提交 f8a25e03 编写于 作者: H HexToString

fix doc and ocr

上级 5c94ff60
...@@ -21,6 +21,7 @@ import os ...@@ -21,6 +21,7 @@ import os
import cv2 import cv2
from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import OCRReader
client = Client() client = Client()
# TODO:load_client need to load more than one client model. # TODO:load_client need to load more than one client model.
...@@ -44,5 +45,13 @@ for img_file in os.listdir(test_img_dir): ...@@ -44,5 +45,13 @@ for img_file in os.listdir(test_img_dir):
feed={"image": image}, feed={"image": image},
fetch=["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"], fetch=["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"],
batch=True) batch=True)
#print("{} {}".format(fetch_map["price"][0], data[0][1][0])) result = {}
print(fetch_map) result["score"] = fetch_map["softmax_0.tmp_0"]
del fetch_map["softmax_0.tmp_0"]
rec_res = OCRReader().postprocess(fetch_map, with_score=False)
res_lst = []
for res in rec_res:
res_lst.append(res[0])
result["res"] = res_lst
print(result)
...@@ -7,7 +7,7 @@ tar zxvf bert_base_chinese.tar.gz ...@@ -7,7 +7,7 @@ tar zxvf bert_base_chinese.tar.gz
``` ```
### convert model ### convert model
``` ```
python3 -m paddle_serving_client.convert --dirname bert_base_chinese --model_filename bert_base_chinese/model.pdmodel --params_filename bert_base_chinese/model.pdiparams python3 -m paddle_serving_client.convert --dirname bert_base_chinese --model_filename model.pdmodel --params_filename model.pdiparams
``` ```
### or, you can get the serving saved model directly ### or, you can get the serving saved model directly
``` ```
......
...@@ -108,7 +108,7 @@ def pad_batch_data(insts, ...@@ -108,7 +108,7 @@ def pad_batch_data(insts,
input_mask_data = np.array( input_mask_data = np.array(
[[1] * len(inst) + [0] * (max_len - len(inst)) for inst in insts]) [[1] * len(inst) + [0] * (max_len - len(inst)) for inst in insts])
input_mask_data = np.expand_dims(input_mask_data, axis=-1) input_mask_data = np.expand_dims(input_mask_data, axis=-1)
return_list += [input_mask_data.astype("float32")] return_list += [input_mask_data]
if return_max_len: if return_max_len:
return_list += [max_len] return_list += [max_len]
......
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