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# Copyright (c) 2020 Huawei Technologies Co., Ltd.
# Licensed under CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International) (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
#
# The code is released for academic research use only. For commercial use, please contact Huawei Technologies Co., Ltd.
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This file contains content licensed by https://github.com/xinntao/BasicSR/blob/master/LICENSE/LICENSE
import glob
import sys
from collections import OrderedDict
from natsort import natsort
import options.options as option
from Measure import Measure, psnr
from imresize import imresize
from models import create_model
import torch
from utils.util import opt_get
import numpy as np
import pandas as pd
import os
import cv2
def fiFindByWildcard(wildcard):
return natsort.natsorted(glob.glob(wildcard, recursive=True))
def load_model(conf_path):
opt = option.parse(conf_path, is_train=False)
opt['gpu_ids'] = None
opt = option.dict_to_nonedict(opt)
model = create_model(opt)
model_path = opt_get(opt, ['model_path'], None)
model.load_network(load_path=model_path, network=model.netG)
return model, opt
def predict(model, lr):
model.feed_data({"LQ": t(lr)}, need_GT=False)
model.test()
visuals = model.get_current_visuals(need_GT=False)
return visuals.get('rlt', visuals.get("SR"))
def t(array): return torch.Tensor(np.expand_dims(array.transpose([2, 0, 1]), axis=0).astype(np.float32)) / 255
def rgb(t): return (
np.clip((t[0] if len(t.shape) == 4 else t).detach().cpu().numpy().transpose([1, 2, 0]), 0, 1) * 255).astype(
np.uint8)
def imread(path):
return cv2.imread(path)[:, :, [2, 1, 0]]
def imwrite(path, img):
os.makedirs(os.path.dirname(path), exist_ok=True)
cv2.imwrite(path, img[:, :, [2, 1, 0]])
def imCropCenter(img, size):
h, w, c = img.shape
h_start = max(h // 2 - size // 2, 0)
h_end = min(h_start + size, h)
w_start = max(w // 2 - size // 2, 0)
w_end = min(w_start + size, w)
return img[h_start:h_end, w_start:w_end]
def impad(img, top=0, bottom=0, left=0, right=0, color=255):
return np.pad(img, [(top, bottom), (left, right), (0, 0)], 'reflect')
def main():
conf_path = sys.argv[1]
conf = conf_path.split('/')[-1].replace('.yml', '')
model, opt = load_model(conf_path)
data_dir = opt['dataroot']
# this_dir = os.path.dirname(os.path.realpath(__file__))
test_dir = os.path.join('/kaggle/working/', 'results', conf)
print(f"Out dir: {test_dir}")
measure = Measure(use_gpu=False)
fname = f'measure_full.csv'
fname_tmp = fname + "_"
path_out_measures = os.path.join(test_dir, fname_tmp)
path_out_measures_final = os.path.join(test_dir, fname)
if os.path.isfile(path_out_measures_final):
df = pd.read_csv(path_out_measures_final)
elif os.path.isfile(path_out_measures):
df = pd.read_csv(path_out_measures)
else:
df = None
scale = opt['scale']
pad_factor = 2
data_sets = [
'Set5',
'Set14',
'Urban100',
'BSD100'
]
final_df = pd.DataFrame()
for data_set in data_sets:
lr_paths = fiFindByWildcard(os.path.join(data_dir, data_set, '*LR.png'))
hr_paths = fiFindByWildcard(os.path.join(data_dir, data_set, '*HR.png'))
df = pd.DataFrame(columns=['conf', 'heat', 'data_set', 'name', 'PSNR', 'SSIM', 'LPIPS', 'LRC PSNR'])
for lr_path, hr_path, idx_test in zip(lr_paths, hr_paths, range(len(lr_paths))):
with torch.no_grad(), torch.cuda.amp.autocast():
lr = imread(lr_path)
hr = imread(hr_path)
# Pad image to be % 2
h, w, c = lr.shape
lq_orig = lr.copy()
lr = impad(lr, bottom=int(np.ceil(h / pad_factor) * pad_factor - h),
right=int(np.ceil(w / pad_factor) * pad_factor - w))
lr_t = t(lr)
heat = opt['heat']
if df is not None and len(df[(df['heat'] == heat) & (df['name'] == idx_test)]) == 1:
continue
sr_t = model.get_sr(lq=lr_t, heat=heat)
sr = rgb(torch.clamp(sr_t, 0, 1))
sr = sr[:h * scale, :w * scale]
path_out_sr = os.path.join(test_dir, data_set, "{:0.2f}".format(heat).replace('.', ''), "{:06d}.png".format(idx_test))
imwrite(path_out_sr, sr)
meas = OrderedDict(conf=conf, heat=heat, data_set=data_set, name=idx_test)
meas['PSNR'], meas['SSIM'], meas['LPIPS'] = measure.measure(sr, hr)
lr_reconstruct_rgb = imresize(sr, 1 / opt['scale'])
meas['LRC PSNR'] = psnr(lq_orig, lr_reconstruct_rgb)
str_out = format_measurements(meas)
print(str_out)
df = df._append(pd.DataFrame([meas]), ignore_index=True)
final_df = pd.concat([final_df, df])
final_df.to_csv(path_out_measures, index=False)
os.rename(path_out_measures, path_out_measures_final)
# str_out = format_measurements(df.mean())
# print(f"Results in: {path_out_measures_final}")
# print('Mean: ' + str_out)
def format_measurements(meas):
s_out = []
for k, v in meas.items():
v = f"{v:0.2f}" if isinstance(v, float) else v
s_out.append(f"{k}: {v}")
str_out = ", ".join(s_out)
return str_out
if __name__ == "__main__":
main()
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