def generate_and_save_images(model, epoch, test_input):
predictions = model(test_input, training=False)
fig = plt.figure(figsize=(4, 4))
for i in range(predictions.shape[0]):
plt.subplot(4, 4, i + 1)
plt.imshow(normalize(predictions[i, :, :, 0],
input_range=(-1, 1),
output_range=(0, 255)),
cmap='gray')
plt.axis('off')
plt.savefig('./images/epoch_{:04d}.png'.format(epoch))
plt.close()
def preprocess(raw, frames):
global noise
global count
global approach
img = frames[0]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces, _, confidence = classifier.detectMultiScale3(gray, outputRejectLevels=True, minSize=(25, 25), maxSize=(80, 80))
max_ind = -1
max_metric = 0
max_ind_2 = -1
for i in range(len(faces)):
metric = confidence[i]
if metric > max_metric:
max_metric = metric
max_ind = i
max_metric = 0
for i in range(len(faces)):
if i == max_ind:
continue
metric = confidence[i]
if metric > max_metric:
max_metric = metric
max_ind_2 = i
for i in [max_ind, max_ind_2]:
if i < 0:
continue
x, y, w, h = faces[i]
noise = (1 - r) * noise + r * approach
count += 1
if count >= stability:
count = 0
approach = tf.random.normal([1, 100])
gen_img = colorifier(generator(noise, training=False), training=False)
gen_img = normalize(gen_img, input_range=(-1, 1), output_range=(0, 255))
gen_img = np.reshape(gen_img, (28,28,3))
gen_img = cv2.resize(gen_img, (w, h))
weights = cv2.resize(gaussian_tensor, (w, h))
img[y: y + h, x: x + w] = gen_img * weights + (1 - weights) * img[y: y + h, x: x + w]
return img
capture = cv2.VideoCapture(0)
classifier = cv2.CascadeClassifier('../dataset/haarcascade_frontalface_default.xml')
colorifier = tf.keras.models.load_model(path)
while True:
cur_time = time.time()
_, img = capture.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = classifier.detectMultiScale(gray)
if len(faces) > 0:
faces = list(faces)
faces.sort(key=lambda rect: -rect[2] * rect[3])
x, y, w, h = faces[0]
inp_img = normalize(cv2.resize(gray[y: y + h, x: x + h], (28, 28)))
inp_img = np.reshape(inp_img, (1, 28, 28, 1))
gen_img = colorifier(inp_img, training=False)
gen_img = normalize(gen_img, input_range=(-1, 1), output_range=(0, 255))
gen_img = np.reshape(gen_img, (28,28,3))
gen_img = cv2.resize(gen_img, (w, h))
img[y: y + h, x: x + w] = gen_img
cv2.imshow('img', img)
k = cv2.waitKey(30) & 0xff
if k==27:
break
capture.release()
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import PIL
from gan.config import *
from gan.generative_network import make_generator_model
from gan.discriminative_network import make_discriminator_model
from dataset.preproccesing import load_data, normalize
if os.path.exists(data_file) and not update_data_file:
gray = np.load(data_file)
else:
gray = load_data('../dataset/images/', should_log=True, load_color=False)
gray = normalize(gray)
np.save(data_file, gray)
dataset = tf.data.Dataset.from_tensor_slices(gray).shuffle(BUFFER_SIZE).batch(
BATCH_SIZE)
generator = make_generator_model()
discriminator = make_discriminator_model()
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
total_loss = real_loss + fake_loss
import numpy as np
import tensorflow as tf
import cv2
from dataset.preproccesing import load_data, normalize
from colorify.make_model import make_model
from colorify.config import *
if os.path.exists(gray_data_file) and not update_data_file:
color = np.load(color_data_file)
gray = np.load(gray_data_file)
else:
color, gray = load_data('../dataset/images/', should_log=True)
color, gray = normalize(color), normalize(gray)
np.save(color_data_file, color)
np.save(gray_data_file, gray)
gray = tf.data.Dataset.from_tensor_slices(gray)
color = tf.data.Dataset.from_tensor_slices(color)
dataset = tf.data.Dataset.zip(
(gray, color)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
log_dir = ".\\logs\\fit\\" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)
model = make_model()
model.compile(optimizer='adam', loss='mse')
model.fit(dataset, epochs=10, callbacks=[tensorboard_callback])
generator = tf.keras.models.load_model(gen_path)
noise = tf.random.normal(
[1, 100]) if rand_start else np.load('./inputs/best_noise_input.npy')
while True:
cur_time = time.time()
_, img = capture.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = classifier.detectMultiScale(gray)
if len(faces) > 0:
faces = list(faces)
faces.sort(key=lambda rect: -rect[2] * rect[3])
x, y, w, h = faces[0]
noise = r * noise + (1 - r) * tf.random.normal([1, 100])
gen_img = generator(noise, training=False)
gen_img = normalize(gen_img,
input_range=(-1, 1),
output_range=(0, 255))
gen_img = np.reshape(gen_img, (28, 28))
gen_img = cv2.resize(cv2.cvtColor(gen_img, cv2.COLOR_GRAY2BGR), (w, h))
img[y:y + h, x:x + w] = gen_img
cv2.imshow('img', img)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
capture.release()
matcher = tf.keras.models.load_model(path)
while True:
cur_time = time.time()
_, img = capture.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = classifier.detectMultiScale(gray)
if len(faces) > 0:
faces = list(faces)
faces.sort(key=lambda rect: -rect[2] * rect[3])
x, y, w, h = faces[0]
inp_img = img[y:y + h, x:x + w]
inp_img = cv2.resize(inp_img, (28, 28))
inp_img = cv2.cvtColor(inp_img, cv2.COLOR_BGR2GRAY)
inp_img = normalize(inp_img)
inp_img = np.reshape(inp_img, (1, 28, 28, 1))
out_img = generator(matcher(inp_img))
out_img = np.reshape(out_img, (28, 28))
out_img = normalize(out_img,
input_range=(-1, 1),
output_range=(0, 255))
out_img = cv2.resize(out_img, (w, h))
out_img = cv2.cvtColor(out_img, cv2.COLOR_GRAY2BGR)
img[y:y + h, x:x + w] = out_img
cv2.imshow('img', img)
k = cv2.waitKey(30) & 0xff
if k == 27:
break