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 process_one_image(image_file, output_dir):
start = time.time()
im = cv2.imread(image_file)
file_name = filename(image_file)
persons, bounding_boxes, heat_map = predict(im)
# visualization(im, persons, bounding_boxes, heat_map)
save_results(im, bounding_boxes, output_dir, file_name)
print('{} done, time spent: {}'.format(file_name, time.time() - start))
def predict(images_dir):
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
image_files = images_in_dir(images_dir)
for im_f in image_files:
f_name = filename(im_f)
im = cv2.imread(im_f)
face = extract(im_f)
results = find_top_n_closest(face)
im = overlay(im, results)
cv2.imwrite(os.path.join(OUTPUT_DIR, f_name), im)
def main(images_dir, output_dir):
image_files = images_in_dir(images_dir=images_dir)
for im_f in image_files:
im = cv2.imread(im_f)
start = time.time()
faces = detect_faces(im)
end = time.time()
print('{}: {}'.format(im_f, end - start))
for face in faces:
x1, y1, x2, y2 = to_rectangle(face)
cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.imwrite(os.path.join(output_dir, filename(im_f)), 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)
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)
args = parser.parse_args()
input_images = args.images
outputs = args.output
keras_weights_file = args.model
print('start processing...')
# load model
# authors of original model don't use
# vgg normalization (subtracting mean) on input images
model = get_testing_model()
model.load_weights(keras_weights_file)
# load config
params, model_params = config_reader()
print('params:\n {}'.format(params))
print('model_params:\n {}'.format(model_params))
# generate image with body parts
image_files = images_in_dir(input_images)
for im_f in image_files:
tic = time.time()
canvas = process(im_f, params, model_params)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
cv2.imwrite(os.path.join(outputs, filename(im_f)), canvas)
cv2.destroyAllWindows()