I have two images - the images are identical but of different sizes.
Currently I complete a Canny analysis of the smaller image using track bars in an interactive environment.
I want to have this output created on the second (larger) image - when I apply the same parameters the output is different
I've tried to use cv.resize however the output is blurred significantly
Any help is appreciated
Thanks in advance
To archieve comparable results you should resize the bigger image to the size of the smaller one. Image upscaling is "creating" information which isn't contained in your image, that's why you see the blur. Using interpolation=cv2.INTER_AREA should deliver good results, if you used a camera for images acquisition.
Related
Forgive me but I'm new in OpenCV.
I would like to delete the common background in 3 images, where there is a landscape and a man.
I tried some subtraction codes but I can't solve the problem.
I would like output each image only with the man and without landscape
Are there in OpenCV Algorithms what do this do? (then without any manual operation so no markers or other)
I tried this python code CV - Extract differences between two images
but not works because in my case i don't have an image with only background (without man).
I thinks that good solution should to Compare all the images and save those "points" that are the same at least in an image.
In this way I can extrapolate a background (which we call "Result.jpg") and finally analyze each image and cut those portions that are also present in "Result.jpg".
You say it's a good idea? Do you have other simplest ideas?
Without semantic segmentation, you can't do that.
Because all you can compute is where two images differ, and this does not give you the silhouette of the person, but an overlapping of two silhouettes. You'll never know the exact outline.
I have a database of original images and for each original images there are various cropped versions.
This is an example of how the image look like:
Original
Horizontal Crop
Square Crop
This is a very simple example, but most images are like this, some might taken a smaller section of the original image than others.
I was looking at OpenCV in python but I'm very new to this kind of image processing.
The idea is to be able to save the cropping information separate from the image to save space and then generate all the cropping and different aspect ratio on the fly with a cache system instead.
The method you are looking for is called "template matching". You find examples here
https://docs.opencv.org/trunk/d4/dc6/tutorial_py_template_matching.html
For your problem, given the large images, it might be a good idea to constrain the search space by resizing both images by the same factor. So that searching a position that isn't as precise, but allows then to constrain the actual full pixel sized search to a smaller region around that point.
I've been trying to detect if a printed image has any defects(shape and color) when compared to either a proof of another printed image which has no defects or the digital version of the image, which also has no defects. I'm using opencv(cv2) and python.
I first take a picture of the printed image. Then, I perform perspective transformation to get the picture of the printed image cropped sufficiently. I am then using Zernike moments, SSIM, and color histograms to compare the color and shape of the image. However, the resulting values vary too much and I am not able to create a threshold for a misprinted image.
I have also tried to subdivide the image into smaller sections and compare those. This is also not creating distinguishable values to determine if there is a misprint or not.
The differences in the print can be subtle or very apparent. Are there any other techniques that I can try? Thanks!
This is an example of a correctly printed image:
This is an example of an incorrectly printed image, it has too much blue ink on the right side:
This is another example of a correct print:
This is an example of a misprint when compared to the one above:
I am looking at an application where I have two images from a single object (a smartphone) acquired using x-ray. The two images have been acquired at different instant. The intensity content of the two images is thus different and I would like to be able to fuse the two images in order to extract some information about the phones.
In between the two images, the set up have been slightly changed such that the phone is not placed at the same pixel value in the two images. To be able to correctly compare the two images, I would need to translate and rotate the images of the phones such that they overlap as much as possible.
For this I am using python and open cv (cv2). I was thinking to use a thresholding and then find the coordinate of the two thresholded images and use the coordinate to map the yellow image on the red one (or the opposite). The attached image shows what I have obtained so far.
The pseudo code is given in the following:
ret1, thresh1 = cv2.threshold(img1.astype(np.uint8),200,255,cv2.THRESH_BINARY_INV)
ret2, thresh2 = cv2.threshold(template.astype(np.uint8),200,255,cv2.THRESH_BINARY_INV)
plt.figure(1)
plt.subplot(121)
plt.imshow(thresh1)
plt.subplot(122)
plt.imshow(thresh2)
plt.show()
where img1 is one image acquired with the first filter and template is the image acquired with the second filter. One can see that the phones are at different position in the yellow and green images respectivelly.
My question is how to perform the next step. How can I find the coordinate of this thresholded images and then superpose the images of the two phones? Is it the right strategy at all or are there better solutions?
I have been looking at this link on template matching, but for the moment I have had no success.
Hei,
Image-registration did the tricks, thanks! I followed the following tutorial:
image-registration and manage to do what I was looking for.
Thanks!
Greg
there is this project am currently working on, which requires me to watermark every uploaded image. i have tried series of examples online, but they are not giving me what i really want as result
for example
i have an image A with image B watermarked on it, the two images are of the same dimensions. i applied opacity of 0.5 on image B before placing it on image A
now, i would really appreciate if anyone could help with a boolean function to check if image A has already been watermarked with image B before watermarking it.
thanks.
This depends on several factors that you'll need to provide more information for.
For instance, how complex are these images? Is there a lot of noise? Are the images that are uploaded similar in any way, or are they heterogenous? Are the watermarks always the same, or are they different?
As a general principle for extracting objects from images, you should look into processes such as color deconvolution, thresholding, and blob extraction.
In short--some sample images would go a long way...
yeah, finally found a dubious way to solve the problem, by hiding a specific text on the alpha layer of the image after watermarking it using steganography.
so on every upload, i get the image, iterate through the lowest pixels of the image's alpha layer, then compare the result to the text. if the result matches the text, definitely, the image has been watermarked.