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