mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
Zheng-Bicheng
69 lines
2.1 KiB
Python
69 lines
2.1 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",
|
|
default="./picodet_s_416_coco_lcnet_non_postprocess/picodet_xs_416_coco_lcnet.onnx",
|
|
help="Path of rknn model.")
|
|
parser.add_argument(
|
|
"--config_file",
|
|
default="./picodet_s_416_coco_lcnet_non_postprocess/infer_cfg.yml",
|
|
help="Path of config.")
|
|
parser.add_argument(
|
|
"--image",
|
|
type=str,
|
|
default="./000000014439.jpg",
|
|
help="Path of test image file.")
|
|
return parser.parse_args()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_arguments()
|
|
|
|
model_file = args.model_file
|
|
params_file = ""
|
|
config_file = args.config_file
|
|
|
|
# 配置runtime，加载模型
|
|
runtime_option = fd.RuntimeOption()
|
|
runtime_option.use_rknpu2()
|
|
|
|
model = fd.vision.detection.PPYOLOE(
|
|
model_file,
|
|
params_file,
|
|
config_file,
|
|
runtime_option=runtime_option,
|
|
model_format=fd.ModelFormat.RKNN)
|
|
model.preprocessor.disable_normalize()
|
|
model.preprocessor.disable_permute()
|
|
model.postprocessor.apply_decode_and_nms()
|
|
|
|
# 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_detection(im, result, score_threshold=0.5)
|
|
cv2.imwrite("visualized_result.jpg", vis_im)
|
|
print("Visualized result save in ./visualized_result.jpg")
|