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I have an image that I'm masking, but even the text gets thresholded. Here's a thresdholded image below:
As you can see, the time, letters and such are also a part of the mask. I want to get rid of them. How do I go about it?
If the images follow that kind of pattern, you could try to apply a morphological closing.
Whith a mask the right size for the closing and opening, it will get rid of the text in the image, leaving it black.
Note that applying a closing may also slightly modify the rest of the image (for instance, a circular mask may smooth the edges of the circle in the center of the mask).
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Well currently I'm working on a personal project which is the identification of products in a scanned image taken from a store catalog.
As you may see in the image there's no lines separation between products, so using Hough lines to locate the products won't really solve the problem!
Using Tesseract is really amazing to extract the image content, the only problem that I'm facing is finding the image products automatically, I mean not cropping the image manually but I want to detect the products, cropping them with their text description and price and then extract content using OCR.
I have tried many image processing techniques but still nothing (I'm using Python and OpenCV).
Thanks in advance :)
The problem you have is usually called background removal, or alternatively foreground extraction. In this example, it might actually be relatively easy, as the background is mostly in shades of the same color - my recommendation would be to look at the GrabCut algorithm which is described here: https://docs.opencv.org/3.4.3/d8/d83/tutorial_py_grabcut.html
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I am having a short sequence of grayscale images ( 20 images ), which is consecutive frames collected from a camera. I need to identify the location of a small moving object in each image. I have tried to use background subtraction, and can identify the moving pixels in each image. How can I proceed to detect the exact location of that moving object ?
You can try this way.
(1) find the object using template matching at first time.
https://docs.opencv.org/3.0-beta/doc/tutorials/imgproc/histograms/template_matching/template_matching.html?highlight=template%20matching
(2) Tracking the object using camshift or other tracking algorithm.
https://github.com/opencv/opencv/blob/master/samples/python/camshift.py
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I would like to code a program that will use a phones camera to read a node graph and then perform dijkstra's algorithm on it, displaying the shortest path.
Could pytesseract or other OCR tools for python read node graphs (such as in the image. Real use would be on printed ones) and give me enough information to get node coordinates and what letters are next to them, as well as the positions of the edges/which nodes they connect and what numbers are next to them?
Any help would be much appreciated.
https://i.stack.imgur.com/RUZwA.jpg
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I am trying to enhance blood vein part in forearm image shown below.
After grayscale conversion I got following image.
Then I followed histogram equalization and got better result as shown below
Based on the histogram equalized image, I need to enhance blood vein part in original image. For that purpose first I need to extract blood vein from histogram equalized image, but I am not getting any result. Can anyone suggest proper method to enhance blood vein in original infrared image. ?? I am using python+OpenCV for image processing
Area of interest to extract is shown below
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I need to process a lot of images using Python. All these images have some transparent region (alpha channel) of different sizes.
I need to programmatically fill in RGB noise in the transparent region of those images, but keep the non-transparent region unchanged. This is an example of changing the images.
How to do this programmatically in Python?
In my opinion you need to:
Create a Mat that contains Gaussian noise (or what kind of noise you need to add in the images).
For each image you copy the noise Mat into another one based on the alpha channel (used as mask)
Add the two images (initial and noise_mask) to he initial image (or the inital_noisy_background)