cap_img = ImageCapture()
frame = cap_img.capture_image()
if frame is not None:
im_conv = ImageConverter()
temp_img = im_conv.crop_frame_to_square(
im_conv.convert_frame_to_grey_scale(frame))
img_48 = im_conv.rescale(temp_img, size=48)
img_48 = im_conv.normalize_frame(img_48)
img_array = im_conv.reshape_frame_to_array(img_48)
dtype = torch.float
device = torch.device("cpu")
model_name = f'best'
m_importer = ModelImporter('best_model')
model = m_importer.load_nn_model(model_name, )
model.eval()
x = model.reshape_data(
torch.tensor([img_48], device=device, dtype=dtype))
predicted_emotion = None
predicted_emotion2 = None
with torch.no_grad():
results = model(x).squeeze().detach().numpy()
print(results)
sort = np.sort(results, axis=0)
idx1 = np.where(results == sort[6])
idx2 = np.where(results == sort[5])
print(idx1[0][0])
print(idx2[0][0])
pre.load_data(filename='test_public_norm.csv', name='test')
X_df = pre.get(name='test').drop(columns=['emotion'])
y_df = pre.get(name='test')['emotion']
dtype = torch.float
device = torch.device("cpu")
n_classes = 7
n_totalepochs = 100
learning_rate = 0.0001
batch_size = 32
epoch_n = 50
model_name = f'cnn_simple_reduced_{learning_rate}_{batch_size}_{n_totalepochs}_{n_classes}_epoch{epoch_n}'
m_importer = ModelImporter('temp')
model = m_importer.load_nn_model(model_name)
model.eval()
X_test = model.reshape_data(
torch.tensor(X_df.values, device=device, dtype=dtype))
y_test = torch.tensor(y_df.values, device=device, dtype=torch.long)
y_pred = model(X_test).argmax(1)
print(y_pred)
print(y_test)
accuracy_soft = (y_pred == y_test).float().mean()
print(f'test accuracy {accuracy_soft.item()}')
pre.load_data(filename='test_public_norm.csv', name='test')
X_df = pre.get(name='test').drop(columns=['emotion'])
y_df = pre.get(name='test')['emotion']
n_classes = 7
n_totalepochs = 200
learning_rate = 0.00005
batch_size = 32
epoch_n = 40#for the temp folder
dtype = torch.float
device = torch.device("cpu")
model_name = f'cnn_double_layer_reduced_{learning_rate}_{batch_size}_{n_totalepochs}_{n_classes}'
model_name_bestvalloss = f'{model_name}_epoch150'
m_importer = ModelImporter('fer2013_DatasetA')
model = m_importer.load_nn_model(model_name_bestvalloss)
model.eval()
X_test = model.reshape_data(torch.tensor(X_df.values, device=device, dtype=dtype))
y_test = torch.tensor(y_df.values, device=device, dtype=torch.long)
y_pred = model(X_test).argmax(1)
print(y_pred)
print(y_test)
accuracy_soft = (y_pred == y_test).float().mean()
print(f'test accuracy {accuracy_soft.item()}')
print(file)
frame = cv2.imread(file, 0)
if frame is not None:
im_conv = ImageConverter()
temp_img = im_conv.crop_frame_to_square(
im_conv.convert_frame_to_grey_scale(frame))
img_48 = im_conv.rescale(temp_img, size=48)
img_48 = im_conv.normalize_frame(img_48)
img_array = im_conv.reshape_frame_to_array(img_48)
dtype = torch.float
device = torch.device("cpu")
model_name = f'best_model'
m_importer = ModelImporter('best')
model = m_importer.load_nn_model(model_name)
model.eval()
x = model.reshape_data(
torch.tensor([img_48], device=device, dtype=dtype))
predicted_emotion = None
predicted_emotion2 = None
with torch.no_grad():
results = model(x).squeeze().detach().numpy()
sort = np.sort(results, axis=0)
idx1 = np.where(results == sort[6])
idx2 = np.where(results == sort[5])
print(results)
predicted_emotion = emotion_dict[idx1[0][0]]
predicted_emotion2 = emotion_dict[idx2[0][0]]