| ''' | |
| 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 |