readme update. image changed.

README.md

@@ -118,10 +118,22 @@ CMU's original model can not be executed due to 'out of memory' on '368x368' siz
 
 ## Demo
 
+### Test Inference
+
+You can test the inference feature with a single image.
+
+```
+$ python3 inference.py --model=mobilenet --imgpath=...
+```
+
+Then you will see the screen as below with pafmap, heatmap, result and etc.
+
+![inferent_result](./etcs/inference_result.png)
+
 ### Realtime Webcam
 
 ```
-$ python3 realtime_webcam.py --camera=0 --model=mobilenet
+$ python3 realtime_webcam.py --camera=0 --model=mobilenet --zoom=1.0
 ```
 
 ## Training

common_test.py

@@ -42,7 +42,7 @@ def test_mobilenet(self):
         elapsed = time.time() - t
         logging.info('[test_mobilenet] elapsed=%f' % elapsed)
 
-        self._show('./images/person3.jpg', inpmat, heatmat, pafmat, humans)
+        self._show('./images/p3.jpg', inpmat, heatmat, pafmat, humans)
 
     def test_cmu(self):
         inpmat = np.load('./tests/person3.pickle')
@@ -54,7 +54,7 @@ def test_cmu(self):
         elapsed = time.time() - t
         logging.info('[test_mobilenet] elapsed=%f' % elapsed)
 
-        # self._show('./images/person3.jpg', inpmat, heatmat, pafmat, humans)
+        # self._show('./images/p3.jpg', inpmat, heatmat, pafmat, humans)
 
 if __name__ == '__main__':
     unittest.main()

inference.py

@@ -22,7 +22,7 @@
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Tensorflow Openpose Inference')
-    parser.add_argument('--imgpath', type=str, default='./images/person3.jpg')
+    parser.add_argument('--imgpath', type=str, default='./images/p2.jpg')
     parser.add_argument('--input-width', type=int, default=368)
     parser.add_argument('--input-height', type=int, default=368)
     parser.add_argument('--stage-level', type=int, default=6)

networks.py

@@ -1,25 +1,33 @@
+import os
+
 import tensorflow as tf
 
 from network_cmu import CmuNetwork
 from network_mobilenet import MobilenetNetwork
 
 
+def _get_base_path():
+    if not os.environ.get('OPENPOSE_MODEL', ''):
+        return './models'
+    return os.environ.get('OPENPOSE_MODEL')
+
+
 def get_network(type, placeholder_input, sess_for_load=None):
     if type == 'mobilenet':
         net = MobilenetNetwork({'image': placeholder_input}, conv_width=0.75, conv_width2=0.50)
-        pretrain_path = './models/pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
+        pretrain_path = 'pretrained/mobilenet_v1_0.75_224_2017_06_14/mobilenet_v1_0.75_224.ckpt'
         last_layer = 'MConv_Stage6_L{aux}_5'
     elif type == 'mobilenet_accurate':
         net = MobilenetNetwork({'image': placeholder_input}, conv_width=1.00)
-        pretrain_path = './models/pretrained/mobilenet_v1_1.0_224_2017_06_14/mobilenet_v1_1.0_224.ckpt'
+        pretrain_path = 'pretrained/mobilenet_v1_1.0_224_2017_06_14/mobilenet_v1_1.0_224.ckpt'
         last_layer = 'MConv_Stage6_L{aux}_5'
     elif type == 'mobilenet_fast':
         net = MobilenetNetwork({'image': placeholder_input}, conv_width=0.50)
-        pretrain_path = './models/pretrained/mobilenet_v1_0.50_224_2017_06_14/mobilenet_v1_0.50_224.ckpt'
+        pretrain_path = 'pretrained/mobilenet_v1_0.50_224_2017_06_14/mobilenet_v1_0.50_224.ckpt'
         last_layer = 'MConv_Stage6_L{aux}_5'
     elif type == 'cmu':
         net = CmuNetwork({'image': placeholder_input})
-        pretrain_path = './models/numpy/openpose_coco.npy'
+        pretrain_path = 'numpy/openpose_coco.npy'
         last_layer = 'Mconv7_stage6_L{aux}'
     else:
         raise Exception('Invalid Mode.')
@@ -30,11 +38,11 @@ def get_network(type, placeholder_input, sess_for_load=None):
         else:
             s = '%dx%d' % (placeholder_input.shape[2], placeholder_input.shape[1])
             ckpts = {
-                'mobilenet': './models/trained/mobilenet_%s/model-release' % s,
-                'mobilenet_fast': './models/trained/mobilenet_fast/model-163000',
-                'mobilenet_accurate': './models/trained/mobilenet_accurate/model-170000'
+                'mobilenet': 'trained/mobilenet_%s/model-release' % s,
+                'mobilenet_fast': 'trained/mobilenet_fast/model-163000',
+                'mobilenet_accurate': 'trained/mobilenet_accurate/model-170000'
             }
             loader = tf.train.Saver()
-            loader.restore(sess_for_load, ckpts[type])
+            loader.restore(sess_for_load, os.path.join(_get_base_path(), ckpts[type]))
 
-    return net, pretrain_path, last_layer
+    return net, os.path.join(_get_base_path(), pretrain_path), last_layer

train.py

@@ -65,9 +65,9 @@
     for images_test, heatmaps, vectmaps in df_valid.get_data():
         validation_cache.append((images_test, heatmaps, vectmaps))
 
-    val_image = read_imgfile('./images/person1.jpg', args.input_width, args.input_height)
-    val_image2 = read_imgfile('./images/person2.jpg', args.input_width, args.input_height)
-    val_image3 = read_imgfile('./images/person3.jpg', args.input_width, args.input_height)
+    val_image = read_imgfile('./images/p1.jpg', args.input_width, args.input_height)
+    val_image2 = read_imgfile('./images/p2.jpg', args.input_width, args.input_height)
+    val_image3 = read_imgfile('./images/p3.jpg', args.input_width, args.input_height)
 
     # define model for multi-gpu
     q_inp_split = tf.split(q_inp, args.gpus)