Clean dependencies and add to README

README.md

@@ -30,11 +30,17 @@ obman_train/
 - Download model files from TODO
 - unzip `unzip release_models.zip`
 
+## Install python dependencies
+
+- create conda environment with dependencies: `conda env create -f environment.yml`
+- activate environment: `conda activate obman_train`
+
 
 ## Install the MANO PyTorch layer
 
 - Follow the instructions from [here](https://github.com/hassony2/manopth)
 
+
 ### Download the MANO model files
 
 - Go to [MANO website](http://mano.is.tue.mpg.de/)
@@ -59,6 +65,8 @@ obman_render/
 ```
 
 
+
+
 # Launch
 
 ## Training
@@ -113,6 +121,19 @@ Note that the model trained on First Hand Action Benchmark strongly overfits to
   - In addition to the models, we also provide a hand-only model trained on various hand datasets, including our ObMan dataset, that captures a wider variety of hand poses
   - to try it, launch `python webcam_demo.py --resume release_models/hands_only/checkpoint.pth.tar`
 
+# Citations
+
+If you find this code useful for your research, consider citing:
+
+```
+@INPROCEEDINGS{hasson19_obman,
+  title     = {Learning joint reconstruction of hands and manipulated objects},
+  author    = {Hasson, Yana and Varol, G{\"u}l and Tzionas, Dimitris and Kalevatykh, Igor and Black, Michael J. and Laptev, Ivan and Schmid, Cordelia},
+  booktitle = {CVPR},
+  year      = {2019}
+}
+```
+
 # Acknowledgements
 
 ## AtlasNet code

environment.yml

@@ -0,0 +1,18 @@
+name: obman_train
+channels:
+  - menpo
+  - defaults
+  - conda-forge
+dependencies:
+  - opencv
+  - python=3.7
+  - matplotlib
+  - numpy
+  - pillow
+  - pytorch
+  - trimesh
+  - tqdm
+  - pip
+  - pip:
+    - git+https://github.com/hassony2/chumpy.git
+    - opencv-python

handobjectdatasets/viz3d.py

@@ -1,33 +1,33 @@
-from mpl_toolkits.mplot3d import Axes3D
-from matplotlib import pyplot as plt
 import numpy as np
-import plotly.graph_objs as go
 
 
 def equal_aspect_3d(ax):
     extents = np.array(
-        [getattr(ax, 'get_{}lim'.format(dim))() for dim in 'xyz'])
+        [getattr(ax, "get_{}lim".format(dim))() for dim in "xyz"]
+    )
     sz = extents[:, 1] - extents[:, 0]
     centers = np.mean(extents, axis=1)
     maxsize = max(abs(sz))
     r = maxsize / 2
-    for ctr, dim in zip(centers, 'xyz'):
-        getattr(ax, 'set_{}lim'.format(dim))(ctr - r, ctr + r)
+    for ctr, dim in zip(centers, "xyz"):
+        getattr(ax, "set_{}lim".format(dim))(ctr - r, ctr + r)
 
 
-def visualize_joints_3d(ax,
-                        joints,
-                        joint_idxs=True,
-                        alpha=1,
-                        links=None,
-                        scatter_color='r'):
+def visualize_joints_3d(
+    ax, joints, joint_idxs=True, alpha=1, links=None, scatter_color="r"
+):
     """
     Args:
         ax = fig.add_subplot(111, projection='3d')
     """
     if links is None:
-        links = [(0, 1, 2, 3, 4), (0, 5, 6, 7, 8), (0, 9, 10, 11, 12),
-                 (0, 13, 14, 15, 16), (0, 17, 18, 19, 20)]
+        links = [
+            (0, 1, 2, 3, 4),
+            (0, 5, 6, 7, 8),
+            (0, 9, 10, 11, 12),
+            (0, 13, 14, 15, 16),
+            (0, 17, 18, 19, 20),
+        ]
     # Scatter hand joints on image
     x = joints[:, 0]
     y = joints[:, 1]
@@ -43,7 +43,7 @@ def visualize_joints_3d(ax,
 
 
 def _draw3djoints(ax, annots, links, alpha=1):
-    colors = ['r', 'm', 'b', 'c', 'g']
+    colors = ["r", "m", "b", "c", "g"]
 
     for finger_idx, finger_links in enumerate(links):
         for idx in range(len(finger_links) - 1):
@@ -53,57 +53,16 @@ def _draw3djoints(ax, annots, links, alpha=1):
                 finger_links[idx],
                 finger_links[idx + 1],
                 c=colors[finger_idx],
-                alpha=alpha)
+                alpha=alpha,
+            )
 
 
-def _draw3dseg(ax, annot, idx1, idx2, c='r', alpha=1):
+def _draw3dseg(ax, annot, idx1, idx2, c="r", alpha=1):
     ax.plot(
-        [annot[idx1, 0], annot[idx2, 0]], [annot[idx1, 1], annot[idx2, 1]],
+        [annot[idx1, 0], annot[idx2, 0]],
+        [annot[idx1, 1], annot[idx2, 1]],
         [annot[idx1, 2], annot[idx2, 2]],
         c=c,
         alpha=alpha,
-        linewidth=1)
-
-
-def pyplot_hands(joints):
-    joint_nb = joints.shape[0]
-    if joint_nb == 21:
-        links = [(0, 1, 2, 3, 4), (0, 5, 6, 7, 8), (0, 9, 10, 11, 12),
-                 (0, 13, 14, 15, 16), (0, 17, 18, 19, 20)]
-    elif joint_nb == 20:
-        links = [(0, 1, 2, 3), (4, 5, 6, 7), (8, 9, 10, 11), (12, 13, 14, 15),
-                 (16, 17, 18, 19)]
-    else:
-        raise ValueError('{} joints not supported for pyplot visualization'.
-                         format(joint_nb))
-    trace1 = go.Scatter3d(
-        x=joints[:, 0],
-        y=joints[:, 1],
-        z=joints[:, 2],
-        marker=dict(size=1),
-        mode='markers',
-        name='markers')
-
-    def add_trace(finger_links, joints):
-        x_lines = list()
-        y_lines = list()
-        z_lines = list()
-        # create the coordinate list for the lines
-        for idx in finger_links:
-            # print(idx)
-            x_lines.append(joints[idx, 0])
-            y_lines.append(joints[idx, 1])
-            z_lines.append(joints[idx, 2])
-        x_lines.append(None)
-        y_lines.append(None)
-        z_lines.append(None)
-        trace = go.Scatter3d(
-            x=x_lines, y=y_lines, z=z_lines, mode='lines', name='lines')
-        return trace
-
-    traces = []
-    for finger_idx, finger_links in enumerate(links):
-        trace = add_trace(finger_links, joints)
-        traces.append(trace)
-    hand_traces = [trace1, *traces]
-    return hand_traces
+        linewidth=1,
+    )