Add OCR/Handwriting Recognition examples

egs/iam/s5/cmd.sh

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+# you can change cmd.sh depending on what type of queue you are using.
+# If you have no queueing system and want to run on a local machine, you
+# can change all instances 'queue.pl' to run.pl (but be careful and run
+# commands one by one: most recipes will exhaust the memory on your
+# machine).  queue.pl works with GridEngine (qsub).  slurm.pl works
+# with slurm.  Different queues are configured differently, with different
+# queue names and different ways of specifying things like memory;
+# to account for these differences you can create and edit the file
+# conf/queue.conf to match your queue's configuration.  Search for
+# conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information,
+# or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl.
+
+export cmd="queue.pl"
+export train_cmd="queue.pl"
+export decode_cmd="queue.pl --mem 4G"

egs/iam/s5/image

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+../../cifar/v1/image/

egs/iam/s5/local/augment_and_make_feature_vect.py

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+#!/usr/bin/env python
+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="""Generates and saves the feature vectors""")
+parser.add_argument(
+    'dir', type=str, help='directory of images.scp and is also output directory')
+parser.add_argument('--seg', type=str, default='1',
+                    help='JOB number of images.JOB.scp if run in parallel mode')
+parser.add_argument('--out-ark', type=str, default='-',
+                    help='where to write the output feature file')
+parser.add_argument('--scale-size', type=int, default=40,
+                    help='size to scale the height of all images')
+parser.add_argument('--padding', type=int, default=5,
+                    help='size to scale the height of all images')
+parser.add_argument('--vertical-shift', type=int, default=10,
+                    help='total number of padding pixel per column')
+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("\n")
+    file_handle.write(" ]\n")
+
+
+def get_scaled_image(im):
+    scale_size = args.scale_size
+    sx = im.shape[1]  # width
+    sy = im.shape[0]  # height
+    scale = (1.0 * scale_size) / sy
+    nx = int(scale_size)
+    ny = int(scale * sx)
+    im = misc.imresize(im, (nx, ny))
+    padding_x = max(5, int((args.padding / 100) * im.shape[1]))
+    padding_y = im.shape[0]
+    im_pad = np.concatenate(
+        (255 * np.ones((padding_y, padding_x), dtype=int), im), axis=1)
+    im_pad1 = np.concatenate(
+        (im_pad, 255 * np.ones((padding_y, padding_x), dtype=int)), axis=1)
+    return im_pad1
+
+
+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(im):
+    rows = im.shape[0]
+    cols = im.shape[1]
+    sum_max = 0
+    slant_degree = 0
+    for shear_degree in range(-45, 45, 5):
+        sum = 0
+        shear_rad = shear_degree / 360.0 * 2 * math.pi
+        shear_matrix = np.array([[1, 0],
+                                 [np.tan(shear_rad), 1]])
+        sheared_im = affine_transform(im, shear_matrix, cval=255.0)
+        for j in range(cols):
+            foreground = (sheared_im[:, j] < 100)
+            number = np.sum(foreground)
+            # print(number)
+            if number != 0:
+                start_point = -1
+                end_point = -1
+                start_point = 0
+                for i in range(rows):
+                    if foreground[i] == 1:
+                        start_point = i
+                        break
+                for i in range(rows - 1, -1, -1):
+                    if foreground[i] == 1:
+                        end_point = i
+                        break
+                length = end_point - start_point + 1
+                #print(number, length)
+                if length == number:
+                    sum = sum + number * number
+        #print(shear_degree, sum)
+        if sum > sum_max:
+            sum_max = sum
+            slant_degree = shear_degree
+    return slant_degree
+
+
+def deslant(im, shear):
+    padding_x = int(abs(np.tan(shear)) * im.shape[0])
+    padding_y = im.shape[0]
+    if shear > 0:
+        im_pad = np.concatenate(
+            (255 * np.ones((padding_y, padding_x), dtype=int), im), axis=1)
+    else:
+        im_pad = np.concatenate(
+            (im, 255 * np.ones((padding_y, padding_x), dtype=int)), axis=1)
+
+    shear_matrix = np.array([[1, 0],
+                             [np.tan(shear), 1]])
+    # sheared_im = affine_transform(image, shear_matrix, output_shape=(
+    # im.shape[0], im.shape[1] + abs(int(im.shape[0] * np.tan(shear)))), cval=128.0)
+    sheared_im = affine_transform(im_pad, shear_matrix, cval=255.0)
+    return sheared_im
+
+
+def vertical_shift(im, mode='mid'):
+    total = args.vertical_shift
+    if mode == 'mid':
+        top = 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), im), axis=0)
+    im_pad = np.concatenate(
+        (im_pad, 255 * np.ones((bottom, width), dtype=int)), axis=0)
+    return im_pad
+
+
+def image_augment(im, out_fh, image_id):
+    random.seed(1)
+    shift_setting = ['mid', 'top', 'bottom']
+    image_shift_id = []
+    for i in range(3):
+        image_shift_id.append(image_id + '_shift' + str(i + 1))
+        im_shift = vertical_shift(im, shift_setting[i])
+        im_scaled = get_scaled_image(im_shift)
+        data = np.transpose(im_scaled, (1, 0))
+        data = np.divide(data, 255.0)
+        new_scp_list.append(image_id + '_shift' + str(i + 1))
+        write_kaldi_matrix(out_fh, data, image_shift_id[i])
+
+
+# main #
+new_scp_list = list()
+text_file = os.path.join(args.dir, 'backup', 'text')
+text_dict = dict()  # stores imageID and text
+
+with open(text_file) as text_fh:
+    for uttID_text in text_fh:
+        uttID_text = uttID_text.strip()
+        uttID_text_vect = uttID_text.split(" ")
+        uttID = uttID_text_vect[0]
+        imageID = uttID.split("_")[1]
+        text_vect = uttID_text_vect[1:]
+        text = " ".join(text_vect)
+        text_dict[imageID] = text
+
+utt2spk_file = os.path.join(args.dir, 'backup', 'utt2spk')
+uttID_spk_dict = dict()  # stores imageID and speaker
+
+with open(utt2spk_file) as utt2spk_fh:
+    for uttID_spk in utt2spk_fh:
+        uttID_spk = uttID_spk.strip()
+        uttID_spk_vect = uttID_spk.split(" ")
+        uttID = uttID_spk_vect[0]
+        imageID = uttID.split("_")[1]
+        spk = uttID_spk_vect[1]
+        uttID_spk_dict[imageID] = spk
+
+image_file = os.path.join(args.dir, 'backup', 'images.scp')
+uttID_path_dict = dict()  # stores imageID and image path
+
+with open(image_file) as image_fh:
+    for uttID_path in image_fh:
+        uttID_path = uttID_path.strip()
+        uttID_path_vect = uttID_path.split(" ")
+        uttID = uttID_path_vect[0]
+        imageID = uttID.split("_")[1]
+        path = uttID_path_vect[1]
+        uttID_path_dict[imageID] = path
+
+scp_name = 'images.scp'
+data_list_path = os.path.join(args.dir, 'backup', scp_name)
+
+if args.out_ark == '-':
+    out_fh = sys.stdout
+else:
+    out_fh = open(args.out_ark, 'wb')
+
+text_file = os.path.join(args.dir, 'text')
+text_fh = open(text_file, 'w+')
+
+utt2spk_file = os.path.join(args.dir, 'utt2spk')
+utt2spk_fh = open(utt2spk_file, 'w+')
+
+image_file = os.path.join(args.dir, 'images.scp')
+image_fh = open(image_file, 'w+')
+
+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)
+        #im_contrast = contrast_normalization(im, 0.05, 0.2)
+        #shear = (find_slant(im_contrast) / 360.0) * 2 * math.pi
+        im_scaled = get_scaled_image(im)
+        image_augment(im_scaled, out_fh, image_id)
+
+for uttID in new_scp_list:
+    imageID = uttID.split("_")[1]
+    text_fh.write(uttID + ' ' + text_dict[imageID] + '\n')
+    utt2spk_fh.write(uttID + ' ' + uttID_spk_dict[imageID] + '\n')
+    image_fh.write(uttID + ' ' + uttID_path_dict[imageID] + '\n')