mirror of
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101 lines
4.1 KiB
Python
101 lines
4.1 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import fastdeploy as fd
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import cv2
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import os
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import numpy as np
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import runtime_config as rc
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def test_keypointdetection_pptinypose():
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pp_tinypose_model_url = "https://bj.bcebos.com/fastdeploy/tests/PP_TinyPose_256x192_test.tgz"
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fd.download_and_decompress(pp_tinypose_model_url, "resources")
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model_path = "./resources/PP_TinyPose_256x192_test"
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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model_file = os.path.join(model_path, "model.pdmodel")
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params_file = os.path.join(model_path, "model.pdiparams")
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config_file = os.path.join(model_path, "infer_cfg.yml")
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image_file = os.path.join(model_path, "hrnet_demo.jpg")
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baseline_file = os.path.join(model_path, "baseline.npy")
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model = fd.vision.keypointdetection.PPTinyPose(
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model_file, params_file, config_file, runtime_option=rc.test_option)
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# 预测图片关键点
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im = cv2.imread(image_file)
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result = model.predict(im)
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result = np.concatenate(
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(np.array(result.keypoints), np.array(result.scores)[:, np.newaxis]),
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axis=1)
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baseline = np.load(baseline_file)
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diff = np.fabs(result - np.array(baseline))
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thres = 1e-05
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assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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print("No diff")
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def test_keypointdetection_det_keypoint_unite():
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det_keypoint_unite_model_url = "https://bj.bcebos.com/fastdeploy/tests/PicoDet_320x320_TinyPose_256x192_test.tgz"
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fd.download_and_decompress(det_keypoint_unite_model_url, "resources")
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model_path = "./resources/PicoDet_320x320_TinyPose_256x192_test"
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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tinypose_model_file = os.path.join(
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model_path, "PP_TinyPose_256x192_infer/model.pdmodel")
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tinypose_params_file = os.path.join(
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model_path, "PP_TinyPose_256x192_infer/model.pdiparams")
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tinypose_config_file = os.path.join(
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model_path, "PP_TinyPose_256x192_infer/infer_cfg.yml")
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picodet_model_file = os.path.join(
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model_path, "PP_PicoDet_V2_S_Pedestrian_320x320_infer/model.pdmodel")
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picodet_params_file = os.path.join(
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model_path, "PP_PicoDet_V2_S_Pedestrian_320x320_infer/model.pdiparams")
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picodet_config_file = os.path.join(
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model_path, "PP_PicoDet_V2_S_Pedestrian_320x320_infer/infer_cfg.yml")
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image_file = os.path.join(model_path, "000000018491.jpg")
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# image_file = os.path.join(model_path, "hrnet_demo.jpg")
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baseline_file = os.path.join(model_path, "baseline.npy")
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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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det_model = fd.vision.detection.PicoDet(
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picodet_model_file,
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picodet_params_file,
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picodet_config_file,
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runtime_option=rc.test_option)
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# 预测图片关键点
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im = cv2.imread(image_file)
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pipeline = fd.pipeline.PPTinyPose(det_model, tinypose_model)
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pipeline.detection_model_score_threshold = 0.5
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result = pipeline.predict(im)
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print(result)
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result = np.concatenate(
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(np.array(result.keypoints), np.array(result.scores)[:, np.newaxis]),
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axis=1)
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print(result)
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np.save("resources/baseline.npy", result)
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baseline = np.load(baseline_file)
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diff = np.fabs(result - np.array(baseline))
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thres = 1e-05
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assert diff.max() < thres, "The diff is %f, which is bigger than %f" % (
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diff.max(), thres)
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print("No diff")
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