Fix CBCL example

examples/cbcl/cbcl.py

@@ -1,30 +1,31 @@
 import numpy, datetime, json, subprocess, sys, os, glob
+from PIL import Image
 import scipy.misc
 
 def load(dir):
 	images = []
 	vecs = []
 	for f in glob.glob(os.path.join(dir,'*.pgm')):
-		image = numpy.array(scipy.misc.imread(f))
+		image = numpy.array(Image.open(f))
 		images.append((f,image))
 		vecs.append(image.ravel())
 	return numpy.vstack(vecs), images
 
 def embed(feature_matrix):
 	input_file = 'tmp_cbcl_input'
-	numpy.savetxt(input_file,feature_matrix)
+	numpy.savetxt(input_file, feature_matrix, delimiter=',')
 	output_file = 'tmp_cbcl_output.dat'
-	run_string = './bin/tapkee_cli -i %s -o %s -m ltsa -k 20 --transpose --verbose --benchmark' % (input_file,output_file)
-	output = subprocess.check_output(run_string, shell=True)
-	embedding = numpy.loadtxt(output_file)
-	os.remove(output_file)
+	runner_string = './bin/tapkee -i %s -o %s -m ltsa -k 80 --transpose-output --verbose --benchmark' % (input_file, output_file)
+	process = subprocess.run(runner_string, shell=True, capture_output=True, text=True)
+	print(process.stderr)
+	if process.returncode != 0:
+		raise Exception('Failed to embed')
+	embedding = numpy.loadtxt(output_file, delimiter=',')
 	return embedding
 
-def export_json(outfile,embedding,images):
+def export_json(outfile, embedding, images):
 	json_dict = {}
 	N = embedding.shape[1]
-	print 'N', N
-	import scipy.misc
 	json_dict['data'] = [{'cx':embedding[0,i], 'cy':embedding[1,i], 'fname':images[i][0]} for i in xrange(N)]
 	json.dump(json_dict, open(outfile, 'w'))
 
@@ -34,21 +35,21 @@ def plot_embedding(embedding,images):
 	fig = plt.figure()
 	ax = fig.add_subplot(111)
 	ax.scatter(embedding[0],embedding[1],alpha=0.0)
-	for i in xrange(embedding.shape[1]):
-		img = numpy.zeros((images[i][1].shape[0],images[i][1].shape[1],4))
-		img[:,:,0] = 255*images[i][1]
-		img[:,:,1] = 255*images[i][1]
-		img[:,:,2] = 255*images[i][1]
-		img[:,:,3] = 1
-		img[(images[i][1]==28),3] = 0
+	for i in range(embedding.shape[1]):
+		img = numpy.zeros((images[i][1].shape[0], images[i][1].shape[1], 4))
+		img[:,:,0] = images[i][1]/255.0
+		img[:,:,1] = images[i][1]/255.0
+		img[:,:,2] = images[i][1]/255.0
+		img[:,:,3] = 1.0
+		img[(images[i][1]==28), 3] = 0
 		imagebox = OffsetImage(img,cmap=plt.cm.gray,zoom=0.2)
-		ab = AnnotationBbox(imagebox, (embedding[0][i], embedding[1,i]),pad=0.001,frameon=False)
+		ab = AnnotationBbox(imagebox, (embedding[0][i], embedding[1,i]), pad=0.001, frameon=False)
 		ax.add_artist(ab)
 	plt.show()
 
 if __name__ == "__main__":
 	feature_matrix, images = load('data/cbcl')
 	embedding = embed(feature_matrix)
 	if len(sys.argv)==3:
-		export_json(sys.argv[2],embedding, images)
+		export_json(sys.argv[2], embedding, images)
 	plot_embedding(embedding,images)