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iterators.py
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iterators.py
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import json
import os
import random
import time
from Queue import Queue
from math import ceil
from thread import start_new_thread
import numpy as np
from utilities import prepared_dataset_image, patch_centered_at, image_size, list_all_files, index_at_pixel, \
pixel_at_index, TT, load_csv, random_rotation
class BatchGenerator(object):
def __init__(self, dataset, batch_size, pool_size=4000):
"""
:type dataset:Dataset|ImageIterator
:type batch_size:int
:type pool_size:int
"""
self.verbose = TT.verbose
self.dataset = dataset
self.batch_size = batch_size
self.pool_size = pool_size
self.n = int(ceil(len(dataset) * 1.0 / batch_size))
self.MAX_NUM = 2
def __len__(self):
return self.n
def __iter__(self):
data = Queue(self.MAX_NUM)
def append(dst, pool, item):
if item is not None:
pool.append(item)
if len(pool) < min(self.pool_size, self.batch_size):
return dst, pool
if dst is None:
return np.asarray(pool, dtype=np.float64), []
if len(pool):
return np.concatenate((dst, pool)), []
return dst, []
def produce():
i = 1
count = 0
data_x = data_y = None
pool_x = []
pool_y = []
for x, y in self.dataset:
data_x, pool_x = append(data_x, pool_x, x)
data_y, pool_y = append(data_y, pool_y, (y, 1 - y))
count += 1
if count >= self.batch_size:
data_x, pool_x = append(data_x, pool_x, None)
data_y, pool_y = append(data_y, pool_y, None)
data.put([data_x, data_y])
i += 1
count = 0
data_x = data_y = None
if count > 0:
data_x, pool_x = append(data_x, pool_x, None)
data_y, pool_y = append(data_y, pool_y, None)
data.put([data_x, data_y])
start_new_thread(produce, ())
i = 1
while i <= self.n:
start = time.clock()
X, Y = data.get()
if self.verbose:
TT.debug("batch", i, "of", self.n, "completed in", time.clock() - start, "seconds. This batch has",
int(np.sum(Y[:, 0])), "positive pixels and", int(np.sum(Y[:, 1])), "negative pixels.")
yield X, Y
i += 1
class Dataset(object):
def __init__(self, root_path, patch_size=(101, 101), verbose=False, ratio=1.0, name='dataset', mapper=None,
filename_filter=None, rotation=True):
TT.debug("Dataset root path set to:", root_path)
self.name = name
self.patch_size = patch_size
self.ratio = ratio
self.root_path = os.path.abspath(root_path)
self.verbose = verbose
self.label_mapper = mapper
self.filename_filter = filename_filter
self.rotation = rotation
def __iter__(self):
return DatasetIterator(self).generator()
def __len__(self):
_, s = self.data
return s
@property
def files(self):
if not hasattr(self, '_files'):
self._files = list_all_files(self.root_path, filename_filter=self.filename_filter, mapper=self.label_mapper)
TT.debug("Found", len(self._files), "matching files in", self.root_path)
return self._files
@property
def image_size(self):
if not hasattr(self, '_image_size'):
self._image_size = image_size(prepared_dataset_image(os.path.join(self.root_path, self.files[0][0])))
return self._image_size
@property
def dataset_store_path(self):
return os.path.join(self.root_path, self.name+'.dataset.json')
@property
def data(self):
if hasattr(self, '_dataset'):
return self._dataset, self._dataset_size
if self.load():
return self.data
TT.debug("Creating new dataset.")
pos, pos_c = self.positive
sam, sam_c = self.sample
for filename in pos:
if filename not in sam:
sam[filename] = pos[filename]
else:
sam[filename] += pos[filename]
self._dataset = sam
self._dataset_size = sam_c + pos_c
self.dump()
return self.data
def load(self):
if os.path.exists(self.dataset_store_path):
TT.debug("Loading dataset from", self.dataset_store_path)
data = json.load(open(self.dataset_store_path))
self._dataset = data['data']
self._dataset_size = data['size']
self._positive = {}
self._positive_size = data['positive_size']
self._sample = {}
self._sample_size = data['sample_size']
self.positive_in_sample = 0
TT.debug("Current dataset has", self._dataset_size, "images.",
self._positive_size, "positive and", self._sample_size, "negative.")
return True
return False
def dump(self):
_ = self.data # Create data if not already created.
TT.debug("Current dataset has", self._dataset_size, "images.",
self._positive_size, "positive and", self._sample_size, "negative.")
json.dump({'data': self._dataset, 'size': self._dataset_size,
'positive_size': self._positive_size + self.positive_in_sample,
'sample_size': self._sample_size - self.positive_in_sample},
open(self.dataset_store_path, 'w'))
@property
def positive(self):
if hasattr(self, '_positive'):
return self._positive, self._positive_size
TT.debug("Collecting positive samples.")
self._positive = {}
self._positive_size = 0
self._positive_expanded = {}
for data_file, label_file in self.files:
labels = load_csv(os.path.join(self.root_path, label_file))
self._positive[data_file] = labels
self._positive_size += len(labels)
self._positive_expanded[data_file] = {}
for col, row, p in labels:
self._positive_expanded[data_file][index_at_pixel(col=col, row=row, size=self.image_size)] = p
TT.debug("Found", self._positive_size, "positive samples.")
return self.positive
@property
def sample(self):
if hasattr(self, '_sample'):
return self._sample, self._sample_size
self._sample = {}
self._sample_size = 0
_, n = self.positive
positives = self._positive_expanded
n = int(n * self.ratio)
TT.debug("Collecting", n, "random samples.")
pixels_per_image = int(np.prod(self.image_size))
indices = xrange(len(self.files) * pixels_per_image)
ignored = 0
for index in random.sample(indices, n):
data_file, label_file = self.files[index / pixels_per_image]
if data_file not in self._sample:
self._sample[data_file] = []
pixel = index % pixels_per_image
p = 0.0
if data_file in positives and pixel in positives[data_file]:
p = 1.0
ignored += 1
col, row = pixel_at_index(pixel, self.image_size)
self._sample[data_file].append([col, row, p])
self._sample_size += 1
TT.debug(ignored, "samples out of", self._sample_size, "random samples are positive.")
self.positive_in_sample = ignored
return self.sample
class DatasetIterator(object):
"""
DatasetIterator can iterate a dataset sampled with Dataset.
"""
def __init__(self, dataset):
"""
:type dataset:Dataset
:return:
"""
self.patch_size = dataset.patch_size
self.root_path = dataset.root_path
self.dataset, self.dataset_size = dataset.data
self.verbose = dataset.verbose
self.rotation = dataset.rotation
def __len__(self):
return self.dataset_size
def __iter__(self):
return self.generator()
def generator(self):
files = self.dataset.keys()
random.shuffle(files)
for filename in files:
image = prepared_dataset_image(os.path.join(self.root_path, filename), border=self.patch_size)
random.shuffle(self.dataset[filename])
for (col, row, p) in self.dataset[filename]:
patch = patch_centered_at(image, row=row, col=col, size=self.patch_size)
if self.rotation is False:
yield patch, p
else:
yield random_rotation(patch), p
class ImageIterator(object):
def __init__(self, image_file, label_file, patch_size=(101, 101)):
self.input = prepared_dataset_image(image_file, border=patch_size)
self.image_size = image_size(prepared_dataset_image(image_file))
self.patch_size = patch_size
width, height = self.image_size
self.output = np.zeros((height, width))
self.verbose = TT.verbose
# raise Warning("label = %s" %(label_file))
for (col, row, p) in load_csv(label_file):
self.output[row, col] = 1.0
def __len__(self):
return int(np.prod(self.image_size))
def __iter__(self):
for i in xrange(len(self)):
col, row = pixel_at_index(i, self.image_size)
yield patch_centered_at(self.input, row=row, col=col, size=self.patch_size), self.output[row, col]