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I am new to ImageJ macros and I want to use a macro in Fiji. The code for the macro is from this source.

I'm fairly new to GEE, and I'm trying to process some imagery, then download the results. I need to mask about 30 years of Landsat data to isolate different land cover types, remove clouds, and calculate vegetation indices. Then, I need to export these data to do further analyses in R. I've managed to do all of the masking and calculating vegetation indices, but when I go to export the data, it gives me the user memory limit exceeded error. I tried to only download 1 year of data at a time but got the same error. I'm not sure how to accomplish what I'm doing, which is to do the heavy processing of the data in GEE then export it to do the additional analyses elsewhere. Any recommendations? Code below.

Edit: Added the ROI information as recommended.

I'm having problems finding contours on specific columns in a table. The issue when using opencv is that I have to specify which area to draw rectangles in with an if loop on x, y and w (the 2 axes and the width) this works fine if the table lines are perfectly horizontal or vertical but if they're not then you don't get good results.

I used this code to get bounding boxes:

Consider this image:

i want to extract only the number which represent the biggest contour in image but opencv always show the original image and the little contours smaller than number. So when i run this function

I have a large numpy array of shape (32,2048,2048), where every [i,:,:] is a 2D set of pixels which are samples from a spatially correlated statistical distribution. With i=32 samples for each pixel.

I now need to calculate the covariance matrix for everey 2x2 ROI (overlapping) on the 2D image, resulting in a a set of 4x4 covariance matrices with total shape of (4,4,2047,2047).

Looping over all ROIs in a loop is of cause possible and takes about 4 minutes on my maschine:

I tried the code provided bellow to segment each digit in this image and put a contour around it then crop it out but it's giving me bad results, I'm not sure what I need to change or work on.

The best idea I can think of right now is filtering the 4 largest contours in the image except the image contour itself.

The code I'm working with:

the task that I'm trying to accomplish is isolating certain objects in an image through finding contours in the mask of the image, then taking each contour (based on area) and isolating it , and then using this contour to crop the same region in the original image, in order to get the pixel values of the region, e.g.:

the code I wrote in order to get just one contour and then isolating it with the original pixel value:

I am new to deep learning and try to implement a ML algorithm for image clustering. The problem is that I can't crop the objects in an image in Python using OpenCV. Here is the code I have implemented and it works for some objects if the color of the object is very different(in RGB values) from the background but it doesn't work for the image I need for ML algorithm. What kind of parameters should I have/change? Any suggestions?

I'm using pytesseract to extract names from images (the images are the bouding boxes of the names so it's just the name by itself with nothing else)

I get good results but because my roi selection isn't very good sometimes I get bounding boxes on stuff I don't care for.

I got the idea to apply pytesseract-engine to all the images and then only save the ones where the return value on them was all caps and different from two specific words that are all caps but that I still don't care for.

This is the code: