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egs/iam/v2/local/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
""" 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.
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 scipy.io as sio
import numpy as np
from scipy import misc
from scipy.ndimage.interpolation import affine_transform
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('--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=lambda x: (str(x).lower()=='true'), default=False,
help="performs image augmentation")
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
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)
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 contrast_normalization(im, low_pct, high_pct):
element_number = im.size
rows = im.shape[0]
cols = im.shape[1]
im_contrast = np.zeros(shape=im.shape)
low_index = int(low_pct * element_number)
high_index = int(high_pct * element_number)
sorted_im = np.sort(im, axis=None)
low_thred = sorted_im[low_index]
high_thred = sorted_im[high_index]
for i in range(rows):
for j in range(cols):
if im[i, j] > high_thred:
im_contrast[i, j] = 255 # lightest to white
elif im[i, j] < low_thred:
im_contrast[i, j] = 0 # darkest to black
else:
# linear normalization
im_contrast[i, j] = (im[i, j] - low_thred) * \
255 / (high_thred - low_thred)
return im_contrast
def geometric_moment(frame, p, q):
m = 0
for i in range(frame.shape[1]):
for j in range(frame.shape[0]):
m += (i ** p) * (j ** q) * frame[i][i]
return m
def central_moment(frame, p, q):
u = 0
x_bar = geometric_moment(frame, 1, 0) / \
geometric_moment(frame, 0, 0) # m10/m00
y_bar = geometric_moment(frame, 0, 1) / \
geometric_moment(frame, 0, 0) # m01/m00
for i in range(frame.shape[1]):
for j in range(frame.shape[0]):
u += ((i - x_bar)**p) * ((j - y_bar)**q) * frame[i][j]
return u
def height_normalization(frame, w, h):
frame_normalized = np.zeros(shape=(h, w))
alpha = 4
x_bar = geometric_moment(frame, 1, 0) / \
geometric_moment(frame, 0, 0) # m10/m00
y_bar = geometric_moment(frame, 0, 1) / \
geometric_moment(frame, 0, 0) # m01/m00
sigma_x = (alpha * ((central_moment(frame, 2, 0) /
geometric_moment(frame, 0, 0)) ** .5)) # alpha * sqrt(u20/m00)
sigma_y = (alpha * ((central_moment(frame, 0, 2) /
geometric_moment(frame, 0, 0)) ** .5)) # alpha * sqrt(u02/m00)
for x in range(w):
for y in range(h):
i = int((x / w - 0.5) * sigma_x + x_bar)
j = int((y / h - 0.5) * sigma_y + y_bar)
frame_normalized[x][y] = frame[i][j]
return frame_normalized
def find_slant_project(im):
rows = im.shape[0]
cols = im.shape[1]
std_max = 0
alpha_max = 0
col_disp = np.zeros(90, int)
proj = np.zeros(shape=(90, cols + 2 * rows), dtype=int)
for r in range(rows):
for alpha in range(-45, 45, 1):
col_disp[alpha] = int(r * math.tan(alpha / 180.0 * math.pi))
for c in range(cols):
if im[r, c] < 100:
for alpha in range(-45, 45, 1):
proj[alpha + 45, c + col_disp[alpha] + rows] += 1
for alpha in range(-45, 45, 1):
proj_histogram, bin_array = np.histogram(proj[alpha + 45, :], bins=10)
proj_std = np.std(proj_histogram)
if proj_std > std_max:
std_max = proj_std
alpha_max = alpha
proj_std = np.std(proj, axis=1)
return -alpha_max
def horizontal_shear(im, degree):
rad = degree / 180.0 * math.pi
padding_x = int(abs(np.tan(rad)) * im.shape[0])
padding_y = im.shape[0]
if rad > 0:
im_pad = np.concatenate(
(255 * np.ones((padding_y, padding_x), dtype=int), im), axis=1)
elif rad < 0:
im_pad = np.concatenate(
(im, 255 * np.ones((padding_y, padding_x), dtype=int)), axis=1)
else:
im_pad = im
shear_matrix = np.array([[1, 0],
[np.tan(rad), 1]])
sheared_im = affine_transform(im_pad, shear_matrix, cval=255.0)
return sheared_im
### 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
aug_setting = ['normal', 'scaled']
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]
im = misc.imread(image_path)
if args.fliplr:
im = np.fliplr(im)
if args.augment:
im_aug = get_scaled_image_aug(im, aug_setting[0])
im_contrast = contrast_normalization(im_aug, 0.05, 0.2)
slant_degree = find_slant_project(im_contrast)
im_sheared = horizontal_shear(im_contrast, slant_degree)
im_aug = im_sheared
else:
im_aug = get_scaled_image_aug(im, aug_setting[0])
im_horizontal_padded = horizontal_pad(im_aug, allowed_lengths)
if im_horizontal_padded is None:
num_fail += 1
continue
data = np.transpose(im_horizontal_padded, (1, 0))
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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