def __init__(self, device):
self.net = BlazeFace().to(device)
self.net.load_weights("blazeface.pth")
self.net.load_anchors("anchors.npy")
self.mouth_region_size = (64,64)
self.img_dims = (128, 128)
def __init__(self, blaze_weight, anchors, scale: float = 1.0):
super().__init__()
face_detector = BlazeFace().to(device)
face_detector.load_weights(blaze_weight)
face_detector.load_anchors(anchors)
_ = face_detector.train(False)
self.extractor = FaceExtractor(face_detector, margin=scale - 1)
def test_export_onnx(tmpdir):
p = str(tmpdir.join('blazeface.onnx'))
x = torch.randn(1, 3, 128, 128)
model = BlazeFace()
torch.onnx.export(model,
x,
p,
verbose=True,
input_names=['input'],
output_names=['output'])
def detect(self):
model = BlazeFace()
model.load_weights(self.model_weights)
model.load_anchors(self.model_anchors)
# img = cv2.cvtColor(cv2.imread(self.img_path), cv2.COLOR_BGR2RGB)
# img_res = cv2.resize(img, (self.img_size, self.img_size))
img_res = cv2.resize(self.img_arr, (self.img_size, self.img_size))
results = model.predict_on_image(img_res)
self.detections = results
return results
def main():
# Here we check the train data files extensions.
train_list = list(os.listdir(os.path.join(DATA_FOLDER, TRAIN_SAMPLE_FOLDER)))
ext_dict = []
for file in train_list:
file_ext = file.split('.')[1]
if (file_ext not in ext_dict):
ext_dict.append(file_ext)
print(f"Extensions: {ext_dict}")
# Let's count how many files with each extensions there are.
for file_ext in ext_dict:
print(
f"Files with extension `{file_ext}`: {len([file for file in train_list if file.endswith(file_ext)])}")
test_list = list(os.listdir(os.path.join(DATA_FOLDER, TEST_FOLDER)))
ext_dict = []
for file in test_list:
file_ext = file.split('.')[1]
if (file_ext not in ext_dict):
ext_dict.append(file_ext)
print(f"Extensions: {ext_dict}")
for file_ext in ext_dict:
print(
f"Files with extension `{file_ext}`: {len([file for file in train_list if file.endswith(file_ext)])}")
json_file = [file for file in train_list if file.endswith('json')][0]
print(f"JSON file: {json_file}")
meta_train_df = get_meta_from_json(TRAIN_SAMPLE_FOLDER, json_file)
meta_train_df.head()
fake_train_sample_video = list(
meta_train_df.loc[meta_train_df.label == 'FAKE'].sample(3).index)
real_train_sample_video = list(
meta_train_df.loc[meta_train_df.label == 'REAL'].sample(3).index)
print("PyTorch version:", torch.__version__)
print("CUDA version:", torch.version.cuda)
print("cuDNN version:", torch.backends.cudnn.version())
gpu = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(gpu)
net=BlazeFace().to(gpu)
net.load_weights("../input/blazeface.pth")
net.load_anchors("../input/anchors.npy")
for video_file in fake_train_sample_video:
get_frame_faces(os.path.join(
DATA_FOLDER, TRAIN_SAMPLE_FOLDER, video_file), net)
def process_video(video_path, filename, image_path, original):
gpu = torch.device("cuda" if torch.cuda.is_available() else "cpu")
facedet = BlazeFace().to(gpu)
facedet.load_weights("blazeface.pth")
facedet.load_anchors("anchors.npy")
_ = facedet.train(False)
from helpers_read_video_1 import VideoReader
from helpers_face_extract_1 import FaceExtractor
frames_per_video = 10
video_reader = VideoReader()
video_read_fn = lambda x: video_reader.read_random_frames(
x, num_frames=frames_per_video)
face_extractor = FaceExtractor(video_read_fn, facedet)
faces = face_extractor.process_video(video_path)
# Only look at one face per frame.
face_extractor.keep_only_best_face(faces)
n = 0
for frame_data in faces:
for face in frame_data["faces"]:
face_locations = face_recognition.face_locations(face)
for face_location in face_locations:
top, right, bottom, left = face_location
face_image = face[top:bottom, left:right]
resized_face = cv2.resize(face_image, (224, 224),
interpolation=cv2.INTER_AREA)
resized_face = cv2.cvtColor(resized_face, cv2.COLOR_RGB2BGR)
cv2.imwrite(
image_path + "/" + filename[:-4] + original + "_" +
str(n) + ".jpg", resized_face,
[int(cv2.IMWRITE_JPEG_QUALITY), 85])
n += 1
def _load_face_extractor(self):
"""
Init and Return the face extractor object (implemented in deepfakes-inference-demo/helpers/face_extract_1)
that consists of a video reader function and a facedetector
"""
import sys
sys.path.insert(0, os.path.join(self.root_path, "blazeface-pytorch"))
sys.path.insert(
0, os.path.join(self.root_path, "deepfakes-inference-demo"))
#Load the face detection model BlazeFace, based on https://github.com/tkat0/PyTorch_BlazeFace/
from blazeface import BlazeFace
facedet = BlazeFace().to(self.gpu)
#Load the pretrained weights
facedet.load_weights(
os.path.join(self.root_path, "blazeface-pytorch/blazeface.pth"))
facedet.load_anchors(
os.path.join(self.root_path, "blazeface-pytorch/anchors.npy"))
#Set the module in evaluation mode
_ = facedet.train(False)
from helpers.read_video_1 import VideoReader
from helpers.face_extract_1 import FaceExtractor
#set number of frames to be read from the video, taken regulary from the beggining to the end of the video
self.frames_per_video = 17
#init video reader
video_reader = VideoReader()
#create a lambda function to read the frames where x is the video path
video_read_fn = lambda x: video_reader.read_frames(
x, num_frames=self.frames_per_video)
#init the face extractor with the video reader function and the facedetector
face_extractor = FaceExtractor(video_read_fn, facedet)
return face_extractor
for k in range(6):
kp_x = detections[i, 4 + k * 2] * img.shape[1]
kp_y = detections[i, 4 + k * 2 + 1] * img.shape[0]
circle = patches.Circle((kp_x, kp_y),
radius=0.5,
linewidth=1,
edgecolor="lightskyblue",
facecolor="none",
alpha=detections[i, 16])
ax.add_patch(circle)
plt.show()
from blazeface import BlazeFace
net = BlazeFace().to(gpu)
net.load_weights("blazeface.pth")
net.load_anchors("anchors.npy")
# Optionally change the thresholds:
net.min_score_thresh = 0.75
net.min_suppression_threshold = 0.3
img = cv2.imread("test.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
detections = net.predict_on_image(img)
detections.shape
plot_detections(img, detections)
def main(argv):
args = parse_args(argv)
## Parameters parsing
device: torch.device = args.device
source_dir: Path = args.source
facedestination_dir: Path = args.facesfolder
frames_per_video: int = args.fpv
videodataset_path: Path = args.videodf
facesdataset_path: Path = args.facesdf
collateonly: bool = args.collateonly
batch_size: int = args.batch
threads: int = args.threads
offset: int = args.offset
num: int = args.num
lazycheck: bool = args.lazycheck
deepcheck: bool = args.deepcheck
checkpoint_folder: Path = args.checkpoint
index_enable: bool = args.noindex
## Parameters
face_size = 512
print('Loading video DataFrame')
df_videos = pd.read_pickle(videodataset_path)
if num > 0:
df_videos_process = df_videos.iloc[offset:offset + num]
else:
df_videos_process = df_videos.iloc[offset:]
if not collateonly:
## Blazeface loading
print('Loading face extractor')
facedet = BlazeFace().to(device)
facedet.load_weights("blazeface/blazeface.pth")
facedet.load_anchors("blazeface/anchors.npy")
videoreader = VideoReader(verbose=False)
video_read_fn = lambda x: videoreader.read_frames(x, num_frames=frames_per_video)
face_extractor = FaceExtractor(video_read_fn, facedet)
## Face extraction
with ThreadPoolExecutor(threads) as p:
for batch_idx0 in tqdm(np.arange(start=0, stop=len(df_videos_process), step=batch_size),
desc='Extracting faces'):
tosave_list = list(p.map(partial(process_video,
source_dir=source_dir,
facedestination_dir=facedestination_dir,
checkpoint_folder=checkpoint_folder,
face_size=face_size,
face_extractor=face_extractor,
lazycheck=lazycheck,
deepcheck=deepcheck,
),
df_videos_process.iloc[batch_idx0:batch_idx0 + batch_size].iterrows()))
for tosave in tosave_list:
if tosave is not None:
if len(tosave[2]):
list(p.map(save_jpg, tosave[2]))
tosave[1].parent.mkdir(parents=True, exist_ok=True)
tosave[0].to_pickle(str(tosave[1]))
if index_enable:
# Collect checkpoints
df_videos['nfaces'] = np.zeros(len(df_videos), np.uint8)
faces_dataset = []
for idx, record in tqdm(df_videos.iterrows(), total=len(df_videos), desc='Collecting faces results'):
# Checkpoint
video_face_checkpoint_path = checkpoint_folder.joinpath(record['path']).with_suffix('.faces.pkl')
if video_face_checkpoint_path.exists():
try:
df_video_faces = pd.read_pickle(str(video_face_checkpoint_path))
# Fix same attribute issue
df_video_faces = df_video_faces.rename(columns={'subject': 'videosubject'}, errors='ignore')
nfaces = len(
np.unique(df_video_faces.index.map(lambda x: int(x.split('_subj')[1].split('.jpg')[0]))))
df_videos.loc[idx, 'nfaces'] = nfaces
faces_dataset.append(df_video_faces)
except Exception as e:
print('Error while reading: {}'.format(video_face_checkpoint_path))
print(e)
video_face_checkpoint_path.unlink()
if len(faces_dataset) == 0:
raise ValueError(f'No checkpoint found from face extraction. '
f'Is the the source path {source_dir} correct for the videos in your dataframe?')
# Save videos with updated faces
print('Saving videos DataFrame to {}'.format(videodataset_path))
df_videos.to_pickle(str(videodataset_path))
if offset > 0:
if num > 0:
if facesdataset_path.is_dir():
facesdataset_path = facesdataset_path.joinpath(
'faces_df_from_video_{}_to_video_{}.pkl'.format(offset, num + offset))
else:
facesdataset_path = facesdataset_path.parent.joinpath(
str(facesdataset_path.parts[-1]).split('.')[0] + '_from_video_{}_to_video_{}.pkl'.format(offset,
num + offset))
else:
if facesdataset_path.is_dir():
facesdataset_path = facesdataset_path.joinpath('faces_df_from_video_{}.pkl'.format(offset))
else:
facesdataset_path = facesdataset_path.parent.joinpath(
str(facesdataset_path.parts[-1]).split('.')[0] + '_from_video_{}.pkl'.format(offset))
elif num > 0:
if facesdataset_path.is_dir():
facesdataset_path = facesdataset_path.joinpath(
'faces_df_from_video_{}_to_video_{}.pkl'.format(0, num))
else:
facesdataset_path = facesdataset_path.parent.joinpath(
str(facesdataset_path.parts[-1]).split('.')[0] + '_from_video_{}_to_video_{}.pkl'.format(0, num))
else:
if facesdataset_path.is_dir():
facesdataset_path = facesdataset_path.joinpath('faces_df.pkl') # just a check if the path is a dir
# Creates directory (if doesn't exist)
facesdataset_path.parent.mkdir(parents=True, exist_ok=True)
print('Saving faces DataFrame to {}'.format(facesdataset_path))
df_faces = pd.concat(faces_dataset, axis=0, )
df_faces['video'] = df_faces['video'].astype('category')
for key in ['kp1x', 'kp1y', 'kp2x', 'kp2y', 'kp3x',
'kp3y', 'kp4x', 'kp4y', 'kp5x', 'kp5y', 'kp6x', 'kp6y', 'left',
'top', 'right', 'bottom', ]:
df_faces[key] = df_faces[key].astype(np.int16)
df_faces['videosubject'] = df_faces['videosubject'].astype(np.int8)
# Eventually remove duplicates
df_faces = df_faces.loc[~df_faces.index.duplicated(keep='first')]
fields_to_preserve_from_video = [i for i in ['folder', 'subject', 'scene', 'cluster', 'nfaces'] if
i in df_videos]
df_faces = pd.merge(df_faces, df_videos[fields_to_preserve_from_video], left_on='video',
right_index=True)
df_faces.to_pickle(str(facesdataset_path))
print('Completed!')
def test_forward():
x = torch.randn(1, 3, 128, 128)
model = BlazeFace()
h = model(x)
assert h.detach().numpy().shape == (1, 96, 8, 8)
import sys
from blazebase import resize_pad, denormalize_detections
from blazeface import BlazeFace
from blazepalm import BlazePalm
from blazeface_landmark import BlazeFaceLandmark
from blazehand_landmark import BlazeHandLandmark
from visualization import draw_detections, draw_landmarks, draw_roi, HAND_CONNECTIONS, FACE_CONNECTIONS
gpu = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
torch.set_grad_enabled(False)
back_detector = True
face_detector = BlazeFace(back_model=back_detector).to(gpu)
if back_detector:
face_detector.load_weights("blazefaceback.pth")
face_detector.load_anchors("anchors_face_back.npy")
else:
face_detector.load_weights("blazeface.pth")
face_detector.load_anchors("anchors_face.npy")
palm_detector = BlazePalm().to(gpu)
palm_detector.load_weights("blazepalm.pth")
palm_detector.load_anchors("anchors_palm.npy")
palm_detector.min_score_thresh = .75
hand_regressor = BlazeHandLandmark().to(gpu)
hand_regressor.load_weights("blazehand_landmark.pth")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--source',
type=Path,
help='Videos root directory',
required=True)
parser.add_argument('--videodf',
type=Path,
help='Path to read the videos DataFrame')
parser.add_argument('--facesfolder',
type=Path,
help='Faces output root directory',
required=True)
parser.add_argument('--facesdf',
type=Path,
help='Path to save the output DataFrame of faces',
required=True)
parser.add_argument('--checkpoint',
type=Path,
help='Path to save the temporary per-video outputs',
required=True)
parser.add_argument('--fpv', type=int, default=32, help='Frames per video')
parser.add_argument(
'--device',
type=torch.device,
default=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'),
help='Device to use for face extraction')
parser.add_argument('--collateonly',
help='Only perform collation of pre-existing results',
action='store_true')
parser.add_argument('--noindex',
help='Do not rebuild the index',
action='store_false')
parser.add_argument('--batch', type=int, help='Batch size', default=16)
parser.add_argument('--threads',
type=int,
help='Number of threads',
default=8)
parser.add_argument('--offset',
type=int,
help='Offset to start extraction',
default=0)
parser.add_argument('--num',
type=int,
help='Number of videos to process',
default=0)
parser.add_argument('--lazycheck',
action='store_true',
help='Lazy check of existing video indexes')
parser.add_argument('--deepcheck',
action='store_true',
help='Try to open every image')
args = parser.parse_args()
## Parameters parsing
device: torch.device = args.device
source_dir: Path = args.source
facedestination_dir: Path = args.facesfolder
frames_per_video: int = args.fpv
videodataset_path: Path = args.videodf
facesdataset_path: Path = args.facesdf
collateonly: bool = args.collateonly
batch_size: int = args.batch
threads: int = args.threads
offset: int = args.offset
num: int = args.num
lazycheck: bool = args.lazycheck
deepcheck: bool = args.deepcheck
checkpoint_folder: Path = args.checkpoint
index_enable: bool = args.noindex
## Parameters
face_size = 512
print('Loading video DataFrame')
df_videos = pd.read_pickle(videodataset_path)
if num > 0:
df_videos_process = df_videos.iloc[offset:offset + num]
else:
df_videos_process = df_videos.iloc[offset:]
if not collateonly:
## Blazeface loading
print('Loading face extractor')
facedet = BlazeFace().to(device)
facedet.load_weights("blazeface/blazeface.pth")
facedet.load_anchors("blazeface/anchors.npy")
videoreader = VideoReader(verbose=False)
video_read_fn = lambda x: videoreader.read_frames(
x, num_frames=frames_per_video)
face_extractor = FaceExtractor(video_read_fn, facedet)
## Face extraction
with ThreadPoolExecutor(threads) as p:
for batch_idx0 in tqdm(np.arange(start=0,
stop=len(df_videos_process),
step=batch_size),
desc='Extracting faces'):
tosave_list = list(
p.map(
partial(
process_video,
source_dir=source_dir,
facedestination_dir=facedestination_dir,
checkpoint_folder=checkpoint_folder,
face_size=face_size,
face_extractor=face_extractor,
lazycheck=lazycheck,
deepcheck=deepcheck,
), df_videos_process.iloc[batch_idx0:batch_idx0 +
batch_size].iterrows()))
for tosave in tosave_list:
if tosave is not None:
if len(tosave[2]):
list(p.map(save_jpg, tosave[2]))
tosave[1].parent.mkdir(parents=True, exist_ok=True)
tosave[0].to_pickle(str(tosave[1]))
if index_enable:
# Collect checkpoints
df_videos['nfaces'] = np.zeros(len(df_videos), np.uint8)
faces_dataset = []
for idx, record in tqdm(df_videos.iterrows(),
total=len(df_videos),
desc='Collecting faces results'):
# Checkpoint
video_face_checkpoint_path = checkpoint_folder.joinpath(
record['path']).with_suffix('.faces.pkl')
if video_face_checkpoint_path.exists():
try:
df_video_faces = pd.read_pickle(
str(video_face_checkpoint_path))
# Fix same attribute issue
df_video_faces = df_video_faces.rename(
columns={'subject': 'videosubject'}, errors='ignore')
nfaces = len(
np.unique(
df_video_faces.index.map(lambda x: int(
x.split('_subj')[1].split('.jpg')[0]))))
df_videos.loc[idx, 'nfaces'] = nfaces
faces_dataset.append(df_video_faces)
except Exception as e:
print('Error while reading: {}'.format(
video_face_checkpoint_path))
print(e)
video_face_checkpoint_path.unlink()
# Save videos with updated faces
print('Saving videos DataFrame to {}'.format(videodataset_path))
df_videos.to_pickle(str(videodataset_path))
if offset is not None:
if num is not None:
facesdataset_path = facesdataset_path.parent.joinpath(
str(facesdataset_path.parts[-1]).split('.')[0] +
'_from_video_{}_to_video_{}.pkl'.format(
offset, num + offset))
else:
facesdataset_path = facesdataset_path.parent.joinpath(
str(facesdataset_path.parts[-1]).split('.')[0] +
'_from_video_{}.pkl'.format(offset))
elif num is not None:
facesdataset_path = facesdataset_path.parent.joinpath(
str(facesdataset_path.parts[-1]).split('.')[0] +
'_from_video_{}_to_video_{}.pkl'.format(0, num))
# Creates directory (if doesn't exist)
facesdataset_path.parent.mkdir(parents=True, exist_ok=True)
print('Saving faces DataFrame to {}'.format(facesdataset_path))
df_faces = pd.concat(
faces_dataset,
axis=0,
)
df_faces['video'] = df_faces['video'].astype('category')
for key in [
'kp1x',
'kp1y',
'kp2x',
'kp2y',
'kp3x',
'kp3y',
'kp4x',
'kp4y',
'kp5x',
'kp5y',
'kp6x',
'kp6y',
'left',
'top',
'right',
'bottom',
]:
df_faces[key] = df_faces[key].astype(np.int16)
df_faces['videosubject'] = df_faces['videosubject'].astype(np.int8)
# Eventually remove duplicates
df_faces = df_faces.loc[~df_faces.index.duplicated(keep='first')]
fields_to_preserve_from_video = [
i for i in ['folder', 'subject', 'scene', 'cluster', 'nfaces']
if i in df_videos
]
df_faces = pd.merge(df_faces,
df_videos[fields_to_preserve_from_video],
left_on='video',
right_index=True)
df_faces.to_pickle(str(facesdataset_path))
print('Completed!')
with ThreadPoolExecutor(max_workers=num_workers) as ex:
meta = tqdm(ex.map(process_file, range(len(df))), total=len(df))
return pd.DataFrame(meta)
if __name__ == '__main__':
args = parser.parse_args()
assert args.df is not None, 'Need to specify metadata file'
with open(args.config) as f:
config = yaml.load(f)
device = torch.device('cuda:{}'.format(args.gpu))
df = pd.read_csv(args.df)
path = config['data_path']
# Facedet
facedet = BlazeFace().to(device)
facedet.load_weights("./dfdet/BlazeFace/blazeface.pth")
facedet.load_anchors("./dfdet/BlazeFace/anchors.npy")
_ = facedet.train(False)
#
video_reader = VideoReader()
def video_read_fn(x):
return video_reader.read_frames(x, num_frames=config['n_frames'])
face_extractor = FaceExtractor(video_read_fn, facedet)
faces_dataframe = preprocess_on_video_set(df, 4)
faces_dataframe.to_csv('{}/faces_metadata.csv'.format(config['out_path']))
class MouthDetector():
def __init__(self, device):
self.net = BlazeFace().to(device)
self.net.load_weights("blazeface.pth")
self.net.load_anchors("anchors.npy")
self.mouth_region_size = (64,64)
self.img_dims = (128, 128)
def plot_detections(self, img, detections, with_keypoints=True):
fig, ax = plt.subplots(1, figsize=(10, 10))
ax.grid(False)
ax.imshow(img/255.)
if isinstance(detections, torch.Tensor):
detections = detections.cpu().numpy()
if detections.ndim == 1:
detections = np.expand_dims(detections, axis=0)
print("Found %d faces" % detections.shape[0])
for i in range(detections.shape[0]):
ymin = detections[i, 0] * img.shape[0]
xmin = detections[i, 1] * img.shape[1]
ymax = detections[i, 2] * img.shape[0]
xmax = detections[i, 3] * img.shape[1]
rect = patches.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
linewidth=1, edgecolor="r", facecolor="none",
alpha=detections[i, 16])
ax.add_patch(rect)
print(ymin, ymax, xmin, xmax)
if with_keypoints:
for k in range(2,3):
kp_x = detections[i, 4 + k*2 ] * img.shape[1]
kp_y = detections[i, 4 + k*2 + 1] * img.shape[0]
circle = patches.Circle((kp_x, kp_y), radius=0.5, linewidth=1,
edgecolor="lightskyblue", facecolor="none",
alpha=detections[i, 16])
ax.add_patch(circle)
plt.show()
def mouth_detection(self, img, detections, with_keypoints=True, img_dims=(128,128)):
# fig, ax = plt.subplots(1, figsize=(10, 10))
# ax.grid(False)
# ax.imshow(img/255.)
if isinstance(detections, torch.Tensor):
detections = detections.cpu().numpy()
if detections.ndim == 1:
detections = np.expand_dims(detections, axis=0)
print("Found %d faces" % detections.shape[0])
i = 0 # first face detection
k = 2 # nose keypoint
# for i in range(detections.shape[0]): #for all faces
ymin = detections[i, 0] * img_dims[0]
xmin = detections[i, 1] * img_dims[1]
ymax = detections[i, 2] * img_dims[0]
xmax = detections[i, 3] * img_dims[1]
# print(xmin, xmax, ymin, ymax)
# for k in range(2,3): #for all keypoints
kp_x = detections[i, 4 + k*2 ] * img_dims[1]
kp_y = detections[i, 4 + k*2 + 1] * img_dims[0]
print('########')
print(kp_y, kp_x)
mouth_region = img[int(kp_y):int(ymax), int(xmin):int(xmax)]
return mouth_region
def batch_mouth_detection(self, frames, detections, with_keypoints=True, img_dims= (128, 128)):
"""
return mouth regions for a batch of frames along with status if any frame was skipped while keypoint finding
mouth_regions: mouth rois
flag: boolean if a frame is skipped ; True if a frame is skipped else False
"""
resize_frames = []
for frame in frames:
if frame.shape[0] !=self.img_dims[0] or frame.shape[1] != self.img_dims[1]:
frame = resize(frame, self.img_dims)
resize_frames.append(frame)
else:
resize_frames.append(frame)
# print(len(resize_frames))
# frames = torch.from_numpy(np.array(resize_frames))
# if isinstance(detections, torch.Tensor):
# detections = detections.cpu().numpy()
# if len(detections) == 2:
# detections = np.expand_dims(detections, axis=1)
# print("Found %d faces" % detections.shape[0])
i = 0 # first face detection
k = 2 # nose keypoint
# for i in range(detections.shape[0]): #for all faces
# print(len(detections))
# print('########')
# print(kp_y, kp_x)
mouth_regions = []
for index, img in enumerate(frames):
if len(detections[index]) > 0:
try:
ymin = detections[index][i, 0] * img_dims[0]
xmin = detections[index][i, 1] * img_dims[1]
ymax = detections[index][i, 2] * img_dims[0]
xmax = detections[index][i, 3] * img_dims[1]
# print(xmin, xmax, ymin, ymax)
# for k in range(2,3): #for all keypoints
kp_x = detections[index][i, 4 + k*2 ] * img_dims[1]
kp_y = detections[index][i, 4 + k*2 + 1] * img_dims[0]
mouth_region = img[int(kp_y):int(ymax), int(xmin):int(xmax)]
mouth_regions.append(resize(mouth_region.cpu().numpy(), self.mouth_region_size))
except IndexError:
flag = True
break
else:
flag = True
if len(frames) == len(mouth_regions):
flag = False
else:
flag = True
# print(len(mouth_region_size))
return np.array(mouth_regions), flag
########################## old code
# class VideoHandler(object):
# def __init__(self, filepaths):
# self.paths = filepaths
# self.mp4_filenames = filepaths
# self.blaze_detector = MouthDetector()
# # self.mouth_extractor = FaceROIExtractor()
# def read_video_audio_dyn(self, video_path):
# # print(video_path, audio_path)
# clip = VideoFileClip(video_path, verbose=False)
# video_frames = torch.FloatTensor(list(clip.iter_frames()))
# # video_frames = torch.FloatTensor(list(imageio.get_reader(video_path, 'ffmpeg')))
# # waveform, sample_rate = torchaudio.load(audio_path)
# waveform = torch.from_numpy(
# clip.audio.to_soundarray()).float().permute(1, 0)
# specgram = torchaudio.transforms.MelSpectrogram()(waveform)
# return specgram, video_frames
# def read_video_audio_blaze_roi(self, video_path, frame_len, subdir='train', audio_path=None):
# frames = []
# mouth_frames = []
# mouth_indices = []
# video_frames = []
# cap = cv2.VideoCapture(video_path)
# frame_counter = 0
# while cap.isOpened():
# ret, frame = cap.read()
# if ret:
# frame = resize(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), (256,256))*255
# frames.append(frame.astype(np.uint8))
# frame_counter += 1
# else:
# break
# if frame_counter == 31: # frame length
# break
# cap.release()
# frames = np.array(frames)
# frames = (frames - frames.min()) / (frames.max() - frames.min())
# img = torch.from_numpy(frames).permute(0,3,1,2) * 255
# detections = self.blaze_detector.net.predict_on_batch(img)
# mouth_regions, flag = self.blaze_detector.batch_mouth_detection(img.permute(0,2,3,1), detections)
# if not flag:
# mouth_regions /= 255.
# return frames, mouth_regions, flag
# class MouthDetector():
# def __init__(self):
# self.net = BlazeFace().to(device)
# self.net.load_weights("blazeface.pth")
# self.net.load_anchors("anchors.npy")
# self.mouth_region_size = (64,64)
# self.img_dims = (128, 128)
# def plot_detections(self, img, detections, with_keypoints=True):
# fig, ax = plt.subplots(1, figsize=(10, 10))
# ax.grid(False)
# ax.imshow(img/255.)
# if isinstance(detections, torch.Tensor):
# detections = detections.cpu().numpy()
# if detections.ndim == 1:
# detections = np.expand_dims(detections, axis=0)
# print("Found %d faces" % detections.shape[0])
# for i in range(detections.shape[0]):
# ymin = detections[i, 0] * img.shape[0]
# xmin = detections[i, 1] * img.shape[1]
# ymax = detections[i, 2] * img.shape[0]
# xmax = detections[i, 3] * img.shape[1]
# rect = patches.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
# linewidth=1, edgecolor="r", facecolor="none",
# alpha=detections[i, 16])
# ax.add_patch(rect)
# print(ymin, ymax, xmin, xmax)
# if with_keypoints:
# for k in range(2,3):
# kp_x = detections[i, 4 + k*2 ] * img.shape[1]
# kp_y = detections[i, 4 + k*2 + 1] * img.shape[0]
# circle = patches.Circle((kp_x, kp_y), radius=0.5, linewidth=1,
# edgecolor="lightskyblue", facecolor="none",
# alpha=detections[i, 16])
# ax.add_patch(circle)
# plt.show()
# def mouth_detection(self, img, detections, with_keypoints=True, img_dims=(128,128)):
# # fig, ax = plt.subplots(1, figsize=(10, 10))
# # ax.grid(False)
# # ax.imshow(img/255.)
# if isinstance(detections, torch.Tensor):
# detections = detections.cpu().numpy()
# if detections.ndim == 1:
# detections = np.expand_dims(detections, axis=0)
# print("Found %d faces" % detections.shape[0])
# i = 0 # first face detection
# k = 2 # nose keypoint
# # for i in range(detections.shape[0]): #for all faces
# ymin = detections[i, 0] * img_dims[0]
# xmin = detections[i, 1] * img_dims[1]
# ymax = detections[i, 2] * img_dims[0]
# xmax = detections[i, 3] * img_dims[1]
# # print(xmin, xmax, ymin, ymax)
# # for k in range(2,3): #for all keypoints
# kp_x = detections[i, 4 + k*2 ] * img_dims[1]
# kp_y = detections[i, 4 + k*2 + 1] * img_dims[0]
# print('########')
# print(kp_y, kp_x)
# mouth_region = img[int(kp_y):int(ymax), int(xmin):int(xmax)]
# return mouth_region
# def batch_mouth_detection(self, frames, detections, with_keypoints=True, img_dims= (128, 128)):
# """
# return mouth regions for a batch of frames along with status if any frame was skipped while keypoint finding
# mouth_regions: mouth rois
# flag: boolean if a frame is skipped ; True if a frame is skipped else False
# """
# resize_frames = []
# for frame in frames:
# if frame.shape[0] !=self.img_dims[0] or frame.shape[1] != self.img_dims[1]:
# frame = resize(frame, self.img_dims)
# resize_frames.append(frame)
# else:
# resize_frames.append(frame.numpy())
# # print(len(resize_frames))
# frames = torch.from_numpy(np.array(resize_frames))
# if isinstance(detections, torch.Tensor):
# detections = detections.cpu().numpy()
# # if len(detections) == 2:
# # detections = np.expand_dims(detections, axis=1)
# # print("Found %d faces" % detections.shape[0])
# i = 0 # first face detection
# k = 2 # nose keypoint
# # for i in range(detections.shape[0]): #for all faces
# # print(len(detections))
# # print('########')
# # print(kp_y, kp_x)
# mouth_regions = []
# for index, img in enumerate(frames):
# if len(detections[index]) > 0:
# try:
# ymin = detections[index][i, 0] * img_dims[0]
# xmin = detections[index][i, 1] * img_dims[1]
# ymax = detections[index][i, 2] * img_dims[0]
# xmax = detections[index][i, 3] * img_dims[1]
# # print(xmin, xmax, ymin, ymax)
# # for k in range(2,3): #for all keypoints
# kp_x = detections[index][i, 4 + k*2 ] * img_dims[1]
# kp_y = detections[index][i, 4 + k*2 + 1] * img_dims[0]
# mouth_region = img[int(kp_y):int(ymax), int(xmin):int(xmax)]
# mouth_regions.append(resize(mouth_region, self.mouth_region_size))
# except IndexError:
# flag = True
# break
# else:
# flag = True
# if len(frames) == len(mouth_regions):
# flag = False
# else:
# flag = True
# # print(len(mouth_region_size))
# return np.array(mouth_regions), flag
weighted_detection = detection.clone()
if len(overlapping) > 1:
coordinates = output_detections[i][overlapping, :16]
scores = output_detections[i][overlapping, 16:17]
total_score = scores.sum()
weighted = (coordinates * scores).sum(dim=0) / total_score
weighted_detection[:16] = weighted
weighted_detection[16] = total_score / len(overlapping)
faces.append(weighted_detection)
faces = torch.stack(faces) if len(faces) > 0 else torch.zeros((0, 17))
filtered_detections.append(faces)
return filtered_detections[0]
net = BlazeFace().to("cpu")
net.load_state_dict(torch.load("blazeface.pth"))
net.eval()
myNet = MyBlazeFace(net, "anchors.npy").to("cpu")
torch.save(myNet, "myBlazeface.pth")
# from torch.autograd import Variable
# myNet = torch.load("myBlazeface.pth")
# dummy_input = Variable(torch.randn(1, 3, 128, 128)) # nchw
# onnx_filename = "blazeface.onnx"
# torch.onnx.export(myNet, dummy_input,
# onnx_filename,
# verbose=True)
# import onnx
# from onnx_tf.backend import prepare
# from PIL import Image
import numpy as np
import torch
import cv2
from blazeface import BlazeFace
# some useful info
print("PyTorch version:", torch.__version__)
print("CUDA version:", torch.version.cuda)
print("cuDNN version:", torch.backends.cudnn.version())
gpu = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(gpu)
net = BlazeFace().to(gpu)
net.load_weights("blazeface.pth")
net.load_anchors("anchors.npy")
# let's start the capture now
print("starting camera now....")
#adjust based on your device. For most cases, normally 0
cap = cv2.VideoCapture(0)
while (True):
# Capture frame-by-frame
ret, frame = cap.read()
# Our operations on the frame come here
#gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (128, 128))
detections = net.predict_on_image(img)
model_url = weights.weight_url['{:s}_{:s}'.format(net_model, train_db)]
print('=' * 20)
net = getattr(fornet, net_model)().eval().to(device)
print('=' * 20)
net.load_state_dict(load_url(model_url, map_location=device, check_hash=True))
# In[5]:
transf = utils.get_transformer(face_policy,
face_size,
net.get_normalizer(),
train=False)
# In[6]:
facedet = BlazeFace().to(device)
facedet.load_weights("../blazeface/blazeface.pth")
facedet.load_anchors("../blazeface/anchors.npy")
videoreader = VideoReader(verbose=False)
video_read_fn = lambda x: videoreader.read_frames(x,
num_frames=frames_per_video)
face_extractor = FaceExtractor(video_read_fn=video_read_fn, facedet=facedet)
# ## Detect faces
# In[7]:
torch.cuda.is_available()
# In[8]:
for k in range(6):
offset = 4 + k * 2
# raw_boxes[:, offset ] = (raw_boxes[:, offset ] / 128.0) * self.anchors[:, 2] + self.anchors[:, 0] # x
# raw_boxes[:, offset + 1] = (raw_boxes[:, offset + 1] / 128.0) * self.anchors[:, 3] + self.anchors[:, 1] # y
concat_stuff.append((raw_boxes[:, offset] / 128.0) *
self.anchors[:, 2] + self.anchors[:, 0])
concat_stuff.append((raw_boxes[:, offset + 1] / 128.0) *
self.anchors[:, 3] + self.anchors[:, 1])
return torch.stack(concat_stuff, dim=-1)
import coremltools as ct
from coremltools.converters.onnx import convert
bfModel = BlazeFace()
bfModel.load_weights("./blazeface.pth")
bfModel.load_anchors("./anchors.npy")
bfs = BlazeFaceScaled(bfModel)
bfs.eval()
traced_model = torch.jit.trace(bfs,
torch.rand(1, 3, 128, 128),
check_trace=True)
# print(traced_model)
mlmodel = ct.convert(traced_model,
inputs=[
ct.ImageType(name="image",
shape=ct.Shape(shape=(
1,
import cv2
from facemesh import FaceMesh
from blazeface import BlazeFace
# load FaceMesh model
mesh_net = FaceMesh()
mesh_net.load_weights("facemesh.pth")
# load BlazeFace model
blaze_net = BlazeFace()
blaze_net.load_weights("blazeface.pth")
blaze_net.load_anchors("anchors.npy")
# postprocessing for face detector
def get_crop_face(detections, image):
w, h = image.shape[0], image.shape[1]
ymin = int(detections[0, 0] * w)
xmin = int(detections[0, 1] * h)
ymax = int(detections[0, 2] * w)
xmax = int(detections[0, 3] * h)
margin_x = int(0.25 * (xmax - xmin))
margin_y = int(0.25 * (ymax - ymin))
ymin -= margin_y
ymax += margin_y
xmin -= margin_x
xmax += margin_x
import torch
import torch.nn as nn
from network.models import model_selection
test_dir = "/home/dchen/DFDC/test_videos/"
test_videos = sorted([x for x in os.listdir(test_dir) if x[-4:] == ".mp4"])
gpu = torch.device("cuda:3" if torch.cuda.is_available() else "cpu")
import sys
sys.path.insert(0, "/home/dchen/DFDC/blazeface-pytorch")
sys.path.insert(0, "/home/dchen/DFDC/deepfakes-inference-demo")
from blazeface import BlazeFace
facedet = BlazeFace().to(gpu)
facedet.load_weights("/home/dchen/DFDC/blazeface-pytorch/blazeface.pth")
facedet.load_anchors("/home/dchen/DFDC/blazeface-pytorch/anchors.npy")
_ = facedet.train(False)
from helpers.read_video_1 import VideoReader
from helpers.face_extract_1 import FaceExtractor
frames_per_video = 16
video_reader = VideoReader()
video_read_fn = lambda x: video_reader.read_frames(x,
num_frames=frames_per_video)
face_extractor = FaceExtractor(video_read_fn, facedet)
input_size = 299
import face_recognition
import random
from concurrent.futures import ThreadPoolExecutor
sys.path.insert(1, 'helpers')
sys.path.insert(1, 'model')
sys.path.insert(1, 'weight')
from cvit import CViT
from helpers_read_video_1 import VideoReader
from helpers_face_extract_1 import FaceExtractor
device = 'cuda' if torch.cuda.is_available() else 'cpu'
from blazeface import BlazeFace
facedet = BlazeFace().to(device)
facedet.load_weights("helpers/blazeface.pth")
facedet.load_anchors("helpers/anchors.npy")
_ = facedet.train(False)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
normalize_transform = transforms.Compose([transforms.Normalize(mean, std)])
tresh = 50
sample = 'sample__prediction_data/'
ran = random.randint(0, 400)
ran_min = abs(ran - 1)
from torch.utils.data import Dataset, DataLoader
from blazeface import BlazeFace
import os
import cv2
import numpy as np
from matplotlib import pyplot as plt
import random
import pickle
DATA_FOLDER = '../input/deepfake-detection-challenge'
TRAIN_SAMPLE_FOLDER = 'train_sample_videos'
TEST_FOLDER = 'test_videos'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
NET = BlazeFace().to(device)
NET.load_weights("../input/blazeface.pth")
NET.load_anchors("../input/anchors.npy")
class MyLSTM(nn.Module):
def __init__(self, num_layers=2, num_hidden_nodes=512):
super(MyLSTM, self).__init__()
self.num_layers = num_layers
self.num_hidden_nodes = num_hidden_nodes
# input dim is 167, output 200
self.lstm = nn.LSTM(167,
num_hidden_nodes,
batch_first=True,
num_layers=num_layers)
import sys
from blazebase import resize_pad, denormalize_detections
from blazeface import BlazeFace
from blazepalm import BlazePalm
from blazeface_landmark import BlazeFaceLandmark
from blazehand_landmark import BlazeHandLandmark
from visualization import draw_detections, draw_landmarks, draw_roi, HAND_CONNECTIONS, FACE_CONNECTIONS
gpu = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
torch.set_grad_enabled(False)
back_detector = True
face_detector = BlazeFace(back_model=back_detector).to(gpu)
if back_detector:
face_detector.load_weights("blazefaceback.pth")
face_detector.load_anchors("anchors_face_back.npy")
else:
face_detector.load_weights("blazeface.pth")
face_detector.load_anchors("anchors_face.npy")
palm_detector = BlazePalm().to(gpu)
palm_detector.load_weights("blazepalm.pth")
palm_detector.load_anchors("anchors_palm.npy")
palm_detector.min_score_thresh = .75
hand_regressor = BlazeHandLandmark().to(gpu)
hand_regressor.load_weights("blazehand_landmark.pth")
def run_nb(modelname):
# ## Parameters
# In[2]:
"""
Choose an architecture between
- EfficientNetB4
- EfficientNetB4ST
- EfficientNetAutoAttB4
- EfficientNetAutoAttB4ST
- Xception
"""
net_model = modelname
"""
Choose a training dataset between
- DFDC
- FFPP
"""
train_db = 'DFDC'
# In[3]:
device = torch.device(
'cuda:0') if torch.cuda.is_available() else torch.device('cpu')
face_policy = 'scale'
face_size = 224
frames_per_video = 32
# ## Initialization
# In[4]:
print('=' * 20)
model_url = weights.weight_url['{:s}_{:s}'.format(net_model, train_db)]
print('=' * 20)
net = getattr(fornet, net_model)().eval().to(device)
print('=' * 20)
net.load_state_dict(
load_url(model_url, map_location=device, check_hash=True))
# In[5]:
transf = utils.get_transformer(face_policy,
face_size,
net.get_normalizer(),
train=False)
# In[6]:
facedet = BlazeFace().to(device)
facedet.load_weights("../blazeface/blazeface.pth")
facedet.load_anchors("../blazeface/anchors.npy")
videoreader = VideoReader(verbose=False)
video_read_fn = lambda x: videoreader.read_frames(
x, num_frames=frames_per_video)
face_extractor = FaceExtractor(video_read_fn=video_read_fn,
facedet=facedet)
# ## Detect faces
# In[7]:
torch.cuda.is_available()
# In[8]:
torch.cuda.current_device()
# In[9]:
torch.cuda.device(0)
# In[10]:
torch.cuda.device_count()
# In[11]:
torch.cuda.get_device_name(0)
# In[12]:
vid_real_faces = face_extractor.process_video('samples/lynaeydofd.mp4')
vid_fake_faces = face_extractor.process_video('samples/mqzvfufzoq.mp4')
# In[13]:
im_real_face = vid_real_faces[0]['faces'][0]
im_fake_face = vid_fake_faces[0]['faces'][0]
# In[14]:
fig, ax = plt.subplots(1, 2, figsize=(8, 4))
ax[0].imshow(im_real_face)
ax[0].set_title('REAL')
ax[1].imshow(im_fake_face)
ax[1].set_title('FAKE')
# ## Predict scores for each frame
# In[15]:
# For each frame, we consider the face with the highest confidence score found by BlazeFace (= frame['faces'][0])
faces_real_t = torch.stack([
transf(image=frame['faces'][0])['image'] for frame in vid_real_faces
if len(frame['faces'])
])
faces_fake_t = torch.stack([
transf(image=frame['faces'][0])['image'] for frame in vid_fake_faces
if len(frame['faces'])
])
with torch.no_grad():
faces_real_pred = net(faces_real_t.to(device)).cpu().numpy().flatten()
faces_fake_pred = net(faces_fake_t.to(device)).cpu().numpy().flatten()
# In[16]:
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
ax[0].stem([f['frame_idx'] for f in vid_real_faces if len(f['faces'])],
expit(faces_real_pred),
use_line_collection=True)
ax[0].set_title('REAL')
ax[0].set_xlabel('Frame')
ax[0].set_ylabel('Score')
ax[0].set_ylim([0, 1])
ax[0].grid(True)
ax[1].stem([f['frame_idx'] for f in vid_fake_faces if len(f['faces'])],
expit(faces_fake_pred),
use_line_collection=True)
ax[1].set_title('FAKE')
ax[1].set_xlabel('Frame')
ax[1].set_ylabel('Score')
ax[1].set_ylim([0, 1])
ax[1].set_yticks([0, 1], ['REAL', 'FAKE'])
# In[17]:
"""
Print average scores.
An average score close to 0 predicts REAL. An average score close to 1 predicts FAKE.
"""
print('Average score for REAL video: {:.4f}'.format(
expit(faces_real_pred.mean())))
print('Average score for FAKE face: {:.4f}'.format(
expit(faces_fake_pred.mean())))
