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 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