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input.py
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input.py
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import os
import numpy as np
import utils as ut
import json
import scipy.ndimage.filters as filters
import time
from PIL import Image
import tarfile
import io
INPUT_FOLDER = '../data/circle_basic_1/img/32_32'
def _is_combination_of_image_depth(folder):
return '/dep' not in folder and '/img' not in folder
def get_action_data(folder):
folder = folder.replace('/tmp_grey', '')
folder = folder.replace('/tmp', '')
folder = folder.replace('/img', '')
folder = folder.replace('/dep', '')
file = os.path.join(folder, 'action.txt')
if not os.path.exists(file):
return np.asarray([])
action_data = json.load(open(file, 'r'))[:]
# print(action_data)
res = []
# for i, action in enumerate(action_data):
# print(action)
# res.append(
# (
# action[0],
# action[1],
# action[2][3] or action[2][4] or action[2][5] or action[2][6],
# action[2][18] != 0
# )
# )
# print([print(x[0]) for x in action_data[0:10]])
res = [x[3][:2] for x in action_data]
return np.abs(np.asarray(res))
def read_ds_zip(path):
dep, img = {}, {}
tar = tarfile.open(path, "r:gz")
for member in tar.getmembers():
if '.jpg' not in member.name or not ('/dep/' in member.name or '/img/' in member.name):
# print('skipped', member)
continue
collection = dep if '/dep/' in member.name else img
index = int(member.name.split('/')[-1][1:-4])
f = tar.extractfile(member)
if f is not None:
content = f.read()
image = Image.open(io.BytesIO(content))
collection[index] = np.array(image)
assert len(img) == len(dep)
shape = [len(img)] + list(img[index].shape)
shape[-1] += 1
dataset = np.zeros(shape, np.uint8)
for i, k in enumerate(sorted(img)):
dataset[i, ..., :-1] = img[k]
dataset[i, ..., -1] = dep[k]
return dataset#, shape[1:]
def get_shape_zip(path):
tar = tarfile.open(path, "r:gz")
for member in tar.getmembers():
if '.jpg' not in member.name or '/img/' not in member.name:
continue
f = tar.extractfile(member)
content = f.read()
image = Image.open(io.BytesIO(content))
shape = list(np.array(image).shape)
shape[-1] += 1
return shape
def rescale_ds(ds, min, max):
ut.print_info('rescale call: (min: %s, max: %s) %d' % (str(min), str(max), len(ds)))
if max is None:
return np.asarray(ds) - np.min(ds)
ds_min, ds_max = np.min(ds), np.max(ds)
ds_gap = ds_max - ds_min
scale_factor = (max - min) / ds_gap
ds = np.asarray(ds) * scale_factor
shift_factor = min - np.min(ds)
ds += shift_factor
return ds
def get_input_name(input_folder):
spliter = '/img/' if '/img/' in input_folder else '/dep/'
main_part = input_folder.split(spliter)[0]
name = main_part.split('/')[-1]
name = name.replace('.tar.gz', '')
ut.print_info('input folder: %s -> %s' % (input_folder.split('/'), name))
return name
def permute_array(array, random_state=None):
return permute_data((array,))[0]
def permute_data(arrays, random_state=None):
"""Permute multiple numpy arrays with the same order."""
if any(len(a) != len(arrays[0]) for a in arrays):
raise ValueError('All arrays must be the same length.')
if not random_state:
random_state = np.random
order = random_state.permutation(len(arrays[0]))
return [a[order] for a in arrays]
def apply_gaussian(images, sigma):
if sigma == 0:
return images
res = images.copy()
for i, image in enumerate(res):
for channel in range(image.shape[-1]):
image[:, :, channel] = filters.gaussian_filter(image[:, :, channel], sigma)
return res
def permute_array_in_series(array, series_length, allow_shift=True):
res, permutation = permute_data_in_series((array,), series_length)
return res[0], permutation
def permute_data_in_series(arrays, series_length, allow_shift=True):
shift_possibilities = len(arrays[0]) % series_length
series_count = int(len(arrays[0]) / series_length)
shift = 0
if allow_shift:
if shift_possibilities == 0:
shift_possibilities += series_length
series_count -= 1
shift = np.random.randint(0, shift_possibilities+1, 1, dtype=np.int32)[0]
series = np.arange(0, series_count * series_length)\
.astype(np.int32)\
.reshape((series_count, series_length))
series = np.random.permutation(series)
data_permutation = series.reshape((series_count * series_length))
data_permutation += shift
remaining_elements = np.arange(0, len(arrays[0])).astype(np.int32)
remaining_elements = np.delete(remaining_elements, data_permutation)
data_permutation = np.concatenate((data_permutation, remaining_elements))
# print('assert', len(arrays[0]), len(data_permutation))
assert len(data_permutation) == len(arrays[0])
return [a[data_permutation] for a in arrays], data_permutation
def pad_set(set, batch_size):
length = len(set)
if length % batch_size == 0:
return set
padding_len = batch_size - length % batch_size
if padding_len != 0:
pass
# ut.print_info('Non-zero padding: %d' % padding_len, color=31)
# print('pad set', set.shape, select_random(padding_len, set=set).shape)
return np.concatenate((set, select_random(padding_len, set=set)))
def select_random(n, length=None, set=None):
assert length is None or set is None
length = length if set is None else len(set)
select = np.random.permutation(np.arange(length, dtype=np.int))[:n]
if set is None:
return select
else:
return set[select]
if __name__ == '__main__':
print(read_ds_zip('/home/eugene/repo/data/tmp/romb8.5.6.tar.gz').shape)