def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
wtgs = torch.load('temporal_resnet_50_best_loss.pth.tar')
rnet = TimeSeriesLearning(4, 15)
rnet = rnet.to(device)
valT = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
valDataset = torch.utils.data.DataLoader(IngDiscLearnDataSetBlock(
'frames_face', transform=valT, blocksize=5),
batch_size=20,
shuffle=False,
num_workers=4)
rnet.load_state_dict(wtgs['state_dict'])
classes = getDirectoriesInPath(os.path.join('CASME2_formated', 'train'))
createFoldersForEmotions(classes)
blockNumber = 0
with torch.no_grad():
rnet.eval()
for batch_i, (imgs, targets) in enumerate(valDataset):
outputs = rnet(imgs.cuda())
_, predicts = torch.max(outputs, 1)
predictsCPU = predicts.cpu()
for idx, t in enumerate(targets):
for ft in t:
fileName = ft.split(os.path.sep)[-1]
shutil.copyfile(
ft,
os.path.join(
'expressionsEmotions',
classes[predictsCPU[idx].item()],
'block_' + str(blockNumber) + '_' + fileName))
blockNumber += 1
def __init__(self, casmepath, phase, blocksize, transform):
self.blocksize = blocksize
self.transform = transform
self.label = []
self.filesPath = []
raw_data_loaded = getDirectoriesInPath(os.path.join(casmepath, phase))
labelName = ['appex', 'neutral', 'offset', 'onset']
for r in raw_data_loaded:
files = getFilesInPath(os.path.join(casmepath, phase, r))
blockFiles = {}
for f in files:
fileName = '_'.join(f.split(os.path.sep)[-1].split('_')[:-1])
if not fileName in blockFiles.keys():
blockFiles[fileName] = []
blockFiles[fileName].append(f)
#qntdeBlock = int(sum([len(blockFiles[k]) for k in blockFiles]) / len(blockFiles.keys()) / self.blocksize)
for k in blockFiles:
blockFiles[k].sort(key=lambda f: int(re.sub('\D', '', f)))
for nBl in range(len(blockFiles[k]) - self.blocksize):
if (nBl + self.blocksize) > len(blockFiles[k]):
break
blockData = blockFiles[k][nBl:nBl + self.blocksize]
self.filesPath.append(blockData)
self.label.append(labelName.index(r))
def getCASME2BlockData(casmepath, phase, blocksize):
labels = []
filesPath = []
raw_data_loaded = getDirectoriesInPath(os.path.join(casmepath, phase))
labelName = raw_data_loaded
for r in raw_data_loaded:
files = getFilesInPath(os.path.join(casmepath, phase, r))
blockFiles = {}
for f in files:
fileName = '_'.join(f.split(os.path.sep)[-1].split('_')[:-1])
if not fileName in blockFiles.keys():
blockFiles[fileName] = []
blockFiles[fileName].append(f)
# qntdeBlock = int(sum([len(blockFiles[k]) for k in blockFiles]) / len(blockFiles.keys()) / self.blocksize)
for k in blockFiles:
blockFiles[k].sort(key=lambda f: int(re.sub('\D', '', f)))
for nBl in range(len(blockFiles[k]) - blocksize):
if (nBl + blocksize) > len(blockFiles[k]):
break
blockData = blockFiles[k][nBl:nBl + blocksize]
filesPath.append(blockData)
labels.append(labelName.index(r))
return labels, filesPath
def main():
parser = argparse.ArgumentParser(description='Organize AffWild1')
parser.add_argument('--pathBase', help='Path for faces', required=True)
parser.add_argument('--whereTo', help='Where to save', required=True)
parser.add_argument('--deleteOld',
help='Remove old dataset?',
default=1,
type=int)
args = parser.parse_args()
if args.deleteOld:
if os.path.exists(args.whereTo):
shutil.rmtree(args.whereTo)
os.makedirs(args.whereTo)
dirsFaces = getDirectoriesInPath(os.path.join(args.pathBase, 'videos'))
for d in dirsFaces:
print("Extracting from %s" % (d))
if not os.path.exists(os.path.join(args.whereTo, d)):
os.makedirs(os.path.join(args.whereTo, d))
filesFace = getFilesInPath(os.path.join(args.pathBase, 'videos', d))
for f in filesFace:
fileName = f.split(os.path.sep)[-1]
print("Doing video %s" % (fileName))
if not os.path.exists(os.path.join(args.whereTo, d,
fileName[:-4])):
os.makedirs(os.path.join(args.whereTo, d, fileName[:-4]))
faceAnn = openLandmarks(args.pathBase, d, fileName)
vcap = cv2.VideoCapture(f)
frame_number = 0
sucs = True
while sucs:
sucs, imgv = vcap.read()
if sucs and frame_number in faceAnn.keys():
if not os.path.exists(
os.path.join(args.whereTo, d, fileName[:-4],
str(frame_number) + '.jpg')):
x1, y1, x2, y2 = getBoundingbox(faceAnn[frame_number])
fImage = imgv[y1:y2, x1:x2]
if len(fImage) > 0:
try:
cv2.imwrite(
os.path.join(args.whereTo, d,
fileName[:-4],
str(frame_number) + '.jpg'),
fImage)
except:
print('Error in save frame %d' %
(frame_number))
frame_number += 1
def __init__(self, casmepath, phase, transform=None):
self.transform = transform
self.label = []
self.filesPath = []
raw_data_loaded = getDirectoriesInPath(os.path.join(casmepath, phase))
labelName = ['appex', 'neutral', 'offset', 'onset']
for r in raw_data_loaded:
files = getFilesInPath(os.path.join(casmepath, phase, r))
for f in files:
self.filesPath.append(f)
self.label.append(labelName.index(r))
def main():
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D,
flip_input=False)
#if os.path.exists('formated_aff'):
# shutil.rmtree('formated_aff')
face_cascade = cv2.CascadeClassifier(
'cascadeFolder/haarcascade_frontalface_default.xml')
#os.makedirs('formated_aff')
#os.makedirs(os.path.join('formated_aff', 'Train_Set'))
#os.makedirs(os.path.join('formated_aff', 'Validation_Set'))
folders = getDirectoriesInPath("aff_dataset")
for f in folders:
videos = getFilesInPath(os.path.join('aff_dataset', f))
for v in videos:
preProcessWithThread(v, f, fa, face_cascade)
'''