outputfolder += '/'
features = []
surf = cv2.SURF(250, extended=False)
print "Extracting SURF features"
for line in open(inputfile):
category, path = line.strip().split(';')
img = cv2.imread(path)
keypoints, descriptors = surf.detectAndCompute(img, None)
if descriptors is None:
continue
for d in descriptors:
features.append(d)
features = np.array(features)
print "Performing kmeans on: ", features.shape
centroids, labels = kmeans2(features, FEATURE_TYPES)
return centroids
if __name__ == "__main__":
if len(sys.argv) < 3:
print "Usage: $python traincentroids.py inputfile outputfolder"
sys.exit(1)
inputfile = sys.argv[1]
outputfolder = sys.argv[2]
centroids = routine(inputfile, outputfolder)
helpers.saveObject(centroids, outputfolder + 'centroids.txt')
def train_routine(training_file, output_folder):
"""The main training routine.
Input: training_file: <class>;<path to image>
output_folder: A previous created folder where output files
are written.
Runs the routine: Get SURF features, calculate bag of words,
then train the linear SVM using those.
Then writes the required objects onto files.
"""
if output_folder[-1] != '/':
output_folder += '/'
svm_file = output_folder + 'svm.txt'
centroid_file = output_folder + 'centroids.txt'
ids_file = output_folder + 'ids.txt'
surf = cv2.SURF(250, extended=False)
categories = dict()
ids = dict()
id = 1
features = list()
print "Extracting features"
for line in open(training_file):
try:
category, path = line.split(';')
except:
print "Error: File not in proper format. Ensure: <category/class name>; <path to image of said category>"
sys.exit(0)
path = path.strip()
try:
img = cv2.imread(path)
#img = cv2.resize(img, (500, 500))
except Exception as e:
print e
continue
keypoints, descriptors = surf.detectAndCompute(img, None)
if not category in categories:
categories[category] = Category(label=category)
ids[category] = id
id += 1
categories[category].add_feature(descriptors)
#for category in categories:
#f = categories[category].yield_features()
##features.extend(f)
#for i in f:
#features.extend(i)
print "Calculating centroids"
#np_features = numpy.array(features)
#print "Features: ", np_features.shape
#centroids, labels = kmeans2(np_features, FEATURE_TYPES)
centroids = helpers.loadObject(output_folder + 'centroids.txt')
print centroids.shape
print "Forming bag of words"
X, Y = [], []
for category in categories:
categories[category].calc_bagofwords(centroids)
for bow in categories[category].bagofwords:
X.append(bow)
Y.append(ids[category])
print "Fitting linear SVMs onto the bag of words"
lin_clf = svm.LinearSVC()
lin_clf.fit(X, Y)
helpers.saveObject(lin_clf, svm_file)
helpers.saveObject(centroids, centroid_file)
helpers.saveObject(ids, ids_file)
outputfolder += "/"
features = []
surf = cv2.SURF(250, extended=False)
print "Extracting SURF features"
for line in open(inputfile):
category, path = line.strip().split(";")
img = cv2.imread(path)
keypoints, descriptors = surf.detectAndCompute(img, None)
if descriptors is None:
continue
for d in descriptors:
features.append(d)
features = np.array(features)
print "Performing kmeans on: ", features.shape
centroids, labels = kmeans2(features, FEATURE_TYPES)
return centroids
if __name__ == "__main__":
if len(sys.argv) < 3:
print "Usage: $python traincentroids.py inputfile outputfolder"
sys.exit(1)
inputfile = sys.argv[1]
outputfolder = sys.argv[2]
centroids = routine(inputfile, outputfolder)
helpers.saveObject(centroids, outputfolder + "centroids.txt")
