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b565c15bf7
* Add tinypose model * Add PPTinypose python API * Fix picodet preprocess bug && Add Tinypose examples * Update tinypose example code * Update ppseg preprocess if condition * Update ppseg backend support type * Update permute.h * Update README.md * Update code with comments * Move files dir * Delete premute.cc * Add single model pptinypose * Delete pptinypose old code in ppdet * Code format * Add ppdet + pptinypose pipeline model * Fix bug for posedetpipeline * Change Frontend to ModelFormat * Change Frontend to ModelFormat in __init__.py * Add python posedetpipeline/ * Update pptinypose example dir name * Update README.md * Update README.md * Update README.md * Update README.md * Create keypointdetection_result.md * Create README.md * Create README.md * Create README.md * Update README.md * Update README.md * Create README.md * Fix det_keypoint_unite_infer.py bug * Create README.md * Update PP-Tinypose by comment * Update by comment * Add pipeline directory * Add pptinypose dir * Update pptinypose to align accuracy * Addd warpAffine processor * Update GetCpuMat to GetOpenCVMat * Add comment for pptinypose && pipline * Update docs/main_page.md * Add README.md for pptinypose * Add README for det_keypoint_unite * Remove ENABLE_PIPELINE option * Remove ENABLE_PIPELINE option * Change pptinypose default backend * PP-TinyPose Pipeline support multi PP-Detection models * Update pp-tinypose comment * Update by comments * Add single test example Co-authored-by: Jason <jiangjiajun@baidu.com>
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--tinypose_model_dir",
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required=True,
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help="path of paddletinypose model directory")
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parser.add_argument(
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"--image", required=True, help="path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="wether to use tensorrt.")
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return parser.parse_args()
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def build_tinypose_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("image", [1, 3, 256, 192])
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return option
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args = parse_arguments()
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tinypose_model_file = os.path.join(args.tinypose_model_dir, "model.pdmodel")
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tinypose_params_file = os.path.join(args.tinypose_model_dir, "model.pdiparams")
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tinypose_config_file = os.path.join(args.tinypose_model_dir, "infer_cfg.yml")
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# 配置runtime，加载模型
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runtime_option = build_tinypose_option(args)
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tinypose_model = fd.vision.keypointdetection.PPTinyPose(
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tinypose_model_file,
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tinypose_params_file,
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tinypose_config_file,
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runtime_option=runtime_option)
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# 预测图片检测结果
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im = cv2.imread(args.image)
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tinypose_result = tinypose_model.predict(im)
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print("Paddle TinyPose Result:\n", tinypose_result)
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# 预测结果可视化
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vis_im = fd.vision.vis_keypoint_detection(
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im, tinypose_result, conf_threshold=0.5)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("TinyPose visualized result save in ./visualized_result.jpg")
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