I can't get this to work in my AS1 application. I am using the Color.setTransform method.
Am I correct in thinking the following object creation should result in transforming a colour to white?
var AColorTransform = {ra:100, rb:255, ga:100, gb:255, ba:100, bb:255, aa:100, ab:255};
And this one to black?
AColorTransform = {ra:100, rb:-255, ga:100, gb:-255, ba:100, bb:-255, aa:100, ab:-255};
I read on some websites that calling setRGB or setTransform may not result in actually changing the display colour when the object you're performing the operation on has some kind of dynamic behaviour. Does anyone know more about these situations? And how to change the colour under all circumstances?
Regards.
Been a long time since I've had to do anything is AS1, but I'll do my best.
The basic code for a color.setTransform() looks like this...
var AColorTransform = {ra:100, rb:255, ga:100, gb:255, ba:100, bb:255, aa:100, ab:255};
var myColor = new Color(mc);
myColor.setTransform(AColorTransform);
...where mc is a MovieClip on the stage somewhere.
Remember that you're asking about transform, which by its nature is intended to transform colors from what they are to something else. If you want to reliably paint in a specific color (such as black or white), you're usually far better off using setRGB, which would look like this:
var myColor = new Color(mc);
//set to black
myColor.setRGB(0x000000);
//or set to white
myColor.setRGB(0xFFFFFF);
These work reliably, though there can be some gotchas. Generally, just remember that the color is attached to the specific MovieClip...so if that MovieClip falls out of scope (ie, it disappears from the timeline) your color will be deleted with it.
Read further only if you want to understand color transform better:
Let's look at the components of that color transform.
a (multiplier 0 > 100%) b(offset -255 > 255)
r ra rb
g ga gb
b ba bb
a aa bb
There are four channels (r, g, b, and a). The first three are for red, green and blue, and the last one for alpha (transparency). Each channel has an 'a' component and a 'b' component, thus ra, rb, ga, gb, etc. The 'a' component is a percentage multiplier. That is, it will multiply any existing channel by the percent in that value. The 'b' component is an offset. So 'ra' multiplies the existing red channel. 'rb' offsets it. If your red channel starts as 'FF' (full on red), setting ra:100 will have no effect, since multiplying FF by 100% results in no change. Similarly, if red starts at '00' (no red at all), no value of 'ra' will have any effect, since (if you recall your Shakespeare) twice nothing is still nothing. Things in-between will multiply as you'd expect.
Offsets are added after multiplication. So you can multiply by some value, then offset it:
r (result red color) = (RR * ra%) + rb
g (result green color) = (GG * ga%) + gb
b (result blue color) = (BB * ba%) + bb
a (result alpha) = (AA * aa%) + ab
example: RR = 128 (hex 0x80), ra = 50 (50% or .5), rb = -20
resulting red channel: (128 * .5) + (-20) = 44 (hex 0x2C)
Frankly, this all gets so confusing that I tend to prefer the simple sanity of avoiding transforms altogether and go with the much simpler setRGB().
Related
I'm having trouble grasping the meaning of R = 0, R = S, R = S*Da, defined in kCGBlendMode values such as kCGBlendModeClear, kCGBlendModeCopy, kCGBlendModeSourceIn. So, to what do these symbols refer?
R=0 means that the result color will just be 0, meaning it will be cleared.
R=S means the result color is the same as the source color
R=S*Da means the result is the source color times the alpha value of the destination
If you take a look at the documentation and scroll down you will see their meaning listed:
The blend mode constants introduced in OS X v10.5 represent the Porter-Duff blend modes (a little explanation how they work). The symbols in the equations for these blend modes are:
R is the premultiplied result
S is the source color, and includes alpha
D is the destination color, and includes alpha
Ra, Sa, and Da are the alpha components of R, S, and D
If you furthermore take a look at Setting blend modes you can see most of the blend modes applied and what their result may look like.
I am working on some leaf images using OpenCV (Java). The leaves are captured on a white paper and some has shadows like this one:
Of course, it's somehow the extreme case (there are milder shadows).
Now, I want to threshold the leaf and also remove the shadow (while reserving the leaf's details).
My current flow is this:
1) Converting to HSV and extracting the Saturation channel:
Imgproc.cvtColor(colorMat, colorMat, Imgproc.COLOR_RGB2HSV);
ArrayList<Mat> channels = new ArrayList<Mat>();
Core.split(colorMat, channels);
satImg = channels.get(1);
2) De-noising (median) and applying adaptiveThreshold:
Imgproc.medianBlur(satImg , satImg , 11);
Imgproc.adaptiveThreshold(satImg , satImg , 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 401, -10);
And the result is this:
It looks OK, but the shadow is causing some anomalies along the left boundary. Also, I have this feeling that I am not using the white background to my benefit.
Now, I have 2 questions:
1) How can I improve the result and get rid of the shadow?
2) Can I get good results without working on saturation channel?. The reason I ask is that on most of my images, working on L channel (from HLS) gives way better results (apart from the shadow, of course).
Update: Using the Hue channel makes threshdolding better, but makes the shadow situation worse:
Update2: In some cases, the assumption that the shadow is darker than the leaf doesn't always hold. So, working on intensities won't help. I'm looking more toward a color channels approach.
I don't use opencv, instead I was trying to use matlab image processing toolbox to extract the leaf. Hopefully opencv has all the processing functions for you. Please see my result below. I did all the operations in your original image channel 3 and channel 1.
First I used your channel 3, threshold it with 100 (left top). Then I remove the regions on the border and regions with the pixel size smaller than 100, filling in the hole in the leaf, the result is shown in right top.
Next I used your channel 1, did the same thing as I did in channel 3, the result is shown in left bottom. Then I found out the connected regions (there are only two as you can see in the left bottom figure), remove the one with smaller area (shown in right bottom).
Suppose the right top image is I1, and the right bottom image is I, the leaf is extracted by implement ~I && I1. The leaf is:
Hope it helps. Thanks
I tried two different things:
1. other thresholding on the saturation channel
2. try to find two contours: shadow and leaf
I use c++ so your code snippets will look a little different.
trying otsu-thresholding instead of adaptive thresholding:
cv::threshold(hsv_imgs,mask,0,255,CV_THRESH_BINARY|CV_THRESH_OTSU);
leading to following images (just OTSU thresholding on saturation channel):
the other thing is computing gradient information (i used sobel, see oppenCV documentation), thresholding that and after an opening-operator I used findContours giving something like this, not useable yet (gradient contour approach):
I'm trying to do the same thing with photos of butterflies, but with more uneven and unpredictable backgrounds such as this. Once you've identified a good portion of the background (e.g. via thresholding, or as we do, flood filling from random points), what works well is to use the GrabCut algorithm to get all those bits you might miss on the initial pass. In python, assuming you still want to identify an initial area of background by thresholding on the saturation channel, try something like
import cv2
import numpy as np
img = cv2.imread("leaf.jpg")
sat = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)[:,:,1]
sat = cv2.medianBlur(sat, 11)
thresh = cv2.adaptiveThreshold(sat , 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 401, 10);
cv2.imwrite("thresh.jpg", thresh)
h, w = img.shape[:2]
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
grabcut_mask = thresh/255*3 #background should be 0, probable foreground = 3
cv2.grabCut(img, grabcut_mask,(0,0,w,h),bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK)
grabcut_mask = np.where((grabcut_mask ==2)|(grabcut_mask ==0),0,1).astype('uint8')
cv2.imwrite("GrabCut1.jpg", img*grabcut_mask[...,None])
This actually gets rid of the shadows for you in this case, because the edge of the shadow actually has high saturation levels, so is included in the grab cut deletion. (I would post images, but don't have enough reputation)
Usually, however, you can't trust shadows to be included in the background detection. In this case you probably want to compare areas in the image with colour of the now-known background using the chromacity distortion measure proposed by Horprasert et. al. (1999) in "A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection". This measure takes account of the fact that for desaturated colours, hue is not a relevant measure.
Note that the pdf of the preprint you find online has a mistake (no + signs) in equation 6. You can use the version re-quoted in Rodriguez-Gomez et al (2012), equations 1 & 2. Or you can use my python code below:
def brightness_distortion(I, mu, sigma):
return np.sum(I*mu/sigma**2, axis=-1) / np.sum((mu/sigma)**2, axis=-1)
def chromacity_distortion(I, mu, sigma):
alpha = brightness_distortion(I, mu, sigma)[...,None]
return np.sqrt(np.sum(((I - alpha * mu)/sigma)**2, axis=-1))
You can feed the known background mean & stdev as the last two parameters of the chromacity_distortion function, and the RGB pixel image as the first parameter, which should show you that the shadow is basically the same chromacity as the background, and very different from the leaf. In the code below, I've then thresholded on chromacity, and done another grabcut pass. This works to remove the shadow even if the first grabcut pass doesn't (e.g. if you originally thresholded on hue)
mean, stdev = cv2.meanStdDev(img, mask = 255-thresh)
mean = mean.ravel() #bizarrely, meanStdDev returns an array of size [3,1], not [3], so flatten it
stdev = stdev.ravel()
chrom = chromacity_distortion(img, mean, stdev)
chrom255 = cv2.normalize(chrom, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX).astype(np.uint8)[:,:,None]
cv2.imwrite("ChromacityDistortionFromBackground.jpg", chrom255)
thresh2 = cv2.adaptiveThreshold(chrom255 , 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 401, 10);
cv2.imwrite("thresh2.jpg", thresh2)
grabcut_mask[...] = 3
grabcut_mask[thresh==0] = 0 #where thresh == 0, definitely background, set to 0
grabcut_mask[np.logical_and(thresh == 255, thresh2 == 0)] = 2 #could try setting this to 2 or 0
cv2.grabCut(img, grabcut_mask,(0,0,w,h),bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK)
grabcut_mask = np.where((grabcut_mask ==2)|(grabcut_mask ==0),0,1).astype('uint8')
cv2.imwrite("final_leaf.jpg", grabcut_mask[...,None]*img)
I'm afraid with the parameters I tried, this still removes the stalk, though. I think that's because GrabCut thinks that it looks a similar colour to the shadows. Let me know if you find a way to keep it.
I'm attempting to make a photo effect where you subtract one or two channels from a red-green-blue channel triple. Suppose, for example, I don't want any green or red in my final image. One way to do this is to simply zero the green and red components. However, I lose the edges, shape, and shading of many objects with that approach. What I really want is more of a "grayscale with blue hints" effect (especially if that blue can represent the original blue that was in the image). What formula do I use for this?
B = R*0.299 + G*0.587 + B*0.114
R = G = 0
Blue = 0.299×Red + 0.587×Green + 0.114×Blue
This formula is quite popular but its incorrect. It will not give you good results. For correct results you might want to go with below formula: first convert to a linear colorspace, then use different weights:
Blue = 0.2126×Red + 0.7152×Green + 0.0722×Blue
Correct approximation is :
Blue = (0.2126×Red^(2.2) + 0.7152×Green^(2.2) + 0.0722×Blue^(2.2))^(1/2.2)
Green=Red=0
Hello I am trying to do some image processing. I use Microsoft Kinect to detect humans on a room. I get depth data, do some background subtraction work and end up with a video sequence like this when a person enters the scene and walks around:
http://www.screenr.com/h7f8
I put a video so that you can see the behaviour of the noise in the video. Different colors represent different levels of depth. White represents empty. As you can see it is pretty noisy, especially the red noises.
I need to get rid of everything except the human as much as possible. When I do erosion/dilation (using a very big window size) I can get rid of a lot of the noise but I wondered if there are other methods I can use. Especially the red noise in the video is hard to remove using erosion/dilation.
Some notes:
1) A better background subtraction could be done if we knew when there are no humans in the scene but the background subtraction we do is fully automatic and it works even when there are humans in the scene and even when the camera is moved etc. so this is the best background subtraction we can get right now.
2) The algorithm will work on an embedded system, real time. So the more efficient and easy the algorithm the better. And it doesn't have to be perfect. Though complicated signal processing techniques are also welcome (maybe we might use them on another project who does not need embedded, real time processing).
3) I don't need an actual code. Just ideas.
Just my two cents:
If you don't mind using the SDK for that, then you can very easily keep only the person pixels using the PlayerIndexBitmask as Outlaw Lemur shows.
Now you may not want to be dependable on the drivers for that and want to do it in an image processing level. An approach that we had tried in a project and worked pretty good was contour based. We began by a background subtraction and then we detected the largest contour in the image assuming that this was the person (since usually the noise that remained was very small blobs) and we filled that contour and kept that. You could also use some kind of median filtering as a first pass.
Of course, this is not perfect nor suitable in every case and probably there are a lot better methods. But I'm just throwing it out there in case it helps you come up with any ideas.
Take a look at the eyesweb.
It is a platform for designing that supports kinect device and you can apply noise filters on the outputs. It is a very usefull and simple tool for multimodal systems designing.
I may be wrong (I'd need the video without processing for that) but I'd tend to say that you are trying to get rid of illumination changes.
This is what makes people detection really difficult in 'real' environmnents.
You can check out this other SO question for some links.
I used to detect humans real-time in the same configuration than you, but with monocular vision.
In my case, a really good descriptor was the LBPs, that is mainly used for texture classification.
This is quite simple to put into practice (there are implementations all over the web).
The LBPs where basically used to define an area of interest where movement is detected, so that I can process only part of the image and get rid of all that noise.
This paper for example uses LBP for grayscale correction of images.
Hope that brings some new ideas.
This is pretty simple assuming you are using the Kinect SDK. I would follow this video for Depth basics, and do something like this:
private byte[] GenerateColoredBytes(DepthImageFrame depthFrame)
{
//get the raw data from kinect with the depth for every pixel
short[] rawDepthData = new short[depthFrame.PixelDataLength];
depthFrame.CopyPixelDataTo(rawDepthData);
//use depthFrame to create the image to display on-screen
//depthFrame contains color information for all pixels in image
//Height x Width x 4 (Red, Green, Blue, empty byte)
Byte[] pixels = new byte[depthFrame.Height * depthFrame.Width * 4];
//Bgr32 - Blue, Green, Red, empty byte
//Bgra32 - Blue, Green, Red, transparency
//You must set transparency for Bgra as .NET defaults a byte to 0 = fully transparent
//hardcoded locations to Blue, Green, Red (BGR) index positions
const int BlueIndex = 0;
const int GreenIndex = 1;
const int RedIndex = 2;
//loop through all distances
//pick a RGB color based on distance
for (int depthIndex = 0, colorIndex = 0;
depthIndex < rawDepthData.Length && colorIndex < pixels.Length;
depthIndex++, colorIndex += 4)
{
//get the player (requires skeleton tracking enabled for values)
int player = rawDepthData[depthIndex] & DepthImageFrame.PlayerIndexBitmask;
//gets the depth value
int depth = rawDepthData[depthIndex] >> DepthImageFrame.PlayerIndexBitmaskWidth;
//.9M or 2.95'
if (depth <= 900)
{
//we are very close
pixels[colorIndex + BlueIndex] = Colors.White.B;
pixels[colorIndex + GreenIndex] = Colors.White.G;
pixels[colorIndex + RedIndex] = Colors.White.R;
}
// .9M - 2M or 2.95' - 6.56'
else if (depth > 900 && depth < 2000)
{
//we are a bit further away
pixels[colorIndex + BlueIndex] = Colors.White.B;
pixels[colorIndex + GreenIndex] = Colors.White.G;
pixels[colorIndex + RedIndex] = Colors.White.R;
}
// 2M+ or 6.56'+
else if (depth > 2000)
{
//we are the farthest
pixels[colorIndex + BlueIndex] = Colors.White.B;
pixels[colorIndex + GreenIndex] = Colors.White.G;
pixels[colorIndex + RedIndex] = Colors.White.R;
}
////equal coloring for monochromatic histogram
//byte intensity = CalculateIntensityFromDepth(depth);
//pixels[colorIndex + BlueIndex] = intensity;
//pixels[colorIndex + GreenIndex] = intensity;
//pixels[colorIndex + RedIndex] = intensity;
//Color all players "gold"
if (player > 0)
{
pixels[colorIndex + BlueIndex] = Colors.Gold.B;
pixels[colorIndex + GreenIndex] = Colors.Gold.G;
pixels[colorIndex + RedIndex] = Colors.Gold.R;
}
}
return pixels;
}
This turns everything except humans white, and the humans are gold. Hope this helps!
EDIT
I know you didn't necessarily want code just ideas, so I would say find an algorithm that finds the depth, and one that finds the amount of humans, and color everything white except the humans. I have provided all of this, but I didn't know if you knew what was going on. Also I have an image of the final program.
Note: I added the second depth frame for perspective
I'm writing for Android with OpenCV. I'm segmenting an image similar to below using marker-controlled watershed, without the user manually marking the image. I'm planning to use the regional maxima as markers.
minMaxLoc() would give me the value, but how can I restrict it to the blobs which is what I'm interested in? Can I utilize the results from findContours() or cvBlob blobs to restrict the ROI and apply maxima to each blob?
First of all: the function minMaxLoc finds only the global minimum and global maximum for a given input, so it is mostly useless for determining regional minima and/or regional maxima. But your idea is right, extracting markers based on regional minima/maxima for performing a Watershed Transform based on markers is totally fine. Let me try to clarify what is the Watershed Transform and how you should correctly use the implementation present in OpenCV.
Some decent amount of papers that deal with watershed describe it similarly to what follows (I might miss some detail, if you are unsure: ask). Consider the surface of some region you know, it contains valleys and peaks (among other details that are irrelevant for us here). Suppose below this surface all you have is water, colored water. Now, make holes in each valley of your surface and then the water starts to fill all the area. At some point, differently colored waters will meet, and when this happen, you construct a dam such that they don't touch each other. In the end you have a collection of dams, which is the watershed separating all the different colored water.
Now, if you make too many holes in that surface, you end up with too many regions: over-segmentation. If you make too few you get an under-segmentation. So, virtually any paper that suggests using watershed actually presents techniques to avoid these problems for the application the paper is dealing with.
I wrote all this (which is possibly too naïve for anyone that knows what the Watershed Transform is) because it reflects directly on how you should use watershed implementations (which the current accepted answer is doing in a completely wrong manner). Let us start on the OpenCV example now, using the Python bindings.
The image presented in the question is composed of many objects that are mostly too close and in some instances overlapping. The usefulness of watershed here is to separate correctly these objects, not to group them into a single component. So you need at least one marker for each object and good markers for the background. As an example, first binarize the input image by Otsu and perform a morphological opening for removing small objects. The result of this step is shown below in the left image. Now with the binary image consider applying the distance transform to it, result at right.
With the distance transform result, we can consider some threshold such that we consider only the regions most distant to the background (left image below). Doing this, we can obtain a marker for each object by labeling the different regions after the earlier threshold. Now, we can also consider the border of a dilated version of the left image above to compose our marker. The complete marker is shown below at right (some markers are too dark to be seen, but each white region in the left image is represented at the right image).
This marker we have here makes a lot of sense. Each colored water == one marker will start to fill the region, and the watershed transformation will construct dams to impede that the different "colors" merge. If we do the transform, we get the image at left. Considering only the dams by composing them with the original image, we get the result at right.
import sys
import cv2
import numpy
from scipy.ndimage import label
def segment_on_dt(a, img):
border = cv2.dilate(img, None, iterations=5)
border = border - cv2.erode(border, None)
dt = cv2.distanceTransform(img, 2, 3)
dt = ((dt - dt.min()) / (dt.max() - dt.min()) * 255).astype(numpy.uint8)
_, dt = cv2.threshold(dt, 180, 255, cv2.THRESH_BINARY)
lbl, ncc = label(dt)
lbl = lbl * (255 / (ncc + 1))
# Completing the markers now.
lbl[border == 255] = 255
lbl = lbl.astype(numpy.int32)
cv2.watershed(a, lbl)
lbl[lbl == -1] = 0
lbl = lbl.astype(numpy.uint8)
return 255 - lbl
img = cv2.imread(sys.argv[1])
# Pre-processing.
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, img_bin = cv2.threshold(img_gray, 0, 255,
cv2.THRESH_OTSU)
img_bin = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN,
numpy.ones((3, 3), dtype=int))
result = segment_on_dt(img, img_bin)
cv2.imwrite(sys.argv[2], result)
result[result != 255] = 0
result = cv2.dilate(result, None)
img[result == 255] = (0, 0, 255)
cv2.imwrite(sys.argv[3], img)
I would like to explain a simple code on how to use watershed here. I am using OpenCV-Python, but i hope you won't have any difficulty to understand.
In this code, I will be using watershed as a tool for foreground-background extraction. (This example is the python counterpart of the C++ code in OpenCV cookbook). This is a simple case to understand watershed. Apart from that, you can use watershed to count the number of objects in this image. That will be a slightly advanced version of this code.
1 - First we load our image, convert it to grayscale, and threshold it with a suitable value. I took Otsu's binarization, so it would find the best threshold value.
import cv2
import numpy as np
img = cv2.imread('sofwatershed.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
Below is the result I got:
( even that result is good, because great contrast between foreground and background images)
2 - Now we have to create the marker. Marker is the image with same size as that of original image which is 32SC1 (32 bit signed single channel).
Now there will be some regions in the original image where you are simply sure, that part belong to foreground. Mark such region with 255 in marker image. Now the region where you are sure to be the background are marked with 128. The region you are not sure are marked with 0. That is we are going to do next.
A - Foreground region:- We have already got a threshold image where pills are white color. We erode them a little, so that we are sure remaining region belongs to foreground.
fg = cv2.erode(thresh,None,iterations = 2)
fg :
B - Background region :- Here we dilate the thresholded image so that background region is reduced. But we are sure remaining black region is 100% background. We set it to 128.
bgt = cv2.dilate(thresh,None,iterations = 3)
ret,bg = cv2.threshold(bgt,1,128,1)
Now we get bg as follows :
C - Now we add both fg and bg :
marker = cv2.add(fg,bg)
Below is what we get :
Now we can clearly understand from above image, that white region is 100% foreground, gray region is 100% background, and black region we are not sure.
Then we convert it into 32SC1 :
marker32 = np.int32(marker)
3 - Finally we apply watershed and convert result back into uint8 image:
cv2.watershed(img,marker32)
m = cv2.convertScaleAbs(marker32)
m :
4 - We threshold it properly to get the mask and perform bitwise_and with the input image:
ret,thresh = cv2.threshold(m,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
res = cv2.bitwise_and(img,img,mask = thresh)
res :
Hope it helps!!!
ARK
Foreword
I'm chiming in mostly because I found both the watershed tutorial in the OpenCV documentation (and C++ example) as well as mmgp's answer above to be quite confusing. I revisited a watershed approach multiple times to ultimately give up out of frustration. I finally realized I needed to at least give this approach a try and see it in action. This is what I've come up with after sorting out all of the tutorials I've come across.
Aside from being a computer vision novice, most of my trouble probably had to do with my requirement to use the OpenCVSharp library rather than Python. C# doesn't have baked-in high-power array operators like those found in NumPy (though I realize this has been ported via IronPython), so I struggled quite a bit in both understanding and implementing these operations in C#. Also, for the record, I really despise the nuances of, and inconsistencies in most of these function calls. OpenCVSharp is one of the most fragile libraries I've ever worked with. But hey, it's a port, so what was I expecting? Best of all, though -- it's free.
Without further ado, let's talk about my OpenCVSharp implementation of the watershed, and hopefully clarify some of the stickier points of watershed implementation in general.
Application
First of all, make sure watershed is what you want and understand its use. I am using stained cell plates, like this one:
It took me a good while to figure out I couldn't just make one watershed call to differentiate every cell in the field. On the contrary, I first had to isolate a portion of the field, then call watershed on that small portion. I isolated my region of interest (ROI) via a number of filters, which I will explain briefly here:
Start with source image (left, cropped for demonstration purposes)
Isolate the red channel (left middle)
Apply adaptive threshold (right middle)
Find contours then eliminate those with small areas (right)
Once we have cleaned the contours resulting from the above thresholding operations, it is time to find candidates for watershed. In my case, I simply iterated through all contours greater than a certain area.
Code
Say we've isolated this contour from the above field as our ROI:
Let's take a look at how we'll code up a watershed.
We'll start with a blank mat and draw only the contour defining our ROI:
var isolatedContour = new Mat(source.Size(), MatType.CV_8UC1, new Scalar(0, 0, 0));
Cv2.DrawContours(isolatedContour, new List<List<Point>> { contour }, -1, new Scalar(255, 255, 255), -1);
In order for the watershed call to work, it will need a couple of "hints" about the ROI. If you're a complete beginner like me, I recommend checking out the CMM watershed page for a quick primer. Suffice to say we're going to create hints about the ROI on the left by creating the shape on the right:
To create the white part (or "background") of this "hint" shape, we'll just Dilate the isolated shape like so:
var kernel = Cv2.GetStructuringElement(MorphShapes.Ellipse, new Size(2, 2));
var background = new Mat();
Cv2.Dilate(isolatedContour, background, kernel, iterations: 8);
To create the black part in the middle (or "foreground"), we'll use a distance transform followed by threshold, which takes us from the shape on the left to the shape on the right:
This takes a few steps, and you may need to play around with the lower bound of your threshold to get results that work for you:
var foreground = new Mat(source.Size(), MatType.CV_8UC1);
Cv2.DistanceTransform(isolatedContour, foreground, DistanceTypes.L2, DistanceMaskSize.Mask5);
Cv2.Normalize(foreground, foreground, 0, 1, NormTypes.MinMax); //Remember to normalize!
foreground.ConvertTo(foreground, MatType.CV_8UC1, 255, 0);
Cv2.Threshold(foreground, foreground, 150, 255, ThresholdTypes.Binary);
Then we'll subtract these two mats to get the final result of our "hint" shape:
var unknown = new Mat(); //this variable is also named "border" in some examples
Cv2.Subtract(background, foreground, unknown);
Again, if we Cv2.ImShow unknown, it would look like this:
Nice! This was easy for me to wrap my head around. The next part, however, got me quite puzzled. Let's look at turning our "hint" into something the Watershed function can use. For this we need to use ConnectedComponents, which is basically a big matrix of pixels grouped by the virtue of their index. For example, if we had a mat with the letters "HI", ConnectedComponents might return this matrix:
0 0 0 0 0 0 0 0 0
0 1 0 1 0 2 2 2 0
0 1 0 1 0 0 2 0 0
0 1 1 1 0 0 2 0 0
0 1 0 1 0 0 2 0 0
0 1 0 1 0 2 2 2 0
0 0 0 0 0 0 0 0 0
So, 0 is the background, 1 is the letter "H", and 2 is the letter "I". (If you get to this point and want to visualize your matrix, I recommend checking out this instructive answer.) Now, here's how we'll utilize ConnectedComponents to create the markers (or labels) for watershed:
var labels = new Mat(); //also called "markers" in some examples
Cv2.ConnectedComponents(foreground, labels);
labels = labels + 1;
//this is a much more verbose port of numpy's: labels[unknown==255] = 0
for (int x = 0; x < labels.Width; x++)
{
for (int y = 0; y < labels.Height; y++)
{
//You may be able to just send "int" in rather than "char" here:
var labelPixel = (int)labels.At<char>(y, x); //note: x and y are inexplicably
var borderPixel = (int)unknown.At<char>(y, x); //and infuriatingly reversed
if (borderPixel == 255)
labels.Set(y, x, 0);
}
}
Note that the Watershed function requires the border area to be marked by 0. So, we've set any border pixels to 0 in the label/marker array.
At this point, we should be all set to call Watershed. However, in my particular application, it is useful just to visualize a small portion of the entire source image during this call. This may be optional for you, but I first just mask off a small bit of the source by dilating it:
var mask = new Mat();
Cv2.Dilate(isolatedContour, mask, new Mat(), iterations: 20);
var sourceCrop = new Mat(source.Size(), source.Type(), new Scalar(0, 0, 0));
source.CopyTo(sourceCrop, mask);
And then make the magic call:
Cv2.Watershed(sourceCrop, labels);
Results
The above Watershed call will modify labels in place. You'll have to go back to remembering about the matrix resulting from ConnectedComponents. The difference here is, if watershed found any dams between watersheds, they will be marked as "-1" in that matrix. Like the ConnectedComponents result, different watersheds will be marked in a similar fashion of incrementing numbers. For my purposes, I wanted to store these into separate contours, so I created this loop to split them up:
var watershedContours = new List<Tuple<int, List<Point>>>();
for (int x = 0; x < labels.Width; x++)
{
for (int y = 0; y < labels.Height; y++)
{
var labelPixel = labels.At<Int32>(y, x); //note: x, y switched
var connected = watershedContours.Where(t => t.Item1 == labelPixel).FirstOrDefault();
if (connected == null)
{
connected = new Tuple<int, List<Point>>(labelPixel, new List<Point>());
watershedContours.Add(connected);
}
connected.Item2.Add(new Point(x, y));
if (labelPixel == -1)
sourceCrop.Set(y, x, new Vec3b(0, 255, 255));
}
}
Then, I wanted to print these contours with random colors, so I created the following mat:
var watershed = new Mat(source.Size(), MatType.CV_8UC3, new Scalar(0, 0, 0));
foreach (var component in watershedContours)
{
if (component.Item2.Count < (labels.Width * labels.Height) / 4 && component.Item1 >= 0)
{
var color = GetRandomColor();
foreach (var point in component.Item2)
watershed.Set(point.Y, point.X, color);
}
}
Which yields the following when shown:
If we draw on the source image the dams that were marked by a -1 earlier, we get this:
Edits:
I forgot to note: make sure you're cleaning up your mats after you're done with them. They WILL stay in memory and OpenCVSharp may present with some unintelligible error message. I should really be using using above, but mat.Release() is an option as well.
Also, mmgp's answer above includes this line: dt = ((dt - dt.min()) / (dt.max() - dt.min()) * 255).astype(numpy.uint8), which is a histogram stretching step applied to the results of the distance transform. I omitted this step for a number of reasons (mostly because I didn't think the histograms I saw were too narrow to begin with), but your mileage may vary.