def ctpn(sess, net, image_name):
timer = Timer()
timer.tic()
img = cv2.imread(image_name)
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
new_scores = scores[:, np.newaxis]
keep_inds = np.where(new_scores > TextLineCfg.TEXT_PROPOSALS_MIN_SCORE)[0]
boxes, new_scores = boxes[keep_inds], new_scores[keep_inds]
sorted_indices = np.argsort(new_scores.ravel())[::-1]
boxes, new_scores = boxes[sorted_indices], new_scores[sorted_indices]
keep_inds = nms(np.hstack((boxes, new_scores)),
TextLineCfg.TEXT_PROPOSALS_NMS_THRESH)
boxes, new_scores = boxes[keep_inds], new_scores[keep_inds]
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(10, 14))
for key, box in enumerate(boxes):
img_inside = img.copy()
img_inside = cv2.rectangle(img_inside, (box[0], box[1]),
(box[2], box[3]),
color=(255, 0, 0),
thickness=2)
plt.imshow(img_inside)
plt.title('Scores: {0}'.format(scores[key]))
plt.savefig('./data/fig/fig_{0}.jpg'.format(key))
def ctpn(sess, net, image_name, save_path1, save_path2):
timer = Timer()
timer.tic()
#读取图片
img = cv2.imread(image_name)
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
#灰度化处理
#img2 = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
#img2 = cv2.cvtColor(img2,cv2.COLOR_GRAY2RGB)
# base_name = im_name.split('\\')[-1]
# cv2.imwrite(os.path.join("data/results2", base_name), img2)
scores, boxes = test_ctpn(sess, net, img)
#后处理过程，detect包含过滤和合并
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
draw_boxes2(img, boxes, image_name, save_path2, scale)
draw_boxes(img, boxes, image_name, save_path1, scale)
#后处理过程，detect2只过滤小文本框
# textdetector = TextDetector()
# boxes = textdetector.detect2(boxes, scores[:, np.newaxis], img.shape[:2])
# draw_boxes3(img, boxes,image_name, scale)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
def test_net(sess, net, imdb, weights_filename):
timer = Timer()
timer.tic()
np.random.seed(cfg.RNG_SEED)
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
output_dir = get_output_dir(imdb, weights_filename)
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
# all_boxes = []
all_boxes = [[[] for _ in range(imdb.num_classes)]
for _ in range(num_images)]
print(all_boxes)
for i in range(num_images):
print('***********', imdb.image_path_at(i))
img = cv2.imread(imdb.image_path_at(i))
img, scale = resize_im(img, scale=TextLineCfg.SCALE, max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
boxes = check_unreasonable_box(boxes, scale)
all_boxes[i][1] += boxes
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
imdb.evaluate_detections(all_boxes, output_dir)
timer.toc()
def ctpn(sess, net, image_name, boxlabel):
timer = Timer()
timer.tic()
img = cv2.imread(image_name)
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
img = draw_boxes(img, image_name, boxes, scale, None)
boxlabel2 = np.transpose(
np.array([
boxlabel[:, 0], boxlabel[:, 1], boxlabel[:, 2], boxlabel[:, 1],
boxlabel[:, 0], boxlabel[:, 3], boxlabel[:, 2], boxlabel[:, 3],
np.ones(len(boxlabel))
]))
draw_boxes(img, image_name, boxlabel2, 1, (0, 0, 0))
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
boxes = boxes / scale
return boxes
def ctpn(sess, net, image_name):
global true_text, true_non_text, false_text, false_non_text
base_name = image_name.split('/')[-1]
label_name = image_name.split('/')[-2]
img = cv2.imread(image_name)
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
print(len(boxes))
with open('boxes.txt', 'w') as f:
f.write(str(len(boxes)))
if len(boxes) > 0:
if (label_name == 'non_text'):
false_non_text += 1
else:
true_text += 1
cv2.imwrite(os.path.join('data/results/text', base_name), img)
else:
if (label_name == 'text'):
false_text += 1
else:
true_non_text += 1
cv2.imwrite(os.path.join('data/results/non_text', base_name), img)
def get_model(cls):
if cls.sess == None:
cfg_from_file('ctpn/text.yml')
# init session
config = tf.ConfigProto(allow_soft_placement=True)
cls.sess = tf.Session(config=config)
# load network
cls.net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state(cfg.TEST.checkpoints_path)
print('Restoring from {}...'.format(
ckpt.model_checkpoint_path),
end=' ')
saver.restore(cls.sess, ckpt.model_checkpoint_path)
print('done')
except:
raise 'Check your pretrained {:s}'.format(
ckpt.model_checkpoint_path)
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in range(2):
_, _ = test_ctpn(cls.sess, cls.net, im)
def detect(self, image_path):
if self.session is None:
self.load()
regions = []
img = cv2.imread(image_path)
old_h, old_w, channels = img.shape
img, scale = self.resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
new_h, new_w, channels = img.shape
mul_h, mul_w = float(old_h) / float(new_h), float(old_w) / float(new_w)
scores, boxes = test_ctpn(self.session, self.net, img)
boxes = self.textdetector.detect(boxes, scores[:, np.newaxis],
img.shape[:2])
for box in boxes:
left, top = int(box[0]), int(box[1])
right, bottom = int(box[6]), int(box[7])
score = float(box[8])
left, top, right, bottom = int(left * mul_w), int(
top * mul_h), int(right * mul_w), int(bottom * mul_h)
r = {
'score': float(score),
'y': top,
'x': left,
'w': right - left,
'h': bottom - top,
}
regions.append(r)
return regions
def ctpnSource(im_name):
if os.path.exists("data/results/"):
shutil.rmtree("data/results/") #目录下存在多级子目录，递归删除
os.makedirs("data/results/")
cfg_from_file('ctpn/text.yml')
# init session
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# load network
net = get_network("VGGnet_test")
# load model
# print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state(cfg.TEST.checkpoints_path)
# print('Restoring from {}...'.format(ckpt.model_checkpoint_path), end=' ')
saver.restore(sess, ckpt.model_checkpoint_path)
# print('done')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in range(2):
_, _ = test_ctpn(sess, net, im)
# im_names = glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.png')) + \
# glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.jpg'))
# for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print(('Demo for {:s}'.format(im_name)))
ctpn(sess, net, im_name)
def detectorload():
cfg_from_file('TextDetection/ctpn/text.yml')
# init session
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# load network
net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state('TextDetection/checkpoints/')
print(
'Restoring from {}...'.format(ckpt.model_checkpoint_path), end=' ')
saver.restore(sess, ckpt.model_checkpoint_path)
print('done')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in range(2):
_, _ = test_ctpn(sess, net, im)
return net, sess
net, sess = detectorload()
ctpn(sess, net, im_name)
def ctpn(sess, net, image_name):
timer = Timer()
timer.tic()
img = cv2.imread(image_name)
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
#将OPENCV图像转换为PIL图像，
pil_img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
#求图片清晰度
imageVar = cv2.Laplacian(img, cv2.CV_64F).var()
if imageVar <= 5000:
pil_img = ImageEnhance.Sharpness(pil_img).enhance(3.0)
#将PIL图像转换为opencv图像
img = cv2.cvtColor(np.asarray(pil_img), cv2.COLOR_RGB2BGR)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
draw_boxes(img, image_name, boxes, scale)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
def ctpn(sess, net, image_name):
timer = Timer()
timer.tic()
img = cv2.imread(image_name)
height, width = img.shape[:2]
img = img[int(2 * height / 3.0):height, :]
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
# for box in boxes:
# color = (0, 255, 0)
# cv2.line(img, (int(box[0]), int(box[1])), (int(box[2]), int(box[1])), color, 2)
# cv2.line(img, (int(box[0]), int(box[1])), (int(box[0]), int(box[3])), color, 2)
# cv2.line(img, (int(box[2]), int(box[1])), (int(box[2]), int(box[3])), color, 2)
# cv2.line(img, (int(box[0]), int(box[3])), (int(box[2]), int(box[3])), color, 2)
# base_name = image_name.split('/')[-1]
# cv2.imwrite("data/results/test_"+base_name, img)
# draw_boxes(img, image_name, boxes, scale)
# print(boxes)
# assert 0
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
draw_boxes(img, image_name, boxes, scale)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
def ctpn_area(sess,
net,
image_name,
dst,
draw_img=False,
show_area=False,
area_min=-0.1,
area_max=1.1):
#timer = Timer()
#timer.tic()
img = cv2.imread(image_name)
if img is None:
return 0.0
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
ret = compute_area(img,
image_name,
boxes,
scale,
dst,
draw_img=draw_img,
show_area=show_area,
area_min=area_min,
area_max=area_max)
#timer.toc()
#print(('Detection took {:.3f}s for '
# '{:d} object proposals').format(timer.total_time, boxes.shape[0]))
return ret
def ctpn(sess, net, image_path):
timer = Timer()
timer.tic()
img = cv2.imread(image_path)
img_name = image_path.split('/')[-1]
# 将图像进行resize并返回其缩放大小
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
# 送入网络得到1000个得分,1000个bbox
cls, scores, boxes = test_ctpn(sess, net, img)
print('cls, scores, boxes', cls.shape, scores.shape, boxes.shape)
# img_re = img
# for i in range(np.shape(boxes)[0]):
# if cls[i] == 1:
# color = (255, 0, 0)
# else:
# color = (0, 255, 0)
# cv2.rectangle(img_re, (boxes[i][0],boxes[i][1]),(boxes[i][2],boxes[i][3]),color,1)
# cv2.imwrite(os.path.join('./data/proposal_res', img_name), img_re)
handwritten_filter = np.where(cls == 1)[0]
handwritten_scores = scores[handwritten_filter]
handwritten_boxes = boxes[handwritten_filter, :]
print_filter = np.where(cls == 2)[0]
print_scores = scores[print_filter]
print_boxes = boxes[print_filter, :]
handwritten_detector = TextDetector()
handwritten_detector = TextDetector()
print('print_filter', np.array(print_filter).shape)
print('handwritten_boxes, handwritten_scores', handwritten_boxes.shape,
handwritten_scores[:, np.newaxis].shape)
filted_handwritten_boxes = handwritten_detector.detect(
handwritten_boxes, handwritten_scores[:, np.newaxis], img.shape[:2])
filted_print_boxes = handwritten_detector.detect(
print_boxes, print_scores[:, np.newaxis], img.shape[:2])
# boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
draw_boxes(img, filted_handwritten_boxes, (255, 0, 0))
draw_boxes(img, filted_print_boxes, (0, 255, 0))
img = cv2.resize(img,
None,
None,
fx=1.0 / scale,
fy=1.0 / scale,
interpolation=cv2.INTER_LINEAR)
cv2.imwrite(os.path.join("data/results", img_name), img)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
def ctpn(img):
"""
text box detect
"""
scale, max_scale = Config.SCALE, Config.MAX_SCALE
img, f = resize_im(img, scale=scale, max_scale=max_scale)
scores, boxes = test_ctpn(sess, net, img)
return scores, boxes, img
def ctpn(img):
"""
text box detect
"""
scale, max_scale = Config.SCALE,Config.MAX_SCALE
img,f = resize_im(img,scale=scale,max_scale=max_scale)
scores, boxes = test_ctpn(sess, net, img)
return scores, boxes,img
def detect(src,
dst,
draw_img=False,
show_area=False,
area_min=-0.0,
area_max=1.1):
#if os.path.exists("data/results/"):
# shutil.rmtree("data/results/")
if not os.path.exists(dst):
os.makedirs(dst)
cfg_from_file('ctpn/text.yml')
# init session
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# load network
net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state(cfg.TEST.checkpoints_path)
print('Restoring from {}...'.format(ckpt.model_checkpoint_path),
end=' ')
saver.restore(sess, ckpt.model_checkpoint_path)
print('done')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in range(2):
_, _ = test_ctpn(sess, net, im)
'''
im_names = glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.png')) + \
glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.jpg')) + \
glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.jpeg'))
'''
im_names = glob.glob(os.path.join(src, '*.png')) \
+ glob.glob(os.path.join(src, '*.PNG')) \
+ glob.glob(os.path.join(src, '*.jpg')) \
+ glob.glob(os.path.join(src, '*.JPG')) \
+ glob.glob(os.path.join(src, '*.jpeg')) \
+ glob.glob(os.path.join(src, '*.JPEG')) \
+ glob.glob(os.path.join(src, '*.bmp')) \
+ glob.glob(os.path.join(src, '*.BMP'))
print("images:{}".format(len(im_names)))
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print(('Demo for {:s}'.format(im_name)))
ctpn(sess,
net,
im_name,
dst,
draw_img=draw_img,
show_area=show_area,
area_min=area_min,
area_max=area_max)
def text_detect(img):
#ctpn
scale, max_scale = Config.SCALE,Config.MAX_SCALE
img,f = resize_im(img,scale=scale,max_scale=max_scale)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes,scores[:, np.newaxis],img.shape[:2])
text_recs,tmp = draw_boxes(img, boxes, caption='im_name', wait=True,is_display=False)
return text_recs,tmp,img
def ctpn(img):
"""
text box detect
"""
scale, max_scale = Config.SCALE, Config.MAX_SCALE
# 对图像进行resize，输出的图像长宽
print('original_size', img.shape)
img, f = resize_im(img, scale=scale, max_scale=max_scale)
print('resize', img.shape, f)
scores, boxes = test_ctpn(sess, net, img)
return scores, boxes, img
def ctpn(img):
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
return scores, boxes, img, scale
def ctpn(self, image_name):
img = cv2.imread(image_name)
img, scale = self.resize_im(img, scale=600, max_scale=1000) # 参考ctpn论文
scores, boxes = test_ctpn(self.sess, self.net, img)
# ctpn识别实例
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis],
img.shape[:2])
min_y_sort_list, base_name = self.get_coordinates(
img, image_name, boxes, scale)
return min_y_sort_list, base_name
def ctpn(sess, net, image_name):
timer = Timer()
timer.tic()
img = cv2.imread(image_name)
img, scale = resize_im(img, scale=TextLineCfg.SCALE, max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
draw_boxes(img, image_name, boxes, scale)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
def ctpn(sess, net, image_name):
timer = Timer()
timer.tic()
img = cv2.imread(image_name)
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
draw_boxes(img, image_name, boxes, scale)
timer.toc()
def ctpn(sess, net, img):
timer = Timer()
timer.tic()
img, scale = resize_im(img, scale=TextLineCfg.SCALE, max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
sort_index = np.argsort(boxes[:, -1])[::-1]
boxes = boxes[sort_index]
im, bboxes = draw_boxes(img, boxes, scale)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
return im, bboxes
def ctpn(sess, net, image_name, model):
img = cv2.imread(image_name)
#r = image_to_binary(img)
#noise = np.ones(img.shape[:2],dtype="uint8") * 125
#img = cv2.merge((r+noise, r, noise))
img, scale = resize_im(img, scale=600, max_scale=1000) # 参考ctpn论文
print('ctpn', img.shape)
scores, boxes = test_ctpn(sess, net, img)
# ctpn识别实例
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
get_coordinates(img, image_name, boxes, scale, model)
def ctpn(sess, net, image_name):
timer = Timer()
timer.tic()
img = cv2.imread(image_name)
img, scale = resize_im(img, scale=TextLineCfg.SCALE, max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
draw_boxes(img, image_name, boxes, scale)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
def predict(self, image_name):
img = cv2.imread(image_name)
img, scale = self.resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(self.sess, self.net, img)
# print('scores', scores)
# mask = scores > 0.9
# boxes = boxes[mask]
# print('length of boxes', len(boxes))
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis],
img.shape[:2])
return img, boxes, scale
def ctpn(cv_image):
os.chdir(CTPN_DIR)
with ctpn_sess.as_default():
img = cv_image
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(ctpn_sess, ctpn_net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis],
img.shape[:2])
boxes[:, 0:8] /= scale
os.chdir(ROOT_DIR)
return boxes
def ctpn(sess, net, image_name):
img = cv2.imread(image_name)
im = check_img(img)
timer = Timer()
timer.tic()
scores, boxes = test_ctpn(sess, net, im)
timer.toc()
CONF_THRESH = 0.9
NMS_THRESH = 0.3
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
keep = np.where(dets[:, 4] >= 0.7)[0]
dets = dets[keep, :]
line = connect_proposal(dets[:, 0:4], dets[:, 4], im.shape)
save_results(image_name, im, line, thresh=0.9)
def ctpn(img):
timer = Timer()
timer.tic()
img, scale = resize_im(img,
scale=TextLineCfg.SCALE,
max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
timer.toc()
#print("\n----------------------------------------------")
#print(('Detection took {:.3f}s for '
# '{:d} object proposals').format(timer.total_time, boxes.shape[0]))
return scores, boxes, img, scale, timer.total_time, boxes.shape[0]
def ctpn(sess, net, frame, draw):
# timer = Timer()
# timer.tic()
img, scale = resize_im(
frame, scale=TextLineCfg.SCALE, max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
buf = img.copy()
crop = crop_image(buf, boxes, scale)
# timer.toc()
if draw is 1:
draw_boxes(img, boxes, scale)
return crop
def local_result(list_img_path, save_dir, ctpn_path, base_net):
save_dir = os.path.join(cfg.ROOT_DIR, 'data', save_dir)
if os.path.exists(save_dir):
shutil.rmtree(save_dir)
os.makedirs(save_dir)
yml_path = os.path.join(cfg.ROOT_DIR, 'ctpn/text.yml')
cfg_from_file(yml_path)
# init session
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# load network
training_flag = tf.placeholder(tf.bool)
net = get_network(base_net, training_flag)
# load model
print(('Loading network {:s}... '.format(base_net)), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state(ctpn_path)
print('Restoring from {}...'.format(ckpt.model_checkpoint_path), end=' ')
saver.restore(sess, ckpt.model_checkpoint_path)
print('done')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in range(2):
_, _ = test_ctpn(sess, training_flag, net, im)
im_names = list_img_path
# im_names = glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.png')) + \
# glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.jpg'))
xml_dir = '/data/kuaidi01/dataset_detect/VOC2007/Annotations'
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print(('Demo for {:s}'.format(im_name)))
if im_name[-7:]=='164.jpg':
continue
xml_path = os.path.join(xml_dir, os.path.basename(im_name)[:-4]+'.xml')
ctpn(sess,training_flag, net, im_name, save_dir, xml_path)
def ctpn(sess, training_flag, net, image_name, save_all_dir):
timer = Timer()
timer.tic()
img = cv2.imread(image_name)
img, scale = resize_im(img, scale=TextLineCfg.SCALE, max_scale=TextLineCfg.MAX_SCALE)
scores, boxes = test_ctpn(sess, training_flag, net, img)
textdetector = TextDetector()
boxes1, boxes2, boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
# img1 = img.copy()
# draw_middle_boxes(img1, boxes1, scale)
# img2 = img.copy()
# draw_middle_boxes(img2, boxes2, scale)
draw_boxes(img, image_name, boxes, scale, save_all_dir)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0]))
# init session
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# load network
net = get_network("VGGnet_test")
# load model
print(('Loading network {:s}... '.format("VGGnet_test")), end=' ')
saver = tf.train.Saver()
try:
ckpt = tf.train.get_checkpoint_state(cfg.TEST.checkpoints_path)
print('Restoring from {}...'.format(ckpt.model_checkpoint_path), end=' ')
saver.restore(sess, ckpt.model_checkpoint_path)
print('done')
except:
raise 'Check your pretrained {:s}'.format(ckpt.model_checkpoint_path)
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in range(2):
_, _ = test_ctpn(sess, net, im)
im_names = glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.png')) + \
glob.glob(os.path.join(cfg.DATA_DIR, 'demo', '*.jpg'))
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print(('Demo for {:s}'.format(im_name)))
ctpn(sess, net, im_name)
