| ''' |
| utils for vis |
| ''' |
| import argparse |
| import json |
| import tqdm |
| import cv2 |
| import os |
| import numpy as np |
| from pycocotools import mask as mask_utils |
| import random |
| from PIL import Image |
| from natsort import natsorted |
| from pycocotools.mask import encode, decode, frPyObjects |
|
|
|
|
| def blend_mask(input_img, binary_mask, alpha=0.5, color="g"): |
| if input_img.ndim == 2: |
| return input_img |
| mask_image = np.zeros(input_img.shape, np.uint8) |
| if color == "r": |
| mask_image[:, :, 0] = 255 |
| if color == "g": |
| mask_image[:, :, 1] = 255 |
| if color == "b": |
| mask_image[:, :, 2] = 255 |
| mask_image = mask_image * np.repeat(binary_mask[:, :, np.newaxis], 3, axis=2) |
| blend_image = input_img[:, :, :].copy() |
| pos_idx = binary_mask > 0 |
| for ind in range(input_img.ndim): |
| ch_img1 = input_img[:, :, ind] |
| ch_img2 = mask_image[:, :, ind] |
| ch_img3 = blend_image[:, :, ind] |
| ch_img3[pos_idx] = alpha * ch_img1[pos_idx] + (1 - alpha) * ch_img2[pos_idx] |
| blend_image[:, :, ind] = ch_img3 |
| return blend_image |
|
|
|
|
| def upsample_mask(mask, frame): |
| H, W = frame.shape[:2] |
| mH, mW = mask.shape[:2] |
|
|
| if W > H: |
| ratio = mW / W |
| h = H * ratio |
| diff = int((mH - h) // 2) |
| if diff == 0: |
| mask = mask |
| else: |
| mask = mask[diff:-diff] |
| else: |
| ratio = mH / H |
| w = W * ratio |
| diff = int((mW - w) // 2) |
| if diff == 0: |
| mask = mask |
| else: |
| mask = mask[:, diff:-diff] |
|
|
| mask = cv2.resize(mask, (W, H)) |
| return mask |
|
|
|
|
| def downsample(mask, frame): |
| H, W = frame.shape[:2] |
| mH, mW = mask.shape[:2] |
|
|
| mask = cv2.resize(mask, (W, H)) |
| return mask |
