File size: 5,511 Bytes
c6dfc69 | 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 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | import os
import re
import PIL.Image
import matplotlib.pyplot as plt
import numpy
import torch
import pandas
import torchvision
class Visual(torch.utils.data.Dataset):
def __init__(self, augmentation, directory_path, split, image_size, image_embedding_size):
self.augment = augmentation
self.directory_path = directory_path
self.split = split
self.image_size = image_size
self.embedding_size = image_embedding_size
def get_frame_and_label(self, file_prefix, object_id):
# if self.split == 'null':
# frame_path = os.path.join(self.directory_path, 'media_cross', file_prefix, 'frames')
# frame_path = [os.path.join(frame_path, i) for i in os.listdir(frame_path)]
# frame_path.sort(key=lambda x: tuple(map(int, x.split('/')[-1].split("_")[-1].split('.jpg')[0])))
# # dummy empty label.
# frame = [PIL.Image.open(i) for i in frame_path]
# label = [PIL.Image.new('L', frame[0].size)] * len(frame)
# else:
frame_path = os.path.join(self.directory_path, 'media', file_prefix, 'frames')
label_path = os.path.join(self.directory_path, 'gt_mask', file_prefix, 'fid_{}'.format(str(object_id)))
frame_path = [os.path.join(frame_path, i) for i in os.listdir(frame_path)]
label_path = [os.path.join(label_path, i) for i in os.listdir(label_path)]
frame_path.sort(key=lambda x: tuple(map(int, x.split('/')[-1].split("_")[-1].split('.jpg')[0])))
label_path.sort(key=lambda x: tuple(map(int, x.split('/')[-1].split("_")[-1].split('.png')[0])))
frame = [PIL.Image.open(i) for i in frame_path]
label = [PIL.Image.open(i).convert('L') for i in label_path]
return frame, label
def load_data(self, file_prefix, object_id):
frame, label = self.get_frame_and_label(file_prefix, object_id)
label_idx = torch.tensor(list([1] * 10), dtype=torch.bool)
prompts = {}
image_batch = [None]*len(frame)
label_batch = [None]*len(frame)
if self.split == 'train':
# apply sam2 augmentation.
frame, label = self.augment(frame, label)
for i in range(len(frame)):
if 'test_' in self.split:
# note: there is no augmentation in here.
curr_frame, curr_label = self.augment(frame[i], label[i], split=self.split)
else:
curr_frame, curr_label = frame[i], label[i]
curr_label[curr_label > 0.] = 1.
image_batch[i], label_batch[i] = curr_frame, curr_label
# image_batch[i], label_batch[i] = self.augment(frame[i], label[i], split=self.split)
# note: we simply convert the code to binary mask in v1s, v1m;
# to some reason, we failed to load the label in `L' format and had to hardcoding here.
# label_batch[i][label_batch[i] > 0.] = 1.
# prompts['box_coords'][i], prompts['masks'][i] = self.receive_other_prompts(label_batch[i])
# organise the prompts
# prompts.update({'masks': torch.stack(prompts['masks'], dim=0)})
# prompts.update({'box_coords': torch.stack(prompts['box_coords'], dim=0)})
# prompts.update({'point_labels': torch.stack(prompts['point_labels'], dim=0)})
prompts.update({'label_index': label_idx})
return torch.stack(image_batch, dim=0), torch.stack(label_batch, dim=0), prompts
def receive_other_prompts(self, y_):
# y_ = torch.zeros_like(y_)
if len(torch.unique(y_)) > 1:
# foreground point
points_foreground = torch.stack(torch.where(y_ > 0)[::-1], dim=0).transpose(1, 0)
# bbox prompt (left-top corner & right-bottom corner)
bbox_one = torch.min(points_foreground[:, 0]), torch.min(points_foreground[:, 1])
bbox_fou = torch.max(points_foreground[:, 0]), torch.max(points_foreground[:, 1])
bbox_coord = torch.tensor(bbox_one + bbox_fou, dtype=torch.float)
bbox_coord = self.transform_coords(bbox_coord, orig_hw=y_.squeeze().shape)
# mask prompt
low_mask = torchvision.transforms.functional.resize(y_.clone(), [self.embedding_size*4, self.embedding_size*4],
torchvision.transforms.InterpolationMode.NEAREST)
else:
# for the pure background situation.
bbox_coord = torch.zeros([4], dtype=torch.float).fill_(float('nan'))
low_mask = torch.zeros([1, self.embedding_size*4, self.embedding_size*4], dtype=torch.float).fill_(float('nan'))
return bbox_coord, low_mask
# we transfer the coords to SAM's input resolution (1024, 1024).
def transform_coords(self, coords: torch.Tensor, orig_hw=None) -> torch.Tensor:
"""
Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates,
If the coords are in absolute image coordinates, normalize should be set to True and original image size is required.
Returns
Un-normalized coordinates in the range of [0, 1] which is expected by the sam2 model.
"""
h, w = orig_hw
coords = coords.clone().reshape(-1, 2, 2)
coords[..., 0] = coords[..., 0] / w
coords[..., 1] = coords[..., 1] / h
coords = coords * self.image_size # unnormalize coords
return coords.reshape(4)
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