v0.0 TF 2 for DLC release

Author: AlexEMG

.gitignore

@@ -2,6 +2,8 @@
 __pycache__/
 *.py[cod]
 *$py.class
+*.ckpt #TF
+
 
 # C extensions
 *.so

conda-environments/DLCcore-CPU.yaml

@@ -0,0 +1,25 @@
+# DLC-CPU.yaml
+
+#DeepLabCut2.0 Toolbox (deeplabcut.org)
+#© A. & M. Mathis Labs
+#https://github.com/AlexEMG/DeepLabCut
+#Please see AUTHORS for contributors.
+
+#https://github.com/AlexEMG/DeepLabCut/blob/master/AUTHORS
+#Licensed under GNU Lesser General Public License v3.0
+
+# install: conda env create -f DLCcore-CPU.yaml
+# update:  conda env update -f DLCcore-CPU.yaml
+
+name: DLCcore-CPU
+dependencies:
+  - python=3.7
+  - pip
+  - jupyter
+  - nb_conda
+  - tensorflow
+  - wxpython<4.1.0
+  - Shapely
+  - pip:
+    - deeplabcutcore
+    - tf_slim

conda-environments/DLCcore-GPU.yaml

@@ -0,0 +1,27 @@
+# DLC-GPU.yaml
+
+#DeepLabCut2.0 Toolbox (deeplabcut.org)
+#© A. & M. Mathis Labs
+#https://github.com/AlexEMG/DeepLabCut
+#Please see AUTHORS for contributors.
+
+#https://github.com/AlexEMG/DeepLabCut/blob/master/AUTHORS
+#Licensed under GNU Lesser General Public License v3.0
+#
+
+# install: conda env create -f DLCcore-GPU.yaml
+# update:  conda env update -f DLCcore-GPU.yaml
+
+name: DLCcore-GPU
+dependencies:
+  - python=3.7
+  - pip
+  - tensorflow-gpu
+  - cudnn=7
+  - wxpython<4.1.0
+  - jupyter
+  - nb_conda
+  - Shapely
+  - pip:
+    - deeplabcutcore
+    - tf_slim

deeplabcutcore/__init__.py

@@ -12,25 +12,66 @@
 import platform
 
 # Supress tensorflow warning messages
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-DEBUG = True and 'DEBUG' in os.environ and os.environ['DEBUG']
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+DEBUG = True and "DEBUG" in os.environ and os.environ["DEBUG"]
 from deeplabcutcore import DEBUG
 
 
-from deeplabcutcore.create_project import create_new_project, create_new_project_3d, add_new_videos, load_demo_data
-from deeplabcutcore.create_project import create_pretrained_project, create_pretrained_human_project
-from deeplabcutcore.generate_training_dataset import extract_frames, select_cropping_area
-from deeplabcutcore.generate_training_dataset import check_labels,create_training_dataset, mergeandsplit, create_training_model_comparison
-from deeplabcutcore.utils import create_labeled_video,plot_trajectories, auxiliaryfunctions, convertcsv2h5, convertannotationdata_fromwindows2unixstyle, analyze_videos_converth5_to_csv, auxfun_videos
+from deeplabcutcore.create_project import (
+    create_new_project,
+    create_new_project_3d,
+    add_new_videos,
+    load_demo_data,
+)
+from deeplabcutcore.create_project import (
+    create_pretrained_project,
+    create_pretrained_human_project,
+)
+from deeplabcutcore.generate_training_dataset import (
+    extract_frames,
+    select_cropping_area,
+)
+from deeplabcutcore.generate_training_dataset import (
+    check_labels,
+    create_training_dataset,
+    mergeandsplit,
+    create_training_model_comparison,
+)
+from deeplabcutcore.utils import (
+    create_labeled_video,
+    plot_trajectories,
+    auxiliaryfunctions,
+    convertcsv2h5,
+    convertannotationdata_fromwindows2unixstyle,
+    analyze_videos_converth5_to_csv,
+    auxfun_videos,
+)
 from deeplabcutcore.utils.auxfun_videos import ShortenVideo, DownSampleVideo, CropVideo
 
 # Train, evaluate & predict functions / require TF
-from deeplabcutcore.pose_estimation_tensorflow import train_network, return_train_network_path
-from deeplabcutcore.pose_estimation_tensorflow import evaluate_network, return_evaluate_network_data
-from deeplabcutcore.pose_estimation_tensorflow import analyze_videos, analyze_time_lapse_frames
+from deeplabcutcore.pose_estimation_tensorflow import (
+    train_network,
+    return_train_network_path,
+)
+from deeplabcutcore.pose_estimation_tensorflow import (
+    evaluate_network,
+    return_evaluate_network_data,
+)
+from deeplabcutcore.pose_estimation_tensorflow import (
+    analyze_videos,
+    analyze_time_lapse_frames,
+)
 from deeplabcutcore.pose_estimation_tensorflow import export_model
 
-from deeplabcutcore.pose_estimation_3d import calibrate_cameras,check_undistortion,triangulate,create_labeled_video_3d
+from deeplabcutcore.pose_estimation_3d import (
+    calibrate_cameras,
+    check_undistortion,
+    triangulate,
+    create_labeled_video_3d,
+)
 
-from deeplabcutcore.refine_training_dataset import extract_outlier_frames, merge_datasets
+from deeplabcutcore.refine_training_dataset import (
+    extract_outlier_frames,
+    merge_datasets,
+)
 from deeplabcutcore.post_processing import filterpredictions, analyzeskeleton

deeplabcutcore/create_project/__init__.py

@@ -2,4 +2,7 @@
 from deeplabcutcore.create_project.new_3d import create_new_project_3d
 from deeplabcutcore.create_project.add import add_new_videos
 from deeplabcutcore.create_project.demo_data import load_demo_data
-from deeplabcutcore.create_project.modelzoo import create_pretrained_human_project, create_pretrained_project
+from deeplabcutcore.create_project.modelzoo import (
+    create_pretrained_human_project,
+    create_pretrained_project,
+)

deeplabcutcore/create_project/add.py

@@ -9,7 +9,7 @@
 """
 
 
-def add_new_videos(config,videos,copy_videos=False,coords=None):
+def add_new_videos(config, videos, copy_videos=False, coords=None):
     """
     Add new videos to the config file at any stage of the project.
 
@@ -49,26 +49,26 @@ def add_new_videos(config,videos,copy_videos=False,coords=None):
     # Read the config file
     cfg = auxiliaryfunctions.read_config(config)
 
-    video_path = Path(config).parents[0] / 'videos'
-    data_path = Path(config).parents[0] / 'labeled-data'
+    video_path = Path(config).parents[0] / "videos"
+    data_path = Path(config).parents[0] / "labeled-data"
     videos = [Path(vp) for vp in videos]
 
-    dirs = [data_path/Path(i.stem) for i in videos]
+    dirs = [data_path / Path(i.stem) for i in videos]
 
     for p in dirs:
         """
         Creates directory under data & perhaps copies videos (to /video)
         """
-        p.mkdir(parents = True, exist_ok = True)
+        p.mkdir(parents=True, exist_ok=True)
 
     destinations = [video_path.joinpath(vp.name) for vp in videos]
-    if copy_videos==True:
+    if copy_videos == True:
         for src, dst in zip(videos, destinations):
             if dst.exists():
                 pass
             else:
                 print("Copying the videos")
-                shutil.copy(os.fspath(src),os.fspath(dst))
+                shutil.copy(os.fspath(src), os.fspath(dst))
     else:
         for src, dst in zip(videos, destinations):
             if dst.exists():
@@ -79,29 +79,35 @@ def add_new_videos(config,videos,copy_videos=False,coords=None):
                 dst = str(dst)
                 os.symlink(src, dst)
 
-    if copy_videos==True:
-        videos=destinations # in this case the *new* location should be added to the config file
+    if copy_videos == True:
+        videos = (
+            destinations
+        )  # in this case the *new* location should be added to the config file
     # adds the video list to the config.yaml file
-    for idx,video in enumerate(videos):
+    for idx, video in enumerate(videos):
         try:
-# For windows os.path.realpath does not work and does not link to the real video.
-           video_path = str(Path.resolve(Path(video)))
-#           video_path = os.path.realpath(video)
+            # For windows os.path.realpath does not work and does not link to the real video.
+            video_path = str(Path.resolve(Path(video)))
+        #           video_path = os.path.realpath(video)
         except:
-           video_path = os.readlink(video)
+            video_path = os.readlink(video)
 
         vcap = cv2.VideoCapture(video_path)
         if vcap.isOpened():
             # get vcap property
-           width = int(vcap.get(cv2.CAP_PROP_FRAME_WIDTH))
-           height = int(vcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-           if coords == None:
-                cfg['video_sets'].update({video_path : {'crop': ', '.join(map(str, [0, width, 0, height]))}})
-           else:
+            width = int(vcap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(vcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            if coords == None:
+                cfg["video_sets"].update(
+                    {video_path: {"crop": ", ".join(map(str, [0, width, 0, height]))}}
+                )
+            else:
                 c = coords[idx]
-                cfg['video_sets'].update({video_path : {'crop': ', '.join(map(str, c))}})
+                cfg["video_sets"].update({video_path: {"crop": ", ".join(map(str, c))}})
         else:
-           print("Cannot open the video file!")
+            print("Cannot open the video file!")
 
-    auxiliaryfunctions.write_config(config,cfg)
-    print("New video was added to the project! Use the function 'extract_frames' to select frames for labeling.")
+    auxiliaryfunctions.write_config(config, cfg)
+    print(
+        "New video was added to the project! Use the function 'extract_frames' to select frames for labeling."
+    )

deeplabcutcore/create_project/demo_data.py

@@ -13,8 +13,9 @@
 import deeplabcutcore
 from deeplabcutcore.utils import auxiliaryfunctions
 
-def load_demo_data(config,createtrainingset=True):
-  """
+
+def load_demo_data(config, createtrainingset=True):
+    """
   Loads the demo data. Make sure that you are in the same directory where you have downloaded or cloned the deeplabcutcore.
 
   Parameter
@@ -27,13 +28,14 @@ def load_demo_data(config,createtrainingset=True):
     >>> deeplabcutcore.load_demo_data('config.yaml')
     --------
   """
-  config = Path(config).resolve()
-  config = str(config)
+    config = Path(config).resolve()
+    config = str(config)
+
+    transform_data(config)
+    if createtrainingset:
+        print("Loaded, now creating training data...")
+        deeplabcutcore.create_training_dataset(config, num_shuffles=1)
 
-  transform_data(config)
-  if createtrainingset:
-      print("Loaded, now creating training data...")
-      deeplabcutcore.create_training_dataset(config, num_shuffles=1)
 
 def transform_data(config):
     """
@@ -45,15 +47,17 @@ def transform_data(config):
     cfg = auxiliaryfunctions.read_config(config)
     project_path = str(Path(config).parents[0])
 
-    cfg['project_path'] = project_path
-    if 'Reaching' in project_path:
-        video_file = os.path.join(project_path, 'videos','reachingvideo1.avi')
-    elif 'openfield' in project_path:
-        video_file = os.path.join(project_path, 'videos','m4s1.mp4')
+    cfg["project_path"] = project_path
+    if "Reaching" in project_path:
+        video_file = os.path.join(project_path, "videos", "reachingvideo1.avi")
+    elif "openfield" in project_path:
+        video_file = os.path.join(project_path, "videos", "m4s1.mp4")
     else:
         print("This is not an offical demo dataset.")
 
-    if 'WILL BE AUTOMATICALLY UPDATED BY DEMO CODE' in cfg['video_sets'].keys():
-        cfg['video_sets'][str(video_file)] = cfg['video_sets'].pop('WILL BE AUTOMATICALLY UPDATED BY DEMO CODE')
+    if "WILL BE AUTOMATICALLY UPDATED BY DEMO CODE" in cfg["video_sets"].keys():
+        cfg["video_sets"][str(video_file)] = cfg["video_sets"].pop(
+            "WILL BE AUTOMATICALLY UPDATED BY DEMO CODE"
+        )
 
-    auxiliaryfunctions.write_config(config,cfg)
+    auxiliaryfunctions.write_config(config, cfg)