I'm processing some large volumetric image data that are present in three dimensional numpy arrays. I'll explain my task with two small 1D arrays. I have one image: img = [5, 6, 70, 80, 3, 4, 80, 90] and one segmented and labeled version of that image: labels = [0, 0, 1, 1, 0, 0, 2, 2] Each number in labels represents an object in img. Both arrays have the same dimensions. So in this example there's two objects in img: [5, 6, 70, 80, 3, 4, 80, 90] and what I'm trying to do now is finding the location of the maximum value of each object, which in this case would be the 3 and 7. Currently I loop over all labels, create a version of img which contains only the object corresponding to the current label, and look for the maximum value: for label in range(1, num_labels + 1): imgcp = np.copy(img) imgcp[labels != label] = 0 max_pos = np.argmax(imgcp) max_coords = np.unravel_index(pos, imgcp.shape) One problem with this approach is that copying img in every step tends to create memory errors. I feel like memory management should prevent this, but is there a more memory efficient and possibly faster way to to this task? Continue reading...
