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test_single_image.py
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242 lines (194 loc) · 8.48 KB
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import os, argparse
import numpy as np
import torch
from torchvision import transforms
import models.UIQA as UIQA
from PIL import Image
from scipy.optimize import curve_fit
from scipy import stats
import pandas as pd
import random
def logistic_func(X, bayta1, bayta2, bayta3, bayta4):
logisticPart = 1 + np.exp(np.negative(np.divide(X - bayta3, np.abs(bayta4))))
yhat = bayta2 + np.divide(bayta1 - bayta2, logisticPart)
return yhat
def fit_function(y_label, y_output):
beta = [np.max(y_label), np.min(y_label), np.mean(y_output), 0.5]
popt, _ = curve_fit(logistic_func, y_output, \
y_label, p0=beta, maxfev=100000000)
y_output_logistic = logistic_func(y_output, *popt)
return y_output_logistic
def performance_fit(y_label, y_output):
y_output_logistic = fit_function(y_label, y_output)
PLCC = stats.pearsonr(y_output_logistic, y_label)[0]
SRCC = stats.spearmanr(y_output, y_label)[0]
KRCC = stats.stats.kendalltau(y_output, y_label)[0]
RMSE = np.sqrt(((y_output_logistic-y_label) ** 2).mean())
return PLCC, SRCC, KRCC, RMSE
def get_spatial_fragments(
video,
fragments_h=7,
fragments_w=7,
fsize_h=32,
fsize_w=32,
aligned=32,
nfrags=1,
random=False,
random_upsample=False,
fallback_type="upsample",
**kwargs,
):
size_h = fragments_h * fsize_h
size_w = fragments_w * fsize_w
## video: [C,T,H,W]
## situation for images
if video.shape[1] == 1:
aligned = 1
dur_t, res_h, res_w = video.shape[-3:]
ratio = min(res_h / size_h, res_w / size_w)
if fallback_type == "upsample" and ratio < 1:
ovideo = video
video = torch.nn.functional.interpolate(
video / 255.0, scale_factor=1 / ratio, mode="bilinear"
)
video = (video * 255.0).type_as(ovideo)
if random_upsample:
randratio = random.random() * 0.5 + 1
video = torch.nn.functional.interpolate(
video / 255.0, scale_factor=randratio, mode="bilinear"
)
video = (video * 255.0).type_as(ovideo)
assert dur_t % aligned == 0, "Please provide match vclip and align index"
size = size_h, size_w
## make sure that sampling will not run out of the picture
hgrids = torch.LongTensor(
[min(res_h // fragments_h * i, res_h - fsize_h) for i in range(fragments_h)]
)
wgrids = torch.LongTensor(
[min(res_w // fragments_w * i, res_w - fsize_w) for i in range(fragments_w)]
)
hlength, wlength = res_h // fragments_h, res_w // fragments_w
if random:
print("This part is deprecated. Please remind that.")
if res_h > fsize_h:
rnd_h = torch.randint(
res_h - fsize_h, (len(hgrids), len(wgrids), dur_t // aligned)
)
else:
rnd_h = torch.zeros((len(hgrids), len(wgrids), dur_t // aligned)).int()
if res_w > fsize_w:
rnd_w = torch.randint(
res_w - fsize_w, (len(hgrids), len(wgrids), dur_t // aligned)
)
else:
rnd_w = torch.zeros((len(hgrids), len(wgrids), dur_t // aligned)).int()
else:
if hlength > fsize_h:
rnd_h = torch.randint(
hlength - fsize_h, (len(hgrids), len(wgrids), dur_t // aligned)
)
else:
rnd_h = torch.zeros((len(hgrids), len(wgrids), dur_t // aligned)).int()
if wlength > fsize_w:
rnd_w = torch.randint(
wlength - fsize_w, (len(hgrids), len(wgrids), dur_t // aligned)
)
else:
rnd_w = torch.zeros((len(hgrids), len(wgrids), dur_t // aligned)).int()
target_video = torch.zeros(video.shape[:-2] + size).to(video.device)
# target_videos = []
for i, hs in enumerate(hgrids):
for j, ws in enumerate(wgrids):
for t in range(dur_t // aligned):
t_s, t_e = t * aligned, (t + 1) * aligned
h_s, h_e = i * fsize_h, (i + 1) * fsize_h
w_s, w_e = j * fsize_w, (j + 1) * fsize_w
if random:
h_so, h_eo = rnd_h[i][j][t], rnd_h[i][j][t] + fsize_h
w_so, w_eo = rnd_w[i][j][t], rnd_w[i][j][t] + fsize_w
else:
h_so, h_eo = hs + rnd_h[i][j][t], hs + rnd_h[i][j][t] + fsize_h
w_so, w_eo = ws + rnd_w[i][j][t], ws + rnd_w[i][j][t] + fsize_w
target_video[:, t_s:t_e, h_s:h_e, w_s:w_e] = video[
:, t_s:t_e, h_so:h_eo, w_so:w_eo
]
# target_videos.append(video[:,t_s:t_e,h_so:h_eo,w_so:w_eo])
# target_video = torch.stack(target_videos, 0).reshape((dur_t // aligned, fragments, fragments,) + target_videos[0].shape).permute(3,0,4,1,5,2,6)
# target_video = target_video.reshape((-1, dur_t,) + size) ## Splicing Fragments
return target_video
def parse_args():
"""Parse input arguments. """
parser = argparse.ArgumentParser(description="Authentic Image Quality Assessment")
parser.add_argument('--model_path', help='Path of model snapshot.', type=str)
parser.add_argument('--trained_model_file', type=str)
parser.add_argument('--popt_file', type=str)
parser.add_argument('--model', type=str)
parser.add_argument('--n_fragment', type=int, default=12)
parser.add_argument('--image_path', type=str)
parser.add_argument('--resize', type=int)
parser.add_argument('--salient_patch_dimension', type=int, default=384)
parser.add_argument('--crop_size', help='crop_size.',type=int)
parser.add_argument('--gpu_ids', type=list, default=None)
args = parser.parse_args()
return args
if __name__ == '__main__':
random_seed = 2
torch.manual_seed(random_seed) #
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(random_seed)
random.seed(random_seed)
args = parse_args()
model_path = args.model_path
popt_file = args.popt_file
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load the network
model_file = args.trained_model_file
if args.model == 'UIQA':
model = UIQA.UIQA_Model()
# model = torch.nn.DataParallel(model)
model = model.to(device)
model.load_state_dict(torch.load(os.path.join(model_path, model_file)))
popt = np.load(os.path.join(model_path, popt_file))
model.eval()
transform_asethetics = transforms.Compose([transforms.Resize(args.resize),
transforms.CenterCrop(args.crop_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
transform_distortion = transforms.Compose([transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
transform_distortion_preprocessing = transforms.Compose([transforms.ToTensor()])
transform_saliency = transforms.Compose([
transforms.CenterCrop(args.salient_patch_dimension),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
test_image = Image.open(os.path.join(args.image_path))
test_image = test_image.convert('RGB')
test_image_aesthetics = transform_asethetics(test_image)
test_image_saliency = transform_saliency(test_image)
test_image_distortion = transform_distortion_preprocessing(test_image)
test_image_distortion = test_image_distortion.unsqueeze(1)
test_image_distortion = get_spatial_fragments(
test_image_distortion,
fragments_h=args.n_fragment,
fragments_w=args.n_fragment,
fsize_h=32,
fsize_w=32,
aligned=32,
nfrags=1,
random=False,
random_upsample=False,
fallback_type="upsample"
)
test_image_distortion = test_image_distortion.squeeze(1)
test_image_distortion = transform_distortion(test_image_distortion)
test_image_aesthetics = test_image_aesthetics.unsqueeze(0)
test_image_distortion = test_image_distortion.unsqueeze(0)
test_image_saliency = test_image_saliency.unsqueeze(0)
with torch.no_grad():
test_image_aesthetics = test_image_aesthetics.to(device)
test_image_saliency = test_image_saliency.to(device)
test_image_distortion = test_image_distortion.to(device)
outputs = model(test_image_aesthetics, test_image_distortion, test_image_saliency)
score = outputs.item()
print('The quality of the test image is {:.4f}'.format(score))