Hi I am trying to obtain a distance transform to an endge image and I want to obtain similar results in given figures
Figure 1 Figure 2
import cv2
img = cv2.imshow("image.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Applying the Canny Edge filter
edge = cv2.Canny(img, t_lower, t_upper)
#dil = cv2.dilate(edge,(3,3),iterations=20)
distTransform=cv2.distanceTransform(edge,cv2.DIST_L1,5)
However, openCV canny edge detection function gives me 1px thick edges, so the output of distance tranform becomes same as the input.
Figure 3 - Distance transform to edge detection
Dilating the edge works good however, I don't want to cause a change in the image. How can i obtain similar resuts as in figure 1 and 2?
After lots of trying I have found that the distance transform function in OpenCV works differently than the Matlab one. OpenCV one assumes active pixels to be value 0 as #Micka said. Shifting the input is a choice. However, I also found a custom function which also works great in this post.
Related
I have been trying to detect and track vehicle in a video stream. Recently I decided to implement a hard-coded method which find out the shadow of a vehicle and detect entire vehicle with respect to tire position. At the end, I partially done with my implementation. Here is the video link of demonstration.
At the first step I used canny edge detector to subtract edge of the video frames.
Then I used hough transform funciton in opencv.
However this functions finds all the horizontal and vertical lines while I only interested in horizontal lines which are possibly shadow of the vehicle.
My question is how I can use hough line transform function where it only checks the lines which are in a spesific range of angle and within a spesific area. Is there any parameter that tresholds the angle ? Or should I implement the function by myself ?
Since you end up with a binary image after the Canny operation, it may be easiest to convolve the image with a simple horizontal Prewitt operator before applying the Hough transform:
1 1 1
0 0 0
-1 -1 -1
which will give you a map of the grayscale intensities of each pixel, with pixels along horizontal edge giving the strongest signal. The advantage of using only the horizontal operator is that vertical edges receive no amplification, horizontal edges receive maximum amplification, and any edge within 45° of horizontal should have a signal somewhere between the two. You can use the resulting image to decide which pixels from the Canny mask to keep when you apply the detect edges to the original image: If the Prewitt signal is above a certain threshold for a pixel, that pixel is assumed to be along a 'horizontal-enough' edge that gets kept, discard otherwise. I believe opencv has this feature, but it's trivial to implement if not.
I'm trying to detect concentric circles in an image of a paper target using AForge.NET.
I can clean up the image using Threshold(88) or Edges, but can't work out how to detect the circles.
Original image size = 450 x 479 px
Steps so far:
convert image to greyscale
use Edge to find circles (Threshold(88) also works)
Run HoughTransform with radius of = 100, output using 'ToBitmap'
Count the circles found (I get 69750 circles detected)
Draw the 20 most intensive circles
I don't have a background in image processing; any guidance very much appreciated.
The Solution, in this case, was not to use a Hough Transform at all.
I used a threshold and blob detection to find the outer circle which gave me the centre.
Then an horizontal-intensity histogram to find the peaks that correlate with each ring
Then compare this against the peaks from a vertical-intensity histogram, to get the radius of each ring
Then some sanity checking.
I haven't got the peak detection working yet, but it's in progress and it all looks like working.
I'd like to extract the contours of an image, expressed as a sequence of point coordinates.
With Canny I'm able to produce a binary image that contains only the edges of the image. Then, I'm trying to use findContours to extract the contours. The results are not OK, though.
For each edge I often got 2 lines, like if it was considered as a very thin area.
I would like to simplify my contours so I can draw them as single lines. Or maybe extract them with a different function that directly produce the correct result would be even better.
I had a look on the documentation of OpenCV but I was't able to find anything useful, but I guess that I'm not the first one with a similar problem. Is there any function or method I could use?
Here is the Python code I've written so far:
def main():
img = cv2.imread("lena-mono.png", 0)
if img is None:
raise Exception("Error while loading the image")
canny_img = cv2.Canny(img, 80, 150)
contours, hierarchy = cv2.findContours(canny_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
scale = 10
contours_img = cv2.resize(contours_img, (0, 0), fx=scale, fy=scale)
for cnt in contours:
color = np.random.randint(0, 255, (3)).tolist()
cv2.drawContours(contours_img,[cnt*scale], 0, color, 1)
cv2.imwrite("canny.png", canny_img)
cv2.imwrite("contours.png", contours_img)
The scale factor is used to highlight the double lines of the contours.
Here are the links to the images:
Lena greyscale
Edges extracted with Canny
Contours: 10x zoom where you can see the wrong results produced by findContours
Any suggestion will be greatly appreciated.
If I understand you right, your question has nothing to do with finding lines in a parametric (Hough transform) sense.
Rather, it is an issue with the findContours method returning multiple contours for a single line.
This is because Canny is an edge detector - that means it is filter attuned to the image intensity gradient which occurs on both sides of a line.
So your question is more akin to: “how can I convert low-level edge features to single line?”, or perhaps: “how can I navigate the contours hierarchy to detect single lines?"
This is a fairly common topic - and here is a previous post which proposed one solution:
OpenCV converting Canny edges to contours
I have this project where I need (on iOS) to detect simple geometric shapes inside an image.
After searching the internet I have concluded that the best tool for this is OpenCV. The thing is that up until two hours ago I had no idea what OpenCV is and I have never even remotely did anything involving image processing. My main experience is JS/HTML,C#,SQL,Objective-C...
Where do I start with this?
I have found this answer that I was able to digest and by reading already other stuff, I understand that OpenCV should return an Array of shapes with the points/corners, is this true? Also how will it represent a circle or a half circle?
Also what about the shape orientation?
Do you know of any Demo iOS project that can demonstrate a similar functionality?
If you have only these regular shapes, there is a simple procedure as follows :
Find Contours in the image ( image should be binary as given in your question)
Approximate each contour using approxPolyDP function.
First, check number of elements in the approximated contours of all the shapes. It is to recognize the shape. For eg, square will have 4, pentagon will have 5. Circles will have more, i don't know, so we find it. ( I got 16 for circle and 9 for half-circle.)
Now assign the color, run the code for your test image, check its number, fill it with corresponding colors.
Below is my example in Python:
import numpy as np
import cv2
img = cv2.imread('shapes.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,127,255,1)
contours,h = cv2.findContours(thresh,1,2)
for cnt in contours:
approx = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)
print len(approx)
if len(approx)==5:
print "pentagon"
cv2.drawContours(img,[cnt],0,255,-1)
elif len(approx)==3:
print "triangle"
cv2.drawContours(img,[cnt],0,(0,255,0),-1)
elif len(approx)==4:
print "square"
cv2.drawContours(img,[cnt],0,(0,0,255),-1)
elif len(approx) == 9:
print "half-circle"
cv2.drawContours(img,[cnt],0,(255,255,0),-1)
elif len(approx) > 15:
print "circle"
cv2.drawContours(img,[cnt],0,(0,255,255),-1)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Below is the output:
Remember, it works only for regular shapes.
Alternatively to find circles, you can use houghcircles. You can find a tutorial here.
Regarding iOS, OpenCV devs are developing some iOS samples this summer, So visit their site : www.code.opencv.org and contact them.
You can find slides of their tutorial here : http://code.opencv.org/svn/gsoc2012/ios/trunk/doc/CVPR2012_OpenCV4IOS_Tutorial.pdf
The answer depends on the presence of other shapes, level of noise if any and invariance you want to provide for (e.g. rotation, scaling, etc). These requirements will define not only the algorithm but also required pre-procesing stages to extract features.
Template matching that was suggested above works well when shapes aren't rotated or scaled and when there are no similar shapes around; in other words, it finds a best translation in the image where template is located:
double minVal, maxVal;
Point minLoc, maxLoc;
Mat image, template, result; // template is your shape
matchTemplate(image, template, result, CV_TM_CCOEFF_NORMED);
minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc); // maxLoc is answer
Geometric hashing is a good method to get invariance in terms of rotation and scaling; this method would require extraction of some contour points.
Generalized Hough transform can take care of invariance, noise and would have minimal pre-processing but it is a bit harder to implement than other methods. OpenCV has such transforms for lines and circles.
In the case when number of shapes is limited calculating moments or counting convex hull vertices may be the easiest solution: openCV structural analysis
You can also use template matching to detect shapes inside an image.
I'm trying to use OpenCV to "parse" screenshots from the iPhone game Blocked. The screenshots are cropped to look like this:
I suppose for right now I'm just trying to find the coordinates of each of the 4 points that make up each rectangle. I did see the sample file squares.c that comes with OpenCV, but when I run that algorithm on this picture, it comes up with 72 rectangles, including the rectangular areas of whitespace that I obviously don't want to count as one of my rectangles. What is a better way to approach this? I tried doing some Google research, but for all of the search results, there is very little relevant usable information.
The similar issue has already been discussed:
How to recognize rectangles in this image?
As for your data, rectangles you are trying to find are the only black objects. So you can try to do a threshold binarization: black pixels are those ones which have ALL three RGB values less than 40 (I've found it empirically). This simple operation makes your picture look like this:
After that you could apply Hough transform to find lines (discussed in the topic I referred to), or you can do it easier. Compute integral projections of the black pixels to X and Y axes. (The projection to X is a vector of x_i - numbers of black pixels such that it has the first coordinate equal to x_i). So, you get possible x and y values as the peaks of the projections. Then look through all the possible segments restricted by the found x and y (if there are a lot of black pixels between (x_i, y_j) and (x_i, y_k), there probably is a line probably). Finally, compose line segments to rectangles!
Here's a complete Python solution. The main idea is:
Apply pyramid mean shift filtering to help threshold accuracy
Otsu's threshold to get a binary image
Find contours and filter using contour approximation
Here's a visualization of each detected rectangle contour
Results
import cv2
image = cv2.imread('1.png')
blur = cv2.pyrMeanShiftFiltering(image, 11, 21)
gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.015 * peri, True)
if len(approx) == 4:
x,y,w,h = cv2.boundingRect(approx)
cv2.rectangle(image,(x,y),(x+w,y+h),(36,255,12),2)
cv2.imshow('thresh', thresh)
cv2.imshow('image', image)
cv2.waitKey()
I wound up just building on my original method and doing as Robert suggested in his comment on my question. After I get my list of rectangles, I then run through and calculate the average color over each rectangle. I check to see if the red, green, and blue components of the average color are each within 10% of the gray and blue rectangle colors, and if they are I save the rectangle, if they aren't I discard it. This process gives me something like this:
From this, it's trivial to get the information I need (orientation, starting point, and length of each rectangle, considering the game window as a 6x6 grid).
The blocks look like bitmaps - why don't you use simple template matching with different templates for each block size/color/orientation?
Since your problem is the small rectangles I would start by removing them.
Since those lines are much thinner than the borders of the rectangles I would start by applying morphological operations on the image.
Using a structural element that looks like this:
element = [ 1 1
1 1 ]
should remove lines that are less than two pixels wide. After the small lines are removed the rectangle finding algorithm of OpenCV will most likely do the rest of the job for you.
The erosion can be done in OpenCV by the function cvErode
Try one of the many corner detectors like harris corner detector. also it is in general a good idea to try that at multiple resolutions : so do some preprocessing of of varying magnification.
It appears that you want some sort of color dominated square then you can suppress the other colors, by first using something like cvsplit .....and then thresholding the color...so only that region remains....follow that with a cropping operation ...I think that could work as well ....