Python opencv flood fill, day 21

Dream eraser 2021-01-21 00:49:28
python opencv flood day

Learning goals

Every day 1 Hours ,365 It's a miracle .

Learning today Python OpenCV Flood filling knowledge , Flood filling is also called flood filling algorithm .

The principle is Start with a pixel , A point that meets the pixel requirements nearby , Fill all in the specified color , Until it comes to the point where it doesn't meet the requirements .

If you remember conceptually , There are four common neighborhood pixel filling method , Eight neighborhood pixel filling method , Pixel filling method based on scan line .

For these concepts , Skip first , Before the establishment of the whole cognition , There's no value in learning any basic concepts .

The function prototype

The syntax prototype of flood fill is as follows :

floodFill(image, mask, seedPoint, newVal[, loDiff[, upDiff[, flags]]]) -> retval, image, mask, rect

This function has seven parameters , They are as follows :

  • image: The image of the operation
  • mask: A mask , Than image It's a lot of height 2 Pixel , Wide 2 Pixel . Fill cannot pass through non-zero pixels in the input mask
  • seedPoint: The starting pixel
  • newVal: New fill value ( Color )
  • loDiff: Low value of fill color ( Subtract the value from the color at the starting pixel )
  • upDiff: High value of fill color ( Add this value to the color at the starting pixel )
  • flags: Operation bit identifier , Color images are generally FLOODFILL_FIXED_RANGE Specify color fill

About flags And find a clear explanation :

When it comes to CV_FLOODFILL_FIXED_RANGE when , The pixel to be processed is compared with the seed point , If meet (s – loDiff, s + upDiff) Between (s Is the pixel value of the seed point ), Then fill in ;
When it comes to CV_FLOODFILL_MASK_ONLY when , be mask Can't be empty , here , Function does not fill the original image img, It's filling in the mask image .

Case code

The test picture is as follows :

Python OpenCV Flood fill , The journey of learning from scriptures No 21 God

The test code is as follows :

import cv2 as cv
import numpy as np
# Color image filling 
def fill_color_demo(src):
img_copy = src.copy()
h, w, ch = src.shape
# Declare a rectangular shape , Notice that the height and width increase 2 Pixel 
# np.zeros Returns a function of a given shape and type 0 Filled array 
mask = np.zeros([h+2, w+2], np.uint8)
# Parameters 1, Images to be filled with flooding 
# Parameters 2, A mask , Using a mask can specify in which area the algorithm is used , If you want to use it for the whole image , The mask size is the number of lines in the original image + 2, Number of columns + 2
# A mask , It's a two-dimensional 0 matrix , Because only the mask corresponds to 0 The location of the flood 
# Parameters 3, Flood filling seed points , Based on the pixels of the point, judge the pixels of similar colors , Whether it is flooded or not 
# Parameters 4, New colors for flood areas (BGR Format )
# Parameters 5, The seed pixel can be down the pixel value 
# Parameters 6, Seed point pixel can be up pixel value 
# Parameters 7, The processing mode of flooding algorithm 
cv.floodFill(img_copy, mask, (20, 20), (0, 255, 0),
(50, 50, 50), (100, 100, 100), cv.FLOODFILL_FIXED_RANGE)
cv.imshow("color_demo", img_copy)
if __name__ == "__main__":
src = cv.imread('./25.jpg')

The results are as follows :
Python OpenCV Flood fill , The journey of learning from scriptures No 21 God
About setting the mask , Why do pixels +2, The explanation given in part is : When from 0 That's ok 0 Column begins flooding fill scan ,mask Extra 2 It can ensure that the pixels on the boundary of scanning will be processed . Let's understand for a moment .

About parameters 5 With the parameters 6, Find the following information :

Start from the starting seed point , Fill the connected pixels with the specified color . Connectivity depends on the color and brightness of adjacent pixels , Pixels belong to the repainted area in the following cases , The formula is as follows .
Python OpenCV Flood fill , The journey of learning from scriptures No 21 God
The popular explanation is as follows :

  • (20, 20): Is the location of the seed point ;
  • (0, 255, 0): Color flooding , green ;
  • (50, 50, 50): Three channel values of seed pixels [ b, g, r ] Benchmarking , The lowest value of the three channels in the original picture is [ b-50, g-50, r-50 ];
  • (100, 100, 100): Three channel values of seed pixels [ b, g, r ] Benchmarking , The filled range is in the original picture. The highest value of three channels is [ b+100, g+100, r+100];
  • cv.FLOODFILL_FIXED_RANGE: The pixel to be processed is compared with the seed point , Within the scope of , Then fill this pixel .

In the original image, there are only three channel values of pixels [ b-50, g-50, r-50 ] <= [ B , G, R] <=[ b+100, g+100, r+100] Within this range will be designated green (0, 255, 0) fill .

Binary image filling

Look at the code first , Pay attention to the notes .

import cv2 as cv
import numpy as np
def fill_binary():
# Set up a 400*400 The rectangular 
image = np.zeros([400, 400, 3], np.uint8)
# Fill the inside with a white square 
image[100:300, 100:300, :] = 255
cv.imshow("fill_binary", image)
# Set the mask 
mask = np.ones([402, 402], np.uint8)
mask[101:301, 101:301] = 0
# mask Not for 0 The area of is not filled ,mask by 0 It's the area that's filled in 
cv.floodFill(image, mask, (200, 200), (255, 255, 0),
cv.imshow("filled_binary", image)

This part of the code comes from the Internet , Focus on understanding FLOODFILL_MASK_ONLY that will do , This value indicates starting from the seed point , Fill the mask area .

OpenCV The end of the

1 Two hours later , Yes Python OpenCV Related knowledge , Have you got it ?

As a beginner , There are still many places where learning is not in-depth , I hope you will stick with me .

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