mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
5b143219ce
* [Docs] Pick seg fastdeploy docs from PaddleSeg * [Docs] update seg docs * [Docs] Add c&csharp examples for seg * [Docs] Add c&csharp examples for seg * [Doc] Update paddleseg README.md * Update README.md
62 lines
1.7 KiB
Python
Executable File
62 lines
1.7 KiB
Python
Executable File
import fastdeploy as fd
|
|
import cv2
|
|
import os
|
|
|
|
|
|
def parse_arguments():
|
|
import argparse
|
|
import ast
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--model", required=True, help="Path of PaddleSeg model.")
|
|
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 'kunlunxin', 'cpu' or 'gpu'.")
|
|
parser.add_argument(
|
|
"--use_trt",
|
|
type=ast.literal_eval,
|
|
default=False,
|
|
help="Wether to use tensorrt.")
|
|
return parser.parse_args()
|
|
|
|
|
|
def build_option(args):
|
|
option = fd.RuntimeOption()
|
|
|
|
if args.device.lower() == "gpu":
|
|
option.use_gpu()
|
|
|
|
if args.use_trt:
|
|
option.use_trt_backend()
|
|
# If use original Tensorrt, not Paddle-TensorRT,
|
|
# comment the following two lines
|
|
option.enable_paddle_to_trt()
|
|
option.enable_paddle_trt_collect_shape()
|
|
option.set_trt_input_shape("x", [1, 3, 256, 256], [1, 3, 1024, 1024],
|
|
[1, 3, 2048, 2048])
|
|
return option
|
|
|
|
|
|
args = parse_arguments()
|
|
|
|
# settting for runtime
|
|
runtime_option = build_option(args)
|
|
model_file = os.path.join(args.model, "model.pdmodel")
|
|
params_file = os.path.join(args.model, "model.pdiparams")
|
|
config_file = os.path.join(args.model, "deploy.yaml")
|
|
model = fd.vision.segmentation.PaddleSegModel(
|
|
model_file, params_file, config_file, runtime_option=runtime_option)
|
|
|
|
# predict
|
|
im = cv2.imread(args.image)
|
|
result = model.predict(im)
|
|
print(result)
|
|
|
|
# visualize
|
|
vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
|
|
cv2.imwrite("vis_img.png", vis_im)
|