Files
FastDeploy/examples/vision/segmentation/paddleseg/rknpu2/python/infer.py
T
Zheng_Bicheng 188dcedc02 [RKNN2] Fix bugs (#851)
* 修复picodet格式

* * 修正错误文档
* 修复rknpu2 backend后端的部分错误
* 更新pphumanseg example格式

* * 更新pphumanseg example格式

* * 更新picodet example格式

* * 更新scrfd example格式

* * 更新ppseg rknpu2 python example中的错误

* * 修复代码格式问题

* * 修复代码格式问题

* * 修复代码格式问题

* * 修复代码格式问题

* * 修复代码格式问题

* * 修复代码格式问题

Co-authored-by: Jason <jiangjiajun@baidu.com>
2022-12-12 15:37:31 +08:00

62 lines
1.7 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(
"--model_file", required=True, help="Path of PaddleSeg model.")
parser.add_argument(
"--config_file", required=True, help="Path of PaddleSeg config.")
parser.add_argument(
"--image", type=str, required=True, help="Path of test image file.")
return parser.parse_args()
def build_option(args):
option = fd.RuntimeOption()
option.use_rknpu2()
return option
args = parse_arguments()
# 配置runtime,加载模型
runtime_option = build_option(args)
model_file = args.model_file
params_file = ""
config_file = args.config_file
model = fd.vision.segmentation.PaddleSegModel(
model_file,
params_file,
config_file,
runtime_option=runtime_option,
model_format=fd.ModelFormat.RKNN)
model.preprocessor.disable_normalize_and_permute()
# 预测图片分割结果
im = cv2.imread(args.image)
result = model.predict(im)
print(result)
# 可视化结果
vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
cv2.imwrite("vis_img.png", vis_im)