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data_processor.py
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data_processor.py
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# 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]
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