mindspore/model_zoo/official/cv/dncnn/eval.py

147 lines
5.5 KiB
Python
Executable File

#!/usr/bin/env python3
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
# ============================================================================
import os
import io
import argparse
import glob
import PIL
import numpy as np
import cv2
import skimage.metrics
import mindspore
import mindspore.dataset as ds
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.dataset.transforms.c_transforms as C
from src.model import DnCNN
class DnCNN_eval_Dataset():
def __init__(self, dataset_path, task_type, noise_level):
self.im_list = []
self.im_list.extend(glob.glob(os.path.join(dataset_path, "*.png")))
self.im_list.extend(glob.glob(os.path.join(dataset_path, "*.bmp")))
self.im_list.extend(glob.glob(os.path.join(dataset_path, "*.jpg")))
self.task_type = task_type
self.noise_level = noise_level
def __getitem__(self, i):
img = cv2.imread(self.im_list[i], 0)
if self.task_type == "denoise":
noisy = self.add_noise(img, self.noise_level)
elif self.task_type == "super-resolution":
h, w = img.shape
noisy = cv2.resize(img, (int(w/self.noise_level), int(h/self.noise_level)))
noisy = cv2.resize(noisy, (w, h))
elif self.task_type == "jpeg-deblock":
noisy = self.jpeg_compression(img, self.noise_level)
#add channel dimension
noisy = noisy[np.newaxis, :, :]
noisy = noisy / 255.0
return noisy, img
def __len__(self):
return len(self.im_list)
def add_noise(self, im, sigma):
gauss = np.random.normal(0, sigma, im.shape)
noisy = im + gauss
noisy = np.clip(noisy, 0, 255)
noisy = noisy.astype('float32')
return noisy
def jpeg_compression(self, img, severity):
im_pil = PIL.Image.fromarray(img)
output = io.BytesIO()
im_pil.save(output, 'JPEG', quality=severity)
im_pil = PIL.Image.open(output)
img_np = np.asarray(im_pil)
return img_np
def create_eval_dataset(data_path, task_type, noise_level, batch_size=1):
# define dataset
dataset = DnCNN_eval_Dataset(data_path, task_type, noise_level)
dataloader = ds.GeneratorDataset(dataset, ["noisy", "clear"])
# apply map operations on images
dataloader = dataloader.map(input_columns="noisy", operations=C.TypeCast(mindspore.float32))
dataloader = dataloader.map(input_columns="clear", operations=C.TypeCast(mindspore.uint8))
dataloader = dataloader.batch(batch_size, drop_remainder=False)
return dataloader
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="DnCNN")
parser.add_argument("--dataset_path", type=str, default="/code/12imgs-TestingSet/", help='eval image path')
parser.add_argument('--ckpt_path', type=str, default=False, help='trained ckpt')
parser.add_argument('--model_type', type=str, default='DnCNN-S', \
choices=['DnCNN-S', 'DnCNN-B', 'DnCNN-3'], help='type of DnCNN')
parser.add_argument('--noise_type', type=str, default=False, \
choices=["denoise", "super-resolution", "jpeg-deblock"], help='trained ckpt')
parser.add_argument('--noise_level', type=int, default=False, help='trained ckpt')
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
ds_eval = create_eval_dataset(args.dataset_path, args.noise_type, args.noise_level, batch_size=1)
print("evaluation image number:", ds_eval.get_dataset_size())
if args.model_type == 'DnCNN-S':
network = DnCNN(1, num_of_layers=17)
elif args.model_type == 'DnCNN-3' or args.model_type == 'DnCNN-B':
network = DnCNN(1, num_of_layers=20)
else:
print("wrong model type")
exit()
# load parameter to the network
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
mean_psnr = 0
mean_ssim = 0
count = 0
for data in ds_eval.create_dict_iterator():
clear = data["clear"].asnumpy()
#get denoised image
residual = network(data["noisy"]).asnumpy() * 255
noisy_img = data["noisy"].asnumpy() * 255
denoised = np.clip(noisy - residual, 0, 255).astype("uint8")
denoised = np.squeeze(denoised)
clear = np.squeeze(clear)
noisy_img = np.squeeze(noisy_img)
if count == 0: #save example result
cv2.imwrite("noisy.jpg", noisy_img.astype("uint8"))
cv2.imwrite("denoised.jpg", denoised)
cv2.imwrite("original.jpg", clear)
#calculate psnr
mse = np.mean((clear - denoised)**2)
psnr = 10*np.log10(255*255/mse)
#calculate ssim
ssim = skimage.metrics.structural_similarity(clear, denoised, data_range=255) #skimage 0.18
mean_psnr += psnr
mean_ssim += ssim
count += 1
mean_psnr = mean_psnr / count
mean_ssim = mean_ssim / count
print("mean psnr", mean_psnr)
print("mean_ssim", mean_ssim)