PaddleHub可以便捷地获取PaddlePaddle生态下的预训练模型，完成模型的管理和一键预测。配合使用Fine-tune API，可以基于大规模预训练模型快速完成迁移学习，让预训练模型能更好地服务于用户特定场景的应用。
模型概述
人体骨骼关键点检测(Pose Estimation) 是计算机视觉的基础性算法之一，在诸多计算机视觉任务起到了基础性的作用，如行为识别、人物跟踪、步态识别等相关领域。具体应用主要集中在智能视频监控，病人监护系统，人机交互，虚拟现实，人体动画，智能家居，智能安防，运动员辅助训练等等。 该模型的论文《Simple Baselines for Human Pose Estimation and Tracking》由 MSRA 发表于 ECCV18，使用 MPII 数据集训练完成。
代码及效果示例：
import paddlehub as hub
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
#pose_resnet50_mpii
module = hub.Module(name="pose_resnet50_mpii")
test_img_path = "./body2.jpg"
# 预测结果展示
img = mpimg.imread(test_img_path)
plt.imshow(img)
plt.axis('off')
plt.show()
# set input dict
input_dict = {"image": [test_img_path]}
# execute predict and print the result
results = module.keypoint_detection(data=input_dict)
for result in results:
print(result)
test_img_path = "./output_pose/rendered_body2.jpg"
img = mpimg.imread(test_img_path)
plt.imshow(img)
plt.axis('off')
plt.show()
[2020-01-05 12:20:51,492] [ INFO] - Installing pose_resnet50_mpii module
2020-01-05 12:20:51,492-INFO: Installing pose_resnet50_mpii module
[2020-01-05 12:20:51,508] [ INFO] - Module pose_resnet50_mpii already installed in /home/aistudio/.paddlehub/modules/pose_resnet50_mpii
2020-01-05 12:20:51,508-INFO: Module pose_resnet50_mpii already installed in /home/aistudio/.paddlehub/modules/pose_resnet50_mpii
[2020-01-05 12:20:51,958] [ INFO] - 282 pretrained paramaters loaded by PaddleHub
2020-01-05 12:20:51,958-INFO: 282 pretrained paramaters loaded by PaddleHub
{'path': 'output_pose/rendered_body2.jpg', 'data': {'left_ankle': [178, 250], 'left_knee': [220, 224], 'left_hip': [201, 215], 'right_hip': [243, 215], 'right_knee': [243, 224], 'right_ankle': [234, 247], 'pelvis': [225, 215], 'thorax': [220, 123], 'upper neck': [220, 98], 'head top': [220, 57], 'right_wrist': [112, 212], 'right_elbow': [168, 180], 'right_shoulder': [182, 126], 'left_shoulder': [257, 120], 'left_elbow': [276, 180], 'left_wrist': [309, 218]}}
module = hub.Module(name="pose_resnet50_mpii")
test_img_path = "./body10.jpg"
# 预测结果展示
img = mpimg.imread(test_img_path)
plt.imshow(img)
plt.axis('off')
plt.show()
# set input dict
input_dict = {"image": [test_img_path]}
# execute predict and print the result
results = module.keypoint_detection(data=input_dict)
for result in results:
print(result)
test_img_path = "./output_pose/rendered_body10.jpg"
img = mpimg.imread(test_img_path)
plt.imshow(img)
plt.axis('off')
plt.show()
[2020-01-05 12:22:10,407] [ INFO] - Installing pose_resnet50_mpii module
2020-01-05 12:22:10,407-INFO: Installing pose_resnet50_mpii module
[2020-01-05 12:22:10,422] [ INFO] - Module pose_resnet50_mpii already installed in /home/aistudio/.paddlehub/modules/pose_resnet50_mpii
2020-01-05 12:22:10,422-INFO: Module pose_resnet50_mpii already installed in /home/aistudio/.paddlehub/modules/pose_resnet50_mpii
[2020-01-05 12:22:10,919] [ INFO] - 282 pretrained paramaters loaded by PaddleHub
2020-01-05 12:22:10,919-INFO: 282 pretrained paramaters loaded by PaddleHub
{'path': 'output_pose/rendered_body10.jpg', 'data': {'left_ankle': [335, 870], 'left_knee': [306, 642], 'left_hip': [313, 555], 'right_hip': [335, 533], 'right_knee': [298, 653], 'right_ankle': [408, 761], 'pelvis': [328, 533], 'thorax': [298, 435], 'upper neck': [306, 413], 'head top': [364, 337], 'right_wrist': [320, 348], 'right_elbow': [298, 391], 'right_shoulder': [291, 424], 'left_shoulder': [313, 457], 'left_elbow': [408, 500], 'left_wrist': [495, 533]}}
