torch.IntTensor([l]),
raw=raw))
index += l
return texts
def label_dict(self):
return self.dict
def label_constant(self):
return self.alphabet
if __name__ == '__main__':
sys.path.append('../')
from model.cnn import CNN
img = Image.open('../test/ocr/test.jpg')
alphabet = process_alphabet('../weight/ocr/ocr.json')
model = CNN(1).cuda()
checkpoint = torch.load('../weight/ocr/ocr.pth.tar')
model.load_state_dict(checkpoint)
res = predict(model, img, alphabet)
print(res)
# os.environ["CUDA_VISIBLE_DEVICES"] = "3,2,1"
# load image for text
image_path = './test/text/1032838434.jpg'
img = cv2.imread(image_path) # ndarray
# load model
textModel = VGG(3).cuda()
checkpoint = torch.load(textPath)
textModel.load_state_dict(checkpoint)
print(textModel)
ocrModel = CNN(1).cuda()
checkpoint = torch.load(ocrPath)
ocrModel.load_state_dict(checkpoint)
print(ocrModel)
end = time.perf_counter()
print('Load Model time is {}'.format(end - start_p))
text = text_ocr(img, textModel, ocrModel)
end_p = time.perf_counter()
print('OCR processing time is {}'.format(end_p - start_p))
print(text)
