final commit in branch after break

after my 1 year break in which I can not overview
the changes I try to set a new start

itten/contrasts.py

 import numpy as np
+import math
 import cv2 as cv
+from skimage import color
 
 # TODO create generic class
 
@@ -9,12 +11,12 @@ class Contrast(object):
         self._img = img
         
 
-class LightDark(Contrast):
-    """docstring for LightDark"""
+class Monochromatic(Contrast):
+    """docstring for Monochromatic"""
     def __init__(self, img, method='luminance'):
-        super(LightDark, self).__init__(img)
+        super(Monochromatic, self).__init__(img)
 
-        meth = getattr(LightDark, method)
+        meth = getattr(Monochromatic, method)
         self.ctrst = meth(self)
 
     def luminance(self):
@@ -49,6 +51,47 @@ class LightDark(Contrast):
 
         return lightness
 
+    def redniss_lab(self):
+        """Returns the redness contrast on the basis of CIE Lab
+
+           The a channel of the representation the image in CIE Lab is
+           isolated and the value 127 set as 0 to eliminate the green
+           shares of a.
+        """
+        img = cv.cvtColor(self._img, cv.COLOR_BGR2Lab)
+        lab_a = img[:, :, 1].copy()
+
+        lab_a[lab_a < 128] = 0
+        redness = np.subtract(lab_a[lab_a > 127], 128)
+
+        return redness
+
+    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"
+        imglab = color.rgb2lab(imgrgb)
+        imglch = color.lab2lch(imglab)
+
+        value = Monochromatic(self._img, method='value')
+        value = value.value()
+
+        imgcv = imglch[:,:,1].astype(np.uint8)
+        imgcv = np.dstack((imgcv, value))
+
+        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"""
@@ -65,24 +108,49 @@ class Saturation(Contrast):
 
         return saturations
 
-    def chroma(self):
+    # CTRL stimmt vom Ergebnis noch nicht, liegt es am Rückgabedatentyp Float?
+    def colourfulness(self):
         """docstring for chroma"""
-        pass
+
+        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)
+        colourfulness = imglch[:, :, 1]
+
+        return colourfulness
 
 
 class Hue(Contrast):
     """docstring for Saturation"""
-    def __init__(self, img, method='hue'):
+    def __init__(self, img, method='hsV'):
         super(Hue, self).__init__(img)
 
         meth = getattr(Hue, method)
         self.ctrst = meth(self)
 
-    def hue(self):
-        """docstring for hue"""
-        
+    def hsV(self):
+        """calculates the Hue value by using the HSV colorspace"""
+
         img = cv.cvtColor(self._img, cv.COLOR_BGR2HSV)
         hues = img[:, :, 0].copy()
 
+        print('hsv')
+
         return hues
 
+    def labHcl(self) -> np.uint16:
+        """calculates the Hue value by using the CIE LabHCL colorspace"""
+
+        imglab = color.rgb2lab(self._img[:,:,::-1])
+        imglch = color.lab2lch(imglab)
+        hues = imglch[:, :, 2]
+
+        vdegrees = np.vectorize(math.degrees)
+        hues = vdegrees(hues)
+
+        hues = np.around(hues).astype(np.uint16)
+
+        return hues

itten/helpers.py

@@ -2,6 +2,58 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
+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=1):
+    """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
+
+    if len(timestmp) > 2:
+        in_sec += int(timestmp[0]) * 3600
+
+    in_sec += int(timestmp[-2]) * 60
+    in_sec += int(timestmp[-1])
+
+    return in_sec
 
 
 def timelabels(val, pos):

itten/movie.py

 #!/ur/bin/env python
 # -*- coding: utf-8 -*-
 
+import numpy as np
 from pathlib import Path  # TODO wie kann ich third-party module nach außen verstecken
 import pickle
+from subprocess import Popen, PIPE, STDOUT
 
 # TODO Ist der import aus dem selben Pakte so korrekt?
 from . import views
 from . import visuals
-import numpy as np
+from . import helpers
 
 
 # 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='./'):  # TODO platform independent
+    def __init__(self, prefix, folder='./', fps=1):  # TODO platform independent
         self._frames = Frames(folder, prefix)
+        self.fps = fps
         self.fsize = self._frames.frm_cnt
 
-    # TODO Warum ist die hier?
-    def light_dark(self):
-        """compute the light/dark contrast of the movie """
-        light_dark_ctrst = views.LightDark(self._frames)
-        return light_dark_ctrst.hist_vstack()
+    def showf(self, start, end=0, type='time', mltp=1, viewer='sxiv -t'):
+        """Opens frames within the given timespan in an image viewer
+
+           start : timestamp of the start position [hh:]mm:ss or
+                  frame number as a string
+           end   : timestamp of the end position [hh:]mm:ss or
+                  frame number as a string
+           type : measure type of from and to. Values 'time' or 'frame'
+                  or list
+           mltp : use a different multiplyer to multiply frame number
+                  than the frame step of the Movie instance
+           viewer : image view of choiche (defaults to sxiv)
+        """
+
+        # transform time to frames if type is time
+        if type is 'time':
+            start = helpers.time2framenr(start, self.fps)
+            end = helpers.time2framenr(end, self.fps)
+        elif type is 'frame':
+            start = int(start) * mltp
+            end = int(end) * mltp
+
+        # Popen sxiv with Path and image count
+        if '_0' in str(self._frames.frames[0]):
+            glob = self._frames.folder + self._frames.prefix + '{' + '{0:04d}'.format(start) + '..'\
+                    + '{0:04d}'.format(end) + '}' + '.png'
+        else:
+            glob = self._frames.folder + self._frames.prefix + '{' + str(start) + '..'\
+                + str(end) + '}' + '.png'
+        cmd = viewer + ' ' + glob
+        Popen(cmd, stdout=PIPE, stderr=STDOUT, shell=True)
+
+        return True
+
+    def visualize(self, ctrst, meth, vtype, start=1, end=0, stp=5,
+                  save=False, desc=False):
+        """Creates a visualization for a specific contrast in the movie
+
+        Parameters
+        ----------
+        ctrst: the contrast to be plotted
+        method: the method which should be used by the contrast class in order
+                to calculate the contrast
+        vtype: type of the visualization (distribution, seqmean, seqmad, etc)
+        start: Int which defines the start frame for the description
+        end: Int which defines the end frame for the description (0 defines
+             the unknown end frame)
+        stp: the frequency in sec by which frames are used to calculate the
+             contrast
+        save: wether the plot should be saved to a file or not TODO
+        desc: a description which is used as a title and filename for the
+              visualization
+
+        Returns
+        -------
+        a tuple containing a view and a matplotlib object
+        """
+        # set default plot description if no description is provided
+        if not(desc):
+            desc = 'Frame ' + str(start) + ' bis ' + str(end)
+
+        data = self.analyze(ctrst, meth, vtype, start, end, stp)
+
+        # build visualization for the selected visualization type
+        if vtype == 'distribution':
+            vis = visuals.MultivariatePlot(data)
+        elif vtype in ['seqmean', 'seqmad', 'seqvar', 'seqper']:
+            vis = visuals.UnivariatePlot(data)
+
+        vis.plot(data)
+
+        return data, vis
+
+        # export plot to file
+        if save:
+            filename = self._frames.prefix + vtype + 'plot' + '_' + ctrst + \
+                       '_' + desc
+            header = self._frames.prefix + vtype + 'plot' + ' for ' + \
+                ctrst + ' - ' + desc
+            vis.saveplt(fname=filename, title=header)
+
+    def analyze(self, ctrst, meth, dtype, start=1, end=0, stp=5):
+        """Calculates contrast data for a specific contrast in the movie
+
+        Parameters
+        ----------
+        ctrst: the contrast to be plotted
+        method: the method which should be used by the contrast class in order
+                to calculate the contrast
+        dtype: type of the visualization (distribution, seqmean, seqmad, etc)
+        start: Int which defines the start frame for the description
+        end: Int which defines the end frame for the description (0 defines
+             the unknown end frame)
+        stp: the frequency in sec by which frames are used to calculate the
+             contrast
+
+        Returns
+        -------
+        a view object
+        """
+
+        # set movie start and end postion for the visualization
+        self._frames.start = start
+        self._frames.end = end
+
+        if dtype == 'distribution':
+            # CTRL funktioniert start/end hier wenn ich es nicht noch einmal
+            # mit übergebe ?
+            data = views.MultivariateSequence(self._frames,)
+            data.populate(ctrst=ctrst, method=meth,
+                          frm_stp=stp)
+            return data
+
+        elif dtype in ['seqmean', 'seqmad', 'seqvar', 'seqper']:
+            data = views.UnivariateSequence(self._frames,)
+            getattr(data, dtype)(ctrst=ctrst, method=meth, frm_stp=stp)
+
+            return data
+
+    def illustrate():
+        """Visualizes the value of a contrast on frame images
+        """
+        pass
 
     def describe(self, desc=False, start=1, end=0, stp=5):
         """Calculates each feature provided by Itten for the movie instance
@@ -53,6 +174,7 @@ class Movie(object):
         - outsource summary/feature loop to function which can be accessed
           by the view class
         - pickle view data
+        - visualize und derive integrieren und dann entsprechend aussortieren
         """
         # set properties of the current function call
         self._frames.start = start
@@ -75,6 +197,7 @@ class Movie(object):
 
             # compute multivariatee contrast representation
             multivariate = views.MultivariateSequence(self._frames)
+            # frm_stp wird um 2 erhöht weil es sonst scheiße aussieht
             multivariate.populate(ctrst=ctrst, frm_stp=stp+2)
 
             # TODO: pickle instance instead of just deleating it!
@@ -181,7 +304,7 @@ class Frames(object):
 
     def get_frame_list(self):
         frm_path = Path(self.folder)
-        return list(frm_path.glob('*.png'))
+        return list(sorted(frm_path.glob('*.png')))
 
     def count_total_frames(self):
         return len(self.frames)

itten/views.py

@@ -20,7 +20,7 @@ from . import movie
 class View(np.ndarray):
     """Core class for the representation of colour contrasts in Movies
 
-       View is the basis class for specific ways to represent colour contrasts.
+       View is the base class for specific ways to represent colour contrasts.
        It does hold the definitions of contrasts itself. Objects of class View
        subclass the numpy array class and hence inherit numpy methods. However,
        it is not recommended to use functions which manipulate the array in
@@ -88,7 +88,7 @@ class View(np.ndarray):
                Class object which corresponds to the parameter
         """
 
-        cls_names = {'light_dark': 'LightDark',
+        cls_names = {'monochromatic': 'Monochromatic',
                      'saturation': 'Saturation',
                      'hue': 'Hue',
                      'cold-warm': 'ColdWarm',
@@ -98,6 +98,7 @@ class View(np.ndarray):
 
         return cls
 
+    # scheint niemals verwendet zu werden
     def frms_from_indcs(self, indices):
         """results frame numbers from a list of indices"""
         indices = np.add(indices, 1)
@@ -134,7 +135,7 @@ class MultivariateSequence(View):
         View.__array_finalize__(self, obj)
         self._threshold = getattr(obj, '_threshold', None)
 
-    def populate(self, ctrst='light_dark', method='luminance', frm_stp=10, bins=16, thrsh=60000, start=1, end=0):
+    def populate(self, ctrst='monochromatic', method='luminance', frm_stp=10, bins=16, thrsh=60000, start=1, end=0):
         """doc (aus __new__ zusammentragen)
         """
 
@@ -145,19 +146,30 @@ class MultivariateSequence(View):
         self._bins = bins
         self._threshold = thrsh
         # TODO dafür müssen erst getters und setters in movie.frames definiert werden
-        # selef._frames.start = start
+        # 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):
-            pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+
+            # 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 + '{0:04d}'.format(frm_nr) + '.png'
+            else:
+                pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
 
             img = cv2.imread(pwd)  # BGR
 
+            print('ctrst: {0} and meth: {1}'.format(self._contrast, self._method))
+
+            # Kontrast Parameternamen auf Klassennamen mappen
             ctrst_cls = self._get_ctrst_cls_name(self._contrast)
-            ctrst_img = ctrst_cls(img).ctrst
+            ctrst_img = ctrst_cls(img, self._method).ctrst
 
             shape = ctrst_img.shape
             ctrst_img = np.reshape(ctrst_img, (shape[0] * shape[1]))
@@ -203,7 +215,7 @@ class BivariateSequence(View):
         View.__array_finalize__(self, obj)
 
     # das kann ich jetzt auch mit quantilen machen
-    def populate(self, variant='peak', ctrst='light_dark',
+    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
 
@@ -233,7 +245,12 @@ class BivariateSequence(View):
         # 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 = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+            pwd = ""
+            if '_0' in str(self._frames.frames[0]):
+                pwd = self._frames.folder + self._frames.prefix + '{0:04d}'.format(frm_nr) + '.png'
+            else:
+                pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+
             img = cv2.imread(pwd)
 
             # BREAK: Funktioniert nicht
@@ -357,7 +374,7 @@ class UnivariateSequence(View):
         return mean, mad
 
     # TODO: Kurve muß unbeding von cuts bereinigt werde und interpolation funktioniert da nicht
-    def seqmean(self, ctrst='light_dark',
+    def seqmean(self, ctrst='monochromatic',
                 method='luminance', frm_stp=10, bins=256):
         """Creates a scatterplot for the dynamic of contrast across movie frames
 
@@ -383,11 +400,16 @@ class UnivariateSequence(View):
         # 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 = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+            pwd = ""
+            if '_0' in str(self._frames.frames[0]):
+                pwd = self._frames.folder + self._frames.prefix + '{0:04d}'.format(frm_nr) + '.png'
+            else:
+                pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+
             img = cv2.imread(pwd)
 
             ctrst_cls = self._get_ctrst_cls_name(self._contrast)
-            ctrst_img = ctrst_cls(img).ctrst
+            ctrst_img = ctrst_cls(img, method=self._method).ctrst
 
             hist_value, _ = np.histogram(ctrst_img.flatten(),
                                          bins=self._bins, range=(0, 256))
@@ -411,7 +433,7 @@ class UnivariateSequence(View):
         contrast_points = None
 
     # TODO: Kurve muß unbeding von cuts bereinigt werde und interpolation funktioniert da nicht
-    def seqmad(self, ctrst='light_dark',
+    def seqmad(self, ctrst='monochromatic',
                method='luminance', frm_stp=10, bins=256):
         """Creates a scatterplot for the dynamic of contrast across movie frames
 
@@ -437,7 +459,12 @@ class UnivariateSequence(View):
         # 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 = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+            pwd = ""
+            if '_0' in str(self._frames.frames[0]):
+                pwd = self._frames.folder + self._frames.prefix + '{0:04d}'.format(frm_nr) + '.png'
+            else:
+                pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+
             img = cv2.imread(pwd)
 
             ctrst_cls = self._get_ctrst_cls_name(self._contrast)
@@ -464,7 +491,7 @@ class UnivariateSequence(View):
         self[:] = contrast_points
         contrast_points = None
 
-    def seqper(self, ctrst='light_dark',
+    def seqper(self, ctrst='monochromatic',
                method='luminance', perc=50, frm_stp=10, bins=256):
         """Creates a scatterplot for the dynamic of contrast across movie frames
 
@@ -490,11 +517,16 @@ class UnivariateSequence(View):
         # 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 = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+            pwd = ""
+            if '_0' in str(self._frames.frames[0]):
+                pwd = self._frames.folder + self._frames.prefix + '{0:04d}'.format(frm_nr) + '.png'
+            else:
+                pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+
             img = cv2.imread(pwd)
 
             ctrst_cls = self._get_ctrst_cls_name(self._contrast)
-            ctrst_img = ctrst_cls(img).ctrst
+            ctrst_img = ctrst_cls(img, method=self._method).ctrst
 
             percentile = np.percentile(ctrst_img.flatten(), perc)
 
@@ -507,7 +539,7 @@ class UnivariateSequence(View):
         self[:] = contrast_points
         contrast_points = None
 
-    def seqvar(self, ctrst='light_dark',
+    def seqvar(self, ctrst='monochromatic',
                method='luminance', perc=50, frm_stp=10, bins=256):
         """Creates a scatterplot for the dynamic of contrast across movie frames
 
@@ -533,7 +565,12 @@ class UnivariateSequence(View):
         # 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 = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+            pwd = ""
+            if '_0' in str(self._frames.frames[0]):
+                pwd = self._frames.folder + self._frames.prefix + '{0:04d}'.format(frm_nr) + '.png'
+            else:
+                pwd = self._frames.folder + self._frames.prefix + str(frm_nr) + '.png'
+
             img = cv2.imread(pwd)
 
             ctrst_cls = self._get_ctrst_cls_name(self._contrast)

itten/visuals.py

@@ -140,11 +140,14 @@ class UnivariatePlot(SequencePlot):
         self._ax = plt.axes()
         self._ax, self._axt = self.ittenstyle(self._ax, view)
 
-    def plot(self, view, mark_gt=False, mark_lt=False, mark=False):
+    def plot(self, view, label=False, mark_gt=False, mark_lt=False, mark=False):
+
+        if not(label):
+            label = view.feature
 
         # Interpolation mit savitzky_golay funktioniert nicht
         # contrast_points = savitzky_golay(np.array(contrast_points), 51, 7)
-        self._axt.plot(self._x, view, label=view.feature)
+        self._axt.plot(self._x, view, label=label)
 
         if any([mark, mark_gt, mark_lt]):
             self._vlines(view, mark, mark_gt, mark_lt)

testing/__init__.py

+ 

testing/bootstrap.py

+# %matplotlib qt5
+# %load_ext autoreload
+# %autoreload 2
 from itten.movie import Movie
 from itten.views import MultivariateSequence
 from itten.views import UnivariateSequence
 from itten.visuals import MultivariatePlot
 from itten.visuals import UnivariatePlot
 
-movie = Movie(prefix='wwz_', folder='../DHd-2017/Data/Frames/WWZ/')
+movie = Movie(prefix='wwz_', folder='../Dissemination/DHd-2017/Data/Frames/WWZ/')
 # movie._frames.start = 367
 # movie._frames.end = 1000
 # cont = MultivariateSequence(movie._frames,)
-cont = UnivariateSequence(movie._frames,)
-cont.seqvar(frm_stp=10, ctrst='light_dark')
+# cont = UnivariateSequence(movie._frames,)
+# cont.seqvar(frm_stp=2, ctrst='light_dark')
 # viz = MultivariatePlot(cont)
 # viz.plot(cont, mark=[243, 1885, 3430, 4237, 4543])
 # cont.seqper(frm_stp=5, ctrst='saturation', perc=50)
@@ -18,3 +21,14 @@ cont.seqvar(frm_stp=10, ctrst='light_dark')
 
 # viz = MultivariatePlot()
 # fig, ax = viz.plot(cont)
+
+# Chroma
+# movie = Movie(prefix='rec_', folder='../Dissemination/DHd-2017/Data/Frames/Rec/')
+# chro = UnivariateSequence(movie._frames,)
+# chrom = MultivariateSequence(movie._frames)
+# chro.seqmean(frm_stp=20, ctrst='saturation', method='colourfulness')
+# chrom.populate(frm_stp=20, ctrst='saturation', method='colourfulness')
+# viz = UnivariatePlot(chro)
+# viz2 = MultivariatePlot(chrom)
+# viz.plot(chro)