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Forked from rkwitt/UCSD_Ped_Counting_Dataset.py
Last active January 1, 2022 22:07
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Data loading for the UCSD pedestrian counting(http://www.svcl.ucsd.edu/projects/peoplecnt/) dataset for images with their respective count from matlab scripts provided along.
import glob
import os
import scipy.io as sio
import numpy as np
import numpy as np
from PIL import *
class UCSD():
"""UCSD pedestrian counting data."""
def __init__(self, data_dir, annotation_dir, transform=None):
# stores name of files and their respective counts in two different lists.
self.file_list = []
self.file_cnts = []
files = glob.glob(
os.path.join(
annotation_dir,
'*count_roi_mainwalkway.mat'))
for f in files:
tmp = sio.loadmat(f)
l_count = tmp['count'][0][0].ravel()
r_count = tmp['count'][0][1].ravel()
t_count = l_count + r_count
[self.file_cnts.append(c) for c in t_count]
file_parts = os.path.basename(f).split('_')
seq_id = "_".join(file_parts[0:3])
for i in np.arange(len(t_count)):
self.file_list.append(
os.path.join(
data_dir,
seq_id + ".y",
"{}_f{:03d}.png".format(seq_id,i+1)))
self.transform = transform
def __len__(self):
return len(self.file_list)
def __getitem__(self, idx):
img_name = os.path.join(self.file_list[idx])
img = Image.open(img_name)
img = img.resize((128,128))
if self.transform:
img = self.transform(img)
return img, self.file_cnts[idx]
# For loading the dataset into an object.
X = UCSD('ucsdpeds/', 'vidf-cvpr/')
dict_imgs = dict(zip(X.file_list, X.file_cnts))
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