train_crnn.py 文件源码

def __iter__(self):
        #print('iter')
        init_state_names = [x[0] for x in self.init_states]
        for k in range(self.count):
            data = []
            label = []
            for i in range(self.batch_size):
                img_name = self.image_set_index[i + k*self.batch_size]
                img = cv2.imread(os.path.join(self.data_path, img_name + '.jpg'), cv2.IMREAD_GRAYSCALE)
                #img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
                img = cv2.resize(img, self.data_shape)
                img = img.reshape((1, data_shape[1], data_shape[0]))
                #print(img)
                #img = img.transpose(1, 0)
                #img = img.reshape((data_shape[0] * data_shape[1]))
                img = np.multiply(img, 1/255.0)
                #print(img)
                data.append(img)
                ret = np.zeros(self.num_label, int)
                plate_str = self.gt[int(img_name)]
                #print(plate_str)
                for number in range(len(plate_str)):
                    ret[number] = self.classes.index(plate_str[number]) + 1
                #print(ret)
                label.append(ret)

            data_all = [mx.nd.array(data)] + self.init_state_arrays
            label_all = [mx.nd.array(label)]
            data_names = ['data'] + init_state_names
            label_names = ['label']


            data_batch = SimpleBatch(data_names, data_all, label_names, label_all)
            yield data_batch