def lda(directories):
images = load(directories, True, permute=False)
f = HaarFeature()
x = []
for idx, im in enumerate(images):
print("%d/%d" % (idx, len(images)))
x.append(np.array(f.process(im)))
y_train = [im.label for im in images]
classes = list(set(y_train))
class_to_index = {key: index for index, key in enumerate(classes)}
labels = np.concatenate(np.array([[class_to_index[name] for name in y_train]]))
clf = ExtraTreesClassifier()
clf = clf.fit(x, labels)
w, h = f.size, f.size
i = 0
filtered = []
for size in f.haarSizes:
for x in range(w - size):
for y in range(h - size):
for haar_type in range(len(f.haars)):
score = clf.feature_importances_[i]
if score > 0.000001:
filtered.append((size, x, y, haar_type, score))
i += 1
sorted_filtered = sorted(filtered, key=lambda tup: tup[4], reverse=True)
text_file = open("haarImportance.txt", "w")
for k in sorted_filtered:
# print("[size=%d][x=%d][y=%d][type=%d] \t=> %f" % k)
text_file.write("[size=%d][x=%d][y=%d][type=%d] \t=> %f\n" % k)
text_file.close()
def load_images(directories, is_train=False, permute=True):
return load(directories, is_train, permute)
def test_feature(directories, trainers):
images = load(directories, True, permute=False)
for image in images:
if '01005_05517' in image.filename:
feature = ColorFeature()
feature.process(image)
def test_feature(directories, trainers):
images = load(directories, True, permute=False)
for image in images:
if '01005_05517' in image.filename:
feature = ColorFeature()
feature.process(image)
def load_images(directories, is_train=False, permute=True):
return load(directories, is_train, permute)
def Task(progress):
for i, url in enumerate(urls):
progress.update(i + 1)
img_path = 'images/' + convertUrlToName(url)
if memo.getUrlData(url)['encoding'] != [] or not os.path.isfile(
img_path):
continue
img = face_recognition.load_image_file(img_path)
locations = face_recognition.face_locations(img)
if len(locations) == 1:
encoding = list(
face_recognition.face_encodings(
img,
known_face_locations=locations,
num_jitters=accurate)[0])
memo.upd_urls({url: {'encoding': encoding}})
else:
memo.remove_urls([url])
execTask(name='calculate encodings:', size=len(urls), task=Task)
if __name__ == '__main__':
image_loader.load()
fillEncodings()
