def process(self, img_list, bbox_list, landmarks_list=None):
# For each input image and corresponding landmarks
for i in range(len(img_list)):
if isinstance(img_list[i], (list, tuple)):
if landmarks_list is None:
img_list[i], _ = self.process(img_list[i], bbox_list[i])
else:
img_list[i], landmarks_list[i] = self.process(img_list[i], bbox_list[i], landmarks_list[i])
else:
if self.det_format:
bbox = np.concatenate((bbox_list[i][:2], bbox_list[i][2:] - bbox_list[i][:2]))
else:
bbox = bbox_list[i]
bbox_scaled = scale_bbox(bbox, self.bbox_scale, self.bbox_square)
if landmarks_list is None:
img_list[i] = crop_img(img_list[i], bbox_scaled, border=self.border, value=self.value)
else:
img_list[i], landmarks_list[i] = crop_img(img_list[i], bbox_scaled, landmarks_list[i], self.border,
self.value)
return img_list, landmarks_list
def __call__(self, img, landmarks, bbox):
img = np.array(img).copy()
if self.align:
img, landmarks = align_crop(img, landmarks, bbox, self.bbox_scale,
self.bbox_square)
else:
bbox_scaled = scale_bbox(bbox, self.bbox_scale, self.bbox_square)
img, landmarks = crop_img(img, landmarks, bbox_scaled)
img = Image.fromarray(img)
return img, landmarks, bbox
def main(input_path,
output_dir=None,
cache_path=None,
seq_postfix='_dsfd_seq.pkl',
out_postfix='.jpg',
resolution=256,
crop_scale=1.2):
cache_path = os.path.splitext(
input_path)[0] + seq_postfix if cache_path is None else cache_path
if output_dir is None:
output_dir = os.path.splitext(input_path)[0]
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
# Verification
if not os.path.isfile(input_path):
raise RuntimeError('Input video does not exist: ' + input_path)
if not os.path.isfile(cache_path):
raise RuntimeError('Cache file does not exist: ' + cache_path)
if not os.path.isdir(output_dir):
raise RuntimeError('Output directory does not exist: ' + output_dir)
print('=> Cropping image sequences from image: "%s"...' %
os.path.basename(input_path))
# Load sequences from file
with open(cache_path, "rb") as fp: # Unpickling
seq_list = pickle.load(fp)
# Read image from file
img = cv2.imread(input_path)
if img is None:
raise RuntimeError('Failed to read image: ' + input_path)
# For each sequence
for s, seq in enumerate(seq_list):
det = seq[0]
# Crop image
bbox = np.concatenate((det[:2], det[2:] - det[:2]))
bbox = scale_bbox(bbox, crop_scale)
img_cropped = crop_img(img, bbox)
img_cropped = cv2.resize(img_cropped, (resolution, resolution),
interpolation=cv2.INTER_CUBIC)
# Write cropped image to file
out_img_name = os.path.splitext(os.path.basename(
input_path))[0] + '_seq%02d%s' % (seq.id, out_postfix)
out_img_path = os.path.join(output_dir, out_img_name)
cv2.imwrite(out_img_path, img_cropped)
def __call__(self, x):
"""
Args:
x (numpy.ndarray or list of numpy.ndarray): Image (H x W x C) or pose (3) or bounding box (4)
Returns:
numpy.ndarray or list of numpy.ndarray: Transformed images or poses
"""
if isinstance(x, (list, tuple)):
if len(x) == 2 and is_img(x[0]) and is_bbox(x[1]):
# Found image and bounding box pair
img, bbox = x
if self.det_format:
bbox = np.concatenate((bbox[:2], bbox[2:] - bbox[:2]))
bbox_scaled = scale_bbox(bbox, self.bbox_scale, self.bbox_square)
return crop_img(img, bbox_scaled, border=self.border_id, value=self.value)
else:
return [self.__call__(a) for a in x]
return x
def align_crop(img, landmarks, bbox, scale=2.0, square=True):
right_eye_center = landmarks[36:42, :].mean(axis=0)
left_eye_center = landmarks[42:48, :].mean(axis=0)
eye_center = (right_eye_center + left_eye_center) / 2.0
dy = right_eye_center[1] - left_eye_center[1]
dx = right_eye_center[0] - left_eye_center[0]
angle = np.degrees(np.arctan2(dy, dx)) - 180
M = cv2.getRotationMatrix2D(tuple(eye_center), angle, 1.)
output = cv2.warpAffine(img,
M, (img.shape[1], img.shape[0]),
flags=cv2.INTER_CUBIC)
new_landmarks = np.concatenate((landmarks, np.ones((68, 1))), axis=1)
new_landmarks = new_landmarks.dot(M.transpose())
bbox_scaled = scale_bbox(bbox, scale, square)
output, new_landmarks = crop_img(output, new_landmarks, bbox_scaled)
return output, new_landmarks
def extract_landmarks_bboxes_euler_from_images(img_dir,
face_pose,
face_align=None,
img_size=(224, 224),
scale=1.2,
device=None,
cache_file=None):
if face_align is None:
face_align = face_alignment.FaceAlignment(
face_alignment.LandmarksType._2D, flip_input=True)
cache_file = img_dir + '.pkl' if cache_file is None else cache_file
if not os.path.exists(cache_file):
frame_indices = []
landmarks = []
bboxes = []
eulers = []
img_paths = glob(os.path.join(img_dir, '*.jpg'))
for i, img_path in tqdm(enumerate(img_paths),
unit='images',
total=len(img_paths)):
img_bgr = cv2.imread(img_path)
if img_bgr is None:
continue
img_rgb = img_bgr[:, :, ::-1]
detected_faces = face_align.face_detector.detect_from_image(
img_bgr.copy())
if len(detected_faces) == 0:
continue
curr_bbox = get_main_bbox(
np.array(detected_faces)[:, :4], img_bgr.shape[:2])
detected_faces = [curr_bbox]
preds = face_align.get_landmarks(img_rgb, detected_faces)
curr_landmarks = preds[0]
curr_bbox[2:] = curr_bbox[2:] - curr_bbox[:2] + 1
scaled_bbox = scale_bbox(curr_bbox, scale)
cropped_frame_rgb, cropped_landmarks = crop_img(
img_rgb, curr_landmarks, scaled_bbox)
scaled_frame_rgb = np.array(
F.resize(Image.fromarray(cropped_frame_rgb), img_size,
Image.BICUBIC))
scaled_frame_tensor = rgb2tensor(
scaled_frame_rgb.copy()).to(device)
curr_euler = face_pose(scaled_frame_tensor)
curr_euler = np.array([x.cpu().numpy() for x in curr_euler])
frame_indices.append(i)
landmarks.append(curr_landmarks)
bboxes.append(curr_bbox)
eulers.append(curr_euler)
frame_indices = np.array(frame_indices)
landmarks = np.array(landmarks)
bboxes = np.array(bboxes)
eulers = np.array(eulers)
with open(cache_file, "wb") as fp:
pickle.dump(frame_indices, fp)
pickle.dump(landmarks, fp)
pickle.dump(bboxes, fp)
pickle.dump(eulers, fp)
else:
with open(cache_file, "rb") as fp:
frame_indices = pickle.load(fp)
landmarks = pickle.load(fp)
bboxes = pickle.load(fp)
eulers = pickle.load(fp)
return frame_indices, landmarks, bboxes, eulers
def main(input_path,
output_dir=None,
cache_path=None,
seq_postfix='_dsfd_seq.pkl',
resolution=256,
crop_scale=2.0,
select='all',
disable_tqdm=False):
cache_path = os.path.splitext(
input_path)[0] + seq_postfix if cache_path is None else cache_path
if output_dir is None:
output_dir = os.path.splitext(input_path)[0]
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
# Verification
if not os.path.isfile(input_path):
raise RuntimeError('Input video does not exist: ' + input_path)
if not os.path.isfile(cache_path):
raise RuntimeError('Cache file does not exist: ' + cache_path)
if not os.path.isdir(output_dir):
raise RuntimeError('Output directory does not exist: ' + output_dir)
print('=> Cropping video sequences from video: "%s"...' %
os.path.basename(input_path))
# Load sequences from file
with open(cache_path, "rb") as fp: # Unpickling
seq_list = pickle.load(fp)
# Select sequences
if select == 'longest':
selected_seq_index = np.argmax([len(s) for s in seq_list])
seq = seq_list[selected_seq_index]
seq.id = 0
seq_list = [seq]
# Open input video file
cap = cv2.VideoCapture(input_path)
if not cap.isOpened():
raise RuntimeError('Failed to read video: ' + input_path)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = cap.get(cv2.CAP_PROP_FPS)
input_vid_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
input_vid_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# For each sequence initialize output video file
out_vids = []
fourcc = cv2.VideoWriter_fourcc(*'avc1')
for seq in seq_list:
curr_vid_name = os.path.splitext(
os.path.basename(input_path))[0] + '_seq%02d.mp4' % seq.id
curr_vid_path = os.path.join(output_dir, curr_vid_name)
out_vids.append(
cv2.VideoWriter(curr_vid_path, fourcc, fps,
(resolution, resolution)))
# For each frame in the target video
cropped_detections = [[] for seq in seq_list]
cropped_landmarks = [[] for seq in seq_list]
pbar = range(total_frames) if disable_tqdm else tqdm(range(total_frames))
for i in pbar:
ret, frame = cap.read()
if frame is None:
continue
# For each sequence
for s, seq in enumerate(seq_list):
if i < seq.start_index or (seq.start_index + len(seq) - 1) < i:
continue
det = seq[i - seq.start_index]
# Crop frame
bbox = np.concatenate((det[:2], det[2:] - det[:2]))
bbox = scale_bbox(bbox, crop_scale)
frame_cropped = crop_img(frame, bbox)
frame_cropped = cv2.resize(frame_cropped, (resolution, resolution),
interpolation=cv2.INTER_CUBIC)
# Write cropped frame to output video
out_vids[s].write(frame_cropped)
# Add cropped detection to list
orig_size = bbox[2:]
axes_scale = np.array([resolution, resolution]) / orig_size
det[:2] -= bbox[:2]
det[2:] -= bbox[:2]
det[:2] *= axes_scale
det[2:] *= axes_scale
cropped_detections[s].append(det)
# Add cropped landmarks to list
if hasattr(seq, 'landmarks'):
curr_landmarks = seq.landmarks[i - seq.start_index]
curr_landmarks[:, :2] -= bbox[:2]
# 3D landmarks case
if curr_landmarks.shape[1] == 3:
axes_scale = np.append(axes_scale, axes_scale.mean())
curr_landmarks *= axes_scale
cropped_landmarks[s].append(curr_landmarks)
# For each sequence write cropped sequence to file
for s, seq in enumerate(seq_list):
# seq.detections = np.array(cropped_detections[s])
# if hasattr(seq, 'landmarks'):
# seq.landmarks = np.array(cropped_landmarks[s])
# seq.start_index = 0
# TODO: this is a hack to change class type (remove this later)
out_seq = Sequence(0)
out_seq.detections = np.array(cropped_detections[s])
if hasattr(seq, 'landmarks'):
out_seq.landmarks = np.array(cropped_landmarks[s])
out_seq.id, out_seq.obj_id, out_seq.size_avg = seq.id, seq.obj_id, seq.size_avg
# Write to file
curr_out_name = os.path.splitext(os.path.basename(
input_path))[0] + '_seq%02d%s' % (out_seq.id, seq_postfix)
curr_out_path = os.path.join(output_dir, curr_out_name)
with open(curr_out_path, "wb") as fp: # Pickling
pickle.dump([out_seq], fp)
def main(input_path,
output_path=None,
seq_postfix='_dsfd_seq.pkl',
output_postfix='_dsfd_seq_lms_euler.pkl',
pose_model_path='weights/hopenet_robust_alpha1.pkl',
smooth_det=False,
smooth_euler=False,
gpus=None,
cpu_only=False,
batch_size=16):
cache_path = os.path.splitext(input_path)[0] + seq_postfix
output_path = os.path.splitext(
input_path)[0] + output_postfix if output_path is None else output_path
# Initialize device
torch.set_grad_enabled(False)
device, gpus = set_device(gpus, not cpu_only)
# Load sequences from file
with open(cache_path, "rb") as fp: # Unpickling
seq_list = pickle.load(fp)
# Load pose model
face_pose = Hopenet().to(device)
checkpoint = torch.load(pose_model_path)
face_pose.load_state_dict(checkpoint)
face_pose.train(False)
# Open input video file
cap = cv2.VideoCapture(input_path)
if not cap.isOpened():
raise RuntimeError('Failed to read video: ' + input_path)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = cap.get(cv2.CAP_PROP_FPS)
input_vid_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
input_vid_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Smooth sequence bounding boxes
if smooth_det:
for seq in seq_list:
seq.smooth()
# For each sequence
total_detections = sum([len(s) for s in seq_list])
pbar = tqdm(range(total_detections), unit='detections')
for seq in seq_list:
euler = []
frame_cropped_tensor_list = []
cap.set(cv2.CAP_PROP_POS_FRAMES, seq.start_index)
# For each detection bounding box in the current sequence
for i, det in enumerate(seq.detections):
ret, frame_bgr = cap.read()
if frame_bgr is None:
raise RuntimeError('Failed to read frame from video!')
frame_rgb = frame_bgr[:, :, ::-1]
# Crop frame
bbox = np.concatenate((det[:2], det[2:] - det[:2]))
bbox = scale_bbox(bbox, 1.2)
frame_cropped_rgb = crop_img(frame_rgb, bbox)
frame_cropped_rgb = cv2.resize(frame_cropped_rgb, (224, 224),
interpolation=cv2.INTER_CUBIC)
frame_cropped_tensor = rgb2tensor(frame_cropped_rgb).to(device)
# Gather batches
frame_cropped_tensor_list.append(frame_cropped_tensor)
if len(frame_cropped_tensor_list) < batch_size and (i +
1) < len(seq):
continue
frame_cropped_tensor_batch = torch.cat(frame_cropped_tensor_list,
dim=0)
# Calculate euler angles
curr_euler_batch = face_pose(
frame_cropped_tensor_batch) # Yaw, Pitch, Roll
curr_euler_batch = curr_euler_batch.cpu().numpy()
# For each prediction in the batch
for b, curr_euler in enumerate(curr_euler_batch):
# Add euler to list
euler.append(curr_euler)
# Render
# render_img = tensor2bgr(frame_cropped_tensor_batch[b]).copy()
# cv2.putText(render_img, '(%.2f, %.2f, %.2f)' % (curr_euler[0], curr_euler[1], curr_euler[2]), (15, 15),
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
# cv2.imshow('render_img', render_img)
# if cv2.waitKey(0) & 0xFF == ord('q'):
# break
# Clear lists
frame_cropped_tensor_list.clear()
pbar.update(len(frame_cropped_tensor_batch))
# Add landmarks to sequence and optionally smooth them
euler = np.array(euler)
if smooth_euler:
euler = smooth(euler)
seq.euler = euler
# Write final sequence list to file
with open(output_path, "wb") as fp: # Pickling
pickle.dump(seq_list, fp)