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I have images (about 1000) with different numbers. Using opencv I extracted ROI from these images. Here's a small sample:
And I don't know how to extract these numbers or identify them. For opencv have a small threshold. I tried VGGnet keras (I rotated each image 1 degree to create 360 images as input for tensorflow), but the control image was mostly not recognized. Does anyone have an idea?
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I want to detect a watermark in an image using OpenCV.
Particularly, I want a rectangular box around the watermark, if present.
Can you please help me out with the python code?
Though the solution would be dependent on the actual image content (that needs to be preserved) and the watermark. But in these kinds of problems, following sequence of steps is usually followed:
Converting the image to grayscale (cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
Applying morphological filtering Erosion, Dilation
Taking the difference of this output from the actual image
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I need a trained deep learning mode can compare two images for two persons and give me the result as if the two image are for the same person or not.
I guess you see the face of the two persons on the image. In this case you can try to use Facenet Paper
You can find an implementation for example here link
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Character engraved on a metal plate
How to extract the characters engraved on a metallic plate?
OCR( Pytesseract) is unable to give good results. I tried Ridge detection but in vain. Any form of threshold doesn't seem to work because the background and the foreground are of the same color. Is there a series of steps that I can follow for such a use-case?
I think Binarization wont work in your image. If any preprocessing improves the quality of this image that doesn't mean that same method will work on all the images you have.
So my suggestion is to create your own Custom OCR using machine learning or CNN.
You can convert your digits into a 28x28 image matrix and then reshape it into 1x784 matrix and perform the training like MNIST dataset is trained.
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I am trying to find the unique area from multiple bounded box generated to find the screen capture by the products in python
You can try threshold the image by colors first (using either HSV threshold, or RGB).
Then having several binary images, you can use Contour Approximation (number 4 on the page) feature, using Douglas-Peucker algorithm. Fill the resulting bounding boxes.
Afterwards, you can subtract resulting binary images from one another to find exact areas of intersections.
Hope it helps!
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This weekend I'll be taking around 50 CCD images with my Celestron CPC 8in. telescope and would like to use python to analyzie the images. Does anyone have any experience doing this?
Check out the python wrapper for OpenCV:
http://docs.opencv.org/trunk/doc/py_tutorials/py_tutorials.html
This should provide all the power you need to do any image processing tasks you require.
And there are some great tutorials to help you get started.