included random mean initialization for gmm training

SRC/Python/.idea/workspace.xml

@@ -24,8 +24,8 @@
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@@ -36,8 +36,8 @@
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@@ -46,8 +46,8 @@
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@@ -66,11 +66,11 @@
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         <option value="$PROJECT_DIR$/PIXELREP_GUI_WRAP.py" />
-        <option value="$PROJECT_DIR$/PixelRep_Demo.py" />
         <option value="$PROJECT_DIR$/PIXEL_REPLICATION_LIB.py" />
+        <option value="$PROJECT_DIR$/GENERATE_ELLIPSE_LIB.py" />
+        <option value="$PROJECT_DIR$/PixelRep_Demo.py" />
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@@ -410,7 +410,7 @@
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@@ -543,16 +543,16 @@
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@@ -561,8 +561,8 @@
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SRC/Python/PIXEL_REPLICATION_LIB.py

@@ -67,29 +67,35 @@ def PixelReplicate(bwim):
             ptsRep[int(nInsert):int(nInsert + nrep), 0:] = rp
         nInsert = nInsert + nrep
 
-    return ptsRep
+    return ptsRep, pts
 
-def fitGMM(ptsRep, k):
-    from sklearn.mixture import GMM
+def fitGMM(ptsRep, k, pts):
     import numpy as np
     import cv2
     print("Fitting GMM...")
+
+    bestGMM = []
+    bestLL = float("-inf")
+
+    uniqPts = pts
+
+    for i in np.arange(0, 10, 1):
+        rndIdx = np.random.randint(low=0, high=uniqPts.shape[0], size=(1, 1, k), dtype=int)
+        rndMeans = np.matrix(uniqPts[np.array(rndIdx), 0:][0][0])
+
         gmm0 = cv2.ml.EM_create()
         gmm0.setCovarianceMatrixType(2)
         gmm0.setClustersNumber(k)
-    termCrit = (20, 400, 1e-15)
+        termCrit = (1000, 1000, 1e-25)
         gmm0.setTermCriteria(termCrit)
-    bestGMM = []
-    bestLL = float("-inf")
-    for i in np.arange(0, 10, 1):
+
         print('Replicate: ', str(i+1))
-        newGMM = gmm0.trainEM(ptsRep)
+        newGMM = gmm0.trainE(samples=ptsRep, means0=rndMeans)
         LL = sum(newGMM[1])
+        print('Likelihood Logarithm of GMM fit = ', LL)
         if LL > bestLL:
             bestGMM = gmm0
             bestLL = LL
-    # gmm0 = GMM(n_components=k, covariance_type='full', n_iter=400, n_init=20, verbose=0, tol=1e-4, init_params='m')
-    # gmmfit = gmm0.fit(ptsRep)
     return bestGMM
 
 def drawClusters(gmm0, bwim, ptsEdge):

SRC/Python/PixelRep_Demo.py

@@ -33,15 +33,14 @@ import GENERATE_ELLIPSE_LIB as GenEllipse
 
 # Generate random ellipse image
 print("Generating Ellipse...")
-K = 4 # number of ellipse to fit
-# bwim, trueBounds = GenEllipse.GetRandomEllipseImage(K)
+K = 3 # number of ellipse to fit
+bwim, trueBounds = GenEllipse.GetRandomEllipseImage(K)
 
 # # You may import an image for demo by uncommenting the following code:
-import cv2
-im = cv2.imread('U:\Angeline\PixelRep\Ellipse_k4.tif', cv2.CV_8UC1)
+# import cv2
 # im = cv2.imread('INSERT FILENAME', cv2.CV_8UC1)
-th, bwim = cv2.threshold(im, 0, 1, cv2.THRESH_BINARY)
-trueBounds = [0, 0]
+# th, bwim = cv2.threshold(im, 0, 1, cv2.THRESH_BINARY)
+# trueBounds = [0, 0]
 # # (Remember to modify K number of ellipses)
 # # ---------------------------------------------------------------------
 
@@ -49,12 +48,12 @@ trueBounds = [0, 0]
 # bwim only has 1 connected component. if bwim has multiple connected
 # components, process each one separately
 
-ptsRep = PR.PixelReplicate(bwim)
+ptsRep, pts = PR.PixelReplicate(bwim)
 
 # Fit GMM to PR points
 # NOTE - more replicates will be more accurate, but will take longer.
 
-gmmfit = PR.fitGMM(ptsRep, K)
+gmmfit = PR.fitGMM(ptsRep, K, pts)
 
 # Draw clusters