I needed the same thing – in my case, to pick the dimension that fits once scaled, and then crop each end to fit the rest to the width. (I’m working in landscape, so might not have noticed any deficiencies in portrait mode.) Here’s my code – it’s part of a categeory on UIImage. Target … Read more
This is a recurring subject in Stackoverflow and since I was unable to find a relevant implementation I decided to accept the challenge. I made some modifications to the squares demo present in OpenCV and the resulting C++ code below is able to detect a sheet of paper in the image: void find_squares(Mat& image, vector<vector<Point> … Read more
For future Googlers, here is an algorithm that I ported from Quasimondo. It’s kind of a mix between a box blur and a gaussian blur, it’s very pretty and quite fast too. Update for people encountering the ArrayIndexOutOfBoundsException problem : @anthonycr in the comments provides this information : I found that by replacing Math.abs with … Read more
fix DPI (if needed) 300 DPI is minimum fix text size (e.g. 12 pt should be ok) try to fix text lines (deskew and dewarp text) try to fix illumination of image (e.g. no dark part of image) binarize and de-noise image There is no universal command line that would fit to all cases (sometimes … Read more
If you’re just wanting (semi) contiguous regions, there’s already an easy implementation in Python: SciPy’s ndimage.morphology module. This is a fairly common image morphology operation. Basically, you have 5 steps: def find_paws(data, smooth_radius=5, threshold=0.0001): data = sp.ndimage.uniform_filter(data, smooth_radius) thresh = data > threshold filled = sp.ndimage.morphology.binary_fill_holes(thresh) coded_paws, num_paws = sp.ndimage.label(filled) data_slices = sp.ndimage.find_objects(coded_paws) return object_slices … Read more
My own favorites are these two: compare image1 image2 -compose src diff.png compare image1 image2 -compose src diff.pdf The only difference between the 2 commands above: the first one shows the visual difference between the two images as a PNG file, the second one as a PDF. The resulting diff file displays all pixels which … Read more
If you wish to use CV2, you need to use the resize function. For example, this will resize both axes by half: small = cv2.resize(image, (0,0), fx=0.5, fy=0.5) and this will resize the image to have 100 cols (width) and 50 rows (height): resized_image = cv2.resize(image, (100, 50)) Another option is to use scipy module, … Read more
from PIL import Image background = Image.open(“test1.png”) foreground = Image.open(“test2.png”) background.paste(foreground, (0, 0), foreground) background.show() First parameter to .paste() is the image to paste. Second are coordinates, and the secret sauce is the third parameter. It indicates a mask that will be used to paste the image. If you pass a image with transparency, then … Read more
Can the screenshot or icon be transformed (scaled, rotated, skewed …)? There are quite a few methods on top of my head that could possibly help you: Simple euclidean distance as mentioned by @carlosdc (doesn’t work with transformed images and you need a threshold). (Normalized) Cross Correlation – a simple metrics which you can use … Read more
Another very simple way to estimate the sharpness of an image is to use a Laplace (or LoG) filter and simply pick the maximum value. Using a robust measure like a 99.9% quantile is probably better if you expect noise (i.e. picking the Nth-highest contrast instead of the highest contrast.) If you expect varying image … Read more