Merge pull request #12 from gbeckers/develop

Parameters project merged for release preparations

README.rst

@@ -50,7 +50,7 @@ Python 3 versions may also work.
 3) Create new environment for Birdwatcher (name is up to you, in example
    here 'mybirdwatcher'). We install Jupter lab and ffmpeg at the same time::
 
-    $ conda create -n mybirdwatcher python=3.9 jupyterlab ffmpeg
+    $ conda create -n mybirdwatcher python=3.9 jupyterlab ffmpeg git
 
 4) Switch to this new environment:
 
@@ -73,7 +73,9 @@ The following dependencies are automatically taken care of when you
 install Birdwatcher from GitHub using the pip method above:
 
 - numpy
+- pandas
 - matplotlib
+- seaborn
 - darr
 - opencv-python
 - opencv-contrib-python

birdwatcher/__init__.py

@@ -1,19 +1,10 @@
 from .video import *
-from .movementdetection import *
 from .backgroundsubtraction import *
 from .coordinatearrays import *
 from .frames import *
-from .utils import *
-try:
-    import pypylon
-    from .recording import *
-except ImportError:
-    pass
 
-#from .plotting import *
 from ._version import get_versions
 __version__ = get_versions()['version']
 del get_versions
 
-
 from .tests import test

birdwatcher/backgroundsubtraction.py

@@ -139,7 +139,7 @@ class BackgroundSubtractorMOG2(BaseBackgroundSubtractor):
 
     Parameters
     ----------
-    History : int, default=5
+    History : int, default=3
         Length of the history.
     ComplexityReductionThreshold : float, default=0.5
         This parameter defines the number of samples needed to accept to prove
@@ -155,11 +155,11 @@ class BackgroundSubtractorMOG2(BaseBackgroundSubtractor):
         The number of gaussian components in the background model.
     VarInit : int, default=15
         The initial variance of each gaussian component.
-    VarMin : int, default=4
+    VarMin : int, default=10
         The minimum variance of each gaussian component.
     VarMax : int, default=75
         The maximum variance of each gaussian component.
-    VarThreshold : int, default=10
+    VarThreshold : int, default=70
         The variance threshold for the pixel-model match. The main threshold
         on the squared Mahalanobis distance to decide if the sample is well
         described by the background model or not. Related to Cthr from the
@@ -180,24 +180,24 @@ class BackgroundSubtractorMOG2(BaseBackgroundSubtractor):
         The shadow threshold is a threshold defining how much darker the
         shadow can be. 0.5 means that if a pixel is more than twice darker
         then it is not shadow.
-    ShadowValue : int, default=127
+    ShadowValue : int, default=0
         Shadow value is the value used to mark shadows in the foreground mask.
         Value 0 in the mask always means background, 255 means foreground.
 
     """
 
-    _setparams = {'History': 5,
+    _setparams = {'History': 3,
                   'ComplexityReductionThreshold': 0.05,
                   'BackgroundRatio': 0.1,
                   'NMixtures': 7,
                   'VarInit': 15,
-                  'VarMin': 4,
+                  'VarMin': 10,
                   'VarMax': 75,
-                  'VarThreshold': 10,
+                  'VarThreshold': 70,
                   'VarThresholdGen': 9,
                   'DetectShadows': False,
                   'ShadowThreshold': 0.5,
-                  'ShadowValue': 127}
+                  'ShadowValue': 0}
 
     _bgsubtractorcreatefunc = cv.createBackgroundSubtractorMOG2
 

birdwatcher/coordinatearrays.py

@@ -1,28 +1,31 @@
 """This module provides objects and functions for coordinate data.
-Coordinates are pixel positions (x,y). This is a convenient way of storing
-output from image algorithms that determine whether or not a pixel is
-categorized as something (e.g. crossing some threshold). Since the number of
-pixels per frame may be different, we use disk-based ragged arrays from the
-python library Darr to store them. This is not the most disk-space efficient
-way of doing this, but it is fast and the data can easily be read in any
-computing platform. Coordinate files can be archived in compressed form
-(lzma) when data is large.
+Coordinates are pixel positions (x,y) in a Frame. Coordinate Arrays are a
+convenient way of storing output from image algorithms that determine if a
+pixel is categorized as something (e.g. crossing some threshold).
+Information is memory-mapped from disk, because data can easily become very
+large and will not fit in RAM memory. Since the number of pixels per frame may
+be variable, we use Ragged Arrays from the python library Darr to store them.
+This is not the most disk-space efficient way of doing this (no compression),
+but it is fast and the data can easily be read in any scientific computing
+environement (Python, Matlab, R, Mathematica, etc.) Coordinate files can be
+archived in compressed form (lzma) to save disk space.
 
 """
 
 import os
-from contextlib import contextmanager
-from pathlib import Path
 import shutil
-import numpy as np
 import tarfile
+from contextlib import contextmanager
+from pathlib import Path
 
+import numpy as np
 from darr import RaggedArray, delete_raggedarray, create_raggedarray
 
 from ._version import get_versions
 from .utils import tempdir
 from .frames import frameiterator
 
+
 __all__ = ['CoordinateArrays', 'open_archivedcoordinatedata',
            'create_coordarray']
 

birdwatcher/ffmpeg.py

@@ -1,10 +1,12 @@
-import subprocess
-import numpy as np
 import json
+import subprocess
 from pathlib import Path
 
+import numpy as np
+
 from .utils import peek_iterable
 
+
 __all__ = ['arraytovideo']
 
 
@@ -104,7 +106,7 @@ def videofileinfo(filepath, ffprobepath='ffprobe'):
 
 ## FIXME inform before raising StopIteration that file has no frames
 def iterread_videofile(filepath, startat=None, nframes=None, color=True,
-                       ffmpegpath='ffmpeg', loglevel= 'quiet'):
+                       ffmpegpath='ffmpeg', loglevel='quiet'):
     """
     Parameters
     ----------
@@ -199,11 +201,11 @@ def get_frame(filepath, framenumber, color=True, ffmpegpath='ffmpeg',
                              dtype=np.uint8).reshape(frameshape)
 
 
-def get_frameat(filepath, time, color=True, ffmpegpath='ffmpeg', loglevel=
-                'quiet'):
-    return next(iterread_videofile(filepath, startat=time, nframes=1, \
-                                   color=color, ffmpegpath=ffmpegpath),
-                loglevel=loglevel)
+def get_frameat(filepath, time, color=True, ffmpegpath='ffmpeg', 
+                loglevel='quiet'):
+    return next(iterread_videofile(filepath, startat=time, nframes=1, 
+                                   color=color, ffmpegpath=ffmpegpath, 
+                                   loglevel=loglevel))
 
 
 # FIXME do not assume things on audio (i.e. number of channels) and make more versatile

birdwatcher/frames.py

@@ -8,16 +8,19 @@
 
 """
 
-import numpy as np
-import cv2 as cv
 from functools import wraps
 from pathlib import Path
 
+import numpy as np
+import cv2 as cv
+
 from .utils import peek_iterable
 
+
 __all__ = ['Frames', 'FramesColor', 'FramesGray', 'FramesNoise', 'framecolor',
            'framegray', 'framenoise']
 
+
 def frameiterator(func):
     @wraps(func)
     def wrapper(*args, **kwargs):
@@ -778,7 +781,7 @@ def calc_meanframe(self, dtype=None):
                                    (0, 0, 0), dtype='float64')
         for i, frame in enumerate(self._frames):
             meanframe += frame
-        meanframe /= i
+        meanframe /= (i+1)
         if dtype is None:
             dtype = self._dtype
         return meanframe.astype(dtype)
@@ -815,17 +818,18 @@ class FramesColor(Frames):
 
     """
 
-    def __init__(self, nframes, width, height, color=(0, 0, 0), dtype='uint8'):
+    def __init__(self, nframes, height, width, color=(0, 0, 0), 
+                 dtype='uint8'):
         """Creates an iterator that yields color frames.
 
         Parameters
         ----------
         nframes : int
             Number of frames to be produced.
-        width : int
-            Width of frame.
         height : int
             Height of frame.
+        width : int
+            Width of frame.
         color : tuple of ints, optional
             Fill value of frame (r, g, b). The default (0, 0, 0) color is
             black.
@@ -837,7 +841,7 @@ def __init__(self, nframes, width, height, color=(0, 0, 0), dtype='uint8'):
         Iterator of numpy ndarrays
 
         """
-        frame = framecolor(width=width, height=height, color=color,
+        frame = framecolor(height=height, width=width, color=color,
                            dtype=dtype)
         frames = (frame.copy() for _ in range(nframes))
         super().__init__(frames=frames)
@@ -850,17 +854,17 @@ class FramesGray(Frames):
 
     """
 
-    def __init__(self, nframes, width, height, value=0, dtype='uint8'):
+    def __init__(self, nframes, height, width, value=0, dtype='uint8'):
         """Creates an iterator that yields gray frames.
 
         Parameters
         ----------
         nframes : int
             Number of frames to be produced.
-        width : int
-            Width of frame.
         height : int
             Height of frame.
+        width : int
+            Width of frame.
         value : int, optional
             Fill value of frame. The default (0) is black.
         dtype : numpy dtype, default='uint8'
@@ -872,9 +876,8 @@ def __init__(self, nframes, width, height, value=0, dtype='uint8'):
 
         """
 
-        frame = framegray(width=width, height=height, value=value,
+        frame = framegray(height=height, width=width, value=value,
                           dtype=dtype)
-
         frames = (frame.copy() for _ in range(nframes))
         super().__init__(frames=frames)
 
@@ -886,17 +889,17 @@ class FramesNoise(Frames):
 
     """
 
-    def __init__(self, nframes,width,  height, dtype='uint8'):
+    def __init__(self, nframes, height, width, dtype='uint8'):
         """Creates an iterator that yields gray frames.
 
         Parameters
         ----------
         nframes : int
             Number of frames to be produced.
-        width : int
-            Width of frame.
         height : int
             Height of frame.
+        width : int
+            Width of frame.
         dtype : numpy dtype, default='uint8'
             Dtype of frame.
 
@@ -911,15 +914,15 @@ def __init__(self, nframes,width,  height, dtype='uint8'):
         super().__init__(frames=frames)
 
 
-def framegray(width, height, value=0, dtype='uint8'):
+def framegray(height, width, value=0, dtype='uint8'):
     """Creates a gray frame.
 
     Parameters
     ----------
-    width : int
-        Width of frame.
     height : int
         Height of frame.
+    width : int
+        Width of frame.
     value : int, optional
         Fill value of frame. The default (0) is black.
     dtype : numpy dtype, default='uint8'
@@ -933,15 +936,15 @@ def framegray(width, height, value=0, dtype='uint8'):
     return np.ones((height, width), dtype=dtype) * value
 
 
-def framecolor(width, height, color=(0, 0, 0), dtype='uint8'):
+def framecolor(height, width, color=(0, 0, 0), dtype='uint8'):
     """Creates a color frame.
 
     Parameters
     ----------
-    width : int
-        Width of frame.
     height : int
         Height of frame.
+    width : int
+        Width of frame.
     color : tuple of ints, optional
         Fill value of frame (r, g, b). The default (0, 0, 0) color is black.
     dtype : numpy dtype, default='uint8'
@@ -952,19 +955,18 @@ def framecolor(width, height, color=(0, 0, 0), dtype='uint8'):
     numpy ndarray
 
     """
-    return np.ones((height, width, 3), dtype=dtype) * np.asanyarray(color,
-                                                                    dtype=dtype)
+    return np.ones((height, width, 3), dtype=dtype) * np.asanyarray(color, dtype=dtype)
 
 
-def framenoise(width, height, dtype='uint8'):
+def framenoise(height, width, dtype='uint8'):
     """Creates a noise frame.
 
     Parameters
     ----------
-    width : int
-        Width of frame.
     height : int
         Height of frame.
+    width : int
+        Width of frame.
     dtype : numpy dtype, default='uint8'
         Dtype of frame.