def find_best_frame_func(self, source, driving):
import face_alignment
def normalize_kp(kp):
kp = kp - kp.mean(axis=0, keepdims=True)
area = ConvexHull(kp[:, :2]).volume
area = np.sqrt(area)
kp[:, :2] = kp[:, :2] / area
return kp
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D,
flip_input=True)
kp_source = fa.get_landmarks(255 * source)[0]
kp_source = normalize_kp(kp_source)
norm = float('inf')
frame_num = 0
for i, image in tqdm(enumerate(driving)):
kp_driving = fa.get_landmarks(255 * image)[0]
kp_driving = normalize_kp(kp_driving)
new_norm = (np.abs(kp_source - kp_driving)**2).sum()
if new_norm < norm:
norm = new_norm
frame_num = i
return frame_num
def make_animation(self,
source_image,
driving_video,
generator,
kp_detector,
relative=True,
adapt_movement_scale=True):
with paddle.no_grad():
predictions = []
source = paddle.to_tensor(source_image[np.newaxis].astype(
np.float32)).transpose([0, 3, 1, 2])
driving = paddle.to_tensor(
np.array(driving_video)[np.newaxis].astype(
np.float32)).transpose([0, 4, 1, 2, 3])
kp_source = kp_detector(source)
kp_driving_initial = kp_detector(driving[:, :, 0])
for frame_idx in tqdm(range(driving.shape[2])):
driving_frame = driving[:, :, frame_idx]
kp_driving = kp_detector(driving_frame)
kp_norm = normalize_kp(
kp_source=kp_source,
kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial,
use_relative_movement=relative,
use_relative_jacobian=relative,
adapt_movement_scale=adapt_movement_scale)
out = generator(source,
kp_source=kp_source,
kp_driving=kp_norm)
predictions.append(
np.transpose(out['prediction'].numpy(), [0, 2, 3, 1])[0])
return predictions
def make_animation(self,
source_image,
driving_video,
generator,
kp_detector,
relative=True,
adapt_movement_scale=True):
with paddle.no_grad():
predictions = []
source = paddle.to_tensor(source_image[np.newaxis].astype(
np.float32)).transpose([0, 3, 1, 2])
driving = paddle.to_tensor(
np.array(driving_video).astype(np.float32)).transpose(
[0, 3, 1, 2])
kp_source = kp_detector(source)
kp_driving_initial = kp_detector(driving[0:1])
kp_source_batch = {}
kp_source_batch["value"] = paddle.tile(
kp_source["value"], repeat_times=[self.batch_size, 1, 1])
kp_source_batch["jacobian"] = paddle.tile(
kp_source["jacobian"], repeat_times=[self.batch_size, 1, 1, 1])
source = paddle.tile(source,
repeat_times=[self.batch_size, 1, 1, 1])
begin_idx = 0
for frame_idx in tqdm(
range(
int(np.ceil(float(driving.shape[0]) /
self.batch_size)))):
frame_num = min(self.batch_size, driving.shape[0] - begin_idx)
driving_frame = driving[begin_idx:begin_idx + frame_num]
kp_driving = kp_detector(driving_frame)
kp_source_img = {}
kp_source_img["value"] = kp_source_batch["value"][0:frame_num]
kp_source_img["jacobian"] = kp_source_batch["jacobian"][
0:frame_num]
kp_norm = normalize_kp(
kp_source=kp_source,
kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial,
use_relative_movement=relative,
use_relative_jacobian=relative,
adapt_movement_scale=adapt_movement_scale)
out = generator(source[0:frame_num],
kp_source=kp_source_img,
kp_driving=kp_norm)
img = np.transpose(out['prediction'].numpy(),
[0, 2, 3, 1]) * 255.0
if self.face_enhancement:
img = self.faceenhancer.enhance_from_batch(img)
predictions.append(img)
begin_idx += frame_num
return np.concatenate(predictions)
