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test.py
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test.py
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import cv2
import time
import math
import os
import numpy as np
import tensorflow as tf
import json
import locality_aware_nms as nms_locality
import lanms
tf.app.flags.DEFINE_string(
'test_data_path',
'/data/20180809/icdar2017/test_images/',
'')
tf.app.flags.DEFINE_string('gpu_list', '0,1', '')
#tf.app.flags.DEFINE_string(
# 'checkpoint_path',
# '/workspace/imagenet-data/EAST/temp_test/east_icdar2015_resnet_v1_50_rbox/',
# '')
tf.app.flags.DEFINE_string(
'checkpoint_path',
'/data/20180809/IncepText/model_save/',
'')
tf.app.flags.DEFINE_string(
'output_dir',
'/data/20180809/IncepText/result/',
'')
tf.app.flags.DEFINE_bool('no_write_images', False, 'do not write images')
tf.app.flags.DEFINE_string('save_pic_jietu','/workspace/imagenet-data/EAST/xuyanqi/crop_result_lsc/','')
tf.app.flags.DEFINE_string('result_last_tsv','temp_tsv.tsv','')
import model
from icdar import restore_rectangle
from math import *
FLAGS = tf.app.flags.FLAGS
def rank_boxes(boxes):
def getKey(item):
return item[1] #sort by y1
sorted_boxes = sorted(boxes,key=getKey)
return sorted_boxes
def ndarray_sort(arr1):
result_list=[]
for arr in arr1:
temp=[]
for ss in arr:
temp.append(ss[0])
temp.append(ss[1])
result_list.append(temp)
result_list = rank_boxes(result_list)
array_result = np.array(result_list).reshape(-1,4,2)
return array_result
def get_images():
'''
find image files in test data path
:return: list of files found
'''
files = []
exts = ['jpg', 'png', 'jpeg', 'JPG','bmp']
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(im, max_side_len=768):
'''
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)
#print(resize_w)
#print(resize_h)
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:
: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()
text_box_restored = restore_rectangle(
xy_text[:, ::-1] * 4, 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
# 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)
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) // 4, 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 dumpRotateImage(img, degree, pt1, pt2, pt3, pt4):
height, width = img.shape[:2]
heightNew = int(width * fabs(sin(radians(degree))) +
height * fabs(cos(radians(degree))))
widthNew = int(height * fabs(sin(radians(degree))) +
width * fabs(cos(radians(degree))))
matRotation = cv2.getRotationMatrix2D((width / 2, height / 2), degree, 1)
matRotation[0, 2] += (widthNew - width) / 2
matRotation[1, 2] += (heightNew - height) / 2
imgRotation = cv2.warpAffine(
img, matRotation, (widthNew, heightNew), borderValue=(
255, 255, 255))
pt1 = list(pt1)
pt3 = list(pt3)
[[pt1[0]], [pt1[1]]] = np.dot(
matRotation, np.array([[pt1[0]], [pt1[1]], [1]]))
[[pt3[0]], [pt3[1]]] = np.dot(
matRotation, np.array([[pt3[0]], [pt3[1]], [1]]))
imgOut = imgRotation[int(pt1[1]):int(pt3[1]), int(pt1[0]):int(pt3[0])]
height, width = imgOut.shape[:2]
return imgOut
def filter_img(img):
if img.shape[0] > img.shape[1] * 1.5:
img = np.rot90(img)
scale = float(img.shape[0]) / 32.0
if scale == 0:
return img
w = int(float(img.shape[1]) / scale)
if w > 280:
w = 280
img = cv2.resize(img, (w, 32), interpolation=cv2.INTER_LINEAR)
else:
img = cv2.resize(img, (w, 32))
expand = 280 - w
r = img[:, img.shape[1] - 1, 0].mean()
g = img[:, img.shape[1] - 1, 1].mean()
b = img[:, img.shape[1] - 1, 2].mean()
img = cv2.copyMakeBorder(
img,
0,
0,
0,
expand,
cv2.BORDER_CONSTANT,
value=(
r,
g,
b))
return img
def main(argv=None):
import os
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu_list
try:
os.makedirs(FLAGS.output_dir)
except OSError as e:
if e.errno != 17:
raise
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(input_images, is_training=False)
variable_averages = tf.train.ExponentialMovingAverage(
0.997, global_step)
saver = tf.train.Saver(variable_averages.variables_to_restore())
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
ckpt_state = tf.train.get_checkpoint_state(FLAGS.checkpoint_path)
#print(type(ckpt_state))
model_path = os.path.join(
FLAGS.checkpoint_path, os.path.basename(
ckpt_state.model_checkpoint_path))
#print(model_path)
print('Restore from {}'.format(model_path))
saver.restore(sess, model_path)
im_fn_list = get_images()
with open(FLAGS.result_last_tsv,"w")as fw:
for im_fn in im_fn_list:
#print(im_fn)
last_name = im_fn.split("/")[-1]
im_just_for_test = cv2.imread(im_fn)
#print("results:"+str(im_just_for_test.shape))
im = cv2.imread(im_fn)[:, :, ::-1]
start_time = time.time()
im_resized, (ratio_h, ratio_w) = resize_image(im)
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
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))
temp_i = 0
# save to file
dict_result_temp = dict()
dict_result_temp["bboxes"] = list()
#print(type(boxes))
#boxes = ndarray_sort(boxes)
if boxes is not None:
res_file = os.path.join(
FLAGS.output_dir,
'{}.txt'.format(
"res_" + os.path.basename(im_fn).split('.')[0]))
save_name_pic = os.path.basename(im_fn).split('.')[0]
with open(res_file, 'w') as f:
for box in boxes:
#print(box)
single_temp = []
pt1 = []
pt2 = []
pt3 = []
pt4 = []
# to avoid submitting errors
box = sort_poly(box.astype(np.int32))
if np.linalg.norm(
box[0] -
box[1]) < 5 or np.linalg.norm(
box[3] -
box[0]) < 5:
continue
t_00 = int(box[0,0])
t_01 = int(box[0,1])
t_10 = int(box[1,0])
t_11 = int(box[1,1])
t_20 = int(box[2,0])
t_21 = int(box[2,1])
t_30 = int(box[3,0])
t_31 = int(box[3,1])
if t_00>=0 and t_01>=0 and t_10>=0 and t_11>=0 and t_20>=0 and t_21>=0 and t_30>=0 and t_31>=0:
f.write('{},{},{},{},{},{},{},{}\r\n'.format(int(box[0, 0]), int(box[0, 1]), int(
box[1, 0]), int(box[1, 1]), int(box[2, 0]), int(box[2, 1]), int(box[3, 0]), int(box[3, 1]), ))
for i in range(4):
for j in range(2):
single_temp.append(box[i][j])
dict_result_temp["bboxes"].append(single_temp)
#pt1.append(box[0, 0])
#pt1.append(box[0, 1])
#pt2.append(box[1, 0])
#pt2.append(box[1, 1])
#pt3.append(box[2, 0])
#pt3.append(box[2, 1])
#pt4.append(box[3, 0])
#pt4.append(box[3, 1])
#partImg = dumpRotateImage(im, degrees(atan2(box[1,1] - box[0,1], box[1,0] - box[0,0])), pt1, pt2, pt3, pt4)
#partImg_new = filter_img(partImg)
#cv2.imwrite(FLAGS.save_pic_jietu+save_name_pic+"_"+str(temp_i)+".png",partImg_new[:,:,::-1])
#temp_i = temp_i+1
cv2.polylines(im[:, :, ::-1], [box.astype(np.int32).reshape(
(-1, 1, 2))], True, color=(255, 0, 0), thickness=2)
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])
fw.write(last_name+"\t"+str(dict_result_temp)+"\n")
if __name__ == '__main__':
tf.app.run()