mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-11-01 04:12:58 +08:00
42d14e7119
* fit yolov7face file path * TODO：添加yolov7facePython接口Predict * resolve yolov7face.py * resolve yolov7face.py * resolve yolov7face.py * add yolov7face example readme file * [Doc] fix yolov7face example readme file * [Doc]fix yolov7face example readme file * support BlazeFace * add blazeface readme file * fix review problem * fix code style error * fix review problem * fix review problem * fix head file problem * fix review problem * fix review problem * fix readme file problem * add English readme file * fix English readme file
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
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 blazeface model dir.")
|
|
parser.add_argument(
|
|
"--image", 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(
|
|
"--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()
|
|
option.set_trt_input_shape("images", [1, 3, 640, 640])
|
|
return option
|
|
|
|
|
|
args = parse_arguments()
|
|
|
|
model_dir = args.model
|
|
|
|
model_file = os.path.join(model_dir, "model.pdmodel")
|
|
params_file = os.path.join(model_dir, "model.pdiparams")
|
|
config_file = os.path.join(model_dir, "infer_cfg.yml")
|
|
|
|
# Configure runtime and load the model
|
|
runtime_option = build_option(args)
|
|
model = fd.vision.facedet.BlazeFace(model_file, params_file, config_file, runtime_option=runtime_option)
|
|
|
|
# Predict image detection results
|
|
im = cv2.imread(args.image)
|
|
result = model.predict(im)
|
|
print(result)
|
|
# Visualization of prediction Results
|
|
vis_im = fd.vision.vis_face_detection(im, result)
|
|
cv2.imwrite("visualized_result.jpg", vis_im)
|
|
print("Visualized result save in ./visualized_result.jpg")
|