def test_model():
#cargar modelo mse
json_file = open('model_1.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights("model_1.h5")
print("Modelo cargado")
image = img_to_array(load_img(path_imags('test', 1)[0]))
image = np.array(image, dtype=float)
X = rgb2lab(image / 255.)[:, :, 0]
X = X.reshape(1, 64, 64, 1)
#print(model.evaluate(X, Y, batch_size=1))
output = model.predict(X)
output = output * 128
out = np.zeros((64, 64, 3))
out[:, :, 0] = X[0][:, :, 0]
out[:, :, 1:] = output[0]
fig = plt.figure(figsize=(15, 15))
ax1 = fig.add_subplot(1, 4, 1)
ax1.imshow(image / 255.)
ax1.axis('off')
ax2 = fig.add_subplot(1, 4, 2)
ax2.imshow(rgb2gray(image), cmap='gray')
ax2.axis('off')
ax3 = fig.add_subplot(1, 4, 3)
ax3.imshow(lab2rgb(out))
ax3.axis('off')
def test_model():
#cargar modelo
json_file = open('model_1_loss.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
model.load_weights("model_1_loss.h5")
print("Modelo cargado")
image = img_to_array(load_img(path_imags('test',1)[0]))
image = np.array(image, dtype=np.float16)
X = rgb2lab(image/255.)[:,:,0]
X = X.reshape(1, 64, 64, 1)
output = model.predict(X)
ab = ab_from_output(output)
out = np.zeros((64, 64, 3))
out[:,:,0] = X[0][:,:,0]
out[:,:,1:] = ab[0]
fig = plt.figure(figsize=(15,15))
ax1 = fig.add_subplot(1,4,1)
ax1.imshow(np.array(image/255., dtype=np.float32))
ax1.axis('off')
ax2 = fig.add_subplot(1,4,2)
ax2.imshow(np.array(rgb2gray(image), dtype=np.float32),cmap='gray')
ax2.axis('off')
ax3 = fig.add_subplot(1,4,3)
ax3.imshow(np.array(lab2rgb(out), dtype=np.float32))
ax3.axis('off')
plt.savefig('results.png')
def get_ab_histogram():
imags = path_imags('train', 10000)
a, b = [], []
for img in imags:
image = img_to_array(load_img(img))
image = np.array(image, dtype=np.float16)
img_lab = rgb2lab(image / 255.)
a.extend(img_lab[:, :, 1].ravel())
b.extend(img_lab[:, :, 2].ravel())
histogram, a_edges, b_edges = np.histogram2d(a, b, bins=22)
plt.figure(figsize=(10, 10))
plt.imshow(np.log(histogram),
cmap='jet',
interpolation='nearest',
origin='low',
extent=[a_edges[0], b_edges[-1], a_edges[0], b_edges[-1]])
plt.gca().invert_yaxis()
plt.xlabel('b')
plt.ylabel('a')
return histogram
def train_model():
data_size = 30000
#data_val = data_size/10
X = []
Y = []
X_val = []
Y_val = []
paths = path_imags('train', data_size)
paths_val = path_imags('val', 2000)
for path in paths:
image = img_to_array(load_img(path))
image = np.array(image, dtype=float)
Xi = rgb2lab(image / 255.)[:, :, 0]
Yi = rgb2lab(image / 255.)[:, :, 1:]
Yi /= 128
Xi = Xi.reshape(1, 64, 64, 1)
Yi = Yi.reshape(1, 64, 64, 2)
X.append(np.squeeze(Xi, axis=0))
Y.append(np.squeeze(Yi, axis=0))
X = np.asarray(X)
Y = np.asarray(Y)
for path in paths_val:
image = img_to_array(load_img(path))
image = np.array(image, dtype=float)
Xi = rgb2lab(image / 255.)[:, :, 0]
Yi = rgb2lab(image / 255.)[:, :, 1:]
Yi /= 128
Xi = Xi.reshape(1, 64, 64, 1)
Yi = Yi.reshape(1, 64, 64, 2)
X_val.append(np.squeeze(Xi, axis=0))
Y_val.append(np.squeeze(Yi, axis=0))
X_val = np.asarray(X_val)
Y_val = np.asarray(Y_val)
inputs = Input(shape=(None, None, 1))
x = Conv2D(8, (3, 3), strides=2, padding='same', activation='relu')(inputs)
x = Conv2D(16, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(16, (3, 3), strides=2, padding='same', activation='relu')(x)
x = Conv2D(32, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(32, (3, 3), strides=2, padding='same', activation='relu')(x)
x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(64, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(32, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(16, (3, 3), padding='same', activation='relu')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(8, (3, 3), padding='same', activation='relu')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(4, (3, 3), padding='same', activation='relu')(x)
x = Conv2D(2, (3, 3), padding='same', activation='tanh')(x)
y = UpSampling2D((2, 2))(x)
model = Model(inputs=[inputs], outputs=[y])
model.compile(
optimizer='adam',
loss='mse',
)
tensorboard = TensorBoard(log_dir="logs/modelo_1" +
datetime.now().strftime("%Y%m%d-%H%M%S"))
model.fit(X,
Y,
epochs=500,
batch_size=64,
validation_data=(X_val, Y_val),
callbacks=[tensorboard])
#guardar modelo
model_json = model.to_json()
with open("model_1.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("model_1.h5")
print("Modelo guardado")
def train_model():
data_size = 5000
#data_val = data_size/10
X = []
Y = []
X_val = []
Y_val = []
paths = path_imags('train', data_size)
paths_val = path_imags('val', 400)
for path in paths:
image = img_to_array(load_img(path))
image = np.array(image, dtype=float)
Xi = rgb2lab(image/255.)[:,:,0]
Yi = rgb2lab(image/255.)[:,:,1:]
Yi /= 128
Xi = Xi.reshape(1, 64, 64, 1)
Yi = Yi.reshape(1, 64, 64, 2)
X.append(np.squeeze(Xi, axis = 0))
Y.append(np.squeeze(Yi, axis = 0))
X = np.asarray(X)
Y = np.asarray(Y)
for path in paths_val:
image = img_to_array(load_img(path))
image = np.array(image, dtype=float)
Xi = rgb2lab(image/255.)[:,:,0]
Yi = rgb2lab(image/255.)[:,:,1:]
Yi /= 128
Xi = Xi.reshape(1, 64, 64, 1)
Yi = Yi.reshape(1, 64, 64, 2)
X_val.append(np.squeeze(Xi, axis = 0))
Y_val.append(np.squeeze(Yi, axis = 0))
X_val = np.asarray(X_val)
Y_val = np.asarray(Y_val)
inputs = Input(shape=(64,64,1))
x = Conv2D(8, (3,3), strides=2, padding='same', activation='relu')(inputs)
x = Conv2D(16, (3,3), padding='same', activation='relu')(x)
x = Conv2D(16, (3,3), strides=2, padding='same', activation='relu')(x)
x = Conv2D(32, (3,3), padding='same', activation='relu')(x)
x = Conv2D(32, (3,3), strides=2, padding='same', activation='relu')(x)
x = Conv2D(64, (3,3), padding='same', activation='relu')(x)
x = Conv2D(64, (3,3), padding='same', activation='relu')(x)
x = Conv2D(32, (3,3), padding='same', activation='relu')(x)
resnet_embedding_input = Input(shape=(1000,))
fusion = RepeatVector(8*8)(resnet_embedding_input)
fusion = Reshape(([8, 8, 1000]))(fusion)
fusion = concatenate([x, fusion], axis=3)
fusion = Conv2D(256, (1,1), padding='same', activation='relu')(fusion)
y = Conv2D(16, (3,3), padding='same', activation='relu')(fusion)
y = UpSampling2D((2, 2))(y)
y = Conv2D(8, (3,3), padding='same', activation='relu')(y)
y = Conv2D(8, (3,3), padding='same', activation='relu')(y)
y = UpSampling2D((2, 2))(y)
y = Conv2D(4, (3,3), padding='same', activation='relu')(y)
y = Conv2D(2, (3,3), padding='same', activation='relu')(y)
y = UpSampling2D((2, 2))(y)
y = Conv2D(484, (1,1), padding='same', activation='softmax')(y)
model = Model(inputs=[inputs, resnet_embedding_input], outputs=[y])
model.compile(optimizer='adam', loss=new_loss)
tensorboard = TensorBoard(log_dir="logs/modelo_2_loss" + datetime.now().strftime("%Y%m%d-%H%M%S"))
model.fit([X, resnet_embedding(X)], output(Y), validation_data=([X_val, resnet_embedding(X_val)], output(Y_val)),
callbacks=[tensorboard], epochs=500, batch_size=64)
#guardar modelo
model_json = model.to_json()
with open("modelo_2_loss.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("modelo_2_loss.h5")
print("Modelo guardado")