frames folder example results in black images

[antithing]

Hi, and thank you for making this code available!

I am running:

python video_infer.py D:\\DeBlur\\SimDeblur-main\\SimDeblur-main\\configs\\dbn\\dbn_gopro.yaml D:\\DeBlur\\SimDeblur-main\\SimDeblur-main\\saves\\checkpoints\\DBN\\dbn_ckpt.pth --frames_folder_path=datasets/input --save_path=deblur

where video_infer.py is:

import os
import sys
import argparse
import numpy as np
import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
from easydict import EasyDict as edict

from simdeblur.config import build_config
from simdeblur.model import build_backbone
from simdeblur.dataset.frames_folder import FramesFolder

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("config", type=str, help="The config .yaml file of deblurring model. ")
    parser.add_argument("ckpt", type=str, help="The trained checkpoint of the selected deblurring model. ")
    parser.add_argument("--frames_folder_path", type=str, help="The video frames folder path. ")
    parser.add_argument("--save_path", type=str, help="The output deblurred path")

    args = parser.parse_args()

    return args

def frames_foler_demo():
    args = parse_args()
    config = build_config(args.config)
    config.args = args
    model = build_backbone(config.model).cuda()

    ckpt = torch.load(args.ckpt, map_location="cuda:0")

    model_ckpt = ckpt["model"]
    model_ckpt = {k[7:]: v for k, v in model_ckpt.items()}
    model.load_state_dict(model_ckpt)

    data_config = edict({
        "root_input": "D:\\DeBlur\\SimDeblur-main\\SimDeblur-main\\datasets\\input",
        "num_frames": 5,
        "overlapping": True,
        "sampling": "n_c"
    })
    frames_data = FramesFolder(data_config)
    frames_dataloader = torch.utils.data.DataLoader(frames_data, 1)

    model.eval()
    with torch.no_grad():
        for i, batch_data in enumerate(frames_dataloader):
            out = model(batch_data["input_frames"].cuda())
            print(batch_data["gt_names"], out.shape)
            save_image_path = f"./deblurred_frame{i}.png"
            cv2.imwrite(save_image_path, out[0].cpu().permute(1, 2, 0).numpy())

if __name__ == "__main__":
    frames_foler_demo()

The code runs, and prints:

Cannot inport EDVR modules!!!
[('00002.jpg',)] torch.Size([1, 3, 1080, 1920])
[('00003.jpg',)] torch.Size([1, 3, 1080, 1920])
....

But all the resulting frames are just solid black. What might be happening here?

Thanks!

[ljzycmd]

Hi @antithing,

With the provided logs, there might be something wrong with your environment. More specifically, some packages are not correctly installed. You may refer to the Colab notebook for the installation.

Hope this can help you.

[antithing]

Hi, thanks! I have run the install.sh steps again, and i no longer see the warning Cannot inport EDVR modules!!!. The result is:

[('00002.jpg',)] torch.Size([1, 3, 1080, 1920])
[('00003.jpg',)] torch.Size([1, 3, 1080, 1920])

But the frames are still black!

Is this line correct?

` cv2.imwrite(save_image_path, out[0].cpu().permute(1, 2, 0).numpy())``

[ljzycmd]

Hi @antithing, I will check what happened and update the results here ASAP.

[ljzycmd]

Hi @antithing, sorry for the late reply. This problem can be attributed to the saving function cv2.imwrite. You should scale the image range to [0, 255] for saving with cv2.imwrite to avoid the black image. I also updated a video inference demo code https://github.com/ljzycmd/SimDeblur/blob/main/inference_video.py, and you can infer the sharp frames with:

python inference_video.py \
CONFIG_PATH  \
CKPT_PATH \
--frames_folder_path FRAME_FOLDER_PATH \
--save_dir OUTPUT_ROOT

Note that the FRAME_FOLDER_PATH should contain consecutive video frames, you may ffmepg to split a blurry video into separate frames.

Hope this can help you.