Merge branch 'master' into 1-pip-tensorlayer

Author: Laicheng0830

.travis.yml

@@ -64,6 +64,7 @@ install:
   - |
     if [[ -v _DOC_AND_YAPF_TEST ]]; then
         pip install tensorflow==2.0.0-rc1
+        pip install opencv-python
         pip install yapf
         pip install -e .[doc]
     else

docs/images/3d_human_pose_result.jpg

@@ -9,7 +9,7 @@ Welcome to TensorLayer
 
 **Documentation Version:** |release|
 
-**Jun 2020** `Deep Reinforcement Learning Book Is Coming <http://deepreinforcementlearningbook.org>`__.
+**Jun 2020** `Deep Reinforcement Learning Book Is Released <http://deepreinforcementlearningbook.org>`__.
 
 **Good News:** We won the **Best Open Source Software Award** `@ACM Multimedia (MM) 2017 <http://www.acmmm.org/2017/mm-2017-awardees/>`_.
 

docs/images/human_pose_points.jpg

@@ -19,6 +19,7 @@ to visualize the model, activations etc. Here we provide more functions for data
    frame
    images2d
    tsne_embedding
+   draw_boxes_and_labels_to_image_with_json
 
 
 Save and read images
@@ -44,6 +45,9 @@ Save image for object detection
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autofunction:: draw_boxes_and_labels_to_image
 
+Save image for object detection with json
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autofunction:: draw_boxes_and_labels_to_image_with_json
 
 Save image for pose estimation (MPII)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

docs/images/yolov4_image_result.png

@@ -0,0 +1,111 @@
+# Quick Start
+TensorLayer Implementation of [YOLOv4: Optimal Speed and Accuracy of Object Detection][1]
+
+TensorLayer Implementation of [Optimizing Network Structure for 3D Human Pose Estimation][2](ICCV2019)
+
+## YOLOv4
+
+Yolov4 was trained on COCO 2017 Dataset in this demo.
+
+### Data
+
+Download yolov4.weights file [yolov4_model.npz][3], Password: `idsz`, and put yolov4.weights under the folder `./examples/app_tutorials/model/`. Your directory structure should look like this:
+
+```
+${root}/examples
+    └── app_tutorials
+            └── model
+                ├── yolov4_model.npz
+                ├── coco.names
+                └── yolov4_weights_congfig.txt
+
+```
+
+
+You can put an image or a video under the folder `./examples/app_tutorials/data/`,like:
+```
+${root}/examples
+    └──app_tutorials
+            └──data
+                └── *.jpg/*.png/*.mp4/..  
+```
+### demo
+
+1. Image
+
+   Modify `image_path` in `./examples/app_tutorials/tutorial_object_detection_yolov4_image.py` according to your demand, then
+   
+```bash
+python tutorial_object_detection_yolov4_image.py
+```
+2. Video
+   
+   Modify `video_path` in `./examples/app_tutorials/tutorial_object_detection_yolov4_video.py` according to your demand, then
+
+```bash
+python tutorial_object_detection_yolov4_video.py
+```
+3. Output
+   
+   -Image
+   
+   <p align="center"><img src="../../docs/images/yolov4_image_result.png" width="640"\></p>
+  
+   -Video
+   
+   <p align="center"><img src="../../docs/images/yolov4_video_result.gif" width="640"\></p>
+
+
+
+## 3D Human Pose Estimation
+
+### Data
+
+Download 3D Human Pose Estimation model weights [lcn_model.npz][4], Password:`ec07`,and put it under the folder `./examples/app_tutorials/model/`, Your directory structure should look like this:
+
+```
+${root}/examples
+    └── app_tutorials
+            └── model
+                ├── lcn_model.npz
+                └── pose_weights_config.txt
+```
+Download finetuned Stacked Hourglass detections and preprocessed H3.6M data([H36M.rar][5],Password:`kw9i`), then uncompress and put them under the folder `./examples/app_tutorials/data/`, like:
+```
+${root}/examples
+    └──app_tutorials
+            └──data
+                ├── h36m_sh_dt_ft.pkl
+                ├── h36m_test.pkl
+                └── h36m_train.pkl
+```
+Each sample is a list with the length of 34 in three `.pkl` files. The list represents `[x,y]` of 17 human pose points:
+<p align="center"><img src="../../docs/images/human_pose_points.jpg" width="300"\></p>
+
+If you would like to know how to prepare the H3.6M data, please have a look at the [pose_lcn][6].
+
+### Demo
+
+For a quick demo, simply run
+
+```bash
+python tutorial_human_3dpose_estimation_LCN.py
+```
+This will produce a visualization similar to this:
+<p align="center"><img src="../../docs/images/3d_human_pose_result.jpg" width="1500"\></p>
+
+This demo maps 2D poses to 3D space. Each 3D space result list represents `[x,y,z]` of 17 human pose points.
+
+# Acknowledgement
+
+Yolov4 is bulit on https://github.com/AlexeyAB/darknet and https://github.com/hunglc007/tensorflow-yolov4-tflite.
+3D Human Pose Estimation is bulit on https://github.com/rujiewu/pose_lcn and https://github.com/una-dinosauria/3d-pose-baseline.
+We would like to thank the authors for publishing their code.
+
+
+[1]:https://arxiv.org/abs/2004.10934
+[2]:https://openaccess.thecvf.com/content_ICCV_2019/papers/Ci_Optimizing_Network_Structure_for_3D_Human_Pose_Estimation_ICCV_2019_paper.pdf
+[3]:https://pan.baidu.com/s/1MC1dmEwpxsdgHO1MZ8fYRQ
+[4]:https://pan.baidu.com/s/1HBHWsAfyAlNaavw0iyUmUQ
+[5]:https://pan.baidu.com/s/1nA96AgMsvs1sFqkTs7Dfaw
+[6]:https://github.com/rujiewu/pose_lcn

docs/images/yolov4_video_result.gif

@@ -0,0 +1,80 @@
+person
+bicycle
+car
+motorbike
+aeroplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+sofa
+potted plant
+bed
+dining table
+toilet
+tvmonitor
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush

docs/index.rst

@@ -0,0 +1,44 @@
+linear_model/w1
+linear_model/b1
+linear_model/batch_normalization/beta
+linear_model/batch_normalization/gamma
+linear_model/batch_normalization/moving_mean
+linear_model/batch_normalization/moving_variance
+linear_model/two_linear_0/w2_0
+linear_model/two_linear_0/b2_0
+linear_model/two_linear_0/batch_normalization10/beta
+linear_model/two_linear_0/batch_normalization10/gamma
+linear_model/two_linear_0/batch_normalization10/moving_mean
+linear_model/two_linear_0/batch_normalization10/moving_variance
+linear_model/two_linear_0/w3_0
+linear_model/two_linear_0/b3_0
+linear_model/two_linear_0/batch_normalization20/beta
+linear_model/two_linear_0/batch_normalization20/gamma
+linear_model/two_linear_0/batch_normalization20/moving_mean
+linear_model/two_linear_0/batch_normalization20/moving_variance
+linear_model/two_linear_1/w2_1
+linear_model/two_linear_1/b2_1
+linear_model/two_linear_1/batch_normalization11/beta
+linear_model/two_linear_1/batch_normalization11/gamma
+linear_model/two_linear_1/batch_normalization11/moving_mean
+linear_model/two_linear_1/batch_normalization11/moving_variance
+linear_model/two_linear_1/w3_1
+linear_model/two_linear_1/b3_1
+linear_model/two_linear_1/batch_normalization21/beta
+linear_model/two_linear_1/batch_normalization21/gamma
+linear_model/two_linear_1/batch_normalization21/moving_mean
+linear_model/two_linear_1/batch_normalization21/moving_variance
+linear_model/two_linear_2/w2_2
+linear_model/two_linear_2/b2_2
+linear_model/two_linear_2/batch_normalization12/beta
+linear_model/two_linear_2/batch_normalization12/gamma
+linear_model/two_linear_2/batch_normalization12/moving_mean
+linear_model/two_linear_2/batch_normalization12/moving_variance
+linear_model/two_linear_2/w3_2
+linear_model/two_linear_2/b3_2
+linear_model/two_linear_2/batch_normalization22/beta
+linear_model/two_linear_2/batch_normalization22/gamma
+linear_model/two_linear_2/batch_normalization22/moving_mean
+linear_model/two_linear_2/batch_normalization22/moving_variance
+linear_model/w4
+linear_model/b4