refac(pkg): correct all flake8 errors

itten/contrasts.py

@@ -5,11 +5,12 @@ from skimage import color
 
 # TODO create generic class
 
+
 class Contrast(object):
     """Base class for color contrasts"""
     def __init__(self, img):
         self._img = img
-        
+
 
 class Monochromatic(Contrast):
     """docstring for Monochromatic"""
@@ -25,10 +26,12 @@ class Monochromatic(Contrast):
            The array has the same dimensions as the image. However the third
            does only have the size 1 which contains the luminance value
         """
-        # Luminance Faktoren nach http://introcs.cs.princeton.edu/python/31datatype/luminance.py.html
+        # Luminance Faktoren nach
+        # http://introcs.cs.princeton.edu/python/31datatype/luminance.py.html
         luminance_factors = np.array([.114, .587, .299])
 
-        # Erzeugung eines eindimensionalen Arrays für die effizientere Berechnung
+        # Erzeugung eines eindimensionalen Arrays für die effizientere
+        # Berechnung
         self._img = np.multiply(self._img, luminance_factors)
 
         # addiert alle Werte auf einer bestimmten Achse
@@ -68,30 +71,31 @@ class Monochromatic(Contrast):
 
     def colf_val_point(self):
         """calculates the most colourful value point in an image
-        
+
         used to identify tinting or toning images with its specific colourful
         areas in very light or darker value regions
         """
 
         # as long as opencv is used, convert RGB to RGB
         imgrgb = self._img[:, :, ::-1]
-        assert(np.array_equal(self._img, imgrgb[:, :, ::-1])), "opencv scikit-image conversion wrong"
+        assert(np.array_equal(self._img,
+               imgrgb[:, :, ::-1])), "opencv scikit-image conversion wrong"
         imglab = color.rgb2lab(imgrgb)
         imglch = color.lab2lch(imglab)
 
         value = Monochromatic(self._img, method='value')
         value = value.value()
 
-        imgcv = imglch[:,:,1].astype(np.uint8)
+        imgcv = imglch[:, :, 1].astype(np.uint8)
         imgcv = np.dstack((imgcv, value))
 
-        perc90 = np.percentile(imgcv[:,:,0], 90)
-        indices_colful = np.where(imgcv[:,:, 0] > perc90)
+        perc90 = np.percentile(imgcv[:, :, 0], 90)
+        indices_colful = np.where(imgcv[:, :, 0] > perc90)
         colful_values = imgcv[indices_colful][:, 1]
         colful_value_max = np.median(colful_values)
 
         return np.array([[[colful_value_max]]])
-        
+
 
 class Saturation(Contrast):
     """docstring for Saturation"""
@@ -114,7 +118,7 @@ class Saturation(Contrast):
 
         imglab = color.rgb2lab(self._img)
         imglch = color.lab2lch(imglab)
-        
+
         # TODO geht garnicht, weil nicht angemessen gerundet wird
         # und skimage auch davor warnt
         imglch = imglch.astype(np.uint16)
@@ -144,7 +148,7 @@ class Hue(Contrast):
     def labHcl(self) -> np.uint16:
         """calculates the Hue value by using the CIE LabHCL colorspace"""
 
-        imglab = color.rgb2lab(self._img[:,:,::-1])
+        imglab = color.rgb2lab(self._img[:, :, ::-1])
         imglch = color.lab2lch(imglab)
         hues = imglch[:, :, 2]
 

itten/helpers.py

@@ -3,48 +3,48 @@
 
 import numpy as np
 import pandas as pd
-import math
+# import math
 
 
 # def lab2hc(labpixel, out='both'):
 #     """returns hue and colorfulness of a pixel
-# 
+#
 #        the image pixel has to have CIE Lab colors.
 #        the conversion follows Fairchild's 2013 conversion equations
-# 
+#
 #        labpixel : pixel from image using CIE Lab color model
 #        out : 'h' (hue), 'c' (colorfulness) or 'both' (tuple containing hue
 #              and colorfulness : default)
 #     """
-# 
+#
 #     def get_hue(a, b):
 #         hue = math.degrees(math.atan(b/a))
 #         return hue
-# 
+#
 #     def get_colorfulness(a, b):
 #         colorfulness = (a**2 + b**2)**(1/2)
 #         return colorfulness
-# 
+#
 #     a = labpixel[1]
 #     b = labpixel[2]
-# 
+#
 #     hc = {}
-# 
+#
 #     if not(out == 'c'):
 #         hc['h'] = get_hue(a, b)
 #     elif not(out == 'h'):
 #         hc['c'] = get_colorfulness(a, b)
-# 
+#
 #     return hc
 
 # TODO fps muss noch implementiert werden
 def time2framenr(time, fps=4):
     """counts the frame index for a given timestamp
-    
+
        time : timestamp needs to have the form [HH:]MM:SS
        fps :  frequency with which frames were taken from the movie
     """
-    
+
     timestmp = time.split(':')
     in_sec = 0
 
@@ -54,16 +54,17 @@ def time2framenr(time, fps=4):
     in_sec += int(timestmp[-2]) * 60
     in_sec += int(timestmp[-1])
 
-    return in_sec *  fps
+    return in_sec * fps
 
 
 def frame2time(val, fps=4):
     """transforms framenumbers into timecodes
-    
+
     Arguments:
         val {int} -- number of frame in the movie
-        fps {int} -- time interval by which frames were extracted from the movie
-    
+        fps {int} -- time interval by which frames were extracted from the
+                     movie
+
     Returns:
         np.datetime64 -- timecode of the frame in the movie
     """
@@ -72,13 +73,15 @@ def frame2time(val, fps=4):
     tstmp = pd.Timedelta(val, unit='s')
     return tstmp
 
+
 def luminance(img):
     """Creates an array of luminance value for each pixel of an image
 
        The array has the same dimensions as the image. However the third
        does only have the size 1 which contains the luminance value
     """
-    # Luminance Faktoren nach http://introcs.cs.princeton.edu/python/31datatype/luminance.py.html
+    # Luminance Faktoren nach
+    # http://introcs.cs.princeton.edu/python/31datatype/luminance.py.html
     luminance_factors = np.array([.299, .587, .114])
 
     # Erzeugung eines eindimensionalen Arrays für die effizientere Berechnung

itten/movie.py

@@ -4,30 +4,32 @@
 import numpy as np
 import pandas as pd
 
-from pathlib import Path  # TODO wie kann ich third-party module nach außen verstecken
+from pathlib import Path
 import pickle
 from subprocess import Popen, PIPE, STDOUT
 
-# TODO Ist der import aus dem selben Pakte so korrekt?
 from . import views
 from . import visuals
 from . import helpers
 
 
-# zum Problem mit privaten und öffentlichen Eigenschaften http://www.python-course.eu/python3_properties.php und 'Fluent Python' relativ weit vorne
+# zum Problem mit privaten und öffentlichen Eigenschaften
+# http://www.python-course.eu/python3_properties.php und 'Fluent Python'
+# relativ weit vorne.
 # numpy gibt beim Versuch zB. size zu schreiben auch ein AttributeError() aus.
 class Movie(object):
     """main class to interact with the colorspace of movieframes"""
     def __init__(self, prefix, folder='./', fps=4):
         """creates an video object holding the frames of the video
-        
+
         Arguments:
             object {self} -- the video object itself
             prefix {str} -- part of the frames filenames representing the movie
-        
+
         Keyword Arguments:
             folder {str} -- folder containing the frames (default: {'./'})
-            fps {int} -- number of frames per second that were extracted from the movie (default: {4})  # before this were 1
+            fps {int} -- number of frames per second that were extracted from
+                the movie (default: {4})  # before this were 1
         """
 
         self._frames = Frames(folder, prefix)
@@ -49,18 +51,20 @@ class Movie(object):
         """
 
         # transform time to frames if type is time
-        if type is 'time':
+        if type == 'time':
             start = helpers.time2framenr(start, self.fps)
             end = helpers.time2framenr(end, self.fps)
-        elif type is 'frame':
+        elif type == 'frame':
             start = int(start) * mltp
             end = int(end) * mltp
 
         # funktioniert nur in zsh
-        # glob = self._frames.folder + self._frames.prefix + '{' + '{0:05d}'.format(start) + '..' + '{0:05d}'.format(end) + '}' + '.png'
+        # glob = self._frames.folder + self._frames.prefix + '{' + '{0:05d}'.
+        # format(start) + '..' + '{0:05d}'.format(end) + '}' + '.png'
         glob_lst = []
         for frm in range(start, end+1):
-            glob_lst.append(self._frames.folder + self._frames.prefix + '{0:05d}'.format(frm) + '.png')
+            glob_lst.append(self._frames.folder + self._frames.prefix +
+                            '{0:05d}'.format(frm) + '.png')
 
         cmd = viewer + ' ' + ' '.join(glob_lst)
         Popen(cmd, stdout=PIPE, stderr=STDOUT, shell=True)
@@ -138,13 +142,13 @@ class Movie(object):
         end: Int which defines the end frame for the description (0 defines
              the unknown end frame)
 
-        stp: the frequency in frame number by which frames are used to calculate
-            the contrast
+        stp: the frequency in frame number by which frames are used to
+            calculate the contrast
 
         Returns
         -------
-        TODO: a pandas DataFrame containing the frame number, timestamp, bin and
-            number of pixels contained in the bin
+        TODO: a pandas DataFrame containing the frame number, timestamp, bin
+            and number of pixels contained in the bin
         """
 
         # set movie start and end postion for the visualization
@@ -157,7 +161,7 @@ class Movie(object):
             data = views.MultivariateSequence(self._frames,)
             data.populate(ctrst=ctrst, method=meth, frm_stp=stp)
             data = pd.DataFrame(data, columns=['frame', 'bin', 'pixels'],
-                dtype=np.int64)
+                                dtype=np.int64)
             data['time'] = data['frame'].apply(helpers.frame2time, fps=4)
 
             return data
@@ -234,7 +238,8 @@ class Movie(object):
             vis = visuals.MultivariatePlot(multivariate)
             vis.plot(multivariate)
 
-            header = prefix[:-1] + ' MultivariatePlot for ' + ctrst + ' - ' + desc
+            header = prefix[:-1] + ' MultivariatePlot for ' + ctrst + ' - '
+            header += desc
             filename = prefix + 'multivariateplot_' + ctrst + '_' + desc
             vis.saveplt(fname=filename, title=header)
 
@@ -251,7 +256,8 @@ class Movie(object):
             vis = visuals.UnivariatePlot(univariate)
 
             # summarizing methods of univariate class
-            summarizations = ['seqmean', 'seqmad']  # TODO Varianz funktioniert so nicht
+            # TODO Varianz funktioniert so nicht
+            summarizations = ['seqmean', 'seqmad']
 
             for feature in summarizations:
                 # compute summarizations for given univariate value
@@ -275,8 +281,10 @@ class Movie(object):
                 vis.plot(univariate)
 
             # save univariate plot
-            header = prefix[:-1] + ' UnivariatePlot for ' + ctrst + ' - ' + desc
-            filename = self._frames.prefix + 'univariateplot_' + ctrst + '_' + desc
+            header = prefix[:-1] + ' UnivariatePlot for ' + ctrst + ' - '
+            header += desc
+            filename = self._frames.prefix + 'univariateplot_' + ctrst + '_'
+            filename += desc
             vis.saveplt(fname=filename, title=header)
 
         with open(prefix[:-1] + '_summary.pickle', 'wb') as f:
@@ -332,9 +340,10 @@ class Frames(object):
 
     def get_frame_list(self):
         """returns the list of paths for all frame files in the current folder
-        
+
         Returns:
-            list -- returns the list of paths for all frame files in the current folder
+            list -- returns the list of paths for all frame files in the
+                current folder
         """
         frm_path = Path(self.folder)
         return list(sorted(frm_path.glob('*.png')))

itten/views.py

@@ -5,11 +5,11 @@
 # TODO import as cv
 import cv2
 import numpy as np
-import peakutils
-from .helpers import luminance
+# import peakutils
+# from .helpers import luminance
 from copy import deepcopy
 from . import contrasts
-from . import movie
+# from . import movie
 
 # subclassing numpy ndarray
 # Vorgehen: https://docs.scipy.org/doc/numpy/user/basics.subclassing.html
@@ -17,6 +17,8 @@ from . import movie
 # andere ownership Probleme könne angeblich mit out= gelöst werden
 # "Use __new__ when you need to control the creation of a new instance.
 # Use __init__ when you need to control initialization of a new instance."
+
+
 class View(np.ndarray):
     """Core class for the representation of colour contrasts in Movies
 
@@ -41,7 +43,8 @@ class View(np.ndarray):
            Parameters
            ----------
            frames : itten.movie.Frames
-           input_array : itten.contrasts.View  # TODO wie schaffte np lower case object
+           input_array : itten.contrasts.View  # TODO wie schaffte np lower
+                         case object
            contrast : String  # Modifizieren Channel Integer to String
            frame_step : Int
            savefig : Boolean
@@ -64,7 +67,8 @@ class View(np.ndarray):
         return obj
 
     def __array_finalize__(self, obj):
-        if obj is None: return
+        if obj is None:
+            return
         self._frames = getattr(obj, '_frames', None)
         self._contrast = getattr(obj, '_contrast', None)
         self._frame_step = getattr(obj, '_frame_step', None)
@@ -106,8 +110,12 @@ class View(np.ndarray):
         frm_nrs = np.subtract(frm_nrs, self._frame_step)
         return frm_nrs.flatten()
 
-# subclassing subclass of numpy http://stackoverflow.com/questions/7342637/how-to-subclass-a-subclass-of-numpy-ndarray
-# TODO es gibt noch das Problem, dass numpy nach mehreren Berechnungen von drive eine max recursion Warnung ausgiebt, warum? Brauche ich __del__
+# subclassing subclass of numpyhttp://stackoverflow.com/questions/7342637/
+# how-to-subclass-a-subclass-of-numpy-ndarray
+# TODO es gibt noch das Problem, dass numpy nach mehreren Berechnungen von
+# drive eine max recursion Warnung ausgiebt, warum? Brauche ich __del__
+
+
 class MultivariateSequence(View):
     def __new__(cls, frames, input_array=None):
         """Represents a movie contrast in terms of stacked histograms
@@ -117,12 +125,12 @@ class MultivariateSequence(View):
 
            Parameters
            ----------
-            TODO threshold : the minimum number of pixels that need to appear in
+            TODO threshold : the minimum number of pixels that need to appear
                 in a certain bin so that the bin will be included in the output
-                data. (Needs to be modifiable in relation to the total number of
-                pixels in the frame, that means on the frame size 240p60 etc.)
-                Oder Threshold ganz raus, weil das eigentlich eine Sache der
-                Visualisierung ist auszusortieren.
+                data. (Needs to be modifiable in relation to the total number
+                of pixels in the frame, that means on the frame size 240p60
+                etc.) Oder Threshold ganz raus, weil das eigentlich eine Sache
+                der Visualisierung ist auszusortieren.
 
            Returns
            -------
@@ -137,11 +145,13 @@ class MultivariateSequence(View):
         return obj
 
     def __array_finalize__(self, obj):
-        if obj is None: return
+        if obj is None:
+            return
         View.__array_finalize__(self, obj)
         self._threshold = getattr(obj, '_threshold', None)
 
-    def populate(self, ctrst='monochromatic', method='luminance', frm_stp=10, bins=16, thrsh=6000, start=1, end=0):
+    def populate(self, ctrst='monochromatic', method='luminance', frm_stp=10,
+                 bins=16, thrsh=6000, start=1, end=0):
         """doc (aus __new__ zusammentragen)
         """
 
@@ -151,23 +161,27 @@ class MultivariateSequence(View):
         self._frame_step = frm_stp
         self._bins = bins
         self._threshold = thrsh
-        # TODO dafür müssen erst getters und setters in movie.frames definiert werden
+        # TODO dafür müssen erst getters und setters in movie.frames definiert
+        # werden
         # self._frames.start = start
         # self._frames.end = end
 
         contrast_points = np.empty((0, 3), dtype=np.uint32)
 
         # pwd list sollte in Frames sein und hier nur durchlaufen werden
-        for frm_nr in range(self._frames.start, self._frames.end, self._frame_step):
+        for frm_nr in range(self._frames.start, self._frames.end,
+                            self._frame_step):
 
             # FIX for legacy numbering scheme of files 1 instead of 0001
             # TODO remove this ridiculous fix and the whole pwd building which
             # now solved by having implemented sorted(Path()…) in Frames
             pwd = ""
             if '_0' in str(self._frames.frames[0]):
-                pwd = self._frames.folder + self._frames.prefix + '{0:05d}'.format(frm_nr) + '.png'
+                pwd = self._frames.folder + self._frames.prefix
+                pwd += '{0:05d}'.format(frm_nr) + '.png'
             else:
-                pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+                pwd = self._frames.folder + self._frames.prefix + str(frm_nr)
+                pwd += '.png'
 
             img = cv2.imread(pwd)  # BGR
 
@@ -183,10 +197,12 @@ class MultivariateSequence(View):
 
             for bin_index, point in enumerate(hist_value):
                 if int(point) > self._threshold:
-                    entry = np.array([[frm_nr, bin_index, point]], dtype=np.uint32)
+                    entry = np.array([[frm_nr, bin_index, point]],
+                                     dtype=np.uint32)
                     contrast_points = np.vstack((contrast_points, entry))
 
-        # irgendwie prüfen, ob ich contrast_points insgesamt durch self ersetzen kann
+        # irgendwie prüfen, ob ich contrast_points insgesamt durch self
+        # ersetzen kann
         contrast_points = np.asarray(contrast_points, np.uint32)
         shape = contrast_points.shape
         self.resize(shape, refcheck=False)
@@ -196,150 +212,6 @@ class MultivariateSequence(View):
         return deepcopy(self)  # TODO does not create a new object
 
 
-class BivariateSequence(View):
-    def __new__(cls, frames, input_array=None):
-        """Represents a movie contrast in terms minbound maxbound values
-
-           For each defined frame a histogram is calculated and each bin that
-           exceeds threshold is considered in the output array
-
-           Parameters
-           ----------
-
-           Returns
-           -------
-           Object : BivariateSequence
-        """
-
-        obj = View.__new__(cls, frames, input_array=input_array)
-        return obj
-
-    def __array_finalize__(self, obj):
-        if obj is None: return
-        View.__array_finalize__(self, obj)
-
-    # das kann ich jetzt auch mit quantilen machen
-    def populate(self, variant='peak', ctrst='monochromatic',
-                 method='luminance', frm_stp=10, bins=256):
-        """Creates a scatterplot for the dynamic of contrast across movie frames
-
-           variant: peak or bin (one of max/min peak; uper/lower mean)
-           threshold: for the peak variant TODO
-           smooth: smooth plot TODO
-        """
-
-        # set class properties
-        self._variant = variant
-        self._contrast = ctrst
-        self._method = method
-        self._frame_step = frm_stp
-        self._bins = bins
-        # TODO um start und end in der Methode zu parametrisieren müssen erst
-        # getters und setters in movie.frames definiert werden
-        # self._frames.start = start
-        # self._frames.end = end
-
-        contrast_points = np.empty((0, 2), dtype=np.uint32)
-
-        # sofern kein oder nur ein Peak gefunden wird, man könnte dann auch
-        # noch einen Durchlauf mit geringeren thres und min_dist versuceh
-        lastmin = 0
-        lastmax = 256
-
-        # pwd list sollte in Frames sein und hier nur durchlaufen werden
-        for frm_nr in range(self._frames.start, self._frames.end,
-                            self._frame_step):
-            pwd = ""
-            if '_0' in str(self._frames.frames[0]):
-                pwd = self._frames.folder + self._frames.prefix + '{0:05d}'.format(frm_nr) + '.png'
-            else:
-                pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
-
-            img = cv2.imread(pwd)
-
-            # BREAK: Funktioniert nicht
-            if method == 'bin':
-                hist_values = cv2.calcHist([img_hsv], [self._channel], None,
-                                           [256], [0, 256])
-                hist_mins = hist_values[0:127].flatten().tolist()
-                hist_maxs = hist_values[128:256].flatten().tolist()
-                # Für jeden Frame wird jeder Kontrastwert mit der Anzahl der
-                # Pixel für diesen Kontrastwert multipliziert
-                # So wird der gesamte Kontrastwert des Frames sowie errechnet
-                bin_total = 0
-                total_value = 0
-
-                for bin_id, points in enumerate(hist_mins):
-                    bin_total = (bin_id + 1) * points
-                    total_value += bin_total
-
-                sum_mins = int(sum(hist_mins))
-                sum_maxs = int(sum(hist_maxs))
-
-                if int(total_value) == 0:
-                    total_value = [(bin_nr, value) for bin_nr, value in
-                                   enumerate(hist_maxs) if
-                                   int(value) > 0][0][0]
-                    mins.append(total_value)
-                else:
-                    mins.append(int(total_value / sum_mins))
-
-                bin_total = 0
-                total_value = 0
-
-                for bin_id, points in enumerate(hist_maxs):
-                    bin_total = (bin_id + 1) * points
-                    total_value += bin_total
-
-                # if there are no values in hist_max take highes bin with value
-                # from hist_mins
-                if int(total_value) == 0:
-                    total_value = [(bin_nr, value) for bin_nr, value in
-                                   enumerate(hist_mins) if
-                                   int(value) > 0][-1][0]
-                    maxs.append(total_value)
-                else:
-                    maxs.append(int(total_value / sum(hist_maxs)) + 127)
-
-            # peak variant
-            else:
-                ctrst_cls = self._get_ctrst_cls_name(self._contrast)
-                ctrst_img = ctrst_cls(img).ctrst
-
-                hist_value, _ = np.histogram(ctrst_img.flatten(),
-                                             bins=self._bins, range=(0, 256))
-
-                peaks = peakutils.indexes(hist_value.flatten(), thres=0.2,
-                                          min_dist=15)
-
-                # Abfangen von von nur 1 oder keinem Peak
-                if len(peaks) > 1:
-                        lastmin = peaks[0]
-                        lastmax = peaks[-1]
-                        contrast_points = np.vstack((contrast_points,
-                                                    [lastmin, lastmax]))
-                elif len(peaks) == 1:
-                    # je nachdem ob der neue peak dem alten min oder max wert
-                    # näher liegt wird er dem einen oder Anderen zugeschlagen
-                    if (lastmax - peaks[0]) < (peaks[0] - lastmin):
-                        lastmax = peaks[0]
-                        contrast_points = np.vstack((contrast_points,
-                                                    [lastmin, lastmax]))
-                    else:
-                        lastmin = peaks[0]
-                        contrast_points = np.vstack((contrast_points,
-                                                    [lastmin, lastmax]))
-                else:
-                    contrast_points = np.vstack((contrast_points,
-                                                [lastmin, lastmax]))
-
-        contrast_points = np.asarray(contrast_points, np.uint32)
-        shape = contrast_points.shape
-        self.resize(shape, refcheck=False)
-        self[:, :] = contrast_points
-        contrast_points = None
-
-
 class UnivariateSequence(View):
     def __new__(cls, frames, input_array=None):
         """Represents a movie contrast in terms of a single feature
@@ -352,16 +224,18 @@ class UnivariateSequence(View):
            Object : BivariateSequence
         """
 
-        obj = View.__new__(cls, frames, input_array=input_array)
+        obj = View.__new__(cls, frames, input_array)
         obj.feature = None
         return obj
 
     def __array_finalize__(self, obj):
-        if obj is None: return
+        if obj is None:
+            return
         self._threshold = getattr(obj, 'feature', None)
         View.__array_finalize__(self, obj)
 
-    # TODO später ersetzen durch numpy mean/deviation in Kombination mit anderen Möglichkeiten
+    # TODO später ersetzen durch numpy mean/deviation in Kombination mit
+    # anderen Möglichkeiten
     @staticmethod
     def meanmad(values):
 
@@ -370,14 +244,15 @@ class UnivariateSequence(View):
         points = np.sum(values)
         # range nicht array und histogram verschachtelt (siehe slice)
         bin_values = np.apply_along_axis(lambda x: x * values[x-1], 0,
-                                         range(1, length+1)) 
+                                         range(1, length+1))
         mean = np.sum(bin_values) / points