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read_sunrgbd_data.py
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read_sunrgbd_data.py
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from __future__ import print_function
import os, sys, glob
import numpy as np
from PIL import Image
from random import randint
# from numba import jit
from tqdm import tqdm
class dataset:
def __init__(self, name, dataset_file, img_type='rgb'):
self.name = name
self.dataset_file = dataset_file
self.rgb_names = []
self.label_names = []
self.img_type = img_type
if dataset_file.endswith('.txt'):
i = 0
with open(dataset_file,"r") as f:
for line in tqdm(f):
self.rgb_names.append(line.split()[0])
self.label_names.append(line.split()[1])
# print(line.split()[0], line.split()[1])
i+=1
# if i == 100000:
# break
self.dataset_size = i
else:
depth_pngs = sorted(glob.glob(dataset_file +'/depth*.png'))
label_pngs = sorted(glob.glob(dataset_file +'/label*.png'))
assert len(depth_pngs) == len(label_pngs)
for i in range(0, len(depth_pngs)):
self.rgb_names.append(dataset_file + '/depthsub' + str(i) + '.png')
self.label_names.append(dataset_file + '/labelssub' + str(i) + '.png')
self.dataset_size = len(depth_pngs)
self.shuffle_indices = list(range(0, self.dataset_size))
np.random.shuffle(self.shuffle_indices)
self.count = 0
# @jit
def get_random_shuffle(self, batch_size):
if self.img_type == 'rgb':
imgarray = np.empty([batch_size, 240, 320, 3],dtype=np.float32)
else:
imgarray = np.empty([batch_size, 240, 320, 1], dtype=np.float32)
labelarray = np.empty([batch_size, 240, 320],dtype=np.float32)
for x in range(0,batch_size):
rand_i = self.shuffle_indices[self.count]
img = Image.open(self.rgb_names[rand_i]).resize((320,240),Image.BILINEAR)
labelImg = Image.open(self.label_names[rand_i]).resize((320,240),Image.NEAREST)
if self.img_type != 'rgb':
imgarray[x] = np.expand_dims(np.asarray(img), axis=2)
else:
imgarray[x] = np.asarray(img)
labelarray[x] = np.asarray(labelImg)
self.count = self.count+1
if self.count >= self.dataset_size:
np.random.shuffle(self.shuffle_indices)
self.count = 0
return imgarray,labelarray
# SUNRGBD_dataset = dataset("SUNRGBD","/Users/ankurhanda/workspace/code/sunrgbd-meta-data/sunrgbd_training.txt")
# img, label = SUNRGBD_dataset.get_random_shuffle(30)
# Image.fromarray(np.uint8(img[1]),'RGB').show()
# label = np.reshape(label,[-1])
# print(label.shape)