update readme and model files.

.gitattributes

@@ -1,2 +1,2 @@
-models/numpy/* filter=lfs diff=lfs merge=lfs -text
+models/numpy/*.npy filter=lfs diff=lfs merge=lfs -text
 *.ckpt* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -4,6 +4,10 @@ Openpose from CMU implemented using Tensorflow. It also provides several variant
 
 Original Repo(Caffe) : https://github.com/CMU-Perceptual-Computing-Lab/openpose
 
+| CMU's Original Model</br> on Macbook Pro 15" | Mobilenet Variant </br>on Macbook Pro 15" | Mobilenet Variant</br>on Jetson TK2 |
+|---------|--------------------|----------------|
+| ![cmu-model](/etcs/openpose_macbook_cmu.gif)     | ![mb-model-macbook](/etcs/openpose_macbook_mobilenet.gif)  | ![mb-model-tx2](/etcs/openpose_tx2_mobilenet.gif) |
+
 **Features**
 
 - [x] CMU's original network architecture and weights.
@@ -30,9 +34,11 @@ Original Repo(Caffe) : https://github.com/CMU-Perceptual-Computing-Lab/openpose
 
 - [ ] Demos
 
-  - [ ] Realtime Webcam Demo
+  - [x] Realtime Webcam Demo
+
+  - [x] Image File Demo
 
-  - [ ] Image/Video File Demo
+  - [ ] Video File Demo
 
 - [ ] ROS Support. 
 
@@ -57,7 +63,6 @@ You need dependencies below.
 ```bash
 $ git clone https://www.github.com/ildoonet/tf-openpose
 $ cd tf-openpose
-$ git lfs pull
 $ pip3 install -r requirements.txt
 ```
 
@@ -97,6 +102,15 @@ $ pip3 install -r requirements.txt
 
 On embedded GPU Board from Nvidia, Test results are as below.
 
+| Dataset | Model              | Inference Time  |
+|---------|--------------------|----------------:|
+| Coco    | cmu                | OOM   @ 368x368 |
+| Coco    | mobilenet_accurate | 0.18s @ 368x368 |
+| Coco    | mobilenet          | 0.10s @ 368x368 |
+| Coco    | mobilenet_fast     | 0.07s @ 368x368 |
+
+CMU's original model can not be executed due to 'out of memory' on '368x368' size.
+
 ## Training
 
 ### Dataset

inference.py

@@ -41,18 +41,17 @@
         elif args.model == 'mobilenet_accurate':
             net = MobilenetNetwork({'image': input_node}, trainable=False, conv_width=1.0)
             loader = tf.train.Saver()
-            loader.restore(sess, '/Users/ildoonet/Downloads/best_mobilenet_p_1.0/model-26000')
+            loader.restore(sess, './models/trained/mobilenet_accurate/model-170000')
             last_layer = 'MConv_Stage{stage}_L{aux}_5'
         elif args.model == 'mobilenet_fast':
             net = MobilenetNetwork({'image': input_node}, trainable=False, conv_width=0.5)
             loader = tf.train.Saver()
-            # loader.restore(sess, '/Users/ildoonet/Downloads/openpose-mobilenet_0.50/model-67000')
-            loader.restore(sess, '/Users/ildoonet/Downloads/openpose-mobilenet_0.50/model-163000')
+            loader.restore(sess, './models/trained/mobilenet_fast/model-163000')
             last_layer = 'MConv_Stage{stage}_L{aux}_5'
         elif args.model == 'mobilenet':
             net = MobilenetNetwork({'image': input_node}, trainable=False, conv_width=0.75, conv_width2=0.50)
             loader = tf.train.Saver()
-            loader.restore(sess, '/Users/ildoonet/Downloads/openpose-mobilenet_0.75_0.50/model-217003')
+            loader.restore(sess, './models/trained/mobilenet/model-241003')
             last_layer = 'MConv_Stage{stage}_L{aux}_5'
         else:
             raise Exception('Invalid Mode.')

realtime_webcam.py

@@ -76,18 +76,17 @@ def cb_showimg(img, preprocessed, heatMat, pafMat, humans, show_process=False):
         elif args.model == 'mobilenet_accurate':
             net = MobilenetNetwork({'image': input_node}, trainable=False, conv_width=1.0)
             loader = tf.train.Saver()
-            loader.restore(sess, '/Users/ildoonet/Downloads/best_mobilenet_p_1.0/model-26000')
+            loader.restore(sess, './models/trained/mobilenet_accurate/model-170000')
             last_layer = 'MConv_Stage{stage}_L{aux}_5'
         elif args.model == 'mobilenet_fast':
             net = MobilenetNetwork({'image': input_node}, trainable=False, conv_width=0.5)
             loader = tf.train.Saver()
-            # loader.restore(sess, '/Users/ildoonet/Downloads/openpose-mobilenet_0.50/model-67000')
-            loader.restore(sess, '/Users/ildoonet/Downloads/openpose-mobilenet_0.50/model-163000')
+            loader.restore(sess, './models/trained/mobilenet_fast/model-163000')
             last_layer = 'MConv_Stage{stage}_L{aux}_5'
         elif args.model == 'mobilenet':
             net = MobilenetNetwork({'image': input_node}, trainable=False, conv_width=0.75, conv_width2=0.50)
             loader = tf.train.Saver()
-            loader.restore(sess, '/Users/ildoonet/Downloads/openpose-mobilenet_0.75_0.50/model-217003')
+            loader.restore(sess, './models/trained/mobilenet/model-241003')
             last_layer = 'MConv_Stage{stage}_L{aux}_5'
         else:
             raise Exception('Invalid Mode.')