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Optimize ocr system code (#209)

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* Support PPYOLOE plus model

* Optimize ocr system code

* modify example code

* fix patchelf of openvino

* optimize demo code of ocr

* remove debug code

* update demo code of ocr

Co-authored-by: Jack Zhou <zhoushunjie@baidu.com>
This commit is contained in:
Jason
2022-09-14 09:46:03 +08:00
committed by GitHub
parent 1452275efe
commit 0dd9ecee65
39 changed files with 1041 additions and 909 deletions
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8
examples/vision/ocr/PPOCRSystemv2/python/README.md
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@@ -19,9 +19,9 @@ tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
wget https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer.tar.gz
tar -xvf ch_PP-OCRv2_rec_infer.tar.gz
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/imgs/12.jpg
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/utils/ppocr_keys_v1.txt
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
#下载部署示例代码
@@ -33,9 +33,7 @@ python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2
# GPU推理
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu
# GPU上使用TensorRT推理
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --det_use_trt True --cls_use_trt True --rec_use_trt True
# OCR还支持det/cls/rec三个模型的组合使用，例如当我们不想使用cls模型的时候，只需要给--cls_model传入一个空的字符串, 例子如下：
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model "" --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu
python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt
```
运行完成可视化结果如下图所示

142
examples/vision/ocr/PPOCRSystemv2/python/infer.py
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@@ -1,3 +1,17 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import fastdeploy as fd
import cv2
import os
@@ -21,7 +35,6 @@ def parse_arguments():
"--rec_label_file",
required=True,
help="Path of Recognization model of PPOCR.")
parser.add_argument(
"--image", type=str, required=True, help="Path of test image file.")
parser.add_argument(
@@ -30,114 +43,83 @@ def parse_arguments():
default='cpu',
help="Type of inference device, support 'cpu' or 'gpu'.")
parser.add_argument(
"--det_use_trt",
type=ast.literal_eval,
default=False,
help="Wether to use tensorrt.")
"--backend",
type=str,
default="default",
help="Type of inference backend, support ort/trt/paddle/openvino, default 'openvino' for cpu, 'tensorrt' for gpu"
)
parser.add_argument(
"--cls_use_trt",
type=ast.literal_eval,
default=False,
help="Wether to use tensorrt.")
"--device_id",
type=int,
default=0,
help="Define which GPU card used to run model.")
parser.add_argument(
"--rec_use_trt",
type=ast.literal_eval,
default=False,
help="Wether to use tensorrt.")
"--cpu_thread_num",
type=int,
default=9,
help="Number of threads while inference on CPU.")
return parser.parse_args()
def build_det_option(args):
def build_option(args):
option = fd.RuntimeOption()
if args.device.lower() == "gpu":
option.use_gpu()
option.use_gpu(0)
if args.det_use_trt:
option.set_cpu_thread_num(args.cpu_thread_num)
if args.backend.lower() == "trt":
assert args.device.lower(
) == "gpu", "TensorRT backend require inference on device GPU."
option.use_trt_backend()
#det_max_side_len 默认为960,当用户更改DET模型的max_side_len参数时，请将此参数同时更改
det_max_side_len = 960
option.set_trt_input_shape("x", [1, 3, 50, 50], [1, 3, 640, 640],
[1, 3, det_max_side_len, det_max_side_len])
return option
def build_cls_option(args):
option = fd.RuntimeOption()
option.use_paddle_backend()
if args.device.lower() == "gpu":
option.use_gpu()
if args.cls_use_trt:
option.use_trt_backend()
option.set_trt_input_shape("x", [1, 3, 32, 100])
return option
def build_rec_option(args):
option = fd.RuntimeOption()
option.use_paddle_backend()
if args.device.lower() == "gpu":
option.use_gpu()
if args.rec_use_trt:
option.use_trt_backend()
option.set_trt_input_shape("x", [1, 3, 48, 10], [1, 3, 48, 320],
[1, 3, 48, 2000])
elif args.backend.lower() == "ort":
option.use_ort_backend()
elif args.backend.lower() == "paddle":
option.use_paddle_backend()
elif args.backend.lower() == "openvino":
assert args.device.lower(
) == "cpu", "OpenVINO backend require inference on device CPU."
option.use_openvino_backend()
return option
args = parse_arguments()
#Det模型
# Detection模型, 检测文字框
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
#Cls模型
# Classification模型，方向分类，可选
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
#Rec模型
# Recognition模型，文字识别模型
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
rec_label_file = args.rec_label_file
#默认
det_model = fd.vision.ocr.DBDetector()
cls_model = fd.vision.ocr.Classifier()
rec_model = fd.vision.ocr.Recognizer()
# 对于三个模型，均采用同样的部署配置
# 用户也可根据自行需求分别配置
runtime_option = build_option(args)
#模型初始化
if (len(args.det_model) != 0):
det_runtime_option = build_det_option(args)
det_model = fd.vision.ocr.DBDetector(
det_model_file, det_params_file, runtime_option=det_runtime_option)
det_model = fd.vision.ocr.DBDetector(
det_model_file, det_params_file, runtime_option=runtime_option)
cls_model = fd.vision.ocr.Classifier(
cls_model_file, cls_params_file, runtime_option=runtime_option)
rec_model = fd.vision.ocr.Recognizer(
rec_model_file,
rec_params_file,
rec_label_file,
runtime_option=runtime_option)
if (len(args.cls_model) != 0):
cls_runtime_option = build_cls_option(args)
cls_model = fd.vision.ocr.Classifier(
cls_model_file, cls_params_file, runtime_option=cls_runtime_option)
if (len(args.rec_model) != 0):
rec_runtime_option = build_rec_option(args)
rec_model = fd.vision.ocr.Recognizer(
rec_model_file,
rec_params_file,
rec_label_file,
runtime_option=rec_runtime_option)
ppocrsysv2 = fd.vision.ocr.PPOCRSystemv2(
ocr_det=det_model._model,
ocr_cls=cls_model._model,
ocr_rec=rec_model._model)
# 创建OCR系统，串联3个模型，其中cls_model可选，如无需求，可设置为None
ocr_system = fd.vision.ocr.PPOCRSystemv2(
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
# 预测图片准备
im = cv2.imread(args.image)
#预测并打印结果
result = ppocrsysv2.predict(im)
result = ocr_system.predict(im)
print(result)
# 可视化结果