I'm having problems using pdb.gimp_by_color_select in Gimp
I've already looked at this question
Here's what I have:
# duplicate layer
duplicate_layer(image, "temp")
tempLayer = pdb.gimp_image_get_active_layer(image)
colour = (0,0,0)
operation = 0
pdb.gimp_selection_none(tempLayer)
pdb.gimp_by_color_select(tempLayer, colour, 0, operation, True, False, 0, True)
Only it doesn't select any of the black pixels in the newly duplicated templayer as I would expect it.
Here's a snippet of the image
The lines are not true black (0,0,0) but I do an auto levels
# Auto layers
pdb.gimp_drawable_levels_stretch(tempLayer)
on the image beforehand
If you look at the picture histogram, the "black" is actually a fairly wide range, from 40 to 100 with a peak around 75:
And even after a level-stretch, most of your black pixels are still not completely black:
You would get a better result by thresholding the image around 100, if necessary using another copy of the layer (a selection applies to any any layer of the image, regardless of the layer used to obtain it).
Related
I have an image given below with black background. I want to separate the background and foreground. The idea is on the assumption that the image contains PURE Black [0,0,0] only at the background part. i.e if there's any other black present in the image, it must be having a bit of non black too for example: 0,0,1, 0,1,0.. or anything else.
I tried 3 different things and got different results each time. I want to know the proper way of doing it.
Below is the code which I tried.
mask_color = [0,0,0]
image = np.array(Image.open('black1.PNG'))
mask = image.copy()
non_black = np.all(mask != mask_color, axis = -1) # ALL pixel which are Non-black
mask[non_black] = [255,255,255] # Make the non black part white
Image.fromarray(mask) # show the mask
I also tried:
non_black = np.any(mask != mask_color, axis = -1) # ANY pixel which are Non-black
And then I tried getting a single channel whic is not so good approach because a single 0 could be anywhere.
mask_color = 0
image = np.array(Image.open('black1.PNG'))[:,:,0] # get only 1 channel
mask = image.copy()
mask[mask != mask_color] = 255 # set to white
could someone please suggest correct way of doing it?
Goal is to create a Black and White mask as mentioned by #Christoph Rackwitz in the comments
I have 26 PNG files, each with an image of a letter of the alphabet. They've all been fully cropped to the letter shape with the result that when I insert them into an image, letters with tails all 'sit on the line'
Each letter is in black, with a transparent background. Each PNG has different dimensions, because of the differing letter shapes
I thought I'd remediate this by adding a transparent border of a different size depending on the source file, to make common datum for all the letters, so that 'a' for example would have some transparent space at the bottom.
I've coded up the calculcation for each letter, but I have two issues:
1) Even before applying the operation, I can't seem to read the file in and write it to a new unchanged file in OpenCV. The transparency in the image is replaced with black.
2) While I can add a colour border, I can't seem to add a transparent border.
Original Image:
Read in, and written out:
Apparenly with a blue border, but maximum transparancy:
I have a feeling that if I can sort out the first problem, the second might fall in line. Here is my code:
img = cv2.imread(file)
img_with_border = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=[-255,0,0,255])
#img_with_border = img
cv2.imwrite(newfile, img_with_border, [cv2.IMWRITE_JPEG_QUALITY, 100])
I'd appreciate some help on what is going on here with transparancy. Is OpenCV the right tool to use?
Thanks,
Jeff.
To load a PNG image with 4 channels in OpenCV, use im = cv2.imread(file, cv2.IMREAD_UNCHANGED). You will obtain a BGRA image.
To change the alpha value, you have to change the fourth channel of the image. This means that to create your transparent border you have to pass a value (B, G, R, 0) and not [-255, 0, 0, 255]. (What is that -255 by the way ?). B, G and R can be 0, it doesn't matter.
Also, make sure you write to a PNG image to keep the transparency. You seem to be writing your result as JPEG.
Let say I have this input image, with any number of boxes. I want to segment out these boxes, so I can eventually extract them out.
input image:
The background could anything that is continuous, like a painted wall, wooden table, carpet.
My idea was that the gradient would be the same throughout the background, and with a constant gradient. I could turn where the gradient is about the same, into zero's in the image.
Through edge detection, I would dilate and fill the regions where edges detected. Essentially my goal is to make a blob of the areas where the boxes are. Having the blobs, I would know the exact location of the boxes, thus being able to crop out the boxes from the input image.
So in this case, I should be able to have four blobs, and then I would be able to crop out four images from the input image.
This is how far I got:
segmented image:
query = imread('AllFour.jpg');
gray = rgb2gray(query);
[~, threshold] = edge(gray, 'sobel');
weightedFactor = 1.5;
BWs = edge(gray,'roberts');
%figure, imshow(BWs), title('binary gradient mask');
se90 = strel('disk', 30);
se0 = strel('square', 3);
BWsdil = imdilate(BWs, [se90]);
%figure, imshow(BWsdil), title('dilated gradient mask');
BWdfill = imfill(BWsdil, 'holes');
figure, imshow(BWdfill);
title('binary image with filled holes');
What a very interesting problem! Here's my solution in an attempt to solve this problem for you. This is assuming that the background has the same colour distribution throughout. First, transform your image from RGB to the HSV colour space with rgb2hsv. The HSV colour space is an ideal transform for analyzing colours. After this, I would look at the saturation and value planes. Saturation is concerned with how "pure" the colour is, while value is the intensity or brightness of the colour itself. If you take a look at the saturation and value planes for the image, this is what is shown:
im = imread('http://i.stack.imgur.com/1SGVm.jpg');
out = rgb2hsv(im);
figure;
subplot(2,1,1);
imshow(out(:,:,2));
subplot(2,1,2);
imshow(out(:,:,3));
This is what I get:
By taking a look at some locations in the gray background, it looks like the majority of the saturation are less than 0.2 as well as the elements in the value plane are greater than 0.3. As such, we want to find the opposite of those pixels to get our objects. As such, we find those pixels whose saturation is greater than 0.2 or those pixels with a value that is less than 0.3:
seg = out(:,:,2) > 0.2 | out(:,:,3) < 0.3;
This is what we get:
Almost there! There are some spurious single pixels, so I'm going to perform an opening with imopen with a line structuring element.
After this, I'll perform a dilation with imdilate to close any gaps, then use imfill with the 'holes' option to fill in the gaps, then use erosion with imerode to shrink the shapes back to their original form. As such:
se = strel('line', 3, 90);
pre = imopen(seg, c);
se = strel('square', 20);
pre2 = imdilate(pre, se);
pre3 = imfill(pre2, 'holes');
final = imerode(pre3, se);
figure;
imshow(final);
final contains the segmented image with the 4 candy boxes. This is what I get:
Try resizing the image. When you make it smaller, it would be easier to join edges. I tried what's shown below. You might have to tune it depending on the nature of the background.
close all;
clear all;
im = imread('1SGVm.jpg');
small = imresize(im, .25); % resize
grad = (double(imdilate(small, ones(3))) - double(small)); % extract edges
gradSum = sum(grad, 3);
bw = edge(gradSum, 'Canny');
joined = imdilate(bw, ones(3)); % join edges
filled = imfill(joined, 'holes');
filled = imerode(filled, ones(3));
imshow(label2rgb(bwlabel(filled))) % label the regions and show
If you have a recent version of MATLAB, try the Color Thresholder app in the image processing toolbox. It lets you interactively play with different color spaces, to see which one can give you the best segmentation.
If your candy covers are fixed or you know all the covers that are coming into the scene then Template matching is best for this. As it is independent of the background in the image.
http://docs.opencv.org/doc/tutorials/imgproc/histograms/template_matching/template_matching.html
I imagine this is a shot in the dark, but is it possible to have a vector file of a shape (in this case a hexagon with rounded corners), and pass an image through some code and have it coming out cropped in the shape of that vector?
I'm attempting to utilize hexagons in my design and have gone through every page I possibly can. I've seen the many HTML and CSS solutions, but none of them achieve what I'm looking for flawlessly.
Another idea I have is maybe overlaying a transparent hexagon shape with white corners on top of the existing image with imagemagick, and then going through and making any white transparent. Thoughts?
I don't have any code for cropping in the shape of a vector file, but here's what I have for overlaying an outline of the shape I want on top of the other picture:
imgfile = "public/" + SecureRandom.uuid + ".png"
SmartCropper.from_file(art.url(:original)).smart_square.resize(225,225).write(imgfile)
overlay = Magick::Image.read("app/assets/images/overlay.png")
img = Magick::Image.read(imgfile)
img.composite(overlay,0,0, Magick::OverCompositeOp)
Right now it's giving me an undefined method error for composite, which is strange because I've followed some other stack overflow questions using the same thing in their models.
Any help is appreciated!
You have fallen for a common ImageMagick trap - the objects you get from the .read method are not Magick::Image objects but Magick::ImageList ones, and for most image types you want the first item from the list.
Without knowing how you have set up your overlay.png file, it is difficult to tell what the best composite option is. However, in a similar situation I found CopyOpacityCompositeOp to be useful, and to have the overlay's transparency control the transparency in the final image.
I tested the following code and it looks like it would do what you want if overlay.png was set up that way:
require 'smartcropper'
imgfile = "test_square.png"
SmartCropper.from_file( 'test_input.png' ).
smart_square.resize( 225, 225 ).write( imgfile )
overlay = Magick::Image.read( 'overlay.png' ).first
img = Magick::Image.read( imgfile ).first
img.composite( overlay, 0, 0, Magick::CopyOpacityCompositeOp ).
write( "test_result.png" )
Instead of reading overlay from a file, you could create it using Magick::Draw like this:
overlay = Magick::Image.new( 225, 225 ) do |i|
i.background_color= "Transparent"
end
gc = Magick::Draw.new
gc.stroke('white').stroke_width(10)
gc.fill('white')
gc.polygon(97.5, 26.25, 178.5, 73.125, 178.5, 167,
97.5, 213.75, 16.5, 167, 16.5, 73.125)
gc.draw( overlay )
NB That's a hexagon, but I've not bothered centering it.
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.