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test.py
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test.py
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# -*- coding=utf-8 -*-
import cv2
import time
import math
import os
import numpy as np
import tensorflow as tf
from utils.tools import bbox_overlaps, cal_TP, cal_FN,cal_FP
import locality_aware_nms as nms_locality
import lanms
tf.app.flags.DEFINE_string('gpu_list', '0', '')
tf.app.flags.DEFINE_string('checkpoint_path', '/tmp/east_icdar2015_resnet_v1_50_rbox/', '')
tf.app.flags.DEFINE_string('output_dir', '/tmp/ch4_test_images/images/', '')
tf.app.flags.DEFINE_bool('no_write_images', False, 'do not write images')
# val_img_dir = '/home/give/Game/OCR/data/ICPR/icpr_mtwi_task2/image_test'
# val_pred_dir = '/home/give/Game/OCR/data/ICPR/icpr_mtwi_task2/pred_txt'
# val_pred_img_dir = '/home/give/Game/OCR/data/ICPR/icpr_mtwi_task2/pred_img'
val_img_dir = '/home/give/Game/OCR/data/ICPR/icpr_mtwi_task2/test'
val_pred_dir = '/home/give/Game/OCR/data/ICPR/icpr_mtwi_task2/test/pred_result'
val_pred_img_dir = '/home/give/Game/OCR/data/ICPR/icpr_mtwi_task2/test/pred_result'
tf.app.flags.DEFINE_string('test_data_path', val_img_dir, '')
import model
from icdar import restore_rectangle, shrink_poly
from tools import show_image, calculate_boundingbox_score
import shutil
import numpy as np
from glob import glob
FLAGS = tf.app.flags.FLAGS
def get_images():
'''
find image files in test data path
:return: list of files found
'''
files = []
exts = ['jpg', 'png', 'jpeg', 'JPG']
for parent, dirnames, filenames in os.walk(FLAGS.test_data_path):
for filename in filenames:
for ext in exts:
if filename.endswith(ext):
print 'file_path: ', os.path.join(parent, filename)
if os.path.join(parent, filename).find('pred') != -1:
continue
files.append(os.path.join(parent, filename))
break
print('Find {} images'.format(len(files)))
return files
def resize_image(im, max_side_len=2400):
'''
resize image to a size multiple of 32 which is required by the network
:param im: the resized image
:param max_side_len: limit of max image size to avoid out of memory in gpu
:return: the resized image and the resize ratio
'''
h, w, _ = im.shape
resize_w = w
resize_h = h
# limit the max side
if max(resize_h, resize_w) > max_side_len:
ratio = float(max_side_len) / resize_h if resize_h > resize_w else float(max_side_len) / resize_w
else:
ratio = 1.
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
resize_h = resize_h if resize_h % 32 == 0 else (resize_h // 32 - 1) * 32
resize_w = resize_w if resize_w % 32 == 0 else (resize_w // 32 - 1) * 32
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
def detect(score_map, geo_map, timer, score_map_thresh=0.8, box_thresh=0.1, nms_thres=0.2):
'''
restore text boxes from score map and geo map
:param score_map:
:param geo_map:[W,H,5]
:param timer:
:param score_map_thresh: threshhold for score map
:param box_thresh: threshhold for boxes
:param nms_thres: threshold for nms
:return:
'''
if len(score_map.shape) == 4:
score_map = score_map[0, :, :, 0]
geo_map = geo_map[0, :, :, ]
# filter the score map
xy_text = np.argwhere(score_map > score_map_thresh)
# sort the text boxes via the y axis
xy_text = xy_text[np.argsort(xy_text[:, 0])]
# restore
start = time.time()
# xy_text[:, ::-1]*4 满足条件的pixel的坐标
# geo_map[xy_text[:, 0], xy_text[:, 1], :] 得到对应点到ｂｏｕｎｄｉｎｇ ｂｏｘ 的距离
text_box_restored = restore_rectangle(xy_text[:, ::-1], geo_map[xy_text[:, 0], xy_text[:, 1], :]) # N*4*2
print('{} text boxes before nms'.format(text_box_restored.shape[0]))
boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)
boxes[:, :8] = text_box_restored.reshape((-1, 8))
boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
timer['restore'] = time.time() - start
# Modify Start
# 我们以bounding box内的平均值作为nms的标准而不是一个点的值
new_boxes = np.copy(boxes)
for i, box in enumerate(new_boxes):
mask = np.zeros_like(score_map, dtype=np.uint8)
cv2.fillPoly(mask, box[:8].reshape((-1, 4, 2)).astype(np.int32), 1)
new_boxes[i, 8] = cv2.mean(score_map, mask)[0]
# end
# nms part
start = time.time()
# boxes = nms_locality.nms_locality(boxes.astype(np.float64), nms_thres)
# boxes = lanms.merge_quadrangle_n9(boxes.astype('float32'), nms_thres)
boxes = lanms.merge_quadrangle_n9(new_boxes.astype('float32'), nms_thres)
timer['nms'] = time.time() - start
if boxes.shape[0] == 0:
return None, timer
# here we filter some low score boxes by the average score map, this is different from the orginal paper
for i, box in enumerate(boxes):
mask = np.zeros_like(score_map, dtype=np.uint8)
cv2.fillPoly(mask, box[:8].reshape((-1, 4, 2)).astype(np.int32), 1)
boxes[i, 8] = cv2.mean(score_map, mask)[0]
boxes = boxes[boxes[:, 8] > box_thresh]
return boxes, timer
def sort_poly(p):
min_axis = np.argmin(np.sum(p, axis=1))
p = p[[min_axis, (min_axis+1)%4, (min_axis+2)%4, (min_axis+3)%4]]
if abs(p[0, 0] - p[1, 0]) > abs(p[0, 1] - p[1, 1]):
return p
else:
return p[[0, 3, 2, 1]]
def read_from_gt(gt_file):
with open(gt_file) as file:
lines = file.readlines()
gt_bboxs = []
for line in lines:
splited_line = line.split(',')
splited_line = splited_line[:8]
splited_line = [int(float(ele)) for ele in splited_line]
gt_bboxs.append(splited_line)
return gt_bboxs
def main(argv=None):
import os
import shutil
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu_list
try:
print FLAGS.output_dir
os.makedirs(FLAGS.output_dir)
except OSError as e:
if e.errno != 17:
raise
# gt_dir = '/home/give/Game/OCR/data/ICPR/rename/1000/whole'
# gt_dir = '/home/give/Game/OCR/data/ICPR/txt_test'
# 每次从新生成都清空文件夹
if os.path.exists(FLAGS.output_dir):
shutil.rmtree(FLAGS.output_dir)
os.makedirs(FLAGS.output_dir)
else:
os.makedirs(FLAGS.output_dir)
P = 0.0
R = 0.0
with tf.get_default_graph().as_default():
input_images = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_images')
global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
# f_score, f_geometry = model.model_InceptionResNet_BLSTM(input_images, is_training=False)
f_score, f_geometry = model.model_InceptionResNet(input_images, is_training=False)
# f_score, f_geometry = model.model_InceptionResNet_symmetry(input_images, is_training=False)
variable_averages = tf.train.ExponentialMovingAverage(0.997, global_step)
saver = tf.train.Saver(variable_averages.variables_to_restore())
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
ckpt_state = tf.train.get_checkpoint_state(FLAGS.checkpoint_path)
model_path = os.path.join(FLAGS.checkpoint_path, os.path.basename(ckpt_state.model_checkpoint_path))
print('Restore from {}'.format(model_path))
saver.restore(sess, model_path)
im_fn_list = get_images()
for im_fn in im_fn_list:
basename = os.path.basename(im_fn).split('.')[0]
# gt_path = os.path.join(val_gt_dir, basename + '.txt')
# gt_bboxs = read_from_gt(gt_path)
im = cv2.imread(im_fn)[:, :, ::-1]
start_time = time.time()
im_resized, (ratio_h, ratio_w) = resize_image(im, max_side_len=2400)
timer = {'net': 0, 'restore': 0, 'nms': 0}
start = time.time()
score, geometry = sess.run([f_score, f_geometry], feed_dict={input_images: [im_resized]})
timer['net'] = time.time() - start
# show_image(im_resized)
# show_image(np.asarray(np.squeeze(score) * 255, np.uint8))
boundingboxs = calculate_boundingbox_score(np.squeeze(score))
boundingboxs = np.asarray(boundingboxs, np.float32)
# print np.shape(boundingboxs)
# print boundingboxs
boundingboxs[::2] /= ratio_w
boundingboxs[1::2] /= ratio_h
# print 'Pred Bounding Box shape is ', np.shape(boundingboxs)
boxes, timer = detect(score_map=score, geo_map=geometry, timer=timer)
print('{} : net {:.0f}ms, restore {:.0f}ms, nms {:.0f}ms'.format(
im_fn, timer['net']*1000, timer['restore']*1000, timer['nms']*1000))
if boxes is not None:
boxes = boxes[:, :8].reshape((-1, 4, 2))
boxes[:, :, 0] /= ratio_w
boxes[:, :, 1] /= ratio_h
duration = time.time() - start_time
print('[timing] {}'.format(duration))
new_boxes = []
# save to file
if boxes is not None:
basename = os.path.basename(im_fn)
basename = basename[:basename.rfind('.')]
res_file = os.path.join(
val_pred_dir,
'{}.txt'.format(
basename))
with open(res_file, 'w') as f:
for box in boxes:
# to avoid submitting errors
box = sort_poly(box.astype(np.int32))
new_box = []
new_box.append(box[0, 0])
new_box.append(box[0, 1])
new_box.append(box[3, 0])
new_box.append(box[3, 1])
new_box.append(box[2, 0])
new_box.append(box[2, 1])
new_box.append(box[1, 0])
new_box.append(box[1, 1])
# ld modify start
# r = [None, None, None, None]
# poly = new_box
# 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]))
# box = shrink_poly(np.reshape(new_box, [4, 2]), r=r, R=0.0001)
#
# box = sort_poly(box.astype(np.int32))
# new_box = []
# new_box.append(box[0, 0])
# new_box.append(box[0, 1])
# new_box.append(box[3, 0])
# new_box.append(box[3, 1])
# new_box.append(box[2, 0])
# new_box.append(box[2, 1])
# new_box.append(box[1, 0])
# new_box.append(box[1, 1])
# end
new_boxes.append(new_box)
# print np.shape(box)
if np.linalg.norm(box[0] - box[1]) < 5 or np.linalg.norm(box[3]-box[0]) < 5:
continue
f.write('{},{},{},{},{},{},{},{}\r\n'.format(
box[0, 0], box[0, 1], box[1, 0], box[1, 1], box[2, 0], box[2, 1], box[3, 0], box[3, 1],
))
cv2.polylines(im[:, :, ::-1], [box.astype(np.int32).reshape((-1, 1, 2))], True,
color=(255, 255, 0), thickness=1)
# for gt_bbox in gt_bboxs:
# gt_bbox = np.array(gt_bbox)
# cv2.polylines(im[:, :, ::-1], [gt_bbox.astype(np.int32).reshape((-1, 1, 2))], True,
# color=(0, 0, 255), thickness=1)
img_path = os.path.join(val_pred_img_dir, basename + '.jpg')
cv2.imwrite(img_path, im[:, :, ::-1])
# if not FLAGS.no_write_images:
# img_path = os.path.join(FLAGS.output_dir, os.path.basename(im_fn))
# cv2.imwrite(img_path, im[:, :, ::-1])
# path_dir, path_base = os.path.split(img_path)
# score_map_path = os.path.join(path_dir, path_base.split('.')[0] + '_score_map.png')
# cv2.imwrite(score_map_path, np.asarray(np.squeeze(score) * 255.0, np.uint8))
# if len(new_boxes) == 0:
# continue
# overlaps = bbox_overlaps(np.array(new_boxes), np.array(gt_bboxs), np.shape(im)[:2])
# if len(boundingboxs) == 0:
# continue
# overlaps = bbox_overlaps(np.array(boundingboxs), np.array(gt_bboxs), np.shape(im)[:2])
# print np.max(overlaps, 1)
# threshold = 0.7
# TP = cal_TP(overlaps, threshold=threshold)
# FP = cal_FP(overlaps, len(overlaps), threshold=threshold)
# FN = cal_FN(overlaps, len(gt_bboxs), threshold=threshold)
# # print 'TP %.4f, FP %.4f, FN %.4f' % (TP, FP, FN)
# if (TP + FP) == 0:
# precision = 0.0
# else:
# precision = (TP * 1.0) / ((TP + FP) * 1.0)
# recall = (TP * 1.0) / ((TP + FN) * 1.0)
# [height, width] = np.shape(im)[:2]
# scale = (1.0 * width) / (1.0 * height)
# # scale = 1.0
# P += scale * precision
# R += scale * recall
# P = P / (len(im_fn_list) * 1.0)
# R = R / (len(im_fn_list) * 1.0)
# print P
# print R
# F1 = (2 * P * R) / (P + R)
# print('Precision is %.4f, Recall is %.4f, F1 score is %.4f' % (P, R, F1))
# submit_dir = os.path.join(val_pred_dir, 'txt')
# print 'submit_dir is ', submit_dir
# if not os.path.exists(submit_dir):
# os.makedirs(submit_dir)
# files = glob( + '/*.txt')
# for file_path in files:
# shutil.copy2(
# file_path,
# os.path.join(submit_dir, os.path.basename(file_path))
# )
if __name__ == '__main__':
tf.app.run()