--- a
+++ b/dummy_pose_estimation.py
@@ -0,0 +1,49 @@
+import numpy as np
+import matplotlib as mpl
+mpl.use('Agg')
+import matplotlib.pyplot as plt
+from scipy.misc import imresize, imread
+
+from human_pose_nn import HumanPoseIRNetwork
+
+net_pose = HumanPoseIRNetwork()
+net_pose.restore('models/MPII+LSP.ckpt')
+
+img = imread('images/dummy.jpg')
+img = imresize(img, [299, 299])
+img_batch = np.expand_dims(img, 0)
+
+y, x, a = net_pose.estimate_joints(img_batch)
+y, x, a = np.squeeze(y), np.squeeze(x), np.squeeze(a)
+
+joint_names = [
+    'right ankle ',
+    'right knee ',
+    'right hip',
+    'left hip',
+    'left knee',
+    'left ankle',
+    'pelvis',
+    'thorax',
+    'upper neck',
+    'head top',
+    'right wrist',
+    'right elbow',
+    'right shoulder',
+    'left shoulder',
+    'left elbow',
+    'left wrist'
+]
+
+# Print probabilities of each estimation
+for i in range(16):
+    print('%s: %.02f%%' % (joint_names[i], a[i] * 100))
+
+# Create image
+colors = ['r', 'r', 'b', 'm', 'm', 'y', 'g', 'g', 'b', 'c', 'r', 'r', 'b', 'm', 'm', 'c']
+for i in range(16):
+    if i < 15 and i not in {5, 9}:
+        plt.plot([x[i], x[i + 1]], [y[i], y[i + 1]], color = colors[i], linewidth = 5)
+
+plt.imshow(img)
+plt.savefig('images/dummy_pose.jpg')