Load from St.Jan

charts.py

@@ -5,7 +5,8 @@ from lxml import etree
 def createPie(data, gaps = 0):
 	total = sum(data.values())
 	colours = ['#268ED7','#6A4F8D', '#A03D4F','#DC7432','#F6DC3B','#76B33B', '#267B60']
-	svg = '<svg viewBox="-115 -115 230 230" xmlns="http://www.w3.org/2000/svg">'
+	className = "hoverTitles" if len(data) > 9 else ""
+	svg = '<svg viewBox="-115 -115 230 230" xmlns="http://www.w3.org/2000/svg" class="%s">' % className
 	numbers = ""
 	position = 90
 	i = 0
@@ -18,8 +19,9 @@ def createPie(data, gaps = 0):
 		d = describeArc(float(diff['x']), float(diff['y']), 60, position, position+arc)
 		# print(position, arc)
 		svg += '<path fill="none" stroke="%s" stroke-width="60" d="%s" />' % (colours[i%len(colours)], d)
+		# tRadius = 105 if i%2 else 95
 		t = polarToCartesian(0,0, 100, position+0.5*arc)
-		numbers += '<text x="%s" y="%s" font-family="sans-serif" font-size="14" style=" stroke:color: black;" text-anchor="middle" transform="rotate(%s %s,%s)">%s</text>' % (t['x'], t['y'], position+0.5*arc, t['x'],t['y'], e)
+		svg += '<text x="%s" y="%s" font-family="sans-serif" font-size="12" style=" stroke:color: black;" text-anchor="middle" transform="rotate(%s %s,%s)">%s</text>' % (t['x'], t['y'], position+0.5*arc, t['x'],t['y'], e)
 		position += arc
 		i+=1
 

colour.py

@@ -7,13 +7,15 @@ import codecs
 import colorsys
 import math
 import numpy as np
+import sklearn.cluster
+from PIL import Image
 
 NUM_CLUSTERS = 64
 
 def getColourAsHex(colour):
     return '#' + ''.join(format(c, '02x') for c in colour.astype(int))
 
-def getColoursForImageByClusters(image):
+def getColoursForImageByKMeansClusters(image):
     """
     Adapted on answers by
     Peter Hansen (http://stackoverflow.com/a/3244061)
@@ -43,6 +45,62 @@ def getColoursForImageByClusters(image):
 #     print(colours)
     return list(zip(codes, percentages))
 
+def getColoursForImageByMeanShiftClusters(image, saveImgName = False):
+    """
+    Adapted on answers by
+    Peter Hansen (http://stackoverflow.com/a/3244061)
+    & Johan Mickos (http://stackoverflow.com/a/34140327)
+    Now using MeanShift clustering
+
+    if saveImgName set to the path/prefix_ for the layer images, save images of each cluster matched pixels
+    """
+    imgSize = (170,170)
+    # imgSize = (int(targetWidth), int(image.size[1] / (image.size[0]/targetWidth)))
+    im = image.copy()      # optional, to reduce time
+    im.thumbnail(imgSize)
+    imgSize = im.size
+    print("\tRender image of", image.size,"for", imgSize)
+    imgAr = scipy.misc.fromimage(im)
+    shape = imgAr.shape
+    ar = imgAr.reshape(scipy.product(shape[:2]), shape[2])
+    total = len(ar)
+
+#     print( 'finding clusters')
+#     codes, dist = scipy.cluster.vq.kmeans(ar.astype(float), NUM_CLUSTERS)
+    bandwidth = sklearn.cluster.estimate_bandwidth(ar.astype(float), quantile=0.1, n_samples=500)
+    ms = sklearn.cluster.MeanShift(bandwidth=bandwidth)
+    ms.fit(ar.astype(float))
+    labels = ms.labels_ # labels per point
+    cluster_centers = ms.cluster_centers_ # centers of found clusters
+
+    labels_unique = np.unique(labels)
+    n_clusters_ = len(labels_unique)
+    
+    print("\tClusters found:", n_clusters_)
+    
+    colours = []
+    for k in labels_unique:
+        cluster_center = cluster_centers[k] # np.array with rgb
+        mask = labels == k # True/False map of flattened array
+        # m = np.ma.masked_where(labels != k, labels)
+        count = len(ar[mask]) # nr of pixels for this label
+
+        # pass on percentages:
+        colours.append((cluster_center, 100 * count / total))
+
+        if saveImgName:
+            # save image as RGBA with transparent pixels, except for there where label == k
+            layer = np.array([np.append(ar[i],255) if l == k else [0,0,0,0] for i,l in enumerate(list(labels))], dtype=np.uint8)
+            layerImgAr = layer.reshape((imgSize[1],imgSize[0],4)) # layers are stacked: vertical, horizontal, rgba
+            layerImg = Image.fromarray(layerImgAr, mode="RGBA")
+            layerFilename = saveImgName + '-%s.png' % k
+            layerImg.save(layerFilename)
+            print(layerFilename)
+        
+        # display(HTML("<span style='background:%s'>%s</span>" % (getColourAsHex(cluster_center),getColourAsHex(cluster_center))))
+    
+    return colours
+
 def getColoursForImageByPxAvg(image):
     im = image.copy().resize((8, 8))
     pixels = np.concatenate(scipy.misc.fromimage(im))
@@ -54,36 +112,41 @@ def getColoursAsHTML(colours):
     return " ".join(['<span style="background:%s">%s - (%s %%)</span>' % (getColourAsHex(colour[0]), getColourAsHex(colour[0]), colour[1]) for colour in colours]);
 
 def loadColoursFromDbImages(images):
-    return [i.colours for i in images]
+    return [i.colours for i in images if not i.colours is None]
     
 
 def getSvgFromDbImages(images, elId = ""):
     # sum concatenates all colour arrays
-    allColours = []
-    for c in loadColoursFromDbImages(images):
-        allColours += c
+    # allColours = []
+    # for c in loadColoursFromDbImages(images):
+    #     allColours += c
     # box 160, because center or circle = 100 => +/- 50 => + r of colour circle (max: 10) => 160
-    svg = '<svg viewBox="-160 -160 320 320" xmlns="http://www.w3.org/2000/svg" id="%s">' % elId
+    svg = '<svg viewBox="-160 -160 320 320" xmlns="http://www.w3.org/2000/svg" id="%s">' % (elId, )
 
     radius = 100
 
-    for colour in allColours:
-        rgb, percentage = colour
-        rgbNorm = rgb/255
-        hsv = colorsys.rgb_to_hsv(rgbNorm[0], rgbNorm[1], rgbNorm[2])
-        # find position on circle
-        radians = 2 * math.pi * hsv[0]
-        x = math.cos(radians)
-        y = math.sin(radians)
-        
-        # based on saturation, we move inwards/outwards
-        # min = 0.5, max = 1.5 (dus + 0.5)
-        pos = np.array([x,y]) * (0.5 + hsv[1]) * radius
-        # Posibilitiy: determine position based on avg(saturation, value) => dark & grey inside, shiney and colourful outside 
-        # pos = np.array([x,y]) * (0.5 + (hsv[1]+hsv[2])/2) * radius
-        r = max(1,-10/percentage+10) # as r, we converge to maximum radius 10, but don't want to get smaller radi then 1
-        c = '<circle cx="%s" cy="%s" r="%s" style="fill:%s" />' % (pos[0], pos[1], r, getColourAsHex(rgb))
-        svg += c
+    for image in images:
+        if image.colours is None:
+            continue
+
+        for i, colour in enumerate(image.colours):
+            colourId = '%s-%s' % (image.id, i)
+            rgb, percentage = colour
+            rgbNorm = rgb/255
+            hsv = colorsys.rgb_to_hsv(rgbNorm[0], rgbNorm[1], rgbNorm[2])
+            # find position on circle
+            radians = 2 * math.pi * hsv[0]
+            x = math.cos(radians)
+            y = math.sin(radians)
+            
+            # based on saturation, we move inwards/outwards
+            # min = 0.5, max = 1.5 (dus + 0.5)
+            pos = np.array([x,y]) * (0.5 + hsv[1]) * radius
+            # Posibilitiy: determine position based on avg(saturation, value) => dark & grey inside, shiney and colourful outside 
+            # pos = np.array([x,y]) * (0.5 + (hsv[1]+hsv[2])/2) * radius
+            r = max(1,-10/percentage+10) # as r, we converge to maximum radius 10, but don't want to get smaller radi then 1
+            c = '<circle cx="%s" cy="%s" r="%s" style="fill:%s" onmouseover="triggerover(\'%s\', this)" />' % (pos[0], pos[1], r, getColourAsHex(rgb), colourId)
+            svg += c
 
     svg += "</svg>"
     return svg