overlaying the ground truth mask on an image-CodePudding

The

And the following is ground truth mask:

and the following is the mapping of ground truth on the frame.

How can I do that. Also, I have two folders that inside each of them, there are several folders that inside each of them the there are stored frames. How can I do this operation with these batch data?

This is the hierarchy of my folders:

frame_folder: folder_1, folder_2, ......

├── frames │ ├── 601 (601 and 602 and etc are folders that in the inside there are image frames that their name is like 0001.png,0002.png, ...) │ ├── 602 . . . │ └── 700 ├── ground truth │ ├── 601 (601 and 602 and etc are folders that in the inside there are ground truth masks that their name is like 0001.png,0002.png, ...) │ ├── 602 . . . │ └── 700

CodePudding user response：

I need to do similar things pretty often. In my favorite StackOverflow fashion, here is a script that you can copy and paste. I hope the code itself is self-explanatory. There are a few things that you can tune and try (e.g., color maps, overlay styles). It uses multiprocessing.Pool for faster batch-processing, resizes the mask to match the shape of the image, assumes the mask is in .png format, and depends on the file structure that you posted.

import os from os import path import cv2 import numpy as np from argparse import ArgumentParser from multiprocessing import Pool def create_overlay(image, mask): """ image: H*W*3 numpy array mask: H*W numpy array If dimensions do not match, the mask is upsampled to match that of the image Returns a H*W*3 numpy array """ h, w = image.shape[:2] mask = cv2.resize(mask, dsize=(w,h), interpolation=cv2.INTER_CUBIC) # color options: https://docs.opencv.org/4.x/d3/d50/group__imgproc__colormap.html mask_color = cv2.applyColorMap(mask, cv2.COLORMAP_HOT).astype(np.float32) mask = mask[:, :, None] # create trailing dimension for broadcasting mask = mask.astype(np.float32)/255 # different other options that you can use to merge image/mask overlay = (image*(1-mask) mask_color*mask).astype(np.uint8) # overlay = (image*0.5 mask_color*0.5).astype(np.uint8) # overlay = (image mask_color).clip(0,255).astype(np.uint8) return overlay def process_video(video_name): """ Processing frames in a single video """ vid_image_path = path.join(image_path, video_name) vid_mask_path = path.join(mask_path, video_name) vid_output_path = path.join(output_path, video_name) os.makedirs(vid_output_path, exist_ok=True) frames = sorted(os.listdir(vid_image_path)) for f in frames: image = cv2.imread(path.join(vid_image_path, f)) mask = cv2.imread(path.join(vid_mask_path, f.replace('.jpg','.png')), cv2.IMREAD_GRAYSCALE) overlay = create_overlay(image, mask) cv2.imwrite(path.join(vid_output_path, f), overlay) if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('--image_path') parser.add_argument('--mask_path') parser.add_argument('--output_path') args = parser.parse_args() image_path = args.image_path mask_path = args.mask_path output_path = args.output_path videos = sorted( list(set(os.listdir(image_path)).intersection( set(os.listdir(mask_path)))) ) print(f'Processing {len(videos)} videos.') pool = Pool() pool.apply(process_video, videos) print('Done')

Output:

CodePudding user response：

This is not much different from @hkchengrex solution, so he deserves the credit, since his answer was first. I mainly wanted to point out the use of cv2.addWeighted

Here is one way to blend the image and ground truth in Python/OpenCV.

I would suggest resizing the ground truth once to the size of the images for all your video frames rather than resizing every video frame to the size of the ground truth.

One simple resizes the ground truth to the size of the image. Then colorize the ground truth using a color map. Then simply use cv2.addWeighted to blend the two for every frame of your video.

I leave it to you to read your video to access each frame. The following simply shows how to process any given frame

Input:

Ground Truth Overlay:

import cv2 import numpy as np # read image img = cv2.imread('bullfight.png') hh, ww = img.shape[:2] # read ground truth overlay overlay = cv2.imread('truth.png') # resize the overlay to match the size of the image over_resize = cv2.resize(overlay, (ww,hh), fx=0, fy=0, interpolation=cv2.INTER_CUBIC) # colorize the over_resized image over_color = cv2.applyColorMap(over_resize, cv2.COLORMAP_HOT) # blend over_color and image (adjust weights for different effects) result = cv2.addWeighted(img, 1, over_color, 1, 0) # save output image cv2.imwrite('bullfight_overlay.png', result) # display images cv2.imshow('overcolor', over_color) cv2.imshow('result', result) cv2.waitKey(0) cv2.destroyAllWindows()

Result: