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 PFLD model.") parser.add_argument("--image", type=str, 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( "--backend", type=str, default="ort", help="inference backend, ort, ov, trt, paddle, paddle_trt.") parser.add_argument( "--enable_trt_fp16", type=bool, default=False, help="whether enable fp16 in trt/paddle_trt backend") return parser.parse_args() def build_option(args): option = fd.RuntimeOption() device = args.device backend = args.backend enable_trt_fp16 = args.enable_trt_fp16 if device == "gpu": option.use_gpu() if backend == "ort": option.use_ort_backend() elif backend == "paddle": option.use_paddle_backend() elif backend in ["trt", "paddle_trt"]: option.use_trt_backend() option.set_trt_input_shape("input", [1, 3, 112, 112]) if backend == "paddle_trt": option.enable_paddle_to_trt() if enable_trt_fp16: option.enable_trt_fp16() elif backend == "default": return option else: raise Exception( "While inference with GPU, only support default/ort/paddle/trt/paddle_trt now, {} is not supported.". format(backend)) elif device == "cpu": if backend == "ort": option.use_ort_backend() elif backend == "ov": option.use_openvino_backend() elif backend == "paddle": option.use_paddle_backend() elif backend == "default": return option else: raise Exception( "While inference with CPU, only support default/ort/ov/paddle now, {} is not supported.". format(backend)) else: raise Exception( "Only support device CPU/GPU now, {} is not supported.".format( device)) return option args = parse_arguments() # 配置runtime,加载模型 runtime_option = build_option(args) model = fd.vision.facealign.PFLD(args.model, runtime_option=runtime_option) # for image im = cv2.imread(args.image) result = model.predict(im.copy()) print(result) # 可视化结果 vis_im = fd.vision.vis_face_alignment(im, result) cv2.imwrite("visualized_result.jpg", vis_im) print("Visualized result save in ./visualized_result.jpg")