mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 11:56:44 +08:00
f36f9324de
* Pick PPOCR fastdeploy docs from PaddleOCR * improve ppocr * improve readme * remove old PP-OCRv2 and PP-OCRv3 folfers * rename kunlun to kunlunxin * improve readme * improve readme * improve readme --------- Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
78 lines
2.1 KiB
Python
Executable File
78 lines
2.1 KiB
Python
Executable File
# 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(
|
|
"--cls_model",
|
|
required=True,
|
|
help="Path of Classification 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', 'kunlunxin' or 'gpu'.")
|
|
parser.add_argument(
|
|
"--device_id",
|
|
type=int,
|
|
default=0,
|
|
help="Define which GPU card used to run model.")
|
|
return parser.parse_args()
|
|
|
|
|
|
def build_option(args):
|
|
|
|
cls_option = fd.RuntimeOption()
|
|
|
|
if args.device.lower() == "gpu":
|
|
cls_option.use_gpu(args.device_id)
|
|
|
|
return cls_option
|
|
|
|
|
|
args = parse_arguments()
|
|
|
|
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
|
|
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
|
|
|
|
# Set the runtime option
|
|
cls_option = build_option(args)
|
|
|
|
# Create the cls_model
|
|
cls_model = fd.vision.ocr.Classifier(
|
|
cls_model_file, cls_params_file, runtime_option=cls_option)
|
|
|
|
# Set the postprocessing parameters
|
|
cls_model.postprocessor.cls_thresh = 0.9
|
|
|
|
# Read the image
|
|
im = cv2.imread(args.image)
|
|
|
|
# Predict and return the results
|
|
result = cls_model.predict(im)
|
|
|
|
# User can infer a batch of images by following code.
|
|
# result = cls_model.batch_predict([im])
|
|
|
|
print(result)
|