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run.py
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run.py
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from __future__ import print_function
import argparse
from os import listdir
from torchvision import transforms
from torchvision import utils as vutils
from PIL import Image
from torchvision import transforms
import os
from os import listdir
import random
from random import randint
import cv2 as cv
from PIL import Image
import numpy as np
import torch
import torch.utils.data as data
from torchvision import transforms
# from dataset import *
from math import exp
import torch.nn.functional as F
# import pytorch_ssim
# from skimage.measure import compare_ssim
from skimage.metrics import structural_similarity as ssim
parser = argparse.ArgumentParser(description='Test')
parser.add_argument('--submit_mode', default=False, help='Test all sequences directly')
parser.add_argument('--input_path', type=str,
default='datasets/hevc_cbr_low_frames/D/BasketballPass_416x240_50/',
help='input path to use')
parser.add_argument('--output_path', default='results/hevc_cbr_low/D/BasketballPass_416x240_50/', type=str,
help='where to save the output image')
parser.add_argument('--gt_path', type=str,
default='datasets/gt_frames/D/BasketballPass_416x240_50/',
help='input path to use')
parser.add_argument('--model', type=str, default='checkpoints/cbr_low.pth',
help='model file to use')
parser.add_argument('--cuda', default=True, action='store_true', help='use cuda')
parser.add_argument('--calc_on_y', default=False, action='store_true', help='calc on y channel')
parser.add_argument('--save_img', default=True, action='store_true', help='save img')
opt = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg", ".bmp"])
class NumpyToTensor(object):
def __init__(self, multi_frame=False):
self.multi_frame = multi_frame
def __call__(self, numpy_input):
numpy_input = numpy_input / 255.0
numpy_input = torch.from_numpy(numpy_input).float()
if self.multi_frame is True:
return numpy_input.permute(0, 3, 1, 2)
else:
return numpy_input.permute(2, 0, 1)
def save_image_tensor(input_tensor: torch.Tensor, filename):
assert (len(input_tensor.shape) == 4 and input_tensor.shape[0] == 1)
# 复制一份
input_tensor = input_tensor.clone().detach()
input_tensor = input_tensor[:, [2, 1, 0], :, :]
# 到cpu
input_tensor = input_tensor.to(torch.device('cpu'))
vutils.save_image(input_tensor, filename)
def save_image_tensor2cv2(input_tensor: torch.Tensor, filename):
assert (len(input_tensor.shape) == 4 and input_tensor.shape[0] == 1)
# 复制一份
input_tensor = input_tensor.clone().detach()
# 到cpu
input_tensor = input_tensor.to(torch.device('cpu'))
# 去掉批次维度
input_tensor = input_tensor.squeeze()
# 从[0,1]转化为[0,255]，再从CHW转为HWC，最后转为cv2
input_tensor = input_tensor.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).type(torch.uint8).numpy()
# RGB转BRG
# input_tensor = cv.cvtColor(input_tensor, cv.COLOR_RGB2BGR)
cv.imwrite(filename, input_tensor)
def save_image_tensor2pillow(input_tensor: torch.Tensor, filename):
assert (len(input_tensor.shape) == 4 and input_tensor.shape[0] == 1)
# 复制一份
input_tensor = input_tensor.clone().detach()
# 到cpu
input_tensor = input_tensor.to(torch.device('cpu'))
input_tensor = input_tensor[:, [2, 1, 0], :, :]
# 反归一化
# input_tensor = unnormalize(input_tensor)
# 去掉批次维度
input_tensor = input_tensor.squeeze()
# 从[0,1]转化为[0,255]，再从CHW转为HWC，最后转为numpy
input_tensor = input_tensor.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).type(torch.uint8).numpy()
# 转成pillow
im = Image.fromarray(input_tensor)
im.save(filename)
def padding8(img):
h, w = img.shape[0:2]
pad_h = 8 - h % 8 if h % 8 != 0 else 0
pad_w = 8 - w % 8 if w % 8 != 0 else 0
img = np.pad(img, ((0, pad_h), (0, pad_w), (0, 0)), 'edge')
return img
def load_img_y(img_name):
img = cv.imread(img_name)
img = cv.cvtColor(img, cv.COLOR_BGR2YCrCb)
img = img[:, :, 0]
img = np.expand_dims(img, -1)
return img
def calc_psnr(img1, img2):
return 10. * torch.log10(1. / torch.mean((img1 - img2) ** 2))
model = torch.load(opt.model, map_location='cuda:0')
if opt.submit_mode is True:
input_root_path = 'datasets/hevc_cbr_low/'
output_root_path = 'results/hevc_cbr_low/'
gt_root_path = 'datasets/gt/'
folder_list = sorted(listdir(input_root_path))
for sequence in folder_list: # A B C D E
if sequence == 'A' or sequence == 'B':
# If the GPU memory size is not large enough to run the sequences A and B,
# pls run test_group_A&B.py
pass
else:
sub_folder_list = sorted(listdir('{}/{}'.format(input_root_path, sequence)))
for video in sub_folder_list: # PeopleOnStreet_2560x1600_30_crop Traffic_2560x1600_30_crop
input_path = '{}{}/{}/'.format(input_root_path, sequence, video)
output_path = '{}{}/{}/'.format(output_root_path, sequence, video)
gt_path = '{}{}/{}/'.format(gt_root_path, sequence, video)
img_list = []
for frame in listdir(input_path):
img_list.append(listdir(input_path))
print(input_path, ' ', len(img_list), 'test images')
input_psnr_avg = 0
output_psnr_avg = 0
input_ssim_avg = 0
output_ssim_avg = 0
index = 0
for i in listdir(input_path):
if is_image_file(i):
with torch.no_grad():
index = index + 1
example_img = cv.imread('{}{}.png'.format(input_path, "%03d" % 1))
h, w, ch = example_img.shape
input_img = np.zeros((5, h, w, ch))
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % (index - 2))
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % (index + 1))
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % (index + 2))
if index == 1:
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % (index + 1))
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % (index + 2))
if index == 2:
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % (index + 1))
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % (index + 2))
if index == len(img_list) - 1:
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % (index - 2))
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % (index + 1))
if index == len(img_list):
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % (index - 2))
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % index)
input_img[0, :, :, :] = cv.imread(input_img_name_1)
input_img[1, :, :, :] = cv.imread(input_img_name_2)
input_img[2, :, :, :] = cv.imread(input_img_name_3)
input_img[3, :, :, :] = cv.imread(input_img_name_4)
input_img[4, :, :, :] = cv.imread(input_img_name_5)
input_tensor = NumpyToTensor(multi_frame=True)(input_img.copy())
input = torch.unsqueeze(input_tensor, dim=0).float().contiguous()
model.eval()
if opt.cuda:
model = model.cuda()
input = input.cuda()
out = model(input)
out = out.cpu()
if not os.path.exists(output_path):
os.makedirs(output_path)
if not os.path.exists('{}output/'.format(output_path)):
os.makedirs('{}output/'.format(output_path))
if not os.path.exists('{}gt/'.format(output_path)):
os.makedirs('{}gt/'.format(output_path))
if not os.path.exists('{}input/'.format(output_path)):
os.makedirs('{}input/'.format(output_path))
# output_tensor = out.clone().detach()
# output_tensor = output_tensor[:, [2, 1, 0], :, :]
# output_tensor = output_tensor.squeeze()
# output_numpy = output_tensor.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).type(
# torch.uint8).numpy()
# out_img = Image.fromarray(output_numpy)
img_original = cv.imread('{}{}.png'.format(gt_path, "%03d" % index))
input_center_img = cv.imread('{}{}.png'.format(input_path, "%03d" % index))
input_y = cv.cvtColor(input_center_img, cv.COLOR_BGR2YUV)
input_y = input_y[:, :, 0]
input_y = transforms.ToTensor()(input_y)
input_y = input_y.squeeze(0)
gt_y = cv.cvtColor(img_original, cv.COLOR_BGR2YUV)
gt_y = gt_y[:, :, 0]
gt_y = transforms.ToTensor()(gt_y)
gt_y = gt_y.squeeze(0)
if opt.save_img is True:
save_image_tensor(out, '{}output/{}.png'.format(output_path, "%03d" % index))
print('saving:', '{}{}.png'.format(output_path, "%03d" % index))
cv.imwrite('{}gt/{}.png'.format(output_path, "%03d" % index), img_original)
cv.imwrite('{}input/{}.png'.format(output_path, "%03d" % index), input_center_img)
out = out.squeeze(0)
out_img = out.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).type(
torch.uint8).numpy()
if opt.calc_on_y is True:
input_center_img = cv.cvtColor(input_center_img, cv.COLOR_BGR2YUV)
input_center_img = input_center_img[:, :, 0]
img_original = cv.cvtColor(img_original, cv.COLOR_BGR2YUV)
img_original = img_original[:, :, 0]
out_img = cv.cvtColor(out_img, cv.COLOR_BGR2YUV)
out_img = out_img[:, :, 0]
input_psnr = calc_psnr(transforms.ToTensor()(input_center_img),
transforms.ToTensor()(img_original))
output_psnr = calc_psnr(transforms.ToTensor()(out_img),
transforms.ToTensor()(img_original))
input_ssim = ssim(input_center_img,
img_original, multichannel=True)
output_ssim = ssim(out_img,
img_original, multichannel=True)
if index > 2 or index < len(img_list) - 1:
input_psnr_avg += input_psnr
output_psnr_avg += output_psnr
input_ssim_avg += input_ssim
output_ssim_avg += output_ssim
input_psnr_avg = input_psnr_avg / (index - 4)
output_psnr_avg = output_psnr_avg / (index - 4)
psnr_increase = output_psnr_avg - input_psnr_avg
input_ssim_avg = input_ssim_avg / (index - 4)
output_ssim_avg = output_ssim_avg / (index - 4)
ssim_increase = output_ssim_avg - input_ssim_avg
# print('input_psnr_avg:', input_psnr_avg)
# print('output_psnr_avg:', output_psnr_avg)
print('psnr_increase:', psnr_increase)
# print('input_ssim_avg:', "%.4f" % input_ssim_avg)
# print('output_ssim_avg:', "%.4f" % output_ssim_avg)
print('ssim_increase:', "%.4f" % ssim_increase)
with open("{}result.txt".format(output_root_path), "a") as f:
f.write(input_path)
f.write('\npsnr_increase:{}'.format(psnr_increase))
f.write('\nssim_increase:{}\n'.format(ssim_increase))
else:
input_path = opt.input_path
img_list = []
for _f in listdir(input_path):
img_list.append(listdir(input_path))
print(len(img_list), 'test images')
input_psnr_avg = 0
output_psnr_avg = 0
input_ssim_avg = 0
output_ssim_avg = 0
index = 0
for i in listdir(input_path):
if is_image_file(i):
with torch.no_grad():
index = index + 1
example_img = cv.imread('{}{}.png'.format(input_path, "%03d" % 1))
h, w, ch = example_img.shape
input_img = np.zeros((5, h, w, ch))
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % (index - 2))
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % (index + 1))
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % (index + 2))
if index == 1:
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % (index + 1))
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % (index + 2))
if index == 2:
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % (index + 1))
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % (index + 2))
if index == len(img_list) - 1:
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % (index - 2))
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % (index + 1))
if index == len(img_list):
input_img_name_1 = '{}{}.png'.format(input_path, "%03d" % (index - 2))
input_img_name_2 = '{}{}.png'.format(input_path, "%03d" % (index - 1))
input_img_name_3 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_4 = '{}{}.png'.format(input_path, "%03d" % index)
input_img_name_5 = '{}{}.png'.format(input_path, "%03d" % index)
input_img[0, :, :, :] = cv.imread(input_img_name_1)
input_img[1, :, :, :] = cv.imread(input_img_name_2)
input_img[2, :, :, :] = cv.imread(input_img_name_3)
input_img[3, :, :, :] = cv.imread(input_img_name_4)
input_img[4, :, :, :] = cv.imread(input_img_name_5)
input_tensor = NumpyToTensor(multi_frame=True)(input_img.copy())
input = torch.unsqueeze(input_tensor, dim=0).float().contiguous()
model.eval()
if opt.cuda:
model = model.cuda()
input = input.cuda()
out = model(input)
out = out.cpu()
if not os.path.exists(opt.output_path):
os.makedirs(opt.output_path)
if not os.path.exists('{}output/'.format(opt.output_path)):
os.makedirs('{}output/'.format(opt.output_path))
if not os.path.exists('{}gt/'.format(opt.output_path)):
os.makedirs('{}gt/'.format(opt.output_path))
if not os.path.exists('{}input/'.format(opt.output_path)):
os.makedirs('{}input/'.format(opt.output_path))
output_tensor = out.clone().detach()
output_tensor = output_tensor[:, [2, 1, 0], :, :]
output_tensor = output_tensor.squeeze()
output_tensor = output_tensor.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).type(
torch.uint8).numpy()
img_original = cv.imread('{}{}.png'.format(opt.gt_path, "%03d" % index))
input_center_img = cv.imread('{}{}.png'.format(opt.input_path, "%03d" % index))
if opt.save_img is True:
save_image_tensor(out, '{}output/{}.png'.format(opt.output_path, "%03d" % index))
print('saving:', '{}{}.png'.format(opt.output_path, "%03d" % index))
cv.imwrite('{}gt/{}.png'.format(opt.output_path, "%03d" % index), img_original)
cv.imwrite('{}input/{}.png'.format(opt.output_path, "%03d" % index), input_center_img)
out = out.squeeze(0)
out_img = out.mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).type(
torch.uint8).numpy()
if opt.calc_on_y is True:
input_center_img = cv.cvtColor(input_center_img, cv.COLOR_BGR2YUV)
input_center_img = input_center_img[:, :, 0]
img_original = cv.cvtColor(img_original, cv.COLOR_BGR2YUV)
img_original = img_original[:, :, 0]
out_img = cv.cvtColor(out_img, cv.COLOR_BGR2YUV)
out_img = out_img[:, :, 0]
input_psnr = calc_psnr(transforms.ToTensor()(input_center_img),
transforms.ToTensor()(img_original))
output_psnr = calc_psnr(transforms.ToTensor()(out_img),
transforms.ToTensor()(img_original))
input_ssim = ssim(input_center_img,
img_original, multichannel=True)
output_ssim = ssim(out_img,
img_original, multichannel=True)
if index > 2 or index < len(img_list) - 1:
input_psnr_avg += input_psnr
output_psnr_avg += output_psnr
input_ssim_avg += input_ssim
output_ssim_avg += output_ssim
print(output_psnr)
input_psnr_avg = input_psnr_avg / (index - 4)
output_psnr_avg = output_psnr_avg / (index - 4)
input_ssim_avg = input_ssim_avg / (index - 4)
output_ssim_avg = output_ssim_avg / (index - 4)
print('input_psnr_avg:', input_psnr_avg)
print('output_psnr_avg:', output_psnr_avg)
print('psnr_increase:', output_psnr_avg - input_psnr_avg)
print('input_ssim_avg:', "%.4f" % input_ssim_avg)
print('output_ssim_avg:', "%.4f" % output_ssim_avg)
print('ssim_increase:', "%.4f" % (output_ssim_avg - input_ssim_avg))