| import torch |
| import os |
| from tqdm import tqdm |
| from torchvision import transforms |
| from pyiqa.archs.musiq_arch import MUSIQ |
| from editboard.utils import load_video, load_dimension_info |
|
|
| def transform(images, preprocess_mode='shorter'): |
| """preprocess_mode is for setting preprocessing in imaging_quality |
| 1. 'shorter': if the shorter side is more than 512, the image is resized so that the shorter side is 512. |
| 2. 'longer': if the longer side is more than 512, the image is resized so that the longer side is 512. |
| 3. 'shorter_centercrop': if the shorter side is more than 512, the image is resized so that the shorter side is 512. |
| Then the center 512 x 512 after resized is used for evaluation. |
| 4. 'None': no preprocessing |
| """ |
| if preprocess_mode.startswith('shorter'): |
| _, _, h, w = images.size() |
| if min(h,w) > 512: |
| scale = 512./min(h,w) |
| images = transforms.Resize(size=( int(scale * h), int(scale * w) ))(images) |
| if preprocess_mode == 'shorter_centercrop': |
| images = transforms.CenterCrop(512)(images) |
|
|
| elif preprocess_mode == 'longer': |
| _, _, h, w = images.size() |
| if max(h,w) > 512: |
| scale = 512./max(h,w) |
| images = transforms.Resize(size=( int(scale * h), int(scale * w) ))(images) |
|
|
| elif preprocess_mode == 'None': |
| return images / 255. |
|
|
| else: |
| raise ValueError("Please recheck imaging_quality_mode") |
| return images / 255. |
|
|
| def technical_quality(model, video_list, device): |
| preprocess_mode = 'longer' |
| video_results = {} |
| for video_path in tqdm(video_list): |
| images = load_video(video_path) |
| images = transform(images, preprocess_mode) |
| acc_score_video = 0. |
| for i in range(len(images)): |
| frame = images[i].unsqueeze(0).to(device) |
| score = model(frame) |
| acc_score_video += float(score) |
| video_results[os.path.dirname(os.path.dirname(video_path))] = (acc_score_video/len(images)) / 100 |
| return video_results |
|
|
|
|
| def compute_imaging_quality(json_dir, device, submodules_list): |
| model_path = submodules_list['model_path'] |
|
|
| model = MUSIQ(pretrained_model_path=model_path) |
| model.to(device) |
| model.training = False |
| |
| video_list = load_dimension_info(json_dir, dimension='imaging_quality') |
| video_results = technical_quality(model, video_list, device) |
| return video_results |
|
|
