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| import cv2 | |
| import pickle as pkl | |
| import numpy as np | |
| def load_dict_picklefile(dictFile): | |
| fp=open(dictFile,'rb') | |
| worddicts=pkl.load(fp) | |
| fp.close() | |
| worddicts_r = [None] * len(worddicts) | |
| i=1 | |
| for kk, vv in worddicts.items(): | |
| if(i<len(worddicts)): | |
| worddicts_r[vv] = kk | |
| else: | |
| break | |
| i=i+1 | |
| return worddicts_r | |
| def preprocess_img(img): | |
| if len(img.shape)>2: | |
| img= cv2.cvtColor(img.astype('float32'), cv2.COLOR_BGR2GRAY) | |
| height=img.shape[0] | |
| width=img.shape[1] | |
| if(width<300): | |
| result = np.ones([img.shape[0], img.shape[1]*2])*255 | |
| result[0:img.shape[0],img.shape[1]:img.shape[1]*2]=img | |
| img=result | |
| img=cv2.resize(img, dsize=(800,100), interpolation = cv2.INTER_AREA) | |
| img=(img-img.min())/(img.max()-img.min()) | |
| xx_pad = np.zeros((100, 800), dtype='float32') | |
| xx_pad[:,:] =1 | |
| xx_pad = xx_pad[None, :, :] | |
| img=img[None, :, :] | |
| return img, xx_pad | |