We provide some task-specific demo scripts to test a single image.
You can use the following commands to test a pair of images for inpainting.
python demo/inpainting_demo.py \
${CONFIG_FILE} \
${CHECKPOINT_FILE} \
${MASKED_IMAGE_FILE} \
${MASK_FILE} \
${SAVE_FILE} \
[--imshow] \
[--device ${GPU_ID}]
If --imshow
is specified, the demo will also show image with opencv. Examples:
python demo/inpainting_demo.py \
configs/inpainting/global_local/gl_256x256_8x12_celeba.py \
https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth \
tests/data/image/celeba_test.png \
tests/data/image/bbox_mask.png \
tests/data/pred/inpainting_celeba.png
The predicted inpainting result will be save in tests/data/pred/inpainting_celeba.png
.
You can use the following commands to test a pair of images and trimap.
python demo/matting_demo.py \
${CONFIG_FILE} \
${CHECKPOINT_FILE} \
${IMAGE_FILE} \
${TRIMAP_FILE} \
${SAVE_FILE} \
[--imshow] \
[--device ${GPU_ID}]
If --imshow
is specified, the demo will also show image with opencv. Examples:
python demo/matting_demo.py \
configs/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k.py \
work_dirs/dim_stage3/latest.pth \
tests/data/merged/GT05.jpg \
tests/data/trimap/GT05.png \
tests/data/pred/GT05.png
The predicted alpha matte will be save in tests/data/pred/GT05.png
.
You can use the following commands to test an image for restoration.
python demo/restoration_demo.py \
${CONFIG_FILE} \
${CHECKPOINT_FILE} \
${IMAGE_FILE} \
${SAVE_FILE} \
[--imshow] \
[--device ${GPU_ID}] \
[--ref-path ${REF_PATH}]
If --imshow
is specified, the demo will also show image with opencv. Examples:
python demo/restoration_demo.py \
configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py \
work_dirs/esrgan_x4c64b23g32_g1_400k_div2k/latest.pth \
tests/data/lq/baboon_x4.png \
demo/demo_out_baboon.png
You can test Ref-SR by providing --ref-path
. Examples:
python demo/restoration_demo.py \
configs/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED.py \
https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth \
tests/data/test_multiple_gt/sequence_1/00000000.png \
work_dirs/demo_out.png \
--ref-path tests/data/test_multiple_gt/sequence_1/00000001.png
You can use the following commands to test an face image for restoration.
python demo/restoration_face_demo.py \
${CONFIG_FILE} \
${CHECKPOINT_FILE} \
${IMAGE_FILE} \
${SAVE_FILE} \
[--upscale-factor] \
[--face-size] \
[--imshow] \
[--device ${GPU_ID}]
If --imshow
is specified, the demo will also show image with opencv. Examples:
python demo/restoration_face_demo.py \
configs/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq.py \
https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth \
tests/data/face/000001.png \
results/000001.png \
--upscale-factor 4
You can use the following commands to test a video for restoration.
python demo/restoration_video_demo.py \
${CONFIG_FILE} \
${CHECKPOINT_FILE} \
${INPUT_DIR} \
${OUTPUT_DIR} \
[--window-size=${WINDOW_SIZE}] \
[--device ${GPU_ID}]
It supports both the sliding-window framework and the recurrent framework. Examples:
EDVR:
python demo/restoration_video_demo.py \
./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py \
https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth \
data/Vid4/BIx4/calendar/ \
./output \
--window-size=5
BasicVSR:
python demo/restoration_video_demo.py \
./configs/restorers/basicvsr/basicvsr_reds4.py \
https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth \
data/Vid4/BIx4/calendar/ \
./output
The restored video will be save in output/
.
You can use the following commands to test a video for frame interpolation.
python demo/video_interpolation_demo.py \
${CONFIG_FILE} \
${CHECKPOINT_FILE} \
${INPUT_DIR} \
${OUTPUT_DIR} \
[--fps-multiplier ${FPS_MULTIPLIER}] \
[--fps ${FPS}]
${INPUT_DIR}
/ ${OUTPUT_DIR}
can be a path of video file or the folder of a sequence of ordered images.
If ${OUTPUT_DIR}
is a path of video file, its frame rate can be determined by the frame rate of input video and fps_multiplier
, or be determined by fps
directly (the former has higher priority). Examples:
The frame rate of output video is determined by the frame rate of input video and fps_multiplier
:
python demo/video_interpolation_demo.py \
configs/video_interpolators/cain/cain_b5_320k_vimeo-triplet.py \
https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_320k_vimeo-triple_20220117-647f3de2.pth \
tests/data/test_inference.mp4 \
tests/data/test_inference_vfi_out.mp4 \
--fps-multiplier 2.0
The frame rate of output video is determined by fps
:
python demo/video_interpolation_demo.py \
configs/video_interpolators/cain/cain_b5_320k_vimeo-triplet.py \
https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_320k_vimeo-triple_20220117-647f3de2.pth \
tests/data/test_inference.mp4 \
tests/data/test_inference_vfi_out.mp4 \
--fps 60.0
python demo/generation_demo.py \
${CONFIG_FILE} \
${CHECKPOINT_FILE} \
${IMAGE_FILE} \
${SAVE_FILE} \
[--unpaired-path ${UNPAIRED_IMAGE_FILE}] \
[--imshow] \
[--device ${GPU_ID}]
If --unpaired-path
is specified (used for CycleGAN), the model will perform unpaired image-to-image translation. If --imshow
is specified, the demo will also show image with opencv. Examples:
Paired:
python demo/generation_demo.py \
configs/example_config.py \
work_dirs/example_exp/example_model_20200202.pth \
demo/demo.jpg \
demo/demo_out.jpg
Unpaired (also show image with opencv):
python demo/generation_demo.py 、
configs/example_config.py \
work_dirs/example_exp/example_model_20200202.pth \
demo/demo.jpg \
demo/demo_out.jpg \
--unpaired-path demo/demo_unpaired.jpg \
--imshow
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Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
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