Gaussian pyramid
Gaussian pyramid is the top by the bottom image in successive rows and columns removal obtained. Each pixel value is equal to the top of the image layer of the image Gaussian weighted average of five pixels.
This operation once a MxN picture image becomes a M / 2xN / 2 in. So this piece of area of the image becomes a quarter of the original image area.
You can get a resolution declining image pyramid. We can use the function cv2.pyrDown () and cv2.pyrUp () to construct an image pyramid.
Image outline:
The profile can be simply considered as discrete points (boundaries attached) together curve having the same color or gray scale;
Finding the contour of a binary image:
function cv2.findContours () has three parameters, a first input image, the outline of the second search mode, the third contour approximation method. There are three return values, it is a first image, a second profile, and the third is chromatographed structure (profile).
Histogram
A histogram is a grayscale image drawing instead of the color map
OpenCV using an image histogram statistics
Parameters:
cv2.calcHist (Images, channels, mask, HISTSIZE, Ranges [, a hist [, the accumulate]])
1. Images: the original image (or image format uint8 float32). When passed to the function to be [] enclosed in square brackets, for example: [img].
2. channels: also need to be enclosed in brackets, and it will tell histogram function we want to photograph the image. If the input image is a grayscale image, its value is [0]; if it is a color image, the parameters passed may be [0], [1], [2] which correspond to the channels B, G, R.
3. mask: the mask image. To histogram of the entire image to put it to None. But if you want a certain part of the statistical histogram of the image, you will need to make a mask image, and use it. (There are examples of back)
4. HISTSIZE: the BIN number. Should also be enclosed in square brackets, for example: [256].
5. ranges: pixel value range, usually [0,256]
Also can do, Matplotlib Matplotlib have to draw a histogram function: matplotlib.pyplot.hist ()
which can directly draw a histogram and statistics
Possible to use only the drawing function matplotlib, which while drawing a multi-channel (BGR) histogram
CV2 Import Import numpy AS NP from matplotlib Import pyplot AS PLT IMG = cv2.imread ( ' home.jpg ' ) Color = ( ' B ' , ' G ' , ' R & lt ' ) # of a list or array to traverse the index and both to traverse the elements, using the enumerate for I, COL in the enumerate (Color): histr = cv2.calcHist ([IMG], [I], None, [ 256 ], [ 0 , 256 ]) plt.plot (histr, Color = COL) plt.xlim ([0,256]) plt.show()
Histogram equalization
One-dimensional histogram
The resulting histogram may direct some of the pixel values in one place, the pixel values of a high-quality image should be widely distributed.
So you should put it to become evenly distributed histogram, which is the histogram equalization. This operation will normally improve the contrast of the image .
Here directly introduce adaptive histogram equalization function can be effectively retained some details of the image;
In this case, the entire image is divided into many smaller pieces is called "tiles" (i.e. the second parameter is a function of the 8x8 by default), then a small piece of each histogram equalization, respectively of
numpy Import AS NP Import CV2 IMG = cv2.imread ( ' tsukuba_l.png ' , 0 )
# Create a CLAHE objects
CLAHE = cv2.createCLAHE (clipLimit = 2.0 , tileGridSize = ( . 8 , . 8 )) CLl = clahe.apply (IMG ) cv2.imwrite ( ' clahe_2.jpg ' , CLl)
2D histogram
Consider a one-dimensional histogram of gray values of this factor only, 2d histogram and color saturation to consider two factors; (the first two is the color saturation and space hsv)
2D histogram is calculated cv2.calcHist (), to modify the parameters:
• channels = [0,. 1] S and H simultaneously process two channels.
• bins = [180, 256] H is a channel 180, S 256 channels.
• range = [0, 180, 0, 256] H in the range of 0 to 180, S in the range 0-256.
The last line is drawn using the drawing function of plt color histogram, interpolation function with the nearest.
img = cv2.imread('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')
Histogram back projection
The purpose is to find some or other action we are interested in the image;
Projection and outputs the input image (to be searched for) the same size of the image, wherein each pixel value represents the probability that the corresponding point on the input image belongs to the target object. ,
More simple words to explain, the higher the output image pixel values (more white) represents the target point, the more likely we are to be searched (the position of the input image is located).
OpenCV in using the function cv2.calcBackProject () to do the histogram back projection. Its parameters with substantially the same function cv2.calcHist parameters. One argument is that we want to find the target histogram. The third parameter is targeted histogram
Prior to re-use the same target histogram back projection we should do to make their normalization process. Returned result is a probability that the image, we then checked using a disc-shaped convolution operations which do volume, the last used threshold binarization
import cv2 import numpy as np roi = cv2.imread('tar.jpg') hsv = cv2.cvtColor(roi,cv2.COLOR_BGR2HSV) target = cv2.imread('roi.jpg') hsvt = cv2.cvtColor(target,cv2.COLOR_BGR2HSV) # calculating object histogram roihist = cv2.calcHist([hsv],[0, 1], None, [180, 256], [0, 180, 0, 256] ) Apply backprojection and the normalize Histogram # # normalized: the original image, the image a result, the resulting image is mapped to the minimum value, maximum value, a normalized type # cv2.NORM_MINMAX array for all values were transformed to a linear mapping so that they between the minimum and maximum histogram after normalizing # ease of illustration, it would be after normalization 0 to 255 number between a. cv2.normalize (roihist, roihist, 0 , 255 , cv2.NORM_MINMAX) DST = cv2.calcBackProject ([hsvt], [ 0 , . 1 ], roihist, [ 0 , 180 [ , 0 , 256 ], . 1 ) # Now with Convolute Disc Circular # convolution herein can be scattered points together Disc = cv2.getStructuringElement (cv2.MORPH_ELLIPSE, ( . 5 ,. 5 )) DST = cv2.filter2D (DST, - . 1 , Disc) # binary threshold and the AND RET, Thresh = cv2.threshold (DST, 50 , 255 , 0 ) # Remember is a three-channel image, hence the use of merge into 3 channels Thresh = cv2.merge ((Thresh, Thresh, Thresh)) # bitwise operations RES = cv2.bitwise_and (target, Thresh) RES = np.hstack ((target, Thresh, RES)) cv2.imwrite ( ' res.jpg ' , RES) # display image cv2.imshow ( ' . 1 ' , RES) cv2.waitKey ( 0)
The results are shown below:
