mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 17:11:21 +08:00
Jason
152 lines
5.6 KiB
Python
152 lines
5.6 KiB
Python
# 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
|
|
|
|
|
|
def parse_arguments():
|
|
import argparse
|
|
import ast
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--det_model", required=True, help="Path of Detection model of PPOCR.")
|
|
parser.add_argument(
|
|
"--cls_model",
|
|
required=True,
|
|
help="Path of Classification model of PPOCR.")
|
|
parser.add_argument(
|
|
"--rec_model",
|
|
required=True,
|
|
help="Path of Recognization model of PPOCR.")
|
|
parser.add_argument(
|
|
"--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(
|
|
"--device",
|
|
type=str,
|
|
default='cpu',
|
|
help="Type of inference device, support 'cpu' or 'gpu'.")
|
|
parser.add_argument(
|
|
"--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(
|
|
"--device_id",
|
|
type=int,
|
|
default=0,
|
|
help="Define which GPU card used to run model.")
|
|
parser.add_argument(
|
|
"--cpu_thread_num",
|
|
type=int,
|
|
default=9,
|
|
help="Number of threads while inference on CPU.")
|
|
return parser.parse_args()
|
|
|
|
|
|
def build_option(args):
|
|
option = fd.RuntimeOption()
|
|
if args.device.lower() == "gpu":
|
|
option.use_gpu(0)
|
|
|
|
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()
|
|
elif args.backend.lower() == "pptrt":
|
|
assert args.device.lower(
|
|
) == "gpu", "Paddle-TensorRT backend require inference on device GPU."
|
|
option.use_trt_backend()
|
|
option.enable_paddle_trt_collect_shape()
|
|
option.enable_paddle_to_trt()
|
|
elif args.backend.lower() == "ort":
|
|
option.use_ort_backend()
|
|
elif args.backend.lower() == "paddle":
|
|
option.use_paddle_infer_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()
|
|
|
|
# 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")
|
|
# 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")
|
|
# 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
|
|
|
|
# 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)
|
|
|
|
# 当使用TRT时，分别给三个模型的runtime设置动态shape,并完成模型的创建.
|
|
# 注意: 需要在检测模型创建完成后，再设置分类模型的动态输入并创建分类模型, 识别模型同理.
|
|
# 如果用户想要自己改动检测模型的输入shape, 我们建议用户把检测模型的长和高设置为32的倍数.
|
|
det_option = runtime_option
|
|
det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
|
|
[1, 3, 960, 960])
|
|
# 用户可以把TRT引擎文件保存至本地
|
|
# det_option.set_trt_cache_file(args.det_model + "/det_trt_cache.trt")
|
|
det_model = fd.vision.ocr.DBDetector(
|
|
det_model_file, det_params_file, runtime_option=det_option)
|
|
|
|
cls_option = runtime_option
|
|
cls_option.set_trt_input_shape("x", [1, 3, 48, 10], [10, 3, 48, 320],
|
|
[64, 3, 48, 1024])
|
|
# 用户可以把TRT引擎文件保存至本地
|
|
# cls_option.set_trt_cache_file(args.cls_model + "/cls_trt_cache.trt")
|
|
cls_model = fd.vision.ocr.Classifier(
|
|
cls_model_file, cls_params_file, runtime_option=cls_option)
|
|
|
|
rec_option = runtime_option
|
|
rec_option.set_trt_input_shape("x", [1, 3, 48, 10], [10, 3, 48, 320],
|
|
[64, 3, 48, 2304])
|
|
# 用户可以把TRT引擎文件保存至本地
|
|
# rec_option.set_trt_cache_file(args.rec_model + "/rec_trt_cache.trt")
|
|
rec_model = fd.vision.ocr.Recognizer(
|
|
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
|
|
|
|
# 创建PP-OCR，串联3个模型，其中cls_model可选，如无需求，可设置为None
|
|
ppocr_v3 = fd.vision.ocr.PPOCRv3(
|
|
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 = ppocr_v3.predict(im)
|
|
|
|
print(result)
|
|
|
|
# 可视化结果
|
|
vis_im = fd.vision.vis_ppocr(im, result)
|
|
cv2.imwrite("visualized_result.jpg", vis_im)
|
|
print("Visualized result save in ./visualized_result.jpg")
|