I want to take an image that looks like this:
And make it look more like this:
My thinking being you could look a line of 3 pixels and if the left and right most pixel green then fill in the cinter one, and do the same but with 3 horizontal pixels. run that 3 or 4 time's and that would take care of most of it.
You can use the OpenCV Python library for this kind of operation.
More specifically, you can use morphological transformations, which are available in OpenCV:
Morphological transformations are some simple operations based on the
image shape. It is normally performed on binary images. It needs two
inputs, one is our original image, second one is called structuring
element or kernel which decides the nature of operation. Two basic
morphological operators are Erosion and Dilation. Then its variant
forms like Opening, Closing, Gradient etc also comes into play. We
will see them one-by-one with help of following image
You could use a closing operator, which is:
Closing is reverse of Opening, Dilation followed by Erosion. It is
useful in closing small holes inside the foreground objects, or small
black points on the object.
The result would look something like this:
And the code would look something like this (you would need to load the image and define a kernel:
import cv2
import numpy as np
img = cv2.imread('<path_to_your_image>',0)
kernel = np.ones((5,5),np.uint8)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
I am trying to extract rectangular big boxes from document images with signatures in it. Since i don't have training data (for deep learning), i want to cut rectangular boxes (3 in all images) from these images using OpenCV.
Here is what I tried:
import numpy as np
import cv2
img = cv2.imread('S-0330-444-20012800.jpg')
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,0.02*cv2.arcLength(cnt,True),True)
if len(approx)==4:
cv2.drawContours(img,[cnt],0,(26,60,232),-1)
cv2.imshow('img',img)
cv2.waitKey(0)
sample image
With the above code, I get a lot of squares (around 152 small points like squares) and of course not the 3 boxes.
Replies appreciated. [sample image is attached]
I would suggest you read up on template matching. There is also a good OpenCV tutorial on this.
For your use case, the idea would be to generate a stereotyped image of a rectangular box with the same shape (width/height ratio) as the boxes found on your documents. Depending on whether your input images show the document always in the same scaling or not, your would need to either resize the inputs to keep their magnification constant, or you would need to operate with a template bank (e.g. an array of box templates in various scalings).
Briefly, you would then cross-correlate the template box(es) with the input image and (in case of well-matched scaling) would find ideally relatively sharp peaks indicating the centers of your document boxes.
In the code above, use image pyramids (to merge unwanted contour noises) and cv2.findContours in combination. Post to that filtering list of Contours based on contour area cv2.contourArea will lead to only bigger squares.
There is also an alternate solution. Looking at images, we can see that the signature text usually is bigger than that of printed text in that ROI. so we can filter out contours smaller than signature contours and extract only the signature.
Its always good to remove noise before using cv2.findContours e.g. dilate, erode, blurring etc.
I would like to darken one image based on the mask of an edge-detected second image.
Image 1: Original (greyscale) image
Image 2: Edge detected (to be used as mask)
Image 3: Failed example showing cv2.subtract processing
In my failed example (Image 3), I subtracted the white pixels (255) from the original image but what I want to do is DARKEN the original image based on a mask of the edge detected image.
In this article: How to fast change image brightness with python + OpenCV?, Bill Gates describes how he converts the image to HSV, splits out then modifies Value, and then finally merges back. This seems like a reasonable approach but I only want to modify the Value where the mask is white i.e. the edge exists.
Ultimately, I am trying to enhance the edge of a low resolution thermal video stream in a similar way to the FLIR One VividIR technology.
I believe that I've made it really far as a complete novice to image processing, OpenCV and Python but after days now of trying just about every function OpenCV offers, I've got myself stuck.
## get the edge coordinates
pos = np.where(edge >0)
## divide
img[pos] //=2
I'm attempting to do some image analysis using OpenCV in python, but I think the images themselves are going to be quite tricky, and I've never done anything like this before so I want to sound out my logic and maybe get some ideas/practical code to achieve what I want to do, before I invest a lot of time going down the wrong path.
This thread comes pretty close to what I want to achieve, and in my opinion, uses an image that should be even harder to analyse than mine. I'd be interested in the size of those coloured blobs though, rather than their distance from the top left. I've also been following this code, though I'm not especially interested in a reference object (the dimensions in pixels alone would be enough for now and can be converted afterwards).
Here's the input image:
What you're looking at are ice crystals, and I want to find the average size of each. The boundaries of each are reasonably well defined, so conceptually this is my approach, and would like to hear any suggestions or comments if this is the wrong way to go:
Image in RGB is imported and converted to 8bit gray (32 would be better based on my testing in ImageJ, but I haven't figured out how to do that in OpenCV yet).
The edges are optionally Gaussian blurred to remove noise
A Canny edge detector picks up the lines
Morphological transforms (erosion + dilation) are done to attempt to close the boundaries a bit further.
At this point it seems like I have a choice to make. I could either binarise the image, and measure blobs above a threshold (i.e. max value pixels if the blobs are white), or continue with the edge detection by closing and filling contours more fully. Contours seems complicated though looking at that tutorial, and though I can get the code to run on my images, it doesn't detect the crystals properly (unsurprisingly). I'm also not sure if I should morph transform before binarizing too?
Assuming I can get all that to work, I'm thinking a reasonable measure would be the longest axis of the minimum enclosing box or ellipse.
I haven't quite ironed out all the thresholds yet, and consequently some of the crystals are missed, but since they're being averaged, this isn't presenting a massive problem at the moment.
The script stores the processed images as it goes along, so I'd also like the final output image similar to the 'labelled blobs' image in the linked SO thread, but with each blob annotated with its dimensions maybe.
Here's what an (incomplete) idealised output would look like, each crystal is identified, annotated and measured (pretty sure I can tackle the measurement when I get that far).
Abridged the images and previous code attempts as they are making the thread overly long and are no longer that relevant.
Edit III:
As per the comments, the watershed algorithm looks to be very close to achieving what I'm after. The problem here though is that it's very difficult to assign the marker regions that the algorithm requires (http://docs.opencv.org/3.2.0/d3/db4/tutorial_py_watershed.html).
I don't think this is something that can be solved with thresholds through the binarization process, as the apparent colour of the grains varies by much more than the toy example in that thread.
Edit IV
Here are a couple of the other test images I've played with. It fares much better than I expected with the smaller crystals, and theres obviously a lot of finessing that could be done with the thresholds that I havent tried yet.
Here's 1, top left to bottom right correspond to the images output in Alex's steps below.
And here's a second one with bigger crystals.
You'll notice these tend to be more homogeneous in colour, but with harder to discern edges. Something I found a little suprising is that the edge floodfilling is a little overzealous with some of the images, I would have thought this would be particularly the case for the image with the very tiny crystals, but actually it appears to have more of an effect on the larger ones. There is probably a lot of room to improve the quality of the input images from our actual microscopy, but the more 'slack' the programming can take from the system, the easier our lives will be!
As I mentioned in the comments, watershed looks to be an ok approach for this problem. But as you replied, defining the foreground and the background for the markers is the hard part! My idea was to use the morphological gradient to get good edges along the ice crystals and work from there; the morphological gradient seems to work great.
import numpy as np
import cv2
img = cv2.imread('image.png')
blur = cv2.GaussianBlur(img, (7, 7), 2)
h, w = img.shape[:2]
# Morphological gradient
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
gradient = cv2.morphologyEx(blur, cv2.MORPH_GRADIENT, kernel)
cv2.imshow('Morphological gradient', gradient)
cv2.waitKey()
From here, I binarized the gradient using some thresholding. There's probably a cleaner way to do this...but this happens to work better than the dozen other ideas I tried.
# Binarize gradient
lowerb = np.array([0, 0, 0])
upperb = np.array([15, 15, 15])
binary = cv2.inRange(gradient, lowerb, upperb)
cv2.imshow('Binarized gradient', binary)
cv2.waitKey()
Now we have a couple issues with this. It needs some cleaning up as it's messy, and further, the ice crystals that are on the edge of the image are showing up---but we don't know where those crystals actually end so we should actually ignore those. To remove those from the mask, I looped through the pixels on the edge and used floodFill() to remove them from the binary image. Don't get confused here on the orders of rows and columns; the if statements are specifying rows and columns of the image matrix, while the input to floodFill() expects points (i.e. x, y form, which is opposite from row, col).
# Flood fill from the edges to remove edge crystals
for row in range(h):
if binary[row, 0] == 255:
cv2.floodFill(binary, None, (0, row), 0)
if binary[row, w-1] == 255:
cv2.floodFill(binary, None, (w-1, row), 0)
for col in range(w):
if binary[0, col] == 255:
cv2.floodFill(binary, None, (col, 0), 0)
if binary[h-1, col] == 255:
cv2.floodFill(binary, None, (col, h-1), 0)
cv2.imshow('Filled binary gradient', binary)
cv2.waitKey()
Great! Now just to clean this up with some opening and closing...
# Cleaning up mask
foreground = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
foreground = cv2.morphologyEx(foreground, cv2.MORPH_CLOSE, kernel)
cv2.imshow('Cleanup up crystal foreground mask', foreground)
cv2.waitKey()
So this image was labeled as "foreground" because it has the sure foreground of the objects we want to segment. Now we need to create a sure background of the objects. Now, I did this in the naïve way, which just is to grow your foreground a bunch, so that your objects are probably all defined within that foreground. However, you could probably use the original mask or even the gradient in a different way to get a better definition. Still, this works OK, but is not very robust.
# Creating background and unknown mask for labeling
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (17, 17))
background = cv2.dilate(foreground, kernel, iterations=3)
unknown = cv2.subtract(background, foreground)
cv2.imshow('Background', background)
cv2.waitKey()
So all the black there is "sure background" for the watershed. Also I created the unknown matrix, which is the area between foreground and background, so that we can pre-label the markers that get passed to watershed as "hey, these pixels are definitely in the foreground, these others are definitely background, and I'm not sure about these ones between." Now all that's left to do is run the watershed! First, you label the foreground image with connected components, identify the unknown and background portions, and pass them in:
# Watershed
markers = cv2.connectedComponents(foreground)[1]
markers += 1 # Add one to all labels so that background is 1, not 0
markers[unknown==255] = 0 # mark the region of unknown with zero
markers = cv2.watershed(img, markers)
You'll notice that I ran watershed() on img. You might experiment running it on a blurred version of the image (maybe median blurring---I tried this and got a little smoother of boundaries for the crystals) or other preprocessed versions of the images which define better boundaries or something.
It takes a little work to visualize the markers as they're all small numbers in a uint8 image. So what I did was assign them some hue in 0 to 179 and set inside a HSV image, then convert to BGR to display the markers:
# Assign the markers a hue between 0 and 179
hue_markers = np.uint8(179*np.float32(markers)/np.max(markers))
blank_channel = 255*np.ones((h, w), dtype=np.uint8)
marker_img = cv2.merge([hue_markers, blank_channel, blank_channel])
marker_img = cv2.cvtColor(marker_img, cv2.COLOR_HSV2BGR)
cv2.imshow('Colored markers', marker_img)
cv2.waitKey()
And finally, overlay the markers onto the original image to check how they look.
# Label the original image with the watershed markers
labeled_img = img.copy()
labeled_img[markers>1] = marker_img[markers>1] # 1 is background color
labeled_img = cv2.addWeighted(img, 0.5, labeled_img, 0.5, 0)
cv2.imshow('watershed_result.png', labeled_img)
cv2.waitKey()
Well, that's the pipeline in it's entirety. You should be able to copy/paste each section in a row and you should be able to get the same results. The weakest parts of this pipeline is binarizing the gradient and defining the sure background for watershed. The distance transform might be useful in binarizing the gradient somehow, but I haven't gotten there yet. Either way...this was a cool problem, I would be interested to see any changes you make to this pipeline or how it fares on other ice-crystal images.
I'm trying to blur around specific regions in a 2D image (the data is an array of size m x n).
The points are specified by an m x n mask. cv2 and scikit avaiable.
I tried:
Simply applying blur filters to the masked image. But that isn't not working.
Extracting the points to blur by np.nan the rest, blurring and reassembling. Also not working, because the blur obviously needs the surrounding points to work correctly.
Any ideas?
Cheers
What was the result in the first case? It sounds like a good approach. What did you expect and what you get?
You can also try something like that:
Either create a copy of a whole image or just slightly bigger ROI (to include samples that will be used for blurring)
Apply blur on the created image
Apply masks on two images (from original image take everything except ROI and from blurred image take ROI)
Add two masked images
If you want more smooth transition make sure that masks aren't binary. You can smooth them using another blurring (blur one mask and create the second one by calculating: mask2 = 1 - mask1. By doing so you will be sure that weights always add up to one).