istopmotion have comic tyle bubbles added in

Istopmotion have comic tyle bubbles added in how to#
Istopmotion have comic tyle bubbles added in code#

Th, im_th2 = cv2.threshold(im_out, 130, 255, cv2.THRESH_BINARY)Ĭontour,hier = cv2.findContours(im_th3,cv2.RETR_CCOMP,cv2. # Create binary image from segments with holes # Combine the two images to get the foreground Im_floodfill_inv = cv2.bitwise_not(im_floodfill) # Notice the size needs to be 2 pixels than the image.Ĭv2.floodFill(im_floodfill, mask, (80,400), 128) Ĭv2.floodFill(im_floodfill, mask, (610,90), 128) Im_in = cv2.imread('gIEXY.png', cv2.IMREAD_GRAYSCALE) Keep us posted on your progress! import cv2 You may find more helpful information there.

Istopmotion have comic tyle bubbles added in code#

I took the liberty to fill all internal holes created by the letters you found, If you do not want this, you can skip the code from line 36 on.įor the solution I have actually taken ideas from two pieces of code which I cited in the snipped below. As soon as you provide proper code, one can adjust that interface. For this I just took 2 fixed coordinates, you would take white points close to blob centers created from text you have extracted in step 1. Below are some fun examples: Whispers (simply make your speech bubble smaller and closer to your characters’ bodies. Since you do not give source code, I had to create something from scratch which hopefully interfaces well with your output from step 1. Speech bubbles can be used for many effects to make your character’s intentions even more obvious.

Istopmotion have comic tyle bubbles added in how to#

While your overall task aims further, your actual question is about your step 2, how to implement a flood fill algorithm on a data set which has detected text in bubbles. I'm open to any suggestions, including ones in OpenCV, etc. starting with step 1 image, working on original image and producing output to separate output image). I suspect that intricate morphology stuff like binary_erosion or generate_binary_structure in ndimage or scikit-image, but I struggle to understand all that morphology terminology and basically how do I implement such a custom flood fill with it (i.e. Obviously, I can use something trivial like breadth-first traversal, basically as suggested here, but it's really slow when done in Python. I'm currently working with scikit-image, and I don't see any ready-made algorithms like flood fill implemented there. So far, steps 1 and 3 work, but I'm struggling with step 2. regions that correspond to text) inside bubbles). Use some sort of binary_closing operation to remove dark areas (i.e. Use a flood fill or some sort of graph traversal, starting from every white pixel detected as a pixel-inside-bubble on step 1, but working on initial image, flooding white pixels (which are supposed to be inside the bubble) and stopping on dark pixels (which are supposed to be borders or text). Dilate these regions somewhat and apply threshold to get a better starting ground I've done this part: Detect where the text is - plant at least one pixel in every bubble.So, my basic idea of the algorithm was something like: The same image, mask overlaid, to be totally clear: I want to detect these regions and create a mask (binary is ok) that will cover all the inside regions of dialogue bubbles, i.e. I have an grayscale image of a comic strip page that features several dialogue bubbles (=speech baloons, etc), that are enclosed areas with white background and solid black borders that contain text inside, i.e.