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generators.py
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generators.py
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import Queue
import threading
import os
from PIL import ImageEnhance
from PIL import Image, ImageChops, ImageOps
import numpy as np
from utils import get_img_ids_from_iter
# A lot of this code is a bad fork from https://github.com/benanne/kaggle-ndsb.
def make_thumb(image, size=(80, 80), pad=False):
# http://stackoverflow.com/questions/9103257/resize-image-
# maintaining-aspect-ratio-and-making-portrait-and-landscape-images-e
image.thumbnail(size, Image.BILINEAR)
image_size = image.size
if pad:
thumb = image.crop((0, 0, size[0], size[1]))
offset_x = max((size[0] - image_size[0]) / 2, 0)
offset_y = max((size[1] - image_size[1]) / 2, 0)
thumb = ImageChops.offset(thumb, offset_x, offset_y)
else:
thumb = ImageOps.fit(image, size, Image.BILINEAR, (0.5, 0.5))
return thumb
def load_image_and_process(im, im_dst, dim_dst, output_shape=(80, 80),
prefix_path='data/train_ds5_crop/',
transfo_params=None,
rand_values=None):
im = Image.open(prefix_path + im + '.jpeg', mode='r')
sort_dim = list(np.sort(im.size))
dim_dst[0] = sort_dim[1] / 700.0
dim_dst[1] = sort_dim[0] / 700.0
im_new = im
# Dict to keep track of random values.
chosen_values = {}
if transfo_params.get('extra_width_crop', False):
w, h = im_new.size
if w / float(h) >= 1.3:
cols_thres = np.where(
np.max(
np.max(
np.asarray(im_new),
axis=2),
axis=0) > 35)[0]
# Extra cond compared to orig crop.
if len(cols_thres) > output_shape[0] // 2:
min_x, max_x = cols_thres[0], cols_thres[-1]
else:
min_x, max_x = 0, -1
im_new = im_new.crop((min_x, 0,
max_x, h))
if transfo_params.get('crop_height', False):
w, h = im_new.size
if w > 1 and 0.98 <= h / float(w) <= 1.02:
# "Normal" without height crop, do height crop.
im_new = im_new.crop((0, int(0.05 * h),
w, int(0.95 * h)))
if transfo_params.get('crop', False) and not \
transfo_params.get('crop_after_rotation', False):
if rand_values:
do_crop = rand_values['do_crop']
else:
do_crop = transfo_params['crop_prob'] > np.random.rand()
chosen_values['do_crop'] = do_crop
if do_crop:
out_w, out_h = im_new.size
w_dev = int(transfo_params['crop_w'] * out_w)
h_dev = int(transfo_params['crop_h'] * out_h)
# If values are supplied.
if rand_values:
w0, w1 = rand_values['w0'], rand_values['w1']
h0, h1 = rand_values['h0'], rand_values['h1']
else:
w0 = np.random.randint(0, w_dev + 1)
w1 = np.random.randint(0, w_dev + 1)
h0 = np.random.randint(0, h_dev + 1)
h1 = np.random.randint(0, h_dev + 1)
# Add params to dict.
chosen_values['w0'] = w0
chosen_values['w1'] = w1
chosen_values['h0'] = h0
chosen_values['h1'] = h1
im_new = im_new.crop((0 + w0, 0 + h0,
out_w - w1, out_h - h1))
# if transfo_params.get('new_gen', False):
# im_new = im_new.crop(im_new.getbbox())
# im_new = im_new.resize(map(lambda x: x*2, output_shape),
# resample=Image.BICUBIC)
if transfo_params.get('shear', False):
# http://stackoverflow.com/questions/14177744/how-does-
# perspective-transformation-work-in-pil
if transfo_params['shear_prob'] > np.random.rand():
# print 'shear'
# TODO: No chosen values because shear not really used.
shear_min, shear_max = transfo_params['shear_range']
m = shear_min + np.random.rand() * (shear_max - shear_min)
out_w, out_h = im_new.size
xshift = abs(m) * out_w
new_width = out_w + int(round(xshift))
im_new = im_new.transform((new_width, out_h), Image.AFFINE,
(1, m, -xshift if m > 0 else 0, 0, 1, 0),
Image.BICUBIC)
if transfo_params.get('rotation_before_resize', False):
if rand_values:
rotation_param = rand_values['rotation_param']
else:
rotation_param = np.random.randint(
transfo_params['rotation_range'][0],
transfo_params['rotation_range'][1])
chosen_values['rotation_param'] = rotation_param
im_new = im_new.rotate(rotation_param, resample=Image.BILINEAR,
expand=transfo_params.get('rotation_expand',
False))
if transfo_params.get('rotation_expand',
False):
im_new = im_new.crop(im_new.getbbox())
if transfo_params.get('crop_after_rotation', False):
if rand_values:
do_crop = rand_values['do_crop']
else:
do_crop = transfo_params['crop_prob'] > np.random.rand()
chosen_values['do_crop'] = do_crop
if do_crop:
out_w, out_h = im_new.size
w_dev = int(transfo_params['crop_w'] * out_w)
h_dev = int(transfo_params['crop_h'] * out_h)
# If values are supplied.
if rand_values:
w0, w1 = rand_values['w0'], rand_values['w1']
h0, h1 = rand_values['h0'], rand_values['h1']
else:
w0 = np.random.randint(0, w_dev + 1)
w1 = np.random.randint(0, w_dev + 1)
h0 = np.random.randint(0, h_dev + 1)
h1 = np.random.randint(0, h_dev + 1)
# Add params to dict.
chosen_values['w0'] = w0
chosen_values['w1'] = w1
chosen_values['h0'] = h0
chosen_values['h1'] = h1
im_new = im_new.crop((0 + w0, 0 + h0,
out_w - w1, out_h - h1))
# im_new = im_new.thumbnail(output_shape, resample=Image.BILINEAR)
if transfo_params.get('keep_aspect_ratio', False):
im_new = make_thumb(im_new, size=output_shape,
pad=transfo_params['resize_pad'])
else:
im_new = im_new.resize(output_shape, resample=Image.BILINEAR)
# im_new = im_new.resize(output_shape, resample=Image.BICUBIC)
# im_new = im_new.resize(map(lambda x: int(x * 1.2), output_shape),
# resample=Image.BICUBIC)
# im_new = im_new.crop(im_new.getbbox())
if transfo_params.get('rotation', False) \
and not transfo_params.get('rotation_before_resize', False):
if rand_values:
rotation_param = rand_values['rotation_param']
else:
rotation_param = np.random.randint(
transfo_params['rotation_range'][0],
transfo_params['rotation_range'][1])
chosen_values['rotation_param'] = rotation_param
im_new = im_new.rotate(rotation_param, resample=Image.BILINEAR,
expand=transfo_params.get('rotation_expand',
False))
if transfo_params.get('rotation_expand',
False):
im_new = im_new.crop(im_new.getbbox())
# im_new = im_new.resize(output_shape, resample=Image.BICUBIC)
if transfo_params.get('contrast', False):
contrast_min, contrast_max = transfo_params['contrast_range']
if rand_values:
contrast_param = rand_values['contrast_param']
else:
contrast_param = np.random.uniform(contrast_min, contrast_max)
chosen_values['contrast_param'] = contrast_param
im_new = ImageEnhance.Contrast(im_new).enhance(contrast_param)
if transfo_params.get('brightness', False):
brightness_min, brightness_max = transfo_params['brightness_range']
if rand_values:
brightness_param = rand_values['brightness_param']
else:
brightness_param = np.random.uniform(brightness_min,
brightness_max)
chosen_values['brightness_param'] = brightness_param
im_new = ImageEnhance.Brightness(im_new).enhance(brightness_param)
if transfo_params.get('color', False):
color_min, color_max = transfo_params['color_range']
if rand_values:
color_param = rand_values['color_param']
else:
color_param = np.random.uniform(color_min, color_max)
chosen_values['color_param'] = color_param
im_new = ImageEnhance.Color(im_new).enhance(color_param)
if transfo_params.get('flip', False):
if rand_values:
do_flip = rand_values['do_flip']
else:
do_flip = transfo_params['flip_prob'] > np.random.rand()
chosen_values['do_flip'] = do_flip
if do_flip:
im_new = im_new.transpose(Image.FLIP_LEFT_RIGHT)
if output_shape[0] < 200 and False:
# Otherwise too slow.
# TODO: Disabled for now
if 'rotation' in transfo_params and transfo_params['rotation']:
if rand_values:
rotation_param = rand_values['rotation_param2']
else:
rotation_param = np.random.randint(
transfo_params['rotation_range'][0],
transfo_params['rotation_range'][1])
im_new = im_new.rotate(rotation_param, resample=Image.BILINEAR,
expand=False)
# im_new = im_new.crop(im_new.getbbox())
chosen_values['rotation_param2'] = rotation_param
if transfo_params.get('zoom', False):
if rand_values:
do_zoom = rand_values['do_zoom']
else:
do_zoom = transfo_params['zoom_prob'] > np.random.rand()
chosen_values['do_zoom'] = do_zoom
if do_zoom:
zoom_min, zoom_max = transfo_params['zoom_range']
out_w, out_h = im_new.size
if rand_values:
w_dev = rand_values['w_dev']
else:
w_dev = int(np.random.uniform(zoom_min, zoom_max) / 2 * out_w)
chosen_values['w_dev'] = w_dev
im_new = im_new.crop((0 + w_dev,
0 + w_dev,
out_w - w_dev,
out_h - w_dev))
# im_new = im_new.resize(output_shape, resample=Image.BILINEAR)
if im_new.size != output_shape:
im_new = im_new.resize(output_shape, resample=Image.BILINEAR)
im_new = np.asarray(im_new).astype('float32') / 255
im_dst[:] = np.rollaxis(im_new.astype('float32'), 2, 0)
im.close()
del im, im_new
return chosen_values
def patches_gen_pairs(images, labels, p_x=80, p_y=80, num_channels=3,
chunk_size=1024,
num_chunks=100, rng=np.random,
prefix_path='data/train_ds5_crop/',
transfo_params=None,
paired_transfos=False):
num_patients = len(images)
for n in xrange(num_chunks):
indices = rng.randint(0, num_patients, chunk_size // 2)
chunk_x = np.zeros((chunk_size, num_channels, p_x, p_y),
dtype='float32')
chunk_dim = np.zeros((chunk_size, 2), dtype='float32')
# chunk_y = labels[indices].astype('float32')
chunk_y = np.zeros((chunk_size,), dtype='int32')
chunk_shape = np.zeros((chunk_size, num_channels), dtype='float32')
for k, idx in enumerate(indices):
# First eye.
img = str(images[idx]) + '_left'
chosen_values = load_image_and_process(
img,
im_dst=chunk_x[2 * k],
dim_dst=chunk_dim[2 * k],
output_shape=(p_x, p_y),
prefix_path=prefix_path,
transfo_params=transfo_params)
chunk_shape[2 * k] = chunk_x[2 * k].shape
chunk_y[2 * k] = labels[idx][0]
# Second eye.
img = str(images[idx]) + '_right'
load_image_and_process(
img,
im_dst=chunk_x[2 * k + 1],
dim_dst=chunk_dim[2 * k + 1],
output_shape=(p_x, p_y),
prefix_path=prefix_path,
transfo_params=transfo_params,
rand_values=chosen_values if paired_transfos else None)
chunk_shape[2 * k + 1] = chunk_x[2 * k + 1].shape
chunk_y[2 * k + 1] = labels[idx][1]
yield chunk_x, chunk_dim, np.eye(5)[chunk_y].astype('float32'), \
chunk_shape
# Get rid of relative imports.
main_dir = os.path.abspath(os.path.dirname(__file__))
import pandas as p
# Get all train ids to know if patient id is train or test.
train_labels = p.read_csv(os.path.join(main_dir, 'data/trainLabels.csv'))
all_train_patient_ids = set(get_img_ids_from_iter(train_labels.image))
def patches_gen_pairs_pseudolabel(images, labels, p_x=80, p_y=80,
num_channels=3, chunk_size=1024,
num_chunks=100, rng=np.random,
prefix_train='data/train_ds5_crop/',
prefix_test='data/test_ds5_crop/',
transfo_params=None,
paired_transfos=False):
num_patients = len(images)
for n in xrange(num_chunks):
indices = rng.randint(0, num_patients, chunk_size // 2)
chunk_x = np.zeros((chunk_size, num_channels, p_x, p_y),
dtype='float32')
chunk_dim = np.zeros((chunk_size, 2), dtype='float32')
chunk_y = np.zeros((chunk_size, 5), dtype='float32')
chunk_shape = np.zeros((chunk_size, num_channels), dtype='float32')
int_labels = len(labels.shape) < 3
id_matrix = np.eye(5)
for k, idx in enumerate(indices):
# First check if img id is train or test.
patient_id = images[idx]
if patient_id in all_train_patient_ids:
prefix_path = prefix_train
else:
prefix_path = prefix_test
# First eye.
img_id = str(patient_id) + '_left'
chosen_values = load_image_and_process(
img_id,
im_dst=chunk_x[2 * k],
dim_dst=chunk_dim[2 * k],
output_shape=(p_x, p_y),
prefix_path=prefix_path,
transfo_params=transfo_params)
chunk_shape[2 * k] = chunk_x[2 * k].shape
if int_labels:
chunk_y[2 * k] = id_matrix[int(labels[idx][0])]
else:
chunk_y[2 * k] = labels[idx][0]
# Second eye.
img_id = str(patient_id) + '_right'
load_image_and_process(img_id, im_dst=chunk_x[2 * k + 1],
dim_dst=chunk_dim[2 * k + 1],
output_shape=(p_x, p_y),
prefix_path=prefix_path,
transfo_params=transfo_params,
rand_values=chosen_values
if paired_transfos else None)
chunk_shape[2 * k + 1] = chunk_x[2 * k + 1].shape
if int_labels:
chunk_y[2 * k + 1] = id_matrix[int(labels[idx][1])]
else:
chunk_y[2 * k + 1] = labels[idx][1]
yield chunk_x, chunk_dim, chunk_y, chunk_shape
def patches_gen_fixed_pairs(images, p_x=80, p_y=80, num_channels=3,
chunk_size=1024,
prefix_train='data/train_ds5_crop/',
prefix_test='data/test_ds5_crop/',
transfo_params=None,
paired_transfos=False):
num_patients = len(images)
num_chunks = int(np.ceil((2 * num_patients) / float(chunk_size)))
idx = 0
for n in xrange(num_chunks):
chunk_x = np.zeros((chunk_size, num_channels, p_x, p_y),
dtype='float32')
chunk_dim = np.zeros((chunk_size, 2), dtype='float32')
chunk_shape = np.zeros((chunk_size, num_channels), dtype='float32')
chunk_length = chunk_size
for k in xrange(chunk_size // 2):
if idx >= num_patients:
chunk_length = 2 * k
break
patient_id = images[idx]
if patient_id in all_train_patient_ids:
prefix_path = prefix_train
else:
prefix_path = prefix_test
img_id = str(patient_id) + '_left'
chosen_values = load_image_and_process(
img_id,
im_dst=chunk_x[2 * k],
dim_dst=chunk_dim[2 * k],
output_shape=(p_x, p_y),
prefix_path=prefix_path,
transfo_params=transfo_params)
chunk_shape[2 * k] = chunk_x[2 * k].shape
img_id = str(images[idx]) + '_right'
load_image_and_process(
img_id,
im_dst=chunk_x[2 * k + 1],
dim_dst=chunk_dim[2 * k + 1],
output_shape=(p_x, p_y),
prefix_path=prefix_path,
transfo_params=transfo_params,
rand_values=chosen_values if paired_transfos else None)
chunk_shape[2 * k + 1] = chunk_x[2 * k + 1].shape
idx += 1
yield chunk_x, chunk_dim, chunk_shape, chunk_length
# From https://github.com/benanne.
def buffered_gen_threaded(source_gen, buffer_size=2):
"""
Generator that runs a slow source generator in a separate thread.
Beware of the GIL!
buffer_size: the maximal number of items to pre-generate
(length of the buffer)
"""
if buffer_size < 2:
raise RuntimeError("Minimal buffer size is 2!")
buffer = Queue.Queue(maxsize=buffer_size - 1)
# the effective buffer size is one less, because the generation process
# will generate one extra element and block until there is room in the
# buffer.
def _buffered_generation_thread(source_gen, buffer):
for data in source_gen:
buffer.put(data, block=True)
buffer.put(None) # sentinel: signal the end of the iterator
thread = threading.Thread(target=_buffered_generation_thread,
args=(source_gen, buffer))
thread.daemon = True
thread.start()
for data in iter(buffer.get, None):
yield data
def buffered_gen_threaded_multiple(source_gens,
buffer_size=3):
"""
Generator that runs a slow source generator in a separate thread.
Beware of the GIL!
buffer_size: the maximal number of items to pre-generate
(length of the buffer)
"""
if buffer_size < 2:
raise RuntimeError("Minimal buffer size is 2!")
buffer = Queue.Queue(maxsize=buffer_size - 1)
# the effective buffer size is one less, because the generation process
# will generate one extra element and block until there is room in the
# buffer.
def _buffered_generation_thread(source_gen, buffer):
for data in source_gen:
buffer.put(data, block=True)
buffer.put(None) # sentinel: signal the end of the iterator
for source_gen in source_gens:
thread = threading.Thread(target=_buffered_generation_thread,
args=(source_gen, buffer))
thread.daemon = True
thread.start()
num_sentinels = 0
while num_sentinels < len(source_gens):
data = buffer.get()
if data is not None:
yield data
else:
num_sentinels += 1
class DataLoader(object):
params = ['zmuv_mean', 'zmuv_std', 'p_x', 'p_y', 'num_channels', 'crop',
'prefix_train', 'prefix_test',
'default_transfo_params', 'no_transfo_params',
'images_train_0', 'labels_train_0',
'images_train_1', 'labels_train_1',
'images_train_eval', 'labels_train_eval',
'images_valid_eval', 'labels_valid_eval',
'paired_transfos']
paired_transfos = False
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
def create_random_gen(self, images, labels, chunk_size=512,
num_chunks=100,
prefix_train='data/train_ds5_crop/',
prefix_test='data/test_ds5_crop/',
transfo_params=None,
buffer_size=3, num_generators=5,
paired_transfos=paired_transfos):
if not transfo_params:
raise ValueError("Need transfo_params for gen!")
gens = []
if num_generators > 1:
for i in range(num_generators - 1):
gen = patches_gen_pairs_pseudolabel(
images,
labels,
p_x=self.p_x,
p_y=self.p_y,
num_channels=self.num_channels,
chunk_size=chunk_size,
num_chunks=num_chunks //
num_generators,
prefix_train=prefix_train,
prefix_test=prefix_test,
transfo_params=transfo_params,
paired_transfos=paired_transfos)
gens.append(gen)
num_chunks_remaining = num_chunks - \
(num_generators - 1) * (num_chunks // num_generators)
gen = patches_gen_pairs_pseudolabel(images, labels,
p_x=self.p_x, p_y=self.p_y,
num_channels=self.num_channels,
chunk_size=chunk_size,
num_chunks=num_chunks_remaining,
prefix_train=prefix_train,
prefix_test=prefix_test,
transfo_params=transfo_params,
paired_transfos=paired_transfos)
gens.append(gen)
def random_gen(gen):
for chunk_x, chunk_dim, chunk_y, chunk_shape in gen:
yield [(chunk_x - self.zmuv_mean) /
(0.05 + self.zmuv_std),
chunk_dim], chunk_y, chunk_shape
return buffered_gen_threaded_multiple(map(random_gen, gens),
buffer_size=buffer_size)
def create_fixed_gen(self, images, chunk_size=512,
prefix_train='data/train_ds5_crop/',
prefix_test='data/test_ds5_crop/',
buffer_size=2,
transfo_params=None,
paired_transfos=paired_transfos):
if not transfo_params:
raise ValueError("Need transfo_params for gen!")
gen = patches_gen_fixed_pairs(images, p_x=self.p_x, p_y=self.p_y,
num_channels=self.num_channels,
chunk_size=chunk_size,
prefix_train=prefix_train,
prefix_test=prefix_test,
transfo_params=transfo_params,
paired_transfos=paired_transfos)
def fixed_gen():
for chunk_x, chunk_dim, chunk_shape, chunk_length in gen:
yield [(chunk_x - self.zmuv_mean) /
(0.05 + self.zmuv_std),
chunk_dim], chunk_shape, chunk_length
return buffered_gen_threaded(fixed_gen(), buffer_size=buffer_size)
def estimate_params(self, transfo_params, eps=0.0,
pixel_based_norm=True):
if self.num_channels > 3:
paired = True
else:
paired = False
gen = patches_gen_pairs_pseudolabel(self.images_train_0,
self.labels_train_0,
p_x=self.p_x, p_y=self.p_y,
num_channels=self.num_channels,
chunk_size=512,
num_chunks=1,
prefix_train=self.prefix_train,
prefix_test=self.prefix_test,
transfo_params=transfo_params,
paired_transfos=paired)
chunks_x, _, _, _ = gen.next()
if pixel_based_norm:
self.zmuv_mean = chunks_x.mean(axis=0, keepdims=True)
self.zmuv_std = chunks_x.std(axis=0, keepdims=True) + eps
else:
self.zmuv_mean = chunks_x.mean(keepdims=True)
self.zmuv_std = chunks_x.std(keepdims=True) + eps
del chunks_x, gen
def get_params(self):
return {pname: getattr(self, pname, None)
for pname in self.params}
def set_params(self, p):
self.__dict__.update(p)