def cumulative_ssim_plot(data_dict, path):
""" cum. ssim scores between adversarials and corresponding originals """
originals = data_dict.pop('original').tensors[0]
for name, data in data_dict.items():
ssim_scores = []
image_pairs = zip(originals, data['images'])
for original, adversarial in image_pairs:
ssim_scores.append(metrics.SSIM(original, adversarial))
counts, bins = numpy.histogram(ssim_scores, bins=1000, range=(0, 1))
cdf = numpy.cumsum(counts) / numpy.sum(counts)
plt.plot(
numpy.vstack((bins, numpy.roll(bins, -1))).T.flatten()[:-2],
numpy.vstack((cdf, cdf)).T.flatten() * 100)
plt.title('Cumulative SSIM Scores')
plt.legend(list(data_dict.keys()))
plt.xlabel('SSIM', size=16)
plt.ylabel('% below', size=16)
filename = '{}cumulative_ssim.png'.format(path)
plt.savefig(filename, transparent=False)
plt.close()
def main():
# paths to the models
model_paths = [
os.path.join("..", "models", "SRDense-Type-3_ep80.h5"),
os.path.join("..", "models", "srdense-norm.h5"),
os.path.join("..", "models", "srresnet85.h5"),
os.path.join("..", "models", "gen_model90.h5"),
os.path.join("..", "models", "srgan60.h5"),
os.path.join("..", "models", "srgan-mse-20.h5"), "Nearest"
]
# corresponding names of the models
model_names = [
"SRDense", "SRDense-norm", "SRResNet", "SRGAN-from-scratch",
"SRGAN-percept.-loss", "SRGAN-mse", "NearestNeighbor"
]
# corresponding tile shapes
tile_shapes = [((168, 168), (42, 42)), ((168, 168), (42, 42)),
((504, 504), (126, 126)), ((336, 336), (84, 84)),
((504, 504), (126, 126)), ((504, 504), (126, 126)),
((336, 336), (84, 84))]
# used to load the models with custom loss functions
loss = VGG_LOSS((504, 504, 3))
custom_objects = [{}, {
"tf": tf
}, {
"tf": tf
}, {
"tf": tf
}, {
"tf": tf
}, {
"tf": tf
}, {}]
# creating a list of test images
# [(lr, hr)]
DOWN_SCALING_FACTOR = 4
INTERPOLATION = cv2.INTER_CUBIC
test_images = []
root = os.path.join("..", "DSIDS", "test")
# iterating over all files in the test folder
for img in os.listdir(root):
# chekcing if the file is an image
if not ".jpg" in img:
continue
hr = Utils.crop_into_lr_shape(cv2.cvtColor(
cv2.imread(os.path.join(root, img), cv2.IMREAD_COLOR),
cv2.COLOR_BGR2RGB),
shape=(3024, 4032))
lr = cv2.resize(hr, (0, 0),
fx=1 / DOWN_SCALING_FACTOR,
fy=1 / DOWN_SCALING_FACTOR,
interpolation=INTERPOLATION)
test_images.append((lr, hr))
if TILES:
'''
First calculating performance metrics on single image tiles
'''
tile_performance = {}
for i, mp in tqdm(enumerate(model_paths)):
keras.backend.clear_session()
# first step: load the model
if i < 6:
model = load_model(mp, custom_objects=custom_objects[i])
mse = []
psnr = []
ssim = []
mssim = []
# second step: iterate over the test images
for test_pair in tqdm(test_images):
# third step: tile the test image
lr_tiles = Utils.tile_image(test_pair[0],
shape=tile_shapes[i][1])
hr_tiles = Utils.tile_image(test_pair[1],
shape=tile_shapes[i][0])
m = []
p = []
s = []
ms = []
# fourth step: iterate over the tiles
for lr, hr in zip(lr_tiles, hr_tiles):
# fifth step: calculate the sr tile
if i < 2:
if i == 1:
lr = lr.astype(np.float64)
lr = lr / 255
tmp = np.squeeze(
model.predict(np.expand_dims(lr, axis=0)))
if i == 1:
tmp = tmp * 255
tmp[tmp < 0] = 0
tmp[tmp > 255] = 255
sr = tmp.astype(np.uint8)
elif i < 6:
sr = Utils.denormalize(
np.squeeze(model.predict(
np.expand_dims(rescale_imgs_to_neg1_1(lr),
axis=0)),
axis=0))
else:
sr = cv2.resize(lr, (0, 0),
fx=4,
fy=4,
interpolation=cv2.INTER_NEAREST)
# sixth step: append the calculated metric
m.append(metrics.MSE(hr, sr))
p.append(metrics.PSNR(hr, sr))
s.append(metrics.SSIM(hr, sr))
ms.append(metrics.MSSIM(hr, sr))
# seventh step: append the mean metric for this image
mse.append(np.mean(m))
psnr.append(np.mean(p))
ssim.append(np.mean(s))
mssim.append(np.mean(ms))
# eight step: append the mean metric for this model
tile_performance[model_names[i]] = (np.mean(mse), np.mean(psnr),
np.mean(ssim), np.mean(mssim))
# final output
print("Performance on single tiles:")
f = open("tile_performance.txt", "w")
for key in tile_performance:
print(
key + ": MSE = " + str(tile_performance[key][0]) +
", PSNR = " + str(tile_performance[key][1]) + ", SSIM = " +
str(tile_performance[key][2]),
", MSSIM = " + str(tile_performance[key][3]))
f.write(key + " " + str(tile_performance[key][0]) + " " +
str(tile_performance[key][1]) + " " +
str(tile_performance[key][2]) + " " +
str(tile_performance[key][3]) + "\n")
f.close()
if WHOLE_LR:
'''
Second calculating performance metrics on a single upscaled image
'''
img_performance = {}
for i, mp in tqdm(enumerate(model_paths)):
keras.backend.clear_session()
# first step: load the model
if i < 6:
model = load_model(mp, custom_objects=custom_objects[i])
# second step: changing the input layer
_in = Input(shape=test_images[0][0].shape)
_out = model(_in)
_model = Model(_in, _out)
mse = []
psnr = []
ssim = []
mssim = []
# third step: iterate over the test images
for test_pair in tqdm(test_images):
# fourth step: calculate the sr image
try:
if i < 2:
if i == 1:
lr = test_pair[0].astype(np.float64)
lr = lr / 255
else:
lr = test_pair[0]
tmp = np.squeeze(
_model.predict(np.expand_dims(lr, axis=0)))
if i == 1:
tmp = tmp * 255
tmp[tmp < 0] = 0
tmp[tmp > 255] = 255
sr = tmp.astype(np.uint8)
elif i < 6:
sr = Utils.denormalize(
np.squeeze(_model.predict(
np.expand_dims(rescale_imgs_to_neg1_1(
test_pair[0]),
axis=0)),
axis=0))
else:
sr = cv2.resize(test_pair[0], (0, 0),
fx=4,
fy=4,
interpolation=cv2.INTER_NEAREST)
# fifth step: append the metric for this image
mse.append(metrics.MSE(test_pair[1], sr))
psnr.append(metrics.PSNR(test_pair[1], sr))
ssim.append(metrics.SSIM(test_pair[1], sr))
mssim.append(metrics.MSSIM(test_pair[1], sr))
except:
mse.append("err")
psnr.append("err")
ssim.append("err")
mssim.append("err")
# sixth step: append the mean metric for this model
try:
img_performance[model_names[i]] = (np.mean(mse), np.mean(psnr),
np.mean(ssim),
np.mean(mssim))
except:
img_performance[model_names[i]] = ("err", "err", "err", "err")
# final output
print("Performance on whole lr:")
f = open("whole_lr_performance.txt", "w")
for key in img_performance:
print(
key + ": MSE = " + str(img_performance[key][0]) +
", PSNR = " + str(img_performance[key][1]) + ", SSIM = " +
str(img_performance[key][2]),
", MSSIM = " + str(img_performance[key][3]))
f.write(key + " " + str(img_performance[key][0]) + " " +
str(img_performance[key][1]) + " " +
str(img_performance[key][2]) + " " +
str(img_performance[key][3]) + "\n")
f.close()
if STITCHED:
'''
Second calculating performance metrics on a stitched image
'''
stitch_performance = {}
for i, mp in tqdm(enumerate(model_paths)):
keras.backend.clear_session()
# first step: load the model
if i < 6:
model = load_model(mp, custom_objects=custom_objects[i])
mse = []
psnr = []
ssim = []
mssim = []
o_mse = []
o_psnr = []
o_ssim = []
o_mssim = []
# second step: iterate over the test images
for test_pair in tqdm(test_images):
# third step: tile the test image
lr_tiles = Utils.tile_image(test_pair[0],
shape=tile_shapes[i][1])
lr_tiles_overlap = Utils.tile_image(test_pair[0],
shape=tile_shapes[i][1],
overlap=True)
sr_tiles = []
sr_tiles_overlap = []
# fourth step: iterate over the tiles
for lr in lr_tiles:
# fifth step: calculate the sr tiles
if i < 2:
if i == 1:
lr = lr.astype(np.float64)
lr = lr / 255
tmp = np.squeeze(
model.predict(np.expand_dims(lr, axis=0)))
if i == 1:
tmp = tmp * 255
tmp[tmp < 0] = 0
tmp[tmp > 255] = 255
sr = tmp.astype(np.uint8)
sr_tiles.append(sr)
elif i < 6:
sr_tiles.append(
Utils.denormalize(
np.squeeze(model.predict(
np.expand_dims(rescale_imgs_to_neg1_1(lr),
axis=0)),
axis=0)))
else:
sr_tiles.append(
cv2.resize(lr, (0, 0),
fx=4,
fy=4,
interpolation=cv2.INTER_NEAREST))
for lr in lr_tiles_overlap:
# fifth step: calculate the sr tiles
if i < 2:
if i == 1:
lr = lr.astype(np.float64)
lr = lr / 255
tmp = np.squeeze(
model.predict(np.expand_dims(lr, axis=0)))
if i == 1:
tmp = tmp * 255
tmp[tmp < 0] = 0
tmp[tmp > 255] = 255
sr = tmp.astype(np.uint8)
sr_tiles_overlap.append(sr)
elif i < 6:
sr_tiles_overlap.append(
Utils.denormalize(
np.squeeze(model.predict(
np.expand_dims(rescale_imgs_to_neg1_1(lr),
axis=0)),
axis=0)))
else:
sr_tiles_overlap.append(
cv2.resize(lr, (0, 0),
fx=4,
fy=4,
interpolation=cv2.INTER_NEAREST))
# sixth step: stitch the image
sr_simple = ImageStitching.stitch_images(
sr_tiles, test_pair[1].shape[1], test_pair[1].shape[0],
sr_tiles[0].shape[1], sr_tiles[0].shape[0],
test_pair[1].shape[1] // sr_tiles[0].shape[1],
test_pair[1].shape[0] // sr_tiles[0].shape[0])
sr_advanced = ImageStitching.stitching(
sr_tiles_overlap,
LR=None,
image_size=(test_pair[1].shape[0], test_pair[1].shape[1]),
adjustRGB=False,
overlap=True)
# seventh step: append the mean metric for this image
mse.append(metrics.MSE(test_pair[1], sr_simple))
psnr.append(metrics.PSNR(test_pair[1], sr_simple))
ssim.append(metrics.SSIM(test_pair[1], sr_simple))
mssim.append(metrics.MSSIM(test_pair[1], sr_simple))
o_mse.append(metrics.MSE(test_pair[1], sr_advanced))
o_psnr.append(metrics.PSNR(test_pair[1], sr_advanced))
o_ssim.append(metrics.SSIM(test_pair[1], sr_advanced))
o_mssim.append(metrics.MSSIM(test_pair[1], sr_advanced))
# ninth step: append the mean metric for this model
stitch_performance[model_names[i]] = [
(np.mean(mse), np.mean(psnr), np.mean(ssim), np.mean(mssim)),
(np.mean(o_mse), np.mean(o_psnr), np.mean(o_ssim),
np.mean(o_mssim))
]
# final output
print("Performance on stitched images:")
f = open("stitch_performance.txt", "w")
for key in stitch_performance:
print(
"simple stitch: " + key + ": MSE = " +
str(stitch_performance[key][0][0]) + ", PSNR = " +
str(stitch_performance[key][0][1]) + ", SSIM = " +
str(stitch_performance[key][0][2]),
", MSSIM = " + str(stitch_performance[key][0][3]))
print(
"advanced stitch: " + key + ": MSE = " +
str(stitch_performance[key][1][0]) + ", PSNR = " +
str(stitch_performance[key][1][1]) + ", SSIM = " +
str(stitch_performance[key][1][2]),
", MSSIM = " + str(stitch_performance[key][1][3]))
f.write(key + " " + str(stitch_performance[key][0][0]) + " " +
str(stitch_performance[key][0][1]) + " " +
str(stitch_performance[key][0][2]) + " " +
str(stitch_performance[key][0][3]) + "\n")
f.write(key + " " + str(stitch_performance[key][1][0]) + " " +
str(stitch_performance[key][1][1]) + " " +
str(stitch_performance[key][1][2]) + " " +
str(stitch_performance[key][1][3]) + "\n")
f.close()
if not os.path.exists(save_dirA) or not os.path.exists(save_dirB):
os.makedirs(save_dirA)
os.makedirs(save_dirB)
### Build model
netG_A2C = eval(checkA[0].replace('@G2LAB', ''))(1, 1, int(checkA[2][1])).to(opt.device)
netG_C2B = eval(checkB[0].replace('@G2LAB', ''))(1, 2).to(opt.device)
# load check point
netG_A2C.load_state_dict(torch.load(os.path.join(Check_DIR, os.path.basename(args.netGA))))
netG_C2B.load_state_dict(torch.load(os.path.join(Check_DIR, os.path.basename(args.netGB))))
netG_A2C.eval()
netG_C2B.eval()
print("Starting test Loop...")
# setup data loader
data_loader = DataLoader(testset, opt.batch_size, num_workers=opt.num_works,
shuffle=False, pin_memory=True, )
evaluators = [metrics.MSE(), metrics.PSNR(), metrics.AE(), metrics.SSIM()]
performs = [[] for i in range(len(evaluators))]
for idx, sample in enumerate(data_loader):
realA = sample['src'].to(opt.device)
# realA -= 0.5
realB = sample['tar'].to(opt.device)
# realB -= 0.5
# Y = 0.2125 R + 0.7154 G + 0.0721 B [RGB2Gray, 3=>1 ch]
realBC = realB[:,:1,:,:]
sf = int(checkA[2][1])
realBA = nn.functional.interpolate(realBC, scale_factor=1. / sf, mode='bilinear')
realBA = nn.functional.interpolate(realBA, scale_factor=sf, mode='bilinear')
# realAA = nn.functional.interpolate(realA, scale_factor=1. / sf)
realAA = realA
fake_AC = netG_A2C(realAA)
fake_AB = netG_C2B(fake_AC)
def __init__(self):
super(DSSIMLoss, self).__init__()
self.criterion = metrics.SSIM()