type=bool,
help='Test the model in real time with webcam connect via usb')
args = parser.parse_args()
transformation = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, ), (0.5, ))])
dataset = Plain_Dataset(csv_file=args.data + '/finaltest.csv',
img_dir=args.data + '/' + 'finaltest/',
datatype='finaltest',
transform=transformation)
test_loader = DataLoader(dataset, batch_size=64, num_workers=0)
net = Deep_Emotion()
print("Deep Emotion:-", net)
net.load_state_dict(torch.load(args.model))
net.to(device)
net.eval()
#Model Evaluation on test data
classes = ('Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral')
total = []
if args.test_acc:
with torch.no_grad():
for data, labels in test_loader:
data, labels = data.to(device), labels.to(device)
outputs = net(data)
pred = F.softmax(outputs, dim=1)
classs = torch.argmax(pred, 1)
wrong = torch.where(classs != labels,
torch.tensor([1.]).cuda(),
torch.tensor([0.]).cuda())
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
mapping = {
0: 'Angry',
1: 'Disgust',
2: 'Fear',
3: 'Happy',
4: 'Sad',
5: 'Surprise',
6: 'Neutral'
}
net = Deep_Emotion() # In pytorch we have to first load the model
file_name = 'deep_emotion-100-128-0.005.pt'
net.load_state_dict(torch.load(file_name))
net.to(device)
path = "haarcascade_frontalface_default.xml"
font_scale = 1.5
font = cv2.FONT_HERSHEY_PLAIN
# SET THE RECTANGLE BACKGROUND TO WHITE
rectangle_bgr = (255, 255, 255)
# MAKE A BLACK IMAGE
img = np.zeros((500, 500))
# SET SOME TEXT
text = "Some text in a box!"
validation_img_dir = 'data'+'/'+ 'val/'
transformation= transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,),(0.5,))])
train_dataset= Plain_Dataset(csv_file=traincsv_file, img_dir = train_img_dir, datatype = 'train', transform = transformation)
validation_dataset= Plain_Dataset(csv_file=validationcsv_file, img_dir = validation_img_dir, datatype = 'val', transform = transformation)
train_loader= DataLoader(train_dataset,batch_size=batchsize,shuffle = True,num_workers=0)
val_loader= DataLoader(validation_dataset,batch_size=batchsize,shuffle = True,num_workers=0)
criterion= nn.CrossEntropyLoss()
optmizer= optim.Adam(net.parameters(),lr= lr)
Train(epochs, train_loader, val_loader, criterion, optmizer, device)
torch.save(net.state_dict(), 'new_Emotion_trained_Lea.pt')
net = Deep_Emotion()
net.load_state_dict(torch.load('new_Emotion_trained_Lea.pt'))
net.to(device)
# Test it on a saved image:
import matplotlib.pyplot as plt
get_ipython().system('pip install opencv-python')
import cv2
frame = cv2.imread("C:\\Users\\kassa\\11-test\\Deep-Emotion-master\\happy.jpg")
get_ipython().system('pip install deepface')
from deepface import DeepFace