np
import matplotlib.pyplot as plt#Matplotlib yes RGB
%matplotlib inline 

def cv_show(img,name):
    b,g,r = cv2.split(img)
    img_rgb = cv2.merge((r,g,b))
    plt.imshow(img_rgb)
    plt.show()
def cv_show1(img,name):
    plt.imshow(img)
    plt.show()
    cv2.imshow(name,img)
    cv2.waitKey()
    cv2.destroyAllWindows()

Histogram

cv2.calcHist(images,channels,mask,histSize,ranges)

• images: The original image format is uint8 or float32. When you pass in a function, you should Use brackets [] For example [img]
• channels: In the same way, it will tell us the function in brackets Like histogram . If the input image is a grayscale image, its value is [0] If it's a color image The parameters passed in to can be [0][1][2] They correspond to BGR.
• mask: mask image . The histogram of the whole image is taken as None. But like If you want to unify the histogram of an image, you make a mask image and Use it .
• histSize:BIN Number of . It should also be enclosed in brackets
• ranges: The range of pixel values is usually [0256]

img = cv2.imread('cat.jpg',0) #0 Represents a grayscale image 
hist = cv2.calcHist([img],[0],None,[256],[0,256])
hist.shape

(256, 1)

plt.hist(img.ravel(),256); 
plt.show()

img = cv2.imread('cat.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]) 

mask operation

#  establish mast
mask = np.zeros(img.shape[:2], np.uint8)
print (mask.shape)
mask[100:300, 100:400] = 255
cv_show1(mask,'mask')

(414, 500)

img = cv2.imread('cat.jpg', 0)
cv_show1(img,'img')

masked_img = cv2.bitwise_and(img, img, mask=mask)# And operation 
cv_show1(masked_img,'masked_img')

hist_full = cv2.calcHist([img], [0], None, [256], [0, 256])
hist_mask = cv2.calcHist([img], [0], mask, [256], [0, 256])

plt.subplot(221), plt.imshow(img, 'gray')
plt.subplot(222), plt.imshow(mask, 'gray')
plt.subplot(223), plt.imshow(masked_img, 'gray')
plt.subplot(224), plt.plot(hist_full), plt.plot(hist_mask)
plt.xlim([0, 256])
plt.show()

Histogram equalization

img = cv2.imread('clahe.jpg',0) #0 Represents a grayscale image  #clahe
plt.hist(img.ravel(),256); 
plt.show()

equ = cv2.equalizeHist(img) 
plt.hist(equ.ravel(),256)
plt.show()

res = np.hstack((img,equ))
cv_show1(res,'res')

Adaptive histogram equalization

clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) 

res_clahe = clahe.apply(img)
res = np.hstack((img,equ,res_clahe))
cv_show1(res,'res')

Template matching

Template matching is very similar to convolution , The template slides from the origin on the original image , Computing templates and （ Where the image is covered by the template ） The degree of difference , The calculation of the degree of difference is in opencv Are there in 6 Kind of , Then put the results of each calculation into a matrix , Output as a result . If the original figure is AxB size , And the template is axb size , Then the output matrix is (A-a+1)x(B-b+1)

#  Template matching 
img = cv2.imread('lena.jpg', 0)
template = cv2.imread('face.jpg', 0)
h, w = template.shape[:2] 

img.shape

(263, 263)

template.shape

(110, 85)

• TM_SQDIFF： The square is different , The smaller the calculated value , The more relevant
• TM_CCORR： Calculate correlation , The larger the calculated value , The more relevant
• TM_CCOEFF： Calculate the correlation coefficient , The larger the calculated value , The more relevant
• TM_SQDIFF_NORMED： Calculating the normalized square is different , The closer the calculated value is to 0, The more relevant
• TM_CCORR_NORMED： Calculate the normalized correlation , The closer the calculated value is to 1, The more relevant
• TM_CCOEFF_NORMED： Calculate the normalized correlation coefficient , The closer the calculated value is to 1, The more relevant

The formula ：https://docs.opencv.org/3.3.1/df/dfb/group__imgproc__object.html#ga3a7850640f1fe1f58fe91a2d7583695d

methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
           'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

res = cv2.matchTemplate(img, template, cv2.TM_SQDIFF)
res.shape

(154, 179)

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) # The smaller the value, the better 

min_val

39168.0

max_val

74403584.0

min_loc

(107, 89)

max_loc

(159, 62)

for meth in methods:
    img2 = img.copy()

    #  The true value of the matching method 
    method = eval(meth)
    print (method)
    res = cv2.matchTemplate(img, template, method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

    #  If it's a square difference match TM_SQDIFF Or normalized square difference matching TM_SQDIFF_NORMED, Minimum value 
    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)

    #  Draw a rectangular 
    cv2.rectangle(img2, top_left, bottom_right, 255, 2)

    plt.subplot(121), plt.imshow(res, cmap='gray')
    plt.xticks([]), plt.yticks([])  #  Hide axis 
    plt.subplot(122), plt.imshow(img2, cmap='gray')
    plt.xticks([]), plt.yticks([])
    plt.suptitle(meth)
    plt.show()

4

5

2

3

0

1

Match multiple objects

img_rgb = cv2.imread('mario.jpg')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('mario_coin.jpg', 0)
h, w = template.shape[:2]

res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = 0.8
#  The matching degree is greater than %80 Coordinates of 
loc = np.where(res >= threshold)
for pt in zip(*loc[::-1]):  # * The sign indicates an optional parameter 
    bottom_right = (pt[0] + w, pt[1] + h)
    cv2.rectangle(img_rgb, pt, bottom_right, (0, 0, 255), 2)

cv_show(img_rgb,'img_rgb')