File size: 1,399 Bytes
f89df01 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
import cv2
import torch
import numpy as np
from torch import Tensor
import torch.nn.functional as F
from imantics import Mask
from typing import List
def convert_ann_to_mask(ann: List, height: int, width: int):
mask = np.zeros((height, width), dtype=np.uint8)
poly = ann["segmentation"]
for p in poly:
p = np.array(p).reshape(-1, 2).astype(int)
cv2.fillPoly(mask, [p], 1)
return mask
def convert_mask_to_ann(mask: np.ndarray):
polygons = Mask(mask).polygons()
return polygons.segmentation
# Define a custom argument type for a list of strings
def list_of_strings(arg):
return [float(thr) for thr in arg.split(',')]
def video_interpolation(video: Tensor, frame_sample_rate: int):
expanded_heatmap = []
for i in range(len(video) - 1):
pre_heatmap, post_heatmap = video[i], video[i + 1]
for j in range(frame_sample_rate):
interpolated_heatmap = ((frame_sample_rate - j) / frame_sample_rate) * pre_heatmap \
+ (j / frame_sample_rate) * post_heatmap
expanded_heatmap.append(interpolated_heatmap)
expanded_heatmap.append(video[-1])
return torch.stack(expanded_heatmap).unsqueeze(1)
def heatmap_interpolation(heatmap: Tensor, height: int, width: int):
return F.interpolate(heatmap, size=(height, width), mode='bilinear', align_corners=False).squeeze().numpy() |