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egs/cifar/v1/image/ocr/make_features.py
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#!/usr/bin/env python3
# Copyright 2017 Chun Chieh Chang
# 2017 Ashish Arora
# 2017 Yiwen Shao
# 2018 Hossein Hadian
# 2018 Desh Raj
""" This script converts images to Kaldi-format feature matrices. The input to
this script is the path to a data directory, e.g. "data/train". This script
reads the images listed in images.scp and writes them to standard output
(by default) as Kaldi-formatted matrices (in text form). It also scales the
images so they have the same height (via --feat-dim). It can optionally pad
the images (on left/right sides) with white pixels. It by default performs
augmentation, (directly scaling down and scaling up). It will double the
data but we can turn augmentation off (via --no-augment).
If an 'image2num_frames' file is found in the data dir, it will be used
to enforce the images to have the specified length in that file by padding
white pixels (the --padding option will be ignored in this case). This relates
to end2end chain training.
eg. local/make_features.py data/train --feat-dim 40
"""
import random
import argparse
import os
import sys
import numpy as np
from scipy import misc
import math
from signal import signal, SIGPIPE, SIG_DFL
signal(SIGPIPE, SIG_DFL)
parser = argparse.ArgumentParser(description="""Converts images (in 'dir'/images.scp) to features and
writes them to standard output in text format.""")
parser.add_argument('images_scp_path', type=str,
help='Path of images.scp file')
parser.add_argument('--allowed_len_file_path', type=str, default=None,
help='If supplied, each images will be padded to reach the '
'target length (this overrides --padding).')
parser.add_argument('--out-ark', type=str, default='-',
help='Where to write the output feature file')
parser.add_argument('--feat-dim', type=int, default=40,
help='Size to scale the height of all images')
parser.add_argument('--padding', type=int, default=5,
help='Number of white pixels to pad on the left'
'and right side of the image.')
parser.add_argument('--num-channels', type=int, default=1,
help='Number of color channels')
parser.add_argument('--vertical-shift', type=int, default=0,
help='total number of padding pixel per column')
parser.add_argument('--fliplr', type=lambda x: (str(x).lower()=='true'), default=False,
help="Flip the image left-right for right to left languages")
parser.add_argument('--augment_type', type=str, default='no_aug',
choices=['no_aug', 'random_scale','random_shift'],
help='Subset of data to process.')
args = parser.parse_args()
def write_kaldi_matrix(file_handle, matrix, key):
file_handle.write(key + " [ ")
num_rows = len(matrix)
if num_rows == 0:
raise Exception("Matrix is empty")
num_cols = len(matrix[0])
for row_index in range(len(matrix)):
if num_cols != len(matrix[row_index]):
raise Exception("All the rows of a matrix are expected to "
"have the same length")
file_handle.write(" ".join(map(lambda x: str(x), matrix[row_index])))
if row_index != num_rows - 1:
file_handle.write("
")
file_handle.write(" ]
")
def horizontal_pad(im, allowed_lengths = None):
if allowed_lengths is None:
left_padding = right_padding = args.padding
else: # Find an allowed length for the image
imlen = im.shape[1] # width
allowed_len = 0
for l in allowed_lengths:
if l > imlen:
allowed_len = l
break
if allowed_len == 0:
# No allowed length was found for the image (the image is too long)
return None
padding = allowed_len - imlen
left_padding = int(padding // 2)
right_padding = padding - left_padding
dim_y = im.shape[0] # height
if args.num_channels in [1,4]:
im_pad = np.concatenate((255 * np.ones((dim_y, left_padding),
dtype=int), im), axis=1)
im_pad1 = np.concatenate((im_pad, 255 * np.ones((dim_y, right_padding),
dtype=int)), axis=1)
else:
im_pad = np.concatenate((255 * np.ones((dim_y, left_padding, args.num_channels),
dtype=int), im), axis=1)
im_pad1 = np.concatenate((im_pad, 255 * np.ones((dim_y, right_padding, args.num_channels),
dtype=int)), axis=1)
return im_pad1
def get_scaled_image_aug(im, mode='normal'):
scale_size = args.feat_dim
sx = im.shape[1]
sy = im.shape[0]
scale = (1.0 * scale_size) / sy
nx = int(scale_size)
ny = int(scale * sx)
scale_size = random.randint(10, 30)
scale = (1.0 * scale_size) / sy
down_nx = int(scale_size)
down_ny = int(scale * sx)
if mode == 'normal':
im = misc.imresize(im, (nx, ny))
return im
else:
im_scaled_down = misc.imresize(im, (down_nx, down_ny))
im_scaled_up = misc.imresize(im_scaled_down, (nx, ny))
return im_scaled_up
return im
def vertical_shift(im, mode='normal'):
if args.vertical_shift == 0:
return im
total = args.vertical_shift
if mode == 'notmid':
val = random.randint(0, 1)
if val == 0:
mode = 'top'
else:
mode = 'bottom'
if mode == 'normal':
top = int(total / 2)
bottom = total - top
elif mode == 'top': # more padding on top
top = random.randint(total / 2, total)
bottom = total - top
elif mode == 'bottom': # more padding on bottom
top = random.randint(0, total / 2)
bottom = total - top
width = im.shape[1]
im_pad = np.concatenate(
(255 * np.ones((top, width), dtype=int) -
np.random.normal(2, 1, (top, width)).astype(int), im), axis=0)
im_pad = np.concatenate(
(im_pad, 255 * np.ones((bottom, width), dtype=int) -
np.random.normal(2, 1, (bottom, width)).astype(int)), axis=0)
return im_pad
### main ###
random.seed(1)
data_list_path = args.images_scp_path
if args.out_ark == '-':
out_fh = sys.stdout
else:
out_fh = open(args.out_ark,'w')
allowed_lengths = None
allowed_len_handle = args.allowed_len_file_path
if os.path.isfile(allowed_len_handle):
print("Found 'allowed_lengths.txt' file...", file=sys.stderr)
allowed_lengths = []
with open(allowed_len_handle) as f:
for line in f:
allowed_lengths.append(int(line.strip()))
print("Read {} allowed lengths and will apply them to the "
"features.".format(len(allowed_lengths)), file=sys.stderr)
num_fail = 0
num_ok = 0
with open(data_list_path) as f:
for line in f:
line = line.strip()
line_vect = line.split(' ')
image_id = line_vect[0]
image_path = line_vect[1]
if args.num_channels == 4:
im = misc.imread(image_path, mode='L')
else:
im = misc.imread(image_path)
if args.fliplr:
im = np.fliplr(im)
if args.augment_type == 'no_aug' or 'random_shift':
im = get_scaled_image_aug(im, 'normal')
elif args.augment_type == 'random_scale':
im = get_scaled_image_aug(im, 'scaled')
im = horizontal_pad(im, allowed_lengths)
if im is None:
num_fail += 1
continue
if args.augment_type == 'no_aug' or 'random_scale':
im = vertical_shift(im, 'normal')
elif args.augment_type == 'random_shift':
im = vertical_shift(im, 'notmid')
if args.num_channels in [1,4]:
data = np.transpose(im, (1, 0))
elif args.num_channels == 3:
H = im.shape[0]
W = im.shape[1]
C = im.shape[2]
data = np.reshape(np.transpose(im, (1, 0, 2)), (W, H * C))
data = np.divide(data, 255.0)
num_ok += 1
write_kaldi_matrix(out_fh, data, image_id)
print('Generated features for {} images. Failed for {} (image too '
'long).'.format(num_ok, num_fail), file=sys.stderr)
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