def load(self,path):
f = open(path,'r')
for ln in f.readlines():
lbb = LabeledExample.from_string(ln)
self.labeled_bbs.append(lbb)
f.close()
def main(args):
X = np.array([])
patches = []
neg_patches = []
l = []
#First pass through the images and collect the examples
for image_name in args.images:
image = cv2.imread( image_name, 0 )
#Open the label file
f = open(image_name+'.lab')
for ln in f.readlines():
lbb = LabeledExample.from_string(ln)
if lbb.label_val in args.classes:
patch = lbb.bounding_box.extract(image)
patches.append(patch)
l.append(lbb.label_val)
elif len(neg_patches) < len(patches)*args.negative_ratio:
patch = lbb.bounding_box.extract(image)
neg_patches.append(patch)
f.close()
if os.path.exists(args.model_file):
#If the model already exists, we use the learned filter size
f = open(args.model_file+'.winsize')
ln = f.readline()
mean_width = int(ln[0])
mean_height = int(ln[1])
f.close()
else:
#Otherwise we compute the mean ourselves
mean_width = int(sum([p.shape[0] for p in patches])/float(len(patches)))
mean_height = int(sum([p.shape[1] for p in patches])/float(len(patches)))
l = l + ['NEGATIVE' for n in neg_patches]
for i,patch in enumerate(patches + neg_patches):
patch_scaled = cv2.resize( patch, (mean_width, mean_height) )
feature = compute_feature( patch_scaled, args.feature )
if i == 0:
X = feature
else:
X = np.vstack( (X, feature))
L = PyML.Labels(l)
print L
data = PyML.VectorDataSet(X,L=L)
#Train if the model doesn't exist
if not os.path.exists(args.model_file):
mc = PyML.classifiers.multi.OneAgainstRest(PyML.svm.SVM())
param = PyML.modelSelection.Param(mc, 'C', [0.1, 1, 100, 1000])
m = PyML.modelSelection.ModelSelector(param)
m.train(data)
cl = m.classifier
cl.loo(data)
#Saving data since for some absurd reason we need it
data.save(args.model_file+'.data')
cl.save(args.model_file)
#Saving the window size...I should really wrap all of this
f = open(args.model_file+'.winsize','w')
f.write('%d\t%d'%(mean_width,mean_height))
f.close()
else:
#Otherwise test
mc = PyML.classifiers.multi.OneAgainstRest(PyML.svm.SVM())
train_data = PyML.VectorDataSet(args.model_file+'.data',labelsColumn=1)
mc.load(args.model_file,train_data)
print mc.test(data)
