# -*- coding: utf-8 -*-
"""
Created on Wed Jul 19 17:54:00 2017

@author: sgnosh
"""

#!/usr/bin/python

import sys
import os

import cv2
import numpy as np

print('\nDeeptextdetection.py')
print('       A demo script of text box alogorithm of the paper:')
print('       * Minghui Liao et al.: TextBoxes: A Fast Text Detector with a Single Deep Neural Network https://arxiv.org/abs/1611.06779\n')


if (len(sys.argv) < 2):
  print(' (ERROR) You must call this script with an argument (path_to_image_to_be_processed)\n')
  quit()
#if not cv2.text.cnn_config.caffe_backend.getCaffeAvailable():
#        print"The text module was compiled without Caffe which is the only available DeepCNN backend.\nAborting!\n"
#
#        quit()
# check model and architecture file existance
if not os.path.isfile('textbox.caffemodel') or not os.path.isfile('textbox_deploy.prototxt'):
    print " Model files not found in current directory. Aborting"
    print " Model files should be downloaded from https://github.com/sghoshcvc/TextBox-Models"
    quit()
cv2.text.cnn_config.caffe_backend.setCaffeGpuMode(True);
pathname = os.path.dirname(sys.argv[0])


img      = cv2.imread(str(sys.argv[1]))
textSpotter=cv2.text.textDetector_create(
                "textbox_deploy.prototxt","textbox.caffemodel")
rects,outProbs = textSpotter.textDetectInImage(img);
# for visualization
vis      = img.copy()
# Threshold to select rectangles : All rectangles for which outProbs is more than this threshold will be shown
thres = 0.6


  #Visualization
for r in range(0,np.shape(rects)[0]):
    if outProbs[r] >thres:
        rect = rects[r]
        cv2.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (255, 0, 0), 2)
       # cv2.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (255, 255, 255), 1)


#Visualization
cv2.imshow("Text detection result", vis)
cv2.waitKey(0)