net = SRResnet(lr=learning_rate)
net.visualize()
net.get_model().summary()
img = cv2.imread('div2k_test.png')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
r = DATA.patch_size - img.shape[0] % DATA.patch_size
c = DATA.patch_size - img.shape[1] % DATA.patch_size
img = np.pad(img, [(0, r), (0, c), (0, 0)], 'constant')
Image.fromarray(img).save("div2k_padded_test.png")
lr_img = cv2.resize(img, (int(img.shape[1] / 2), int(img.shape[0] / 2)),
cv2.INTER_CUBIC)
Image.fromarray(lr_img).save("div2k_padded_test_lr.png")
p, r, c = DATA.patchify(lr_img, scale=2)
for i in range(chk + 1, tryout):
print("tryout no: ", i)
samplev = np.random.random_integers(0,
DATA.training_patches_2x.shape[0] - 1,
sample)
net.fit(DATA.training_patches_2x[samplev],
DATA.training_patches_Y[samplev], batch_size, epochs)
net.get_model().save_weights('model_iter' + str(i) + '.h5')
g = net.get_model().predict(np.array(p))
gen = DATA.reconstruct(g, r, c, scale=1)
gen[gen > 255] = 255
gen[gen < 0] = 0
exit()
R = DATA.patch_size - img.shape[0] % DATA.patch_size
C = DATA.patch_size - img.shape[1] % DATA.patch_size
img = np.pad(img, [(0, R), (0, C), (0, 0)], 'constant')
Image.fromarray(img).save("test_image_padded.png")
lr_img = cv2.resize(img,
(int(img.shape[1] / scale), int(img.shape[0] / scale)),
cv2.INTER_CUBIC)
Image.fromarray(lr_img).save("test_" + str(scale) + "x_lr_padded.png")
hr_img_bi = cv2.resize(lr_img, (int(img.shape[1]), int(img.shape[0])),
cv2.INTER_CUBIC)
Image.fromarray(hr_img_bi).save("test_" + str(scale) +
"x_hr_bicubic_padded.png")
p, r, c = DATA.patchify(lr_img, scale=scale)
if not test_only:
for i in range(chk + 1, tryout):
print("tryout no: ", i)
samplev = np.random.random_integers(0, x.shape[0] - 1, sample)
net.fit(x[samplev], y[samplev], batch_size, epochs)
net.get_model().save_weights('model_' + str(scale) + 'x_iter' +
str(i) + '.h5')
g = net.get_model().predict(np.array(p))
gen = DATA.reconstruct(g, r, c, scale=1)
Image.fromarray(gen).save("test_" + str(scale) + "x_gen_" +
str(i) + ".png")
net = SRResnet(lr=learning_rate, scale=scale, chk=chk)
if not test_only:
net.visualize()
net.get_model().summary()
# Temp stuff for barcode #####
os.mkdir('bargen')
loc = '/home/sanchit/Barcodes/HR/detected_barcodes'
for f in os.listdir(loc):
if not os.path.isdir(f):
filename = loc + '/' + f
img = cv2.imread(filename)
r = 32 - img.shape[0] % 32
c = 32 - img.shape[1] % 32
img = np.pad(img, [(0, r), (0, c), (0, 0)], 'constant')
gz, r2, c2 = DATA.patchify(img, scale=scale)
gz = net.get_model().predict(np.array(gz))
genz = DATA.reconstruct(gz, r2, c2, scale=1)
Image.fromarray(genz).save('bargen/' + f)
image_name = values.test_image
try:
img = cv2.imread(image_name)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
except cv2.error as e:
print("Bad image path check the name or path !!")
exit()
if not zoom:
r = DATA.patch_size - img.shape[0] % DATA.patch_size
lr_2x_img = cv2.resize(img,
(int(img.shape[1] / 2), int(img.shape[0] / 2)),
cv2.INTER_CUBIC)
Image.fromarray(lr_2x_img).save("test_2x_lr_padded.png")
lr_4x_img = cv2.resize(img,
(int(img.shape[1] / 4), int(img.shape[0] / 4)),
cv2.INTER_CUBIC)
Image.fromarray(lr_4x_img).save("test_4x_lr_padded.png")
lr_8x_img = cv2.resize(img,
(int(img.shape[1] / 8), int(img.shape[0] / 8)),
cv2.INTER_CUBIC)
Image.fromarray(lr_8x_img).save("test_8x_lr_padded.png")
p, r, c = DATA.patchify(lr_8x_img, scale=8)
if not os.path.isdir('Results'):
os.mkdir('Results')
if not test_only:
for i in range(chk + 1, tryout):
gen_acc = 0.0
dic_acc = 0.0
print("tryout no: ", i)
# samplev = np.random.random_integers(0 , x.shape[0]-1 , sample)
if adv_lambda > 0.0:
net.fit_discriminator(x, x2, x4, x8, batch_size, epoch=epochs)