data_processor.py

# coding:utf-8
import glob
import sys
import csv
import cv2
import time
import os
import argparse
import itertools
from multiprocessing import Pool
import threading
import numpy as np
import scipy.optimize
import matplotlib.pyplot as plt
import matplotlib.patches as Patches
from shapely.geometry import Polygon

import tensorflow as tf

def get_images(data_path):
  files = []
  idx = 0
  for ext in ['jpg', 'png', 'jpeg', 'JPG']:
    files.extend(glob.glob(
      os.path.join(data_path, '*.{}'.format(ext))))
    idx += 1
  return files


def load_annotation(p):
  '''
  load annotation from the text file
  :param p:
  :return:
  '''
  text_polys = []
  text_tags = []
  if not os.path.exists(p):
    return np.array(text_polys, dtype=np.float32)
  with open(p, 'r') as f:
    reader = csv.reader(f)
    for line in reader:
      label = line[-1]
      # strip BOM. \ufeff for python3,  \xef\xbb\bf for python2
      line = [i.strip('\ufeff').strip('\xef\xbb\xbf') for i in line]
      line = line[0].split(' ')

      x1, y1, x2, y2, x3, y3, x4, y4 = list(map(float, line[:8]))
      text_polys.append([[x1, y1], [x2, y2], [x3, y3], [x4, y4]])
      if label == '*' or label == '###':
        text_tags.append(True)
      else:
        text_tags.append(False)
    return np.array(text_polys, dtype=np.float32), np.array(text_tags, dtype=np.bool)


def polygon_area(poly):
  '''
  compute area of a polygon
  :param poly:
  :return:
  '''
  edge = [
    (poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]),
    (poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]),
    (poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]),
    (poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1])
  ]
  return np.sum(edge)/2.


def check_and_validate_polys(FLAGS, polys, tags, size):
  '''
  check so that the text poly is in the same direction,
  and also filter some invalid polygons
  :param polys:
  :param tags:
  :return:
  '''
  (h, w) = size
  if polys.shape[0] == 0:
    return polys
  polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w-1)
  polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h-1)

  validated_polys = []
  validated_tags = []
  for poly, tag in zip(polys, tags):
    p_area = polygon_area(poly)
    if abs(p_area) < 1:
      # print poly
      if not FLAGS.suppress_warnings_and_error_messages:
        print('invalid poly')
      continue
    if p_area > 0:
      if not FLAGS.suppress_warnings_and_error_messages:
        print('poly in wrong direction')
      poly = poly[(0, 3, 2, 1), :]
    validated_polys.append(poly)
    validated_tags.append(tag)
  return np.array(validated_polys), np.array(validated_tags)


def crop_area(FLAGS, im, polys, tags, crop_background=False, max_tries=50):
  '''
  make random crop from the input image
  :param im:
  :param polys:
  :param tags:
  :param crop_background:
  :param max_tries:
  :return:
  '''
  h, w, _ = im.shape
  pad_h = h//10
  pad_w = w//10
  h_array = np.zeros((h + pad_h*2), dtype=np.int32)
  w_array = np.zeros((w + pad_w*2), dtype=np.int32)
  for poly in polys:
    poly = np.round(poly, decimals=0).astype(np.int32)
    minx = np.min(poly[:, 0])
    maxx = np.max(poly[:, 0])
    w_array[minx+pad_w:maxx+pad_w] = 1
    miny = np.min(poly[:, 1])
    maxy = np.max(poly[:, 1])
    h_array[miny+pad_h:maxy+pad_h] = 1
  # ensure the cropped area not across a text
  h_axis = np.where(h_array == 0)[0]
  w_axis = np.where(w_array == 0)[0]
  if len(h_axis) == 0 or len(w_axis) == 0:
    return im, polys, tags
  for i in range(max_tries):
    xx = np.random.choice(w_axis, size=2)
    xmin = np.min(xx) - pad_w
    xmax = np.max(xx) - pad_w
    xmin = np.clip(xmin, 0, w-1)
    xmax = np.clip(xmax, 0, w-1)
    yy = np.random.choice(h_axis, size=2)
    ymin = np.min(yy) - pad_h
    ymax = np.max(yy) - pad_h
    ymin = np.clip(ymin, 0, h-1)
    ymax = np.clip(ymax, 0, h-1)
    if xmax - xmin < FLAGS.min_crop_side_ratio*w or ymax - ymin < FLAGS.min_crop_side_ratio*h:
      # area too small
      continue
    if polys.shape[0] != 0:
      poly_axis_in_area = (polys[:, :, 0] >= xmin) & (polys[:, :, 0] <= xmax) \
                          & (polys[:, :, 1] >= ymin) & (polys[:, :, 1] <= ymax)
      selected_polys = np.where(np.sum(poly_axis_in_area, axis=1) == 4)[0]
    else:
      selected_polys = []
    if len(selected_polys) == 0:
      # no text in this area
      if crop_background:
        return im[ymin:ymax+1, xmin:xmax+1, :], polys[selected_polys], tags[selected_polys]
      else:
        continue
    im = im[ymin:ymax+1, xmin:xmax+1, :]
    polys = polys[selected_polys]
    tags = tags[selected_polys]
    polys[:, :, 0] -= xmin
    polys[:, :, 1] -= ymin
    return im, polys, tags

  return im, polys, tags


def shrink_poly(poly, r):
  '''
  fit a poly inside the origin poly, maybe bugs here...
  used for generating the score map
  :param poly: the text poly
  :param r: r in the paper
  :return: the shrinked poly
  '''
  # shrink ratio
  R = 0.3
  # find the longer pair
  if np.linalg.norm(poly[0] - poly[1]) + np.linalg.norm(poly[2] - poly[3]) > \
          np.linalg.norm(poly[0] - poly[3]) + np.linalg.norm(poly[1] - poly[2]):
    # first move (p0, p1), (p2, p3), then (p0, p3), (p1, p2)
    ## p0, p1
    theta = np.arctan2((poly[1][1] - poly[0][1]), (poly[1][0] - poly[0][0]))
    poly[0][0] += R * r[0] * np.cos(theta)
    poly[0][1] += R * r[0] * np.sin(theta)
    poly[1][0] -= R * r[1] * np.cos(theta)
    poly[1][1] -= R * r[1] * np.sin(theta)
    ## p2, p3
    theta = np.arctan2((poly[2][1] - poly[3][1]), (poly[2][0] - poly[3][0]))
    poly[3][0] += R * r[3] * np.cos(theta)
    poly[3][1] += R * r[3] * np.sin(theta)
    poly[2][0] -= R * r[2] * np.cos(theta)
    poly[2][1] -= R * r[2] * np.sin(theta)
    ## p0, p3
    theta = np.arctan2((poly[3][0] - poly[0][0]), (poly[3][1] - poly[0][1]))
    poly[0][0] += R * r[0] * np.sin(theta)
    poly[0][1] += R * r[0] * np.cos(theta)
    poly[3][0] -= R * r[3] * np.sin(theta)
    poly[3][1] -= R * r[3] * np.cos(theta)
    ## p1, p2
    theta = np.arctan2((poly[2][0] - poly[1][0]), (poly[2][1] - poly[1][1]))
    poly[1][0] += R * r[1] * np.sin(theta)
    poly[1][1] += R * r[1] * np.cos(theta)
    poly[2][0] -= R * r[2] * np.sin(theta)
    poly[2][1] -= R * r[2] * np.cos(theta)
  else:
    ## p0, p3
    # print poly
    theta = np.arctan2((poly[3][0] - poly[0][0]), (poly[3][1] - poly[0][1]))
    poly[0][0] += R * r[0] * np.sin(theta)
    poly[0][1] += R * r[0] * np.cos(theta)
    poly[3][0] -= R * r[3] * np.sin(theta)
    poly[3][1] -= R * r[3] * np.cos(theta)
    ## p1, p2
    theta = np.arctan2((poly[2][0] - poly[1][0]), (poly[2][1] - poly[1][1]))
    poly[1][0] += R * r[1] * np.sin(theta)
    poly[1][1] += R * r[1] * np.cos(theta)
    poly[2][0] -= R * r[2] * np.sin(theta)
    poly[2][1] -= R * r[2] * np.cos(theta)
    ## p0, p1
    theta = np.arctan2((poly[1][1] - poly[0][1]), (poly[1][0] - poly[0][0]))
    poly[0][0] += R * r[0] * np.cos(theta)
    poly[0][1] += R * r[0] * np.sin(theta)
    poly[1][0] -= R * r[1] * np.cos(theta)
    poly[1][1] -= R * r[1] * np.sin(theta)
    ## p2, p3
    theta = np.arctan2((poly[2][1] - poly[3][1]), (poly[2][0] - poly[3][0]))
    poly[3][0] += R * r[3] * np.cos(theta)
    poly[3][1] += R * r[3] * np.sin(theta)
    poly[2][0] -= R * r[2] * np.cos(theta)
    poly[2][1] -= R * r[2] * np.sin(theta)
  return poly


def point_dist_to_line(p1, p2, p3):
  # compute the distance from p3 to p1-p2
  return np.linalg.norm(np.cross(p2 - p1, p1 - p3)) / np.linalg.norm(p2 - p1)


def fit_line(p1, p2):
  # fit a line ax+by+c = 0
  if p1[0] == p1[1]:
    return [1., 0., -p1[0]]
  else:
    [k, b] = np.polyfit(p1, p2, deg=1)
    return [k, -1., b]


def line_cross_point(FLAGS, line1, line2):
  # line1 0= ax+by+c, compute the cross point of line1 and line2
  if line1[0] != 0 and line1[0] == line2[0]:
    if not FLAGS.suppress_warnings_and_error_messages:
      print('Cross point does not exist')
    return None
  if line1[0] == 0 and line2[0] == 0:
    if not FLAGS.suppress_warnings_and_error_messages:
      print('Cross point does not exist')
    return None
  if line1[1] == 0:
    x = -line1[2]
    y = line2[0] * x + line2[2]
  elif line2[1] == 0:
    x = -line2[2]
    y = line1[0] * x + line1[2]
  else:
    k1, _, b1 = line1
    k2, _, b2 = line2
    x = -(b1-b2)/(k1-k2)
    y = k1*x + b1
  return np.array([x, y], dtype=np.float32)


def line_verticle(line, point):
  # get the verticle line from line across point
  if line[1] == 0:
    verticle = [0, -1, point[1]]
  else:
    if line[0] == 0:
      verticle = [1, 0, -point[0]]
    else:
      verticle = [-1./line[0], -1, point[1] - (-1/line[0] * point[0])]
  return verticle


def rectangle_from_parallelogram(FLAGS, poly):
  '''
  fit a rectangle from a parallelogram
  :param poly:
  :return:
  '''
  p0, p1, p2, p3 = poly
  angle_p0 = np.arccos(np.dot(p1-p0, p3-p0)/(np.linalg.norm(p0-p1) * np.linalg.norm(p3-p0)))
  if angle_p0 < 0.5 * np.pi:
    if np.linalg.norm(p0 - p1) > np.linalg.norm(p0-p3):
      # p0 and p2
      ## p0
      p2p3 = fit_line([p2[0], p3[0]], [p2[1], p3[1]])
      p2p3_verticle = line_verticle(p2p3, p0)

      new_p3 = line_cross_point(FLAGS, p2p3, p2p3_verticle)
      ## p2
      p0p1 = fit_line([p0[0], p1[0]], [p0[1], p1[1]])
      p0p1_verticle = line_verticle(p0p1, p2)

      new_p1 = line_cross_point(FLAGS, p0p1, p0p1_verticle)
      return np.array([p0, new_p1, p2, new_p3], dtype=np.float32)
    else:
      p1p2 = fit_line([p1[0], p2[0]], [p1[1], p2[1]])
      p1p2_verticle = line_verticle(p1p2, p0)

      new_p1 = line_cross_point(FLAGS, p1p2, p1p2_verticle)
      p0p3 = fit_line([p0[0], p3[0]], [p0[1], p3[1]])
      p0p3_verticle = line_verticle(p0p3, p2)

      new_p3 = line_cross_point(FLAGS, p0p3, p0p3_verticle)
      return np.array([p0, new_p1, p2, new_p3], dtype=np.float32)
  else:
    if np.linalg.norm(p0-p1) > np.linalg.norm(p0-p3):
      # p1 and p3
      ## p1
      p2p3 = fit_line([p2[0], p3[0]], [p2[1], p3[1]])
      p2p3_verticle = line_verticle(p2p3, p1)

      new_p2 = line_cross_point(FLAGS, p2p3, p2p3_verticle)
      ## p3
      p0p1 = fit_line([p0[0], p1[0]], [p0[1], p1[1]])
      p0p1_verticle = line_verticle(p0p1, p3)

      new_p0 = line_cross_point(FLAGS, p0p1, p0p1_verticle)
      return np.array([new_p0, p1, new_p2, p3], dtype=np.float32)
    else:
      p0p3 = fit_line([p0[0], p3[0]], [p0[1], p3[1]])
      p0p3_verticle = line_verticle(p0p3, p1)

      new_p0 = line_cross_point(FLAGS, p0p3, p0p3_verticle)
      p1p2 = fit_line([p1[0], p2[0]], [p1[1], p2[1]])
      p1p2_verticle = line_verticle(p1p2, p3)

      new_p2 = line_cross_point(FLAGS, p1p2, p1p2_verticle)
      return np.array([new_p0, p1, new_p2, p3], dtype=np.float32)


def sort_rectangle(FLAGS, poly):
  # sort the four coordinates of the polygon, points in poly should be sorted clockwise
  # First find the lowest point
  p_lowest = np.argmax(poly[:, 1])
  if np.count_nonzero(poly[:, 1] == poly[p_lowest, 1]) == 2:
    # if the bottom line is parallel to x-axis, then p0 must be the upper-left corner
    p0_index = np.argmin(np.sum(poly, axis=1))
    p1_index = (p0_index + 1) % 4
    p2_index = (p0_index + 2) % 4
    p3_index = (p0_index + 3) % 4
    return poly[[p0_index, p1_index, p2_index, p3_index]], 0.
  else:
    # find the point that sits right to the lowest point
    p_lowest_right = (p_lowest - 1) % 4
    p_lowest_left = (p_lowest + 1) % 4
    angle = np.arctan(-(poly[p_lowest][1] - poly[p_lowest_right][1])/(poly[p_lowest][0] - poly[p_lowest_right][0]))
    # assert angle > 0
    if angle <= 0:
      if not FLAGS.suppress_warnings_and_error_messages:
        print(angle, poly[p_lowest], poly[p_lowest_right])
    if angle/np.pi * 180 > 45:
      # 这个点为p2 - this point is p2
      p2_index = p_lowest
      p1_index = (p2_index - 1) % 4
      p0_index = (p2_index - 2) % 4
      p3_index = (p2_index + 1) % 4
      return poly[[p0_index, p1_index, p2_index, p3_index]], -(np.pi/2 - angle)
    else:
      # 这个点为p3 - this point is p3
      p3_index = p_lowest
      p0_index = (p3_index + 1) % 4
      p1_index = (p3_index + 2) % 4
      p2_index = (p3_index + 3) % 4
      return poly[[p0_index, p1_index, p2_index, p3_index]], angle


def restore_rectangle_rbox(origin, geometry):
  d = geometry[:, :4]
  angle = geometry[:, 4]
  # for angle > 0
  origin_0 = origin[angle >= 0]
  d_0 = d[angle >= 0]
  angle_0 = angle[angle >= 0]
  if origin_0.shape[0] > 0:
    p = np.array([np.zeros(d_0.shape[0]), -d_0[:, 0] - d_0[:, 2],
                  d_0[:, 1] + d_0[:, 3], -d_0[:, 0] - d_0[:, 2],
                  d_0[:, 1] + d_0[:, 3], np.zeros(d_0.shape[0]),
                  np.zeros(d_0.shape[0]), np.zeros(d_0.shape[0]),
                  d_0[:, 3], -d_0[:, 2]])
    p = p.transpose((1, 0)).reshape((-1, 5, 2))  # N*5*2

    rotate_matrix_x = np.array([np.cos(angle_0), np.sin(angle_0)]).transpose((1, 0))
    rotate_matrix_x = np.repeat(rotate_matrix_x, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1))  # N*5*2

    rotate_matrix_y = np.array([-np.sin(angle_0), np.cos(angle_0)]).transpose((1, 0))
    rotate_matrix_y = np.repeat(rotate_matrix_y, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1))

    p_rotate_x = np.sum(rotate_matrix_x * p, axis=2)[:, :, np.newaxis]  # N*5*1
    p_rotate_y = np.sum(rotate_matrix_y * p, axis=2)[:, :, np.newaxis]  # N*5*1

    p_rotate = np.concatenate([p_rotate_x, p_rotate_y], axis=2)  # N*5*2

    p3_in_origin = origin_0 - p_rotate[:, 4, :]
    new_p0 = p_rotate[:, 0, :] + p3_in_origin  # N*2
    new_p1 = p_rotate[:, 1, :] + p3_in_origin
    new_p2 = p_rotate[:, 2, :] + p3_in_origin
    new_p3 = p_rotate[:, 3, :] + p3_in_origin

    new_p_0 = np.concatenate([new_p0[:, np.newaxis, :], new_p1[:, np.newaxis, :],
                              new_p2[:, np.newaxis, :], new_p3[:, np.newaxis, :]], axis=1)  # N*4*2
  else:
    new_p_0 = np.zeros((0, 4, 2))
  # for angle < 0
  origin_1 = origin[angle < 0]
  d_1 = d[angle < 0]
  angle_1 = angle[angle < 0]
  if origin_1.shape[0] > 0:
    p = np.array([-d_1[:, 1] - d_1[:, 3], -d_1[:, 0] - d_1[:, 2],
                  np.zeros(d_1.shape[0]), -d_1[:, 0] - d_1[:, 2],
                  np.zeros(d_1.shape[0]), np.zeros(d_1.shape[0]),
                  -d_1[:, 1] - d_1[:, 3], np.zeros(d_1.shape[0]),
                  -d_1[:, 1], -d_1[:, 2]])
    p = p.transpose((1, 0)).reshape((-1, 5, 2))  # N*5*2

    rotate_matrix_x = np.array([np.cos(-angle_1), -np.sin(-angle_1)]).transpose((1, 0))
    rotate_matrix_x = np.repeat(rotate_matrix_x, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1))  # N*5*2

    rotate_matrix_y = np.array([np.sin(-angle_1), np.cos(-angle_1)]).transpose((1, 0))
    rotate_matrix_y = np.repeat(rotate_matrix_y, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1))

    p_rotate_x = np.sum(rotate_matrix_x * p, axis=2)[:, :, np.newaxis]  # N*5*1
    p_rotate_y = np.sum(rotate_matrix_y * p, axis=2)[:, :, np.newaxis]  # N*5*1

    p_rotate = np.concatenate([p_rotate_x, p_rotate_y], axis=2)  # N*5*2

    p3_in_origin = origin_1 - p_rotate[:, 4, :]
    new_p0 = p_rotate[:, 0, :] + p3_in_origin  # N*2
    new_p1 = p_rotate[:, 1, :] + p3_in_origin
    new_p2 = p_rotate[:, 2, :] + p3_in_origin
    new_p3 = p_rotate[:, 3, :] + p3_in_origin

    new_p_1 = np.concatenate([new_p0[:, np.newaxis, :], new_p1[:, np.newaxis, :],
                              new_p2[:, np.newaxis, :], new_p3[:, np.newaxis, :]], axis=1)  # N*4*2
  else:
    new_p_1 = np.zeros((0, 4, 2))
  return np.concatenate([new_p_0, new_p_1])


def restore_rectangle(origin, geometry):
  return restore_rectangle_rbox(origin, geometry)


def generate_rbox(FLAGS, im_size, polys, tags):
  h, w = im_size
  shrinked_poly_mask = np.zeros((h, w), dtype=np.uint8)
  orig_poly_mask = np.zeros((h, w), dtype=np.uint8)
  score_map = np.zeros((h, w), dtype=np.uint8)
  geo_map = np.zeros((h, w, 5), dtype=np.float32)
  # mask used during traning, to ignore some hard areas
  overly_small_text_region_training_mask = np.ones((h, w), dtype=np.uint8)
  for poly_idx, poly_data in enumerate(zip(polys, tags)):
    poly = poly_data[0]
    tag = poly_data[1]

    r = [None, None, None, None]
    for i in range(4):
      r[i] = min(np.linalg.norm(poly[i] - poly[(i + 1) % 4]),
                 np.linalg.norm(poly[i] - poly[(i - 1) % 4]))
    # score map
    shrinked_poly = shrink_poly(poly.copy(), r).astype(np.int32)[np.newaxis, :, :]
    cv2.fillPoly(score_map, shrinked_poly, 1)
    cv2.fillPoly(shrinked_poly_mask, shrinked_poly, poly_idx + 1)
    cv2.fillPoly(orig_poly_mask, poly.astype(np.int32)[np.newaxis, :, :], 1)
    # if the poly is too small, then ignore it during training
    poly_h = min(np.linalg.norm(poly[0] - poly[3]), np.linalg.norm(poly[1] - poly[2]))
    poly_w = min(np.linalg.norm(poly[0] - poly[1]), np.linalg.norm(poly[2] - poly[3]))
    if min(poly_h, poly_w) < FLAGS.min_text_size:
      cv2.fillPoly(overly_small_text_region_training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0)
    if tag:
      cv2.fillPoly(overly_small_text_region_training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0)

    xy_in_poly = np.argwhere(shrinked_poly_mask == (poly_idx + 1))
    # if geometry == 'RBOX':
    # generate a parallelogram for any combination of two vertices
    fitted_parallelograms = []
    for i in range(4):
      p0 = poly[i]
      p1 = poly[(i + 1) % 4]
      p2 = poly[(i + 2) % 4]
      p3 = poly[(i + 3) % 4]
      edge = fit_line([p0[0], p1[0]], [p0[1], p1[1]])
      backward_edge = fit_line([p0[0], p3[0]], [p0[1], p3[1]])
      forward_edge = fit_line([p1[0], p2[0]], [p1[1], p2[1]])
      if point_dist_to_line(p0, p1, p2) > point_dist_to_line(p0, p1, p3):
        # parallel lines through p2
        if edge[1] == 0:
          edge_opposite = [1, 0, -p2[0]]
        else:
          edge_opposite = [edge[0], -1, p2[1] - edge[0] * p2[0]]
      else:
        # after p3
        if edge[1] == 0:
          edge_opposite = [1, 0, -p3[0]]
        else:
          edge_opposite = [edge[0], -1, p3[1] - edge[0] * p3[0]]
      # move forward edge
      new_p0 = p0
      new_p1 = p1
      new_p2 = p2
      new_p3 = p3
      new_p2 = line_cross_point(FLAGS, forward_edge, edge_opposite)
      if point_dist_to_line(p1, new_p2, p0) > point_dist_to_line(p1, new_p2, p3):
        # across p0
        if forward_edge[1] == 0:
          forward_opposite = [1, 0, -p0[0]]
        else:
          forward_opposite = [forward_edge[0], -1, p0[1] - forward_edge[0] * p0[0]]
      else:
        # across p3
        if forward_edge[1] == 0:
          forward_opposite = [1, 0, -p3[0]]
        else:
          forward_opposite = [forward_edge[0], -1, p3[1] - forward_edge[0] * p3[0]]
      new_p0 = line_cross_point(FLAGS, forward_opposite, edge)
      new_p3 = line_cross_point(FLAGS, forward_opposite, edge_opposite)
      fitted_parallelograms.append([new_p0, new_p1, new_p2, new_p3, new_p0])
      # or move backward edge
      new_p0 = p0
      new_p1 = p1
      new_p2 = p2
      new_p3 = p3
      new_p3 = line_cross_point(FLAGS, backward_edge, edge_opposite)
      if point_dist_to_line(p0, p3, p1) > point_dist_to_line(p0, p3, p2):
        # across p1
        if backward_edge[1] == 0:
          backward_opposite = [1, 0, -p1[0]]
        else:
          backward_opposite = [backward_edge[0], -1, p1[1] - backward_edge[0] * p1[0]]
      else:
        # across p2
        if backward_edge[1] == 0:
          backward_opposite = [1, 0, -p2[0]]
        else:
          backward_opposite = [backward_edge[0], -1, p2[1] - backward_edge[0] * p2[0]]
      new_p1 = line_cross_point(FLAGS, backward_opposite, edge)
      new_p2 = line_cross_point(FLAGS, backward_opposite, edge_opposite)
      fitted_parallelograms.append([new_p0, new_p1, new_p2, new_p3, new_p0])
    areas = [Polygon(t).area for t in fitted_parallelograms]
    parallelogram = np.array(fitted_parallelograms[np.argmin(areas)][:-1], dtype=np.float32)
    # sort thie polygon
    parallelogram_coord_sum = np.sum(parallelogram, axis=1)
    min_coord_idx = np.argmin(parallelogram_coord_sum)
    parallelogram = parallelogram[
      [min_coord_idx, (min_coord_idx + 1) % 4, (min_coord_idx + 2) % 4, (min_coord_idx + 3) % 4]]

    rectange = rectangle_from_parallelogram(FLAGS, parallelogram)
    rectange, rotate_angle = sort_rectangle(FLAGS, rectange)

    p0_rect, p1_rect, p2_rect, p3_rect = rectange
    for y, x in xy_in_poly:
      point = np.array([x, y], dtype=np.float32)
      # top
      geo_map[y, x, 0] = point_dist_to_line(p0_rect, p1_rect, point)
      # right
      geo_map[y, x, 1] = point_dist_to_line(p1_rect, p2_rect, point)
      # down
      geo_map[y, x, 2] = point_dist_to_line(p2_rect, p3_rect, point)
      # left
      geo_map[y, x, 3] = point_dist_to_line(p3_rect, p0_rect, point)
      # angle
      geo_map[y, x, 4] = rotate_angle

  shrinked_poly_mask = (shrinked_poly_mask > 0).astype('uint8')
  text_region_boundary_training_mask = 1 - (orig_poly_mask - shrinked_poly_mask)

  return score_map, geo_map, overly_small_text_region_training_mask, text_region_boundary_training_mask

def all(iterable):
  for element in iterable:
    if not element:
      return False
  return True

def get_text_file(image_file):
  txt_file = image_file.replace(os.path.basename(image_file).split('.')[1], 'txt')
  txt_file_name = txt_file.split('/')[-1]
  #txt_file = txt_file.replace(txt_file_name, 'gt_' + txt_file_name)
  return txt_file

def pad_image(img, input_size, is_train):
  new_h, new_w, _ = img.shape
  max_h_w_i = np.max([new_h, new_w, input_size])
  img_padded = np.zeros((max_h_w_i, max_h_w_i, 3), dtype=np.uint8)
  if is_train:
    shift_h = np.random.randint(max_h_w_i - new_h + 1)
    shift_w = np.random.randint(max_h_w_i - new_w + 1)
  else:
    shift_h = (max_h_w_i - new_h) // 2
    shift_w = (max_h_w_i - new_w) // 2
  img_padded[shift_h:new_h+shift_h, shift_w:new_w+shift_w, :] = img.copy()
  img = img_padded
  return img, shift_h, shift_w

def resize_image(img, text_polys, input_size, shift_h, shift_w):
  new_h, new_w, _ = img.shape
  img = cv2.resize(img, dsize=(input_size, input_size))
  # pad and resize text polygons
  resize_ratio_3_x = input_size/float(new_w)
  resize_ratio_3_y = input_size/float(new_h)
  text_polys[:, :, 0] += shift_w
  text_polys[:, :, 1] += shift_h
  text_polys[:, :, 0] *= resize_ratio_3_x
  text_polys[:, :, 1] *= resize_ratio_3_y
  return img, text_polys

class threadsafe_iter:
  """Takes an iterator/generator and makes it thread-safe by
  serializing call to the `next` method of given iterator/generator.
  """
  def __init__(self, it):
    self.it = it
    self.lock = threading.Lock()

  def __iter__(self):
    return self

  def __next__(self): # Python 3
    with self.lock:
      return next(self.it)

  def next(self): # Python 2
    with self.lock:
      return self.it.next()

def threadsafe_generator(f):
  """A decorator that takes a generator function and makes it thread-safe.
  """
  def g(*a, **kw):
    return threadsafe_iter(f(*a, **kw))
  return g

@threadsafe_generator
def generator(FLAGS, input_size=512, background_ratio=3./8, is_train=True, idx=None, random_scale=np.array([0.5, 1, 2.0, 3.0]), vis=False):
  image_list = np.array(get_images(FLAGS.training_data_path))
  if not idx is None:
    image_list = image_list[idx]
  print('{} training images in {}'.format(
    image_list.shape[0], FLAGS.training_data_path))
  index = np.arange(0, image_list.shape[0])
  epoch = 1
  while True:
    np.random.shuffle(index)
    images = []
    image_fns = []
    score_maps = []
    geo_maps = []
    overly_small_text_region_training_masks = []
    text_region_boundary_training_masks = []
    for i in index:
      try:
        im_fn = image_list[i]
        im = cv2.imread(im_fn)
        h, w, _ = im.shape
        txt_fn = get_text_file(im_fn)
        if not os.path.exists(txt_fn):
          if not FLAGS.suppress_warnings_and_error_messages:
            print('text file {} does not exists'.format(txt_fn))
          continue

        text_polys, text_tags = load_annotation(txt_fn)

        text_polys, text_tags = check_and_validate_polys(FLAGS, text_polys, text_tags, (h, w))

        # random scale this image
        rd_scale = np.random.choice(random_scale)
        x_scale_variation = np.random.randint(-10, 10) / 100.
        y_scale_variation = np.random.randint(-10, 10) / 100.
        im = cv2.resize(im, dsize=None, fx=rd_scale + x_scale_variation, fy=rd_scale + y_scale_variation)
        text_polys[:, :, 0] *= rd_scale + x_scale_variation
        text_polys[:, :, 1] *= rd_scale + y_scale_variation

        # random crop a area from image
        if np.random.rand() < background_ratio:
          # crop background
          im, text_polys, text_tags = crop_area(FLAGS, im, text_polys, text_tags, crop_background=True)
          if text_polys.shape[0] > 0:
            # cannot find background
            continue
          # pad and resize image
          im, _, _ = pad_image(im, FLAGS.input_size, is_train)
          im = cv2.resize(im, dsize=(input_size, input_size))
          score_map = np.zeros((input_size, input_size), dtype=np.uint8)
          geo_map_channels = 5 if FLAGS.geometry == 'RBOX' else 8
          geo_map = np.zeros((input_size, input_size, geo_map_channels), dtype=np.float32)
          overly_small_text_region_training_mask = np.ones((input_size, input_size), dtype=np.uint8)
          text_region_boundary_training_mask = np.ones((input_size, input_size), dtype=np.uint8)
        else:
          im, text_polys, text_tags = crop_area(FLAGS, im, text_polys, text_tags, crop_background=False)
          if text_polys.shape[0] == 0:
            continue
          h, w, _ = im.shape
          im, shift_h, shift_w = pad_image(im, FLAGS.input_size, is_train)
          im, text_polys = resize_image(im, text_polys, FLAGS.input_size, shift_h, shift_w)
          new_h, new_w, _ = im.shape
          score_map, geo_map, overly_small_text_region_training_mask, text_region_boundary_training_mask = generate_rbox(FLAGS, (new_h, new_w), text_polys, text_tags)

        if vis:
          fig, axs = plt.subplots(3, 2, figsize=(20, 30))
          axs[0, 0].imshow(im[:, :, ::-1])
          axs[0, 0].set_xticks([])
          axs[0, 0].set_yticks([])
          for poly in text_polys:
            poly_h = min(abs(poly[3, 1] - poly[0, 1]), abs(poly[2, 1] - poly[1, 1]))
            poly_w = min(abs(poly[1, 0] - poly[0, 0]), abs(poly[2, 0] - poly[3, 0]))
            axs[0, 0].add_artist(Patches.Polygon(
              poly, facecolor='none', edgecolor='green', linewidth=2, linestyle='-', fill=True))
            axs[0, 0].text(poly[0, 0], poly[0, 1], '{:.0f}-{:.0f}'.format(poly_h, poly_w), color='purple')
          axs[0, 1].imshow(score_map[::, ::])
          axs[0, 1].set_xticks([])
          axs[0, 1].set_yticks([])
          axs[1, 0].imshow(geo_map[::, ::, 0])
          axs[1, 0].set_xticks([])
          axs[1, 0].set_yticks([])
          axs[1, 1].imshow(geo_map[::, ::, 1])
          axs[1, 1].set_xticks([])
          axs[1, 1].set_yticks([])
          axs[2, 0].imshow(geo_map[::, ::, 2])
          axs[2, 0].set_xticks([])
          axs[2, 0].set_yticks([])
          axs[2, 1].imshow(training_mask[::, ::])
          axs[2, 1].set_xticks([])
          axs[2, 1].set_yticks([])
          plt.tight_layout()
          plt.show()
          plt.close()

        im = (im / 127.5) - 1.
        images.append(im[:, :, ::-1].astype(np.float32))
        image_fns.append(im_fn)
        score_maps.append(score_map[::4, ::4, np.newaxis].astype(np.float32))
        geo_maps.append(geo_map[::4, ::4, :].astype(np.float32))
        overly_small_text_region_training_masks.append(overly_small_text_region_training_mask[::4, ::4, np.newaxis].astype(np.float32))
        text_region_boundary_training_masks.append(text_region_boundary_training_mask[::4, ::4, np.newaxis].astype(np.float32))

        if len(images) == FLAGS.batch_size:
          yield [np.array(images), np.array(overly_small_text_region_training_masks), np.array(text_region_boundary_training_masks), np.array(score_maps)], [np.array(score_maps), np.array(geo_maps)]
          images = []
          image_fns = []
          score_maps = []
          geo_maps = []
          overly_small_text_region_training_masks = []
          text_region_boundary_training_masks = []
      except Exception as e:
        import traceback
        if not FLAGS.suppress_warnings_and_error_messages:
          traceback.print_exc()
        continue
    epoch += 1



@threadsafe_generator
def val_generator(FLAGS, idx=None, is_train=False):
  image_list = np.array(get_images(FLAGS.validation_data_path))
  if not idx is None:
    image_list = image_list[idx]
  print('{} validation images in {}'.format(
    image_list.shape[0], FLAGS.training_data_path))
  index = np.arange(0, image_list.shape[0])
  epoch = 1
  while True:
    np.random.shuffle(index)
    images = []
    image_fns = []
    score_maps = []
    geo_maps = []
    overly_small_text_region_training_masks = []
    text_region_boundary_training_masks = []
    for i in index:
      try:
        im_fn = image_list[i]
        im = cv2.imread(im_fn)
        h, w, _ = im.shape
        txt_fn = get_text_file(im_fn)
        if not os.path.exists(txt_fn):
          if not FLAGS.suppress_warnings_and_error_messages:
            print('text file {} does not exists'.format(txt_fn))
          continue

        text_polys, text_tags = load_annotation(txt_fn)

        text_polys, text_tags = check_and_validate_polys(FLAGS, text_polys, text_tags, (h, w))

        im, shift_h, shift_w = pad_image(im, FLAGS.input_size, is_train)
        im, text_polys = resize_image(im, text_polys, FLAGS.input_size, shift_h, shift_w)
        new_h, new_w, _ = im.shape
        score_map, geo_map, overly_small_text_region_training_mask, text_region_boundary_training_mask = generate_rbox(FLAGS, (new_h, new_w), text_polys, text_tags)

        im = (im / 127.5) - 1.
        images.append(im[:, :, ::-1].astype(np.float32))
        image_fns.append(im_fn)
        score_maps.append(score_map[::4, ::4, np.newaxis].astype(np.float32))
        geo_maps.append(geo_map[::4, ::4, :].astype(np.float32))
        overly_small_text_region_training_masks.append(overly_small_text_region_training_mask[::4, ::4, np.newaxis].astype(np.float32))
        text_region_boundary_training_masks.append(text_region_boundary_training_mask[::4, ::4, np.newaxis].astype(np.float32))

        if len(images) == FLAGS.batch_size:
          yield [np.array(images), np.array(overly_small_text_region_training_masks), np.array(text_region_boundary_training_masks), np.array(score_maps)], [np.array(score_maps), np.array(geo_maps)]
          images = []
          image_fns = []
          score_maps = []
          geo_maps = []
          overly_small_text_region_training_masks = []
          text_region_boundary_training_masks = []
      except Exception as e:
        import traceback
        if not FLAGS.suppress_warnings_and_error_messages:
          traceback.print_exc()
        continue
    epoch += 1


def count_samples(FLAGS):
  if sys.version_info >= (3, 0):
    return len([f for f in next(os.walk(FLAGS.training_data_path))[2] if f[-4:] == ".jpg"])
  else:
    return len([f for f in os.walk(FLAGS.training_data_path).next()[2] if f[-4:] == ".jpg"])


def load_data_process(args):
  (image_file, FLAGS, is_train) = args
  try:
    img = cv2.imread(image_file)
    h, w, _ = img.shape
    txt_file = get_text_file(image_file)
    if not os.path.exists(txt_file):
      print('text file {} does not exists'.format(txt_file))

    text_polys, text_tags = load_annotation(txt_file)
    text_polys, text_tags = check_and_validate_polys(FLAGS, text_polys, text_tags, (h, w))
    img, shift_h, shift_w = pad_image(img, FLAGS.input_size, is_train=is_train)
    img, text_polys = resize_image(img, text_polys, FLAGS.input_size, shift_h, shift_w)
    new_h, new_w, _ = img.shape
    score_map, geo_map, overly_small_text_region_training_mask, text_region_boundary_training_mask = generate_rbox(FLAGS, (new_h, new_w), text_polys, text_tags)
    img = (img / 127.5) - 1.
    return img[:, :, ::-1].astype(np.float32), image_file, score_map[::4, ::4, np.newaxis].astype(np.float32), geo_map[::4, ::4, :].astype(np.float32), overly_small_text_region_training_mask[::4, ::4, np.newaxis].astype(np.float32), text_region_boundary_training_mask[::4, ::4, np.newaxis].astype(np.float32)
  except Exception as e:
    import traceback
    if not FLAGS.suppress_warnings_and_error_messages:
      traceback.print_exc()

def load_data(FLAGS, is_train=False):
  image_files = np.array(get_images(FLAGS.validation_data_path))
  images = []
  image_fns = []
  score_maps = []
  geo_maps = []
  overly_small_text_region_training_masks = []
  text_region_boundary_training_masks = []

  pool = Pool(FLAGS.nb_workers)
  if sys.version_info >= (3, 0):
    loaded_data = pool.map_async(load_data_process, zip(image_files, itertools.repeat(FLAGS), itertools.repeat(is_train))).get(9999999)
  else:
    loaded_data = pool.map_async(load_data_process, itertools.izip(image_files, itertools.repeat(FLAGS), itertools.repeat(is_train))).get(9999999)
  pool.close()
  pool.join()

  images = [item[0] for item in loaded_data if not item is None]
  image_fns = [item[1] for item in loaded_data if not item is None]
  score_maps = [item[2] for item in loaded_data if not item is None]
  geo_maps = [item[3] for item in loaded_data if not item is None]
  overly_small_text_region_training_masks = [item[4] for item in loaded_data if not item is None]
  text_region_boundary_training_masks = [item[5] for item in loaded_data if not item is None]
  print('Number of validation images : %d' % len(images))


  return np.array(images), np.array(overly_small_text_region_training_masks), np.array(text_region_boundary_training_masks), np.array(score_maps), np.array(geo_maps)


if __name__ == '__main__':
  pass