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 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 main(samples_dir, output_dir, image_size=160, margin=32):
hierarchy = read_from_hierarchy(samples_dir)
for k in hierarchy.keys():
images_path = hierarchy[k]
image_files = images_in_dir(images_path)
subdir = _subdir(output_dir, k)
start = time.time()
for im_f in image_files:
im = cv2.imread(im_f)
height, width = im.shape[:2]
faces = detect_faces(im)
print('{}, {} faces detected'.format(im_f, len(faces)))
for face in faces:
images_count = len(glob.glob('{}/*.jpg'.format(subdir)))
f_name = '{}_{}.jpg'.format(k, '{0:04d}'.format(images_count))
output_path = os.path.join(subdir, f_name)
x1, y1, x2, y2 = to_rectangle(face)
x1 = max(x1 - margin, 0)
y1 = max(y1 - margin, 0)
x2 = min(x2 + margin, width)
y2 = min(y2 + margin, height)
roi = im[y1:y2, x1:x2, :]
roi = cv2.resize(roi, (image_size, image_size))
cv2.imwrite(output_path, roi)
print('{} done, time spent: {}'.format(k, time.time() - start))
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))
image_files = images_in_dir(test_dir)
f_nums = {}
for im_f in image_files:
f_name = filename(im_f)
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)
#
# f_num = len(glob.glob('{}/{}*.jpg'.format(output_dir, class_names[idx])))
# f_name = '{}_{}.jpg'.format(class_names[idx], '{0:04d}'.format(f_num))
#
f_nums.setdefault(
class_names[idx],
len(
glob.glob('{}/{}*.jpg'.format(output_dir,
class_names[idx]))))
f_name = '{}_{}.jpg'.format(
class_names[idx], '{0:04d}'.format(f_nums[class_names[idx]]))
f_nums[class_names[idx]] += 1
# txt = '{}:{}'.format(class_names[idx], distance)
# put_text(im, txt, font_face=cv2.FONT_HERSHEY_SIMPLEX)
print('{}: {}, of distance :{} '.format(im_f, f_name, distance))
end = time.time()
print('time : ', end - start)
output_path = os.path.join(output_dir, f_name)
cv2.imwrite(output_path, im)
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))
image_files = images_in_dir(test_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)
idx, distance = closest_one(face_descriptors, descriptor)
txt = '{}:{}'.format(class_names[idx], distance)
put_text(im, txt, font_face=cv2.FONT_HERSHEY_SIMPLEX)
print('{}: {}, of distance :{} '.format(im_f, class_names[idx],
distance))
cv2.imwrite(output_path, im)