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read_dataset.py
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read_dataset.py
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import os
import os.path
import matplotlib.pyplot as plt
import torch
import numpy as np
import pandas
import csv
import random
from collections import OrderedDict
from SPT.lib.train.dataset.base_video_dataset import BaseVideoDataset
from SPT.lib.train.data import jpeg4py_loader
from SPT.lib.train.admin import env_settings
import cv2
import argparse
from SPT.lib.train.dataset.depth_utils import get_rgbd_frame
import matplotlib
# UniMod1K Class
class UniMod1K(BaseVideoDataset):
def __init__(self, root=None, nlp_root=None, dtype='rgbcolormap', image_loader=jpeg4py_loader):
"""
args:
image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
is used by default.
vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the
videos with subscripts -1, -3, and -5 from each class will be used for training.
# split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of
# vid_ids or split option can be used at a time.
# data_fraction - Fraction of dataset to be used. The complete dataset is used by default
root - path to the lasot depth dataset.
dtype - colormap or depth,, colormap + depth
if colormap, it returns the colormap by cv2,
if depth, it returns [depth, depth, depth]
"""
super().__init__('UniMod1K', root, image_loader)
self.root_nlp = nlp_root
self.root = root
self.dtype = dtype
self.sequence_list = self._build_sequence_list()
self.seq_per_class, self.class_list = self._build_class_list()
self.class_list.sort()
self.class_to_id = {cls_name: cls_id for cls_id, cls_name in enumerate(self.class_list)}
def _build_sequence_list(self):
ltr_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'SPT/lib/train')
file_path = os.path.join(ltr_path, 'data_specs', 'unimod1k_train_split.txt')
sequence_list = pandas.read_csv(file_path, header=None, squeeze=True).values.tolist()
return sequence_list
def _build_class_list(self):
seq_per_class = {}
class_list = []
for seq_id, seq_name in enumerate(self.sequence_list):
class_name = seq_name.split('/')[0]
if class_name not in class_list:
class_list.append(class_name)
if class_name in seq_per_class:
seq_per_class[class_name].append(seq_id)
else:
seq_per_class[class_name] = [seq_id]
return seq_per_class, class_list
def get_name(self):
return 'unimod1k'
def has_class_info(self):
return True
def has_occlusion_info(self):
return True
def get_num_sequences(self):
return len(self.sequence_list)
def get_num_classes(self):
return len(self.class_list)
def get_sequences_in_class(self, class_name):
return self.seq_per_class[class_name]
def _read_bb_anno(self, seq_path):
bb_anno_file = os.path.join(seq_path, "groundtruth_rect.txt")
gt = pandas.read_csv(bb_anno_file, delimiter=',', header=None, dtype=np.float32, na_filter=False,
low_memory=False).values
return torch.tensor(gt)
def _get_sequence_path(self, seq_id):
'''
Return :
- seq path
'''
seq_name = self.sequence_list[seq_id]
return os.path.join(self.root, seq_name)
def _get_nlp_path(self, seq_id):
'''
Return :
- nlp path
'''
seq_name = self.sequence_list[seq_id]
return os.path.join(self.root_nlp, seq_name)
def get_sequence_info(self, seq_id):
seq_path = self._get_sequence_path(seq_id)
bbox = self._read_bb_anno(seq_path)
nlp_path = self._get_nlp_path(seq_id)
'''
if the box is too small, it will be ignored
'''
valid = (bbox[:, 2] > 0) & (bbox[:, 3] > 0)
visible = valid
nlp = self._read_nlp(nlp_path)
return {'bbox': bbox, 'valid': valid, 'visible': visible, 'nlp': nlp}
def _read_nlp(self, nlp_path):
nlp_file = os.path.join(nlp_path, "nlp.txt")
nlp = ""
try:
nlp = pandas.read_csv(nlp_file, dtype=str, header=None, low_memory=False).values
except Exception as e:
print(e)
print(f'nlp_file:{nlp_file}')
return nlp[0][0]
def get_sequence_nlp(self, seq_id):
seq_path = self._get_sequence_path(seq_id)
nlp = self._read_nlp(seq_path)
return nlp
def _get_frame_path(self, seq_path, frame_id):
'''
return rgb depth image path
'''
return os.path.join(seq_path, 'color', '{:08}.jpg'.format(frame_id + 1)), os.path.join(seq_path, 'depth',
'{:08}.png'.format(
frame_id + 1)) # frames start from 1
def _get_frame(self, seq_path, frame_id, bbox=None):
'''
Return :
- rgb
- colormap from depth image
'''
color_path, depth_path = self._get_frame_path(seq_path, frame_id)
img = get_rgbd_frame(color_path, depth_path, dtype=self.dtype, depth_clip=False)
return img
def _get_class(self, seq_path):
raw_class = seq_path.split('/')[-2]
return raw_class
def get_class_name(self, seq_id):
depth_path = self._get_sequence_path(seq_id)
obj_class = self._get_class(depth_path)
return obj_class
def get_frames(self, seq_id, frame_ids, anno=None):
depth_path = self._get_sequence_path(seq_id)
obj_class = self._get_class(depth_path)
if anno is None:
anno = self.get_sequence_info(seq_id)
anno_frames = {}
for key, value in anno.items():
if key == 'nlp':
anno_frames[key] = [value for _ in frame_ids]
else:
anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids]
frame_list = [self._get_frame(depth_path, f_id, bbox=anno_frames['bbox'][ii]) for ii, f_id in
enumerate(frame_ids)]
object_meta = OrderedDict({'object_class_name': obj_class,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None})
return frame_list, anno_frames, object_meta
def main():
parser = argparse.ArgumentParser(description='Read images and labels.')
parser.add_argument('--data_dir', type=str, help='Name of tracking method.')
parser.add_argument('--nlp_dir', type=str, help='Name of config file.')
parser.add_argument('--seq_id', default=0, type=int, help='Name of config file.')
args = parser.parse_args()
unimod1k = UniMod1K(args.data_dir, args.nlp_dir)
frame_id = [0]
rgbd_img, annos, _ = unimod1k.get_frames(args.seq_id, frame_id)
rgb = rgbd_img[0][:, :, :3]
depth_color = cv2.cvtColor(rgbd_img[0][:, :, 3:], cv2.COLOR_BGR2RGB) # converted to colormap for visualisation
gt_bbox = list(map(int, np.array(annos['bbox'][0]).tolist()))
nlp = annos['nlp'][0]
im_vis = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB)
cv2.rectangle(im_vis, (gt_bbox[0], gt_bbox[1]), (gt_bbox[0] + gt_bbox[2],gt_bbox[1] + gt_bbox[3]), (0, 0, 255), 3)
cv2.putText(im_vis, nlp, (40, 300), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
cv2.rectangle(depth_color, (gt_bbox[0], gt_bbox[1]), (gt_bbox[0] + gt_bbox[2],gt_bbox[1] + gt_bbox[3]), (0, 255, 255), 3)
plt.subplot(211)
plt.imshow(im_vis[:,:, ::-1])
plt.subplot(212)
plt.imshow(depth_color)
plt.show()
if __name__ == '__main__':
main()