Updated conda files and moved them

Generating_a_Training_Set/Step1_SelectRandomFrames_fromVideos.py

@@ -29,6 +29,7 @@
 import os
 import math
 import sys
+
 sys.path.append(os.getcwd().split('Generating_a_Training_Set')[0])
 from myconfig import Task, videopath, videotype, filename, x1, x2, y1, y2, start, stop, \
     date, cropping, numframes2pick, selectionalgorithm, checkcropping

Generating_a_Training_Set/frameselectiontools.py

@@ -18,9 +18,14 @@
 from tqdm import tqdm
 
 def UniformFrames(clip,numframes2pick,start,stop,Index="all"):
-    ''' Temporally uniformly sampling frames in interval (start,stop). Visual information of video is irrelevant for this method. This code is 
-    very fast and sufficient (to extract distinct frames) when behavioral videos naturally covers many states. '''
+    ''' Temporally uniformly sampling frames in interval (start,stop). 
+    Visual information of video is irrelevant for this method. This code is fast and sufficient (to extract distinct frames),
+    when behavioral videos naturally covers many states.
+    
+    The variable Index allows to pass on a subindex for the frames. 
+    '''
     print("Uniformly extracting of frames from", start*clip.duration," seconds to", stop*clip.duration, " seconds.")
+
     if Index=="all":
         if start==0:
             frames2pick = np.random.choice(math.ceil(clip.duration * clip.fps * stop), size=numframes2pick - 1, replace = False)
@@ -30,23 +35,28 @@ def UniformFrames(clip,numframes2pick,start,stop,Index="all"):
     else:
         startindex=int(np.floor(clip.fps*clip.duration*start))
         stopindex=int(np.ceil(clip.fps*clip.duration*stop))
-        Index=np.array(Index)
+        Index=np.array(Index,dtype=np.int)
         Index=Index[(Index>startindex)*(Index<stopindex)] #crop to range!
         if len(Index)>=numframes2pick-1:
-            return list(np.random.permutation(Index)[:numframes2pick-1]+startindex)
+            return list(np.random.permutation(Index)[:numframes2pick-1])
         else:
             return list(Index)
 
 def KmeansbasedFrameselection(clip,numframes2pick,start,stop,Index="all",resizewidth=30,batchsize=100,max_iter=50):
-    ''' This code downsamples the video to a width of resizewidth. The video is extracted as a numpy array, which is then 
-    clustered with kmeans whereby each frames is treated as a vector. Frames from different clusters are then selected for labeling. This 
-    procedure makes sure that the frames "look different", i.e. different postures etc. 
-    On large videos this code is slow. Consider not extracting the frames from the whole video but rather set start and stop to a period around interesting behavior. 
+    ''' This code downsamples the video to a width of resizewidth. 
+    
+    The video is extracted as a numpy array, which is then clustered with kmeans, whereby each frames is treated as a vector. 
+    Frames from different clusters are then selected for labeling. This procedure makes sure that the frames "look different", 
+    i.e. different postures etc. On large videos this code is slow. 
+    
+    Consider not extracting the frames from the whole video but rather set start and stop to a period around interesting behavior. 
+    
     Note: this method can return fewer images than numframes2pick-1.'''
 
     print("Kmeans-quantization based extracting of frames from", start*clip.duration," seconds to", stop*clip.duration, " seconds.")
     startindex=int(np.floor(clip.fps*clip.duration*start))
     stopindex=int(np.ceil(clip.fps*clip.duration*stop))
+
     if Index=="all":
         Index=np.arange(stopindex-startindex)+startindex
     else:
@@ -68,22 +78,23 @@ def KmeansbasedFrameselection(clip,numframes2pick,start,stop,Index="all",resizew
         else:
             ncolors=1
 
-        print("Extracting...",nframes, " (this might take a while).")
+        print("Extracting and downsampling...",nframes, " frames from the video.")
         for counter,index in tqdm(enumerate(Index)):
             if ncolors==1:
-                DATA[counter,:,:] = img_as_ubyte(clipresized.get_frame(start+index * 1. / clipresized.fps))
-            else: #attention averages over color channels to keep size small / perhaps you want to use color information?
-                DATA[counter,:,:] = img_as_ubyte(np.array(np.mean(clipresized.get_frame(start+index * 1. / clipresized.fps),2),dtype=np.uint8))
+                DATA[counter,:,:] = img_as_ubyte(clipresized.get_frame(index * 1. / clipresized.fps))
+            else: #attention: averages over color channels to keep size small / perhaps you want to use color information?
+                DATA[counter,:,:] = img_as_ubyte(np.array(np.mean(clipresized.get_frame(index * 1. / clipresized.fps),2),dtype=np.uint8))
 
-        print("Kmeans clustering...(this might take a while).")
+        print("Kmeans clustering ... (this might take a while)")
         data = DATA - DATA.mean(axis=0)
         data=data.reshape(nframes,-1) #stacking
+
         kmeans=MiniBatchKMeans(n_clusters=numframes2pick - 1, tol=1e-3, batch_size=batchsize,max_iter=max_iter)
         kmeans.fit(data)
-
         frames2pick=[]
         for clusterid in range(numframes2pick - 1): #pick one frame per cluster
             clusterids=np.where(clusterid==kmeans.labels_)[0]
+
             numimagesofcluster=len(clusterids)
             if numimagesofcluster>0:
                 frames2pick.append(Index[clusterids[np.random.randint(numimagesofcluster)]])

dlcDependenciesFORWINDOWS.yaml → conda-files/dlcDependenciesFORWINDOWS.yaml

@@ -71,6 +71,7 @@ dependencies:
     - pyyaml==3.12
     - requests==2.19.1
     - scikit-image==0.14.0
+    - scikit-learn==0.19.2
     - scipy==1.1.0
     - simplegeneric==0.8.1
     - sk-video==1.1.10

dlcDependenciesFORWINDOWSwTF.yaml → conda-files/dlcDependenciesFORWINDOWSwTF.yaml

@@ -71,6 +71,7 @@ dependencies:
     - pyyaml==3.12
     - requests==2.19.1
     - scikit-image==0.14.0
+    - scikit-learn==0.19.2
     - scipy==1.1.0
     - simplegeneric==0.8.1
     - sk-video==1.1.10

dlcdependencies.yml → conda-files/dlcdependencies.yml

@@ -20,34 +20,42 @@ dependencies:
 - xz=5.2.4=h14c3975_4
 - zlib=1.2.11=ha838bed_2
 - pip:
+  - backports.weakref==1.0rc1
+  - bleach==1.5.0
   - chardet==3.0.4
   - cloudpickle==0.5.3
   - cycler==0.10.0
   - dask==0.18.0
   - decorator==4.3.0
   - easydict==1.7
+  - html5lib==0.9999999
   - idna==2.7
   - imageio==2.3.0
   - kiwisolver==1.0.1
+  - markdown==2.6.11
   - matplotlib==2.2.2
   - moviepy==0.2.3.5
   - networkx==2.1
   - numexpr==2.6.5
   - numpy==1.14.5
   - pandas==0.23.1
   - pillow==5.1.0
+  - protobuf==3.6.0
   - pyparsing==2.2.0
   - python-dateutil==2.7.3
   - pytz==2018.4
   - pywavelets==0.5.2
   - pyyaml==3.12
   - requests==2.19.1
   - scikit-image==0.14.0
+  - scikit-learn==0.19.2
   - scipy==1.1.0
   - six==1.11.0
   - sk-video==1.1.10
+  - sklearn==0.0
   - tables==3.4.4
   - toolz==0.9.0
   - tqdm==4.23.4
   - urllib3==1.23
+  - werkzeug==0.14.1
 

dlcdependencieswTF1.2.yml → conda-files/dlcdependencieswTF1.2.yml

@@ -48,9 +48,11 @@ dependencies:
   - pyyaml==3.12
   - requests==2.19.1
   - scikit-image==0.14.0
+  - scikit-learn==0.19.2
   - scipy==1.1.0
   - six==1.11.0
   - sk-video==1.1.10
+  - sklearn==0.0
   - tables==3.4.4
   - tensorflow==1.2.1
   - toolz==0.9.0

docs/installation.md

@@ -17,7 +17,7 @@ For reference, we use e.g. Dell workstations (79xx series) with Ubuntu 16.04 LTS
 [Anaconda](https://anaconda.org/anaconda/python) is perhaps the easiest way to install Python and additional packages across various operating systems. With anaconda just create all the dependencies in an [environment](https://conda.io/docs/user-guide/tasks/manage-environments.html) on your machine by running in a terminal:
 ```
 git clone https://github.com/AlexEMG/DeepLabCut.git
-cd DeepLabCut
+cd DeepLabCut/conda-files
 conda env create -f dlcdependencieswTF1.2.yml
 ```
 

myconfig.py

@@ -33,13 +33,13 @@
 y2 = 624
 
 # Portion of the video to sample from in step 1 (In relative terms of video length, i.e. [0,1] is the full video)
-start = 0
+start = 0.1
 stop =1
 
 # Number of frames to pick
 numframes2pick = 22
 
-# Method to pick frames:
+# Method to pick frames: uniform or kmeans
 selectionalgorithm='uniform' 
 
 # the algorithm 'uniform' temporally uniformly sampes frames in interval (start,stop). Visual information within video is irrelevant for this method. This code is very fast and sufficient (to extract distinct frames) when behavioral videos naturally covers many states.