def main(samples_dir, test_dir, output_dir):
face_descriptors, class_names = load_samples_descriptors(samples_dir)
save2pickle(face_descriptors, class_names, "wg_colleagues.pkl")
# face_descriptors, class_names = load_from_pickle('wg_colleagues.pkl')
print(face_descriptors[0])
print("len face_descriptors: {}".format(len(face_descriptors)))
print("len class_names: {}".format(len(class_names)))
print("class_names: {}".format(class_names))
print("total class: {}".format(len(set(class_names))))
image_files = images_in_dir(test_dir)
for im_f in image_files:
im = cv2.imread(im_f)
faces = detect_faces(im)
start = time.time()
for face in faces:
descriptor = compute_face_descriptor(im, face)
idx, distance = closest_one(face_descriptors, descriptor)
if distance > 0.4:
label = 'unknown'
else:
label = class_names[idx]
subdir = _subdir(output_dir, label)
images_count = len(glob.glob('{}/*.jpg'.format(subdir)))
f_name = '{}_{}.jpg'.format(label, '{0:04d}'.format(images_count))
print('{}: {}, of distance :{} '.format(im_f, f_name, distance))
output_path = os.path.join(subdir, f_name)
cv2.imwrite(output_path, im)
print('time : ', time.time() - start)
def test_load_samples_descriptors():
face_descriptors, class_names = load_samples_descriptors(images_dir)
assert len(face_descriptors) == 3
assert len(class_names) == 3
assert class_names[0] == "Aaron_Eckhart"
assert class_names[1] == "Blythe_Hartley"
assert class_names[2] == "Blythe_Hartley"
def main(samples_dir, test_dir, output_dir):
face_descriptors, class_names = load_samples_descriptors(samples_dir)
# save2pickle(face_descriptors, class_names, "wg_colleagues.pkl")
# face_descriptors, class_names = load_from_pickle('wg_colleagues.pkl')
image_files = images_in_dir(test_dir)
labels, names = _labeled(class_names)
classifier = SVMClassifier()
classifier.train(face_descriptors, labels, names)
for im_f in image_files:
f_name = filename(im_f)
im = cv2.imread(im_f)
faces = detect_faces(im)
start = time.time()
print('{} faces detected'.format(len(faces)))
for face in faces:
descriptor = compute_face_descriptor(im, face)
results = classifier.predict([descriptor])
for r in results:
label = '{}'.format(r)
print('label:', label)
subdir = _subdir(output_dir, label)
images_count = len(glob.glob('{}/*.jpg'.format(subdir)))
f_name = '{}_{}.jpg'.format(label,
'{0:04d}'.format(images_count))
output_path = os.path.join(subdir, f_name)
cv2.imwrite(output_path, im)
print('{} done, time spent: {}'.format(f_name, time.time() - start))
def main(samples_dir, test_images_dir, output_dir):
face_descriptors, class_names = load_samples_descriptors(samples_dir)
# save2pickle(face_descriptors, class_names, 'wg_merged.pkl')
# face_descriptors, class_names = load_from_pickle('wg_merged.pkl')
print(class_names)
labels, names = _labeled(class_names)
classifier = SVMClassifier()
print([names[i] for i in labels])
classifier.train(face_descriptors, labels, names)
# classifier.load("classifier_2018-05-15 13:30:06.213832.pkl")
image_files = images_in_dir(test_images_dir)
for im_f in image_files:
output_path = os.path.join(output_dir, filename(im_f))
im = cv2.imread(im_f)
faces = detect_faces(im)
for face in faces:
descriptor = compute_face_descriptor(im, face)
results = classifier.predict([descriptor])
for r in results:
txt = '{}'.format(r)
put_text(im, txt, font_face=cv2.FONT_HERSHEY_SIMPLEX)
print('{}: {} '.format(im_f, r))
cv2.imwrite(output_path, im)
def test_save_and_load():
face_descriptors, class_names = load_samples_descriptors(images_dir)
print(face_descriptors)
print(class_names)
save2pickle(face_descriptors, class_names, os.path.join(BASE_DIR, "descriptors.pkl"))
fd, names = load_from_pickle("descriptors.pkl")
print(fd)
print(names)
assert np.array_equal(fd, face_descriptors) is True
assert names == class_names
def main(samples_dir, validate_data_dir, output_dir):
classifier_file = "classifier_wgers_160_2018-06-14.pkl"
classifier = SVMClassifier(probability=False)
if classifier_file is None:
face_descriptors, class_names = load_samples_descriptors(samples_dir)
print("len face_descriptors: {}".format(len(face_descriptors)))
print("len class_names: {}".format(len(class_names)))
print("class_names: {}".format(class_names))
print("class_names: {}".format(class_names))
print("total class: {}".format(len(set(class_names))))
labels, names = _labeled(class_names)
print([names[i] for i in labels])
classifier.train(face_descriptors, labels, names)
else:
classifier.load(classifier_file)
hierarchy = read_from_hierarchy(validate_data_dir)
total = 0
accurate = 0
time_spent = 0
for k in hierarchy.keys():
image_files = images_in_dir(hierarchy[k])
total += len(image_files)
for im_f in image_files:
im = cv2.imread(im_f)
# faces = detect_faces(im)
height, width = im.shape[:2]
faces = rect_2_dlib_rectangles((0, 0), (width, height))
a, t = _evaluate_with_classifier(im, faces, classifier, k, output_dir)
accurate += a
time_spent += t
print("-------------------------------------Accuracy is: {} ".format(accurate / total))
print('==================================================================')
print('Accurate is {}, total: {}'.format(accurate, total))
print('Total time spent is: {}'.format(time_spent))
print("Average time spent is: {}".format(time_spent / total))
print("Average Accuracy is: {} ".format(accurate / total))