potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 00:03:03 +04:00
Code Packages Releases Wiki Activity

Doxygen tutorials: python final edits

Browse Source
This commit is contained in:
Maksim Shabunin
2014-12-01 15:46:05 +03:00
parent 875f922332
commit 812ce48c36
49 changed files with 426 additions and 353 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/py_tutorials/py_imgproc/py_canny/py_canny.markdown
+34 -33
View File
@@ -17,52 +17,55 @@ Canny Edge Detection is a popular edge detection algorithm. It was developed by
-# **Noise Reduction**
Since edge detection is susceptible to noise in the image, first step is to remove the noise in the
image with a 5x5 Gaussian filter. We have already seen this in previous chapters.
Since edge detection is susceptible to noise in the image, first step is to remove the noise in the
image with a 5x5 Gaussian filter. We have already seen this in previous chapters.
-# **Finding Intensity Gradient of the Image**
Smoothened image is then filtered with a Sobel kernel in both horizontal and vertical direction to
get first derivative in horizontal direction (\f$G_x\f$) and vertical direction (\f$G_y\f$). From these two
images, we can find edge gradient and direction for each pixel as follows:
Smoothened image is then filtered with a Sobel kernel in both horizontal and vertical direction to
get first derivative in horizontal direction (\f$G_x\f$) and vertical direction (\f$G_y\f$). From these two
images, we can find edge gradient and direction for each pixel as follows:
\f[Edge_Gradient \; (G) = \sqrt{G_x^2 + G_y^2}\f]\f[Angle \; (\theta) = \tan^{-1} \bigg(\frac{G_y}{G_x}\bigg)\f]
\f[
Edge\_Gradient \; (G) = \sqrt{G_x^2 + G_y^2} \\
Angle \; (\theta) = \tan^{-1} \bigg(\frac{G_y}{G_x}\bigg)
\f]
Gradient direction is always perpendicular to edges. It is rounded to one of four angles
representing vertical, horizontal and two diagonal directions.
Gradient direction is always perpendicular to edges. It is rounded to one of four angles
representing vertical, horizontal and two diagonal directions.
-# **Non-maximum Suppression**
After getting gradient magnitude and direction, a full scan of image is done to remove any unwanted
pixels which may not constitute the edge. For this, at every pixel, pixel is checked if it is a
local maximum in its neighborhood in the direction of gradient. Check the image below:
After getting gradient magnitude and direction, a full scan of image is done to remove any unwanted
pixels which may not constitute the edge. For this, at every pixel, pixel is checked if it is a
local maximum in its neighborhood in the direction of gradient. Check the image below:
![image](images/nms.jpg)
![image](images/nms.jpg)
Point A is on the edge ( in vertical direction). Gradient direction is normal to the edge. Point B
and C are in gradient directions. So point A is checked with point B and C to see if it forms a
local maximum. If so, it is considered for next stage, otherwise, it is suppressed ( put to zero).
Point A is on the edge ( in vertical direction). Gradient direction is normal to the edge. Point B
and C are in gradient directions. So point A is checked with point B and C to see if it forms a
local maximum. If so, it is considered for next stage, otherwise, it is suppressed ( put to zero).
In short, the result you get is a binary image with "thin edges".
In short, the result you get is a binary image with "thin edges".
-# **Hysteresis Thresholding**
This stage decides which are all edges are really edges and which are not. For this, we need two
threshold values, minVal and maxVal. Any edges with intensity gradient more than maxVal are sure to
be edges and those below minVal are sure to be non-edges, so discarded. Those who lie between these
two thresholds are classified edges or non-edges based on their connectivity. If they are connected
to "sure-edge" pixels, they are considered to be part of edges. Otherwise, they are also discarded.
See the image below:
This stage decides which are all edges are really edges and which are not. For this, we need two
threshold values, minVal and maxVal. Any edges with intensity gradient more than maxVal are sure to
be edges and those below minVal are sure to be non-edges, so discarded. Those who lie between these
two thresholds are classified edges or non-edges based on their connectivity. If they are connected
to "sure-edge" pixels, they are considered to be part of edges. Otherwise, they are also discarded.
See the image below:
![image](images/hysteresis.jpg)
![image](images/hysteresis.jpg)
The edge A is above the maxVal, so considered as "sure-edge". Although edge C is below maxVal, it is
connected to edge A, so that also considered as valid edge and we get that full curve. But edge B,
although it is above minVal and is in same region as that of edge C, it is not connected to any
"sure-edge", so that is discarded. So it is very important that we have to select minVal and maxVal
accordingly to get the correct result.
The edge A is above the maxVal, so considered as "sure-edge". Although edge C is below maxVal, it is
connected to edge A, so that also considered as valid edge and we get that full curve. But edge B,
although it is above minVal and is in same region as that of edge C, it is not connected to any
"sure-edge", so that is discarded. So it is very important that we have to select minVal and maxVal
accordingly to get the correct result.
This stage also removes small pixels noises on the assumption that edges are long lines.
This stage also removes small pixels noises on the assumption that edges are long lines.
So what we finally get is strong edges in the image.
@@ -74,7 +77,7 @@ argument is our input image. Second and third arguments are our minVal and maxVa
Third argument is aperture_size. It is the size of Sobel kernel used for find image gradients. By
default it is 3. Last argument is L2gradient which specifies the equation for finding gradient
magnitude. If it is True, it uses the equation mentioned above which is more accurate, otherwise it
uses this function: \f$Edge_Gradient \; (G) = |G_x| + |G_y|\f$. By default, it is False.
uses this function: \f$Edge\_Gradient \; (G) = |G_x| + |G_y|\f$. By default, it is False.
@code{.py}
import cv2
import numpy as np
@@ -98,8 +101,7 @@ Additional Resources
--------------------
-# Canny edge detector at [Wikipedia](http://en.wikipedia.org/wiki/Canny_edge_detector)
2. [Canny Edge Detection
Tutorial](http://dasl.mem.drexel.edu/alumni/bGreen/www.pages.drexel.edu/_weg22/can_tut.html) by
-# [Canny Edge Detection Tutorial](http://dasl.mem.drexel.edu/alumni/bGreen/www.pages.drexel.edu/_weg22/can_tut.html) by
Bill Green, 2002.
Exercises
@@ -107,4 +109,3 @@ Exercises
-# Write a small application to find the Canny edge detection whose threshold values can be varied
using two trackbars. This way, you can understand the effect of threshold values.