def ctpn(self, sess, net, img):
im, im_scale = self.check_img(img)
timer = Timer()
timer.tic()
scores, boxes = test_ctpn(sess, net, im)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time,
boxes.shape[0]))
# Visualize detections for each class
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, :]
text_lines = self.connect_proposal(dets[:, :], dets[:, 4],
im.shape[:2])
tmp = im.copy()
text_recs = draw_boxes(tmp, text_lines, caption="im_name", wait=True)
# self.show_results(tmp,im_scale, text_recs, thresh=0.9)
return tmp, text_recs
def get_text_box(self, img):
# saver.restore(sess, os.path.join(os.getcwd(),"checkpoints/model_final_tf13.ckpt"))
# Warmup on a dummy image
im = 128 * np.ones((300, 300, 3), dtype=np.uint8)
for i in range(2):
_, _ = test_ctpn(self.sess, self.net, im)
return self.ctpn(self.sess, self.net, img)
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)
text_filter = TextFilter()
boxes = text_filter.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(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
# 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)
