-
Notifications
You must be signed in to change notification settings - Fork 5
/
demo.py
150 lines (126 loc) · 6.13 KB
/
demo.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
# coding=utf-8
import os
import shutil
import sys
import time
import pytesseract
import cv2
import numpy as np
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) #tắt cảnh báo update tensorflow
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sys.path.append(os.getcwd())
from nets import model_train as model
from utils.rpn_msr.proposal_layer import proposal_layer
from utils.text_connector.detectors import TextDetector
# tf.app.flags.DEFINE_string('test_data_path', 'data/demo/', '')
# tf.app.flags.DEFINE_string('output_path', 'data/output/', '')
# tf.app.flags.DEFINE_string('gpu', '0', '')
# tf.app.flags.DEFINE_string('checkpoint_path', 'checkpoints_mlt/', '')
# FLAGS = tf.app.flags.FLAGS
def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0):
"""Return a sharpened version of the image, using an unsharp mask."""
blurred = cv2.GaussianBlur(image, kernel_size, sigma)
sharpened = float(amount + 1) * image - float(amount) * blurred
sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
sharpened = sharpened.round().astype(np.uint8)
if threshold > 0:
low_contrast_mask = np.absolute(image - blurred) < threshold
np.copyto(sharpened, image, where=low_contrast_mask)
return sharpened
# def get_images():
# 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):
# files.append(os.path.join(parent, filename))
# break
# print('Find {} images'.format(len(files)))
# return files
def resize_image(img):
img_size = img.shape
im_size_min = np.min(img_size[0:2])
im_size_max = np.max(img_size[0:2])
im_scale = float(600) / float(im_size_min)
if np.round(im_scale * im_size_max) > 1200:
im_scale = float(1200) / float(im_size_max)
new_h = int(img_size[0] * im_scale)
new_w = int(img_size[1] * im_scale)
new_h = new_h if new_h // 16 == 0 else (new_h // 16 + 1) * 16
new_w = new_w if new_w // 16 == 0 else (new_w // 16 + 1) * 16
re_im = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)
return re_im, (new_h / img_size[0], new_w / img_size[1])
from rotate_img import rotate_img
def main(argv=None):
# if os.path.exists(FLAGS.output_path):
# shutil.rmtree(FLAGS.output_path)
# os.makedirs(FLAGS.output_path)
# print(FLAGS.output_path)
# os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu
with tf.get_default_graph().as_default():
input_image = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='input_image')
input_im_info = tf.placeholder(tf.float32, shape=[None, 3], name='input_im_info')
global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
bbox_pred, cls_pred, cls_prob = model.model(input_image)
variable_averages = tf.train.ExponentialMovingAverage(0.997, global_step)
saver = tf.train.Saver(variable_averages.variables_to_restore())
print("init sess")
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
ckpt_state = tf.train.get_checkpoint_state('checkpoints_mlt/')
model_path = os.path.join('checkpoints_mlt/', os.path.basename(ckpt_state.model_checkpoint_path))
print('Restore from {}'.format(model_path))
saver.restore(sess, model_path)
#im_fn_list = get_images()
print('===============')
im = rotate_img('hoadontiendien-3.png')
print(im.shape)
cv2.imwrite('rotated2.png', im[:, :, :])
print("write rotate img")
start = time.time()
img, (rh, rw) = resize_image(im)
h, w, c = img.shape
im_info = np.array([h, w, c]).reshape([1, 3])
bbox_pred_val, cls_prob_val = sess.run([bbox_pred, cls_prob], feed_dict={input_image: [img], input_im_info: im_info})
textsegs, _ = proposal_layer(cls_prob_val, bbox_pred_val, im_info)
scores = textsegs[:, 0]
textsegs = textsegs[:, 1:5]
textdetector = TextDetector(DETECT_MODE='H')
boxes = textdetector.detect(textsegs, scores[:, np.newaxis], img.shape[:2])
boxes = np.array(boxes, dtype=np.int)
cost_time = (time.time() - start)
print("cost time: {:.2f}s".format(cost_time))
min_x, max_x, min_y, max_y = 0,w,0,h
box_minx = min([b[0] for b in boxes])
box_miny = min([b[1] for b in boxes])
box_maxx = max([b[4] for b in boxes])
box_maxy = max([b[5] for b in boxes])
print(box_minx,box_miny)
print(box_maxx,box_maxy)
crop_img = img[box_miny:box_maxy, box_minx:box_maxx]
print(crop_img.shape)
# for b in boxes:
# if b[0] <
# texts = []
for i, box in enumerate(boxes):
cv2.polylines(img, [box[:8].astype(np.int32).reshape((-1, 1, 2))], True, color=(0, 255, 0), thickness=1)
#crop_img2 = img[box[1]-5:box[5]+5, box[0]:box[4]]
img = cv2.resize(img, None, None, fx=1.0 / rh, fy=1.0 / rw, interpolation=cv2.INTER_LINEAR)
#print(img[:, :, ::-1].shape)
#cv2.imshow('aaa',img[:, :, ::-1])
#cv2.waitKey()
cv2.imwrite('rotate_cuted2.png', crop_img[:, :, :])
#cv2.imwrite('rotate_cuted_box.png', crop_img2[:, :, ::-1])
# with open(os.path.join(FLAGS.output_path, os.path.splitext(os.path.basename(im_fn))[0]) + ".txt", "w", encoding="UTF-8") as f:
# for i, box in enumerate(boxes):
# line = ",".join(str(box[k]) for k in range(8))
# line += "," + str(texts[i]) + "\r\n"
# #print(line)
# f.writelines(line)
main()
#if __name__ == '__main__':
#tf.app.run()