c# and gdi+ have a simple way to control the colors that are drawn. It’s basically a ColorMatrix. It’s a 5×5 matrix that is applied to each color if it is set. Adjusting brightness is just performing a translate on the color data, and contrast is performing a scale on the color. Gamma is a … Read more
You can achieve this by creating a third array of dtype uint8, plus a bool array (which together are more memory efficient that one uint16 array). np.putmask is useful for avoiding a temp array. a = np.array([100, 200, 250], dtype=np.uint8) b = np.array([50, 50, 50], dtype=np.uint8) c = 255 – b # a temp uint8 … Read more
I described some tips for preparing images for Tesseract here: Using tesseract to recognize license plates In your example, there are several things going on… You need to get the text to be black and the rest of the image white (not the reverse). That’s what character recognition is tuned on. Grayscale is ok, as … Read more
A lot of your questions stem from the fact that you’re not sure how morphological image processing works, but we can put your doubts to rest. You can interpret the structuring element as the “base shape” to compare to. 1 in the structuring element corresponds to a pixel that you want to look at in … Read more
First, check out /samples/c/squares.c in your OpenCV distribution. This example provides a square detector, and it should be a pretty good start on how to detect corner-like features. Then, take a look at OpenCV’s feature-oriented functions like cvCornerHarris() and cvGoodFeaturesToTrack(). The above methods can return many corner-like features – most will not be the “true … Read more
Yes OpenCV is more robust and flexible and can perform most of the image processing routines which are available out there, So probably this filter can be done with OpenCV> However, there may not be a straightforward API for that. Anyways, as far as the conversion of image format from OpenCV to PIL is concerned … Read more
I’ll first give you the code and then explain it a bit: // direction vectors const int dx[] = {+1, 0, -1, 0}; const int dy[] = {0, +1, 0, -1}; // matrix dimensions int row_count; int col_count; // the input matrix int m[MAX][MAX]; // the labels, 0 means unlabeled int label[MAX][MAX]; void dfs(int x, … Read more
I assume you are asking whether these data augmentation transforms (e.g. RandomHorizontalFlip) actually increase the size of the dataset as well, or are they applied on each item in the dataset one by one and not adding to the size of the dataset. Running the following simple code snippet we could observe that the latter … Read more
In 2011, we can know do it with the File API, and canvas. This works for now only in firefox and chrome. Here is an example : var file = YOUR_FILE, fileType = file.type, reader = new FileReader(); reader.onloadend = function() { var image = new Image(); image.src = reader.result; image.onload = function() { var … 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