You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
123 lines
4.0 KiB
123 lines
4.0 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. |
|
# |
|
#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 cv2 |
|
import glob |
|
import numpy as np |
|
from PIL import Image |
|
|
|
def tqdm(x): |
|
return x |
|
import paddle |
|
from ppgan.models.generators import RRDBNet |
|
from ppgan.utils.video import frames2video, video2frames |
|
from ppgan.utils.download import get_path_from_url |
|
from ppgan.utils.logger import get_logger |
|
|
|
from .base_predictor import BasePredictor |
|
|
|
REALSR_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/applications/DF2K_JPEG.pdparams' |
|
|
|
|
|
class RealSRPredictor(BasePredictor): |
|
def __init__(self, output='output', weight_path=None): |
|
self.input = input |
|
self.output = os.path.join(output, 'RealSR') |
|
self.model = RRDBNet(3, 3, 64, 23) |
|
if weight_path is None: |
|
weight_path = get_path_from_url(REALSR_WEIGHT_URL) |
|
|
|
state_dict = paddle.load(weight_path) |
|
self.model.load_dict(state_dict) |
|
self.model.eval() |
|
|
|
def norm(self, img): |
|
img = np.array(img).transpose([2, 0, 1]).astype('float32') / 255.0 |
|
return img.astype('float32') |
|
|
|
def denorm(self, img): |
|
img = img.transpose((1, 2, 0)) |
|
return (img * 255).clip(0, 255).astype('uint8') |
|
|
|
def run_image(self, img): |
|
if isinstance(img, str): |
|
ori_img = Image.open(img).convert('RGB') |
|
elif isinstance(img, np.ndarray): |
|
ori_img = Image.fromarray(img).convert('RGB') |
|
elif isinstance(img, Image.Image): |
|
ori_img = img |
|
|
|
img = self.norm(ori_img) |
|
x = paddle.to_tensor(img[np.newaxis, ...]) |
|
with paddle.no_grad(): |
|
out = self.model(x) |
|
|
|
pred_img = self.denorm(out.numpy()[0]) |
|
pred_img = Image.fromarray(pred_img) |
|
return pred_img |
|
|
|
def run_video(self, video): |
|
base_name = os.path.basename(video).split('.')[0] |
|
output_path = os.path.join(self.output, base_name) |
|
pred_frame_path = os.path.join(output_path, 'frames_pred') |
|
|
|
if not os.path.exists(output_path): |
|
os.makedirs(output_path) |
|
|
|
if not os.path.exists(pred_frame_path): |
|
os.makedirs(pred_frame_path) |
|
|
|
cap = cv2.VideoCapture(video) |
|
fps = cap.get(cv2.CAP_PROP_FPS) |
|
|
|
out_path = video2frames(video, output_path) |
|
|
|
frames = sorted(glob.glob(os.path.join(out_path, '*.png'))) |
|
|
|
for frame in tqdm(frames): |
|
pred_img = self.run_image(frame) |
|
|
|
frame_name = os.path.basename(frame) |
|
pred_img.save(os.path.join(pred_frame_path, frame_name)) |
|
|
|
frame_pattern_combined = os.path.join(pred_frame_path, '%08d.png') |
|
|
|
vid_out_path = os.path.join(output_path, |
|
'{}_realsr_out.mp4'.format(base_name)) |
|
frames2video(frame_pattern_combined, vid_out_path, str(int(fps))) |
|
|
|
return frame_pattern_combined, vid_out_path |
|
|
|
def run(self, input): |
|
if not os.path.exists(self.output): |
|
os.makedirs(self.output) |
|
|
|
if not self.is_image(input): |
|
return self.run_video(input) |
|
else: |
|
pred_img = self.run_image(input) |
|
|
|
out_path = None |
|
if self.output: |
|
try: |
|
base_name = os.path.splitext(os.path.basename(input))[0] |
|
except: |
|
base_name = 'result' |
|
out_path = os.path.join(self.output, base_name + '.png') |
|
pred_img.save(out_path) |
|
logger = get_logger() |
|
logger.info('Image saved to {}'.format(out_path)) |
|
|
|
return pred_img, out_path
|
|
|