+
+
+
+:star: If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! :hugs:
+
+
+### Update
+- **2023.07.20**: Integrated to :panda_face: [OpenXLab](https://openxlab.org.cn/apps). Try out online demo! [](https://openxlab.org.cn/apps/detail/ShangchenZhou/CodeFormer)
+- **2023.04.19**: :whale: Training codes and config files are public available now.
+- **2023.04.09**: Add features of inpainting and colorization for cropped and aligned face images.
+- **2023.02.10**: Include `dlib` as a new face detector option, it produces more accurate face identity.
+- **2022.10.05**: Support video input `--input_path [YOUR_VIDEO.mp4]`. Try it to enhance your videos! :clapper:
+- **2022.09.14**: Integrated to :hugs: [Hugging Face](https://huggingface.co/spaces). Try out online demo! [](https://huggingface.co/spaces/sczhou/CodeFormer)
+- **2022.09.09**: Integrated to :rocket: [Replicate](https://replicate.com/explore). Try out online demo! [](https://replicate.com/sczhou/codeformer)
+- [**More**](docs/history_changelog.md)
+
+### TODO
+- [x] Add training code and config files
+- [x] Add checkpoint and script for face inpainting
+- [x] Add checkpoint and script for face colorization
+- [x] ~~Add background image enhancement~~
+
+#### :panda_face: Try Enhancing Old Photos / Fixing AI-arts
+[
](https://imgsli.com/MTI3NTE2) [
](https://imgsli.com/MTI3NTE1) [
](https://imgsli.com/MTI3NTIw)
+
+#### Face Restoration
+
+
+
+
+#### Face Color Enhancement and Restoration
+
+
+
+#### Face Inpainting
+
+
+
+
+
+### Dependencies and Installation
+
+- Pytorch >= 1.7.1
+- CUDA >= 10.1
+- Other required packages in `requirements.txt`
+```
+# git clone this repository
+git clone https://github.com/sczhou/CodeFormer
+cd CodeFormer
+
+# create new anaconda env
+conda create -n codeformer python=3.8 -y
+conda activate codeformer
+
+# install python dependencies
+pip3 install -r requirements.txt
+python basicsr/setup.py develop
+conda install -c conda-forge dlib (only for face detection or cropping with dlib)
+```
+
+
+### Quick Inference
+
+#### Download Pre-trained Models:
+Download the facelib and dlib pretrained models from [[Releases](https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0) | [Google Drive](https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EvDxR7FcAbZMp_MA9ouq7aQB8XTppMb3-T0uGZ_2anI2mg?e=DXsJFo)] to the `weights/facelib` folder. You can manually download the pretrained models OR download by running the following command:
+```
+python scripts/download_pretrained_models.py facelib
+python scripts/download_pretrained_models.py dlib (only for dlib face detector)
+```
+
+Download the CodeFormer pretrained models from [[Releases](https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0) | [Google Drive](https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EoKFj4wo8cdIn2-TY2IV6CYBhZ0pIG4kUOeHdPR_A5nlbg?e=AO8UN9)] to the `weights/CodeFormer` folder. You can manually download the pretrained models OR download by running the following command:
+```
+python scripts/download_pretrained_models.py CodeFormer
+```
+
+#### Prepare Testing Data:
+You can put the testing images in the `inputs/TestWhole` folder. If you would like to test on cropped and aligned faces, you can put them in the `inputs/cropped_faces` folder. You can get the cropped and aligned faces by running the following command:
+```
+# you may need to install dlib via: conda install -c conda-forge dlib
+python scripts/crop_align_face.py -i [input folder] -o [output folder]
+```
+
+
+#### Testing:
+[Note] If you want to compare CodeFormer in your paper, please run the following command indicating `--has_aligned` (for cropped and aligned face), as the command for the whole image will involve a process of face-background fusion that may damage hair texture on the boundary, which leads to unfair comparison.
+
+Fidelity weight *w* lays in [0, 1]. Generally, smaller *w* tends to produce a higher-quality result, while larger *w* yields a higher-fidelity result. The results will be saved in the `results` folder.
+
+
+🧑🏻 Face Restoration (cropped and aligned face)
+```
+# For cropped and aligned faces (512x512)
+python inference_codeformer.py -w 0.5 --has_aligned --input_path [image folder]|[image path]
+```
+
+:framed_picture: Whole Image Enhancement
+```
+# For whole image
+# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
+# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
+python inference_codeformer.py -w 0.7 --input_path [image folder]|[image path]
+```
+
+:clapper: Video Enhancement
+```
+# For Windows/Mac users, please install ffmpeg first
+conda install -c conda-forge ffmpeg
+```
+```
+# For video clips
+# Video path should end with '.mp4'|'.mov'|'.avi'
+python inference_codeformer.py --bg_upsampler realesrgan --face_upsample -w 1.0 --input_path [video path]
+```
+
+🌈 Face Colorization (cropped and aligned face)
+```
+# For cropped and aligned faces (512x512)
+# Colorize black and white or faded photo
+python inference_colorization.py --input_path [image folder]|[image path]
+```
+
+🎨 Face Inpainting (cropped and aligned face)
+```
+# For cropped and aligned faces (512x512)
+# Inputs could be masked by white brush using an image editing app (e.g., Photoshop)
+# (check out the examples in inputs/masked_faces)
+python inference_inpainting.py --input_path [image folder]|[image path]
+```
+### Training:
+The training commands can be found in the documents: [English](docs/train.md) **|** [简体中文](docs/train_CN.md).
+
+### License
+
+This project is licensed under 
| 
| 
|
+|:-------------------------:|:-------------------------:|:-------------------------:|
+| Low-light w/ blur | RetinexFormer | **DarkIR** (ours) |
+| 
| 
| 
|
+| Low-light w/o blur | LEDNet | **DarkIR** (ours) |
+
+
+
+## Network Architecture
+
+
+
+## Dependencies and Installation
+
+- Python == 3.10.12
+- PyTorch == 2.5.1
+- CUDA == 12.4
+- Other required packages in `requirements.txt`
+
+```
+# git clone this repository
+git clone https://github.com/Fundacion-Cidaut/DarkIR.git
+cd DarkIR
+
+# create python environment
+python3 -m venv venv_DarkIR
+source venv_DarkIR/bin/activate
+
+# install python dependencies
+pip install -r requirements.txt
+```
+
+## Datasets
+The datasets used for training and/or evaluation are:
+
+|Dataset | Sets of images | Source |
+| -----------| :---------------:|------|
+|LOL-Blur | 10200 training pairs / 1800 test pairs| [LEDNet](https://github.com/sczhou/LEDNet) |
+|LOLv2-real | 689 training pairs / 100 test pairs | [Google Drive](https://drive.google.com/file/d/1dzuLCk9_gE2bFF222n3-7GVUlSVHpMYC/view) |
+|LOLv2-synth | 900 training pairs / 100 test pairs | [Google Drive](https://drive.google.com/file/d/1dzuLCk9_gE2bFF222n3-7GVUlSVHpMYC/view) |
+|LOL | 485 training pairs / 15 test pairs | [Official Site](https://daooshee.github.io/BMVC2018website/) |
+|Real-LOLBlur | 1354 unpaired images | [LEDNet](https://github.com/sczhou/LEDNet) |
+|LSRW-Nikon | 3150 training pairs / 20 test pairs | [R2RNet](https://github.com/JianghaiSCU/R2RNet) |
+|LSRW-Huawei | 2450 training pairs / 30 test pairs | [R2RNet](https://github.com/JianghaiSCU/R2RNet) |
+
+
+You can download each specific dataset and put it on the `/data/datasets` folder for testing.
+
+## Results
+We present results in different datasets for DarkIR of different sizes. While **DarkIR-m** has channel depth of 32, 3.31 M parameters and 7.25 GMACs, **DarkIR-l** has channel depth 64, 12.96 M parameters and 27.19 GMACs.
+
+|Dataset | Model| PSNR| SSIM | LPIPS |
+| -----------| :---------------:|:------:|------|------|
+|LOL-Blur | DarkIR-m| 27.00| 0.883| 0.162|
+| | DarkIR-l| 27.30| 0.898| 0.137|
+|LOLv2-real | DarkIR-m| 23.87| 0.880| 0.186|
+|LOLv2-synth | DarkIR-m| 25.54| 0.934| 0.058|
+|LSRW-Both | DarkIR-m| 18.93| 0.583| 0.412|
+
+We present perceptual metrics for Real-LOLBlur dataset:
+
+| Model| MUSIQ| NRQM | NIQE |
+| -----------| :---------------:|:------:|:------:|
+| DarkIR-m| 48.36| 4.983| 4.998|
+| DarkIR-l| 48.79| 4.917| 5.051|
+
+> LOLBlur results were obtained training the network only in this dataset. Best results in LOLv2-real, LOLv2-synth and both LSRW were obtained in a multitask training of the three datasets with LOLBlur (getting 26.63 PSNR and 0.875 SSIM in this dataset). Finally Real-LOLBlur results were obtained with a model trained in LOLBlur.
+
+In addition, we tested our **DarkIR-m** in Real-World LLIE unpaired Datasets (downloaded from [Drive](https://drive.google.com/drive/folders/0B_FjaR958nw_djVQanJqeEhUM1k?usp=sharing)):
+
+| | DICM| MEF | LIME | NPE | VV |
+| -----------| :---------------:|:------:|:------:|:------:|:------:|
+| BRISQUE| 18.688| 13.903| 21.62| 12.877| 26.87|
+| NIQE| 3.759| 3.448| 4.074| 3.991| 3.74|
+
+
+
+## Evaluation
+
+To check our results you could run the evaluation of DarkIR in each of the datasets:
+
+- Download the weights of the model from [OneDrive](https://cidautes-my.sharepoint.com/:f:/g/personal/alvgar_cidaut_es/Epntbl4SucFNpeIT_jyYZ-cB9BamMbacbyq_svrkMCpShA?e=XB9YBB) and put them in `/models`.
+- run `python testing.py -p ./options/test/
| 
| 
| 
| 
|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+| Low-light | SNR-Net | RetinexFormer | **DarkIR** (ours) | Ground Truth |
+
+
| 
| 
| 
| 
|
+|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
+| Low-light | SNR-Net | RetinexFormer | **DarkIR** (ours) | Ground Truth |
+
+
+
+
| 
+
+
+
+### Text-Guided Object Inpainting with ControlNet
+
+Fortunately, PowerPaint is compatible with ControlNet. Therefore, users can generate object with a control image.
+
+|Input| Condition | Control Image |Output|
+|-------|--------|-------|----------|
+|
| Canny| 
| 
+|
| 
+|
| 
+|
| Pose| 
| 
+
+
+### Object Removal
+
+For object removal, you need to select the tab of `Object removal inpainting` and you don't need to input any prompts. PowerPaint is able to fill in the masked region according to context background.
+
+We remain the text box for inputing prompt, allowing users to further suppress object generation by using negative prompts.
+Specifically, we recommend to use 10 or higher value for Guidance Scale. If undesired objects appear in the masked area, you can address this by specifically increasing the Guidance Scale.
+
+|Input|Output|
+|---------------|-----------------|
+| 
| 
+
+
+
+### Image Outpainting
+
+For image outpainting, you don't need to input any text prompt. You can simply select the tab of `Image outpainting` and adjust the slider for `horizontal expansion ratio` and `vertical expansion ratio`, then PowerPaint will extend the image for you.
+
+|Input|Output|
+|---------------|-----------------|
+| 
| 
+
+
+
+### Shape-Guided Object Inpainting
+
+PowerPaint also supports shape-guided object inpainting, which allows users to control the fitting degree of the generated objects to the shape of masks. You can select the tab of `Shape-guided object inpainting` and input the text prompt. Then, you can adjust the slider of `fitting degree` to control the shape of generated object.
+
+Taking the following cases as example, you can draw a square mask and use a high fitting degree, e.g., 0.95, to generate a bread to fit in the mask shape. For the same mask, you can also use a low fitting degree, e.g., 0.55, to generate a reasonable result for rabbit. However, if you use a high fitting degree for the 'square rabit', the result may look funny.
+
+Basically, we recommend to use 0.5-0.6 for fitting degree when you want to generate objects that are not constrained by the mask shape. If you want to generate objects that fit the mask shape, you can use 0.8-0.95 for fitting degree.
+
+
+|Prompt | Fitting Degree | Input| Output|
+|-------|--------|--------|---------|
+|a bread | 0.95| 
| 
+|a rabbit | 0.55| 
| 
+|a rabbit | 0.95|
| 
+|a rabbit | 0.95 | 
| 
+
+
+
+## Training
+
+1. Prepare training data. You may need to rewrite [`Datasets`](./powerpaint/datasets/__init__.py）per your need (e.g., data and storage formats). Here, we use petreloss to read training dataset from cloud storages. Besides, the recipe of datasets for training a versatile model can be tricky but intuitive.
+
+2. Start training. We suggest using PowerPaint-V2 version, which is built upon BrushNet and requires smaller batch size for training. You can train it with the following command,
+```shell
+# running on a single node
+accelerate launch --config_file configs/acc.yaml train_ppt2_bn.py --config configs/ppt2_bn.yaml --output_dir runs/ppt1_sd15
+
+# running on one node by slurm, e.g., 1 nodes with 8 gpus in total
+python submit.py --job-name ppt2_bn --gpus 8 train_ppt2_bn.py --config configs/ppt2_bn.yaml --output_dir runs/ppt2_bn
+```
+where `configs/acc.yaml` is the configuration file for using accelerate, and `configs/ppt2_bn.yaml` is the configuration file for training PowerPaint-V2.
+
+PowerPaint-V1 version often requires much larger training batch size to converge (e.g., 1024). You can train it with the following command,
+
+```shell
+# running on a single node
+accelerate launch --config_file configs/acc.yaml train_ppt1_sd15.py --config configs/ppt1_sd15.yaml --output_dir runs/ppt1_sd15 --gradient_accumulation_steps 2 --train_batch_size 64
+
+# running on two nodes by slurm, e.g., 2 nodes with 8 gpus in total
+python submit.py --job-name ppt1_sd15 --gpus 16 train_ppt1_sd15.py --config configs/ppt1_sd15.yaml --output_dir runs/ppt1_sd15 --train_batch_size 64
+```
+where `configs/acc.yaml` is the configuration file for using accelerate, and `configs/ppt1_sd15.yaml` is the configuration file for training PowerPaint-V1.
+
+
+
+
+## Contact Us
+
+**Junhao Zhuang**: zhuangjh23@mails.tsinghua.edu.cn
+
+**Yanhong Zeng**: zengyh1900@gmail.com
+
+
+
+
+## BibTeX
+
+```
+@misc{zhuang2023task,
+ title={A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting},
+ author={Junhao Zhuang and Yanhong Zeng and Wenran Liu and Chun Yuan and Kai Chen},
+ year={2023},
+ eprint={2312.03594},
+ archivePrefix={arXiv},
+ primaryClass={cs.CV}
+}
+```
diff --git a/PART1/PowerPaint/app.py b/PART1/PowerPaint/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..684b0a1e8932d74a4bce86278cee27b5113a98bc
--- /dev/null
+++ b/PART1/PowerPaint/app.py
@@ -0,0 +1,667 @@
+import argparse
+import os
+
+import cv2
+import gradio as gr
+import numpy as np
+import torch
+from accelerate.utils import set_seed
+from controlnet_aux import HEDdetector, OpenposeDetector
+from PIL import Image, ImageFilter
+from transformers import CLIPTextModel, DPTFeatureExtractor, DPTForDepthEstimation
+
+from diffusers.pipelines.controlnet.pipeline_controlnet import ControlNetModel
+from powerpaint.models import BrushNetModel, UNet2DConditionModel
+from powerpaint.pipelines import (
+ StableDiffusionControlNetInpaintPipeline,
+ StableDiffusionInpaintPipeline,
+ StableDiffusionPowerPaintBrushNetPipeline,
+)
+
+
+# =======================================
+# use the same task prompt as training
+# =======================================
+TASK_LIST = ["text-guided", "object-removal", "image-outpainting", "shape-guided"]
+TASK_PROMPT = {
+ "ppt1": {
+ "text-guided": {
+ "prompt": "",
+ "negative_prompt": "",
+ "promptA": "P_obj {}",
+ "promptB": "P_obj {}",
+ "negative_promptA": "{}",
+ "negative_promptB": "{}",
+ },
+ "object-removal": {
+ "prompt": "",
+ "negative_prompt": "",
+ "promptA": "P_ctxt empty scene blur",
+ "promptB": "P_ctxt empty scene blur",
+ "negative_promptA": "P_obj {}",
+ "negative_promptB": "P_obj {}",
+ },
+ "image-outpainting": {
+ "prompt": "",
+ "negative_prompt": "",
+ "promptA": "P_ctxt empty scene blur, {}",
+ "promptB": "P_ctxt empty scene blur, {}",
+ "negative_promptA": "P_obj {}",
+ "negative_promptB": "P_obj {}",
+ },
+ "shape-guided": {
+ "prompt": "",
+ "negative_prompt": "",
+ "promptA": "P_shape {}",
+ "promptB": "P_ctxt {}",
+ "negative_promptA": "P_shape {}, worst quality, low quality, normal quality, bad quality, blurry",
+ "negative_promptB": "P_ctxt {}, worst quality, low quality, normal quality, bad quality, blurry",
+ },
+ },
+ "ppt2": {
+ "text-guided": {
+ "prompt": "{}",
+ "negative_prompt": "{}, worst quality, low quality, normal quality, bad quality, blurry",
+ "promptA": "P_obj",
+ "promptB": "P_obj",
+ "negative_promptA": "P_obj",
+ "negative_promptB": "P_obj",
+ },
+ "object-removal": {
+ "prompt": "{} empty scene blur",
+ "negative_prompt": "{}, worst quality, low quality, normal quality, bad quality, blurry",
+ "promptA": "P_ctxt",
+ "promptB": "P_ctxt",
+ "negative_promptA": "P_obj",
+ "negative_promptB": "P_obj",
+ },
+ "image-outpainting": {
+ "prompt": "{} empty scene blur",
+ "negative_prompt": "{}, worst quality, low quality, normal quality, bad quality, blurry",
+ "promptA": "P_ctxt",
+ "promptB": "P_ctxt",
+ "negative_promptA": "P_obj",
+ "negative_promptB": "P_obj",
+ },
+ "shape-guided": {
+ "prompt": "{}",
+ "negative_prompt": "{}, worst quality, low quality, normal quality, bad quality, blurry",
+ "promptA": "P_shape",
+ "promptB": "P_ctxt",
+ "negative_promptA": "P_shape",
+ "negative_promptB": "P_ctxt",
+ },
+ },
+}
+
+
+class PowerPaintController:
+ def __init__(
+ self, pretrained_model_path, version, base_model_path=None, weight_dtype=torch.float16, local_files_only=False
+ ) -> None:
+ self.version = version
+ self.pretrained_model_path = pretrained_model_path
+ self.base_model_path = base_model_path
+ self.local_files_only = local_files_only
+ torch.set_grad_enabled(False)
+
+ # initialize powerpaint pipeline
+ if version == "ppt1":
+ self.pipe = StableDiffusionInpaintPipeline.from_pretrained(
+ self.base_model_path,
+ unet=UNet2DConditionModel.from_pretrained(
+ self.pretrained_model_path,
+ subfolder="unet",
+ torch_dtype=weight_dtype,
+ local_files_only=local_files_only,
+ ).to("cuda"),
+ text_encoder=CLIPTextModel.from_pretrained(
+ self.pretrained_model_path,
+ subfolder="text_encoder",
+ torch_dtype=weight_dtype,
+ local_files_only=local_files_only,
+ ).to("cuda"),
+ torch_dtype=weight_dtype,
+ local_files_only=local_files_only,
+ safety_checker=None,
+ )
+ else:
+ # brushnet-based version
+ self.pipe = StableDiffusionPowerPaintBrushNetPipeline.from_pretrained(
+ self.base_model_path,
+ unet=UNet2DConditionModel.from_pretrained(
+ self.base_model_path,
+ subfolder="unet",
+ torch_dtype=weight_dtype,
+ local_files_only=local_files_only,
+ ).to("cuda"),
+ brushnet=BrushNetModel.from_pretrained(
+ self.pretrained_model_path,
+ subfolder="brushnet",
+ torch_dtype=weight_dtype,
+ local_files_only=local_files_only,
+ ).to("cuda"),
+ text_encoder=CLIPTextModel.from_pretrained(
+ self.pretrained_model_path,
+ subfolder="text_encoder",
+ torch_dtype=weight_dtype,
+ local_files_only=local_files_only,
+ ),
+ torch_dtype=weight_dtype,
+ safety_checker=None,
+ local_files_only=local_files_only,
+ )
+
+ # IMPORTANT:
+ # 1. Add tokens in the same order and placeholder with training
+ # 2. set initilize_parameters to False to avoid reinitializing the model
+ self.pipe.add_tokens(
+ placeholder_tokens=["P_obj", "P_ctxt", "P_shape"],
+ initializer_tokens=["a", "a", "a"],
+ num_vectors_per_token=10,
+ initialize_parameters=False,
+ )
+
+ self.pipe.enable_model_cpu_offload()
+ self.pipe = self.pipe.to("cuda")
+
+ if self.version == "ppt1":
+ # initialize controlnet-related models
+ self.depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
+ self.feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
+ self.openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+ self.hed = HEDdetector.from_pretrained("lllyasviel/ControlNet")
+
+ base_control = ControlNetModel.from_pretrained(
+ "lllyasviel/sd-controlnet-canny", torch_dtype=weight_dtype, local_files_only=local_files_only
+ )
+ self.control_pipe = StableDiffusionControlNetInpaintPipeline(
+ self.pipe.vae,
+ self.pipe.text_encoder,
+ self.pipe.tokenizer,
+ self.pipe.unet,
+ base_control,
+ self.pipe.scheduler,
+ None,
+ None,
+ False,
+ )
+ self.control_pipe = self.control_pipe.to("cuda")
+ self.current_control = "canny"
+ # controlnet_conditioning_scale = 0.8
+
+ def get_depth_map(self, image):
+ image = self.feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
+ with torch.no_grad(), torch.autocast("cuda"):
+ depth_map = self.depth_estimator(image).predicted_depth
+
+ depth_map = torch.nn.functional.interpolate(
+ depth_map.unsqueeze(1),
+ size=(1024, 1024),
+ mode="bicubic",
+ align_corners=False,
+ )
+ depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
+ depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
+ depth_map = (depth_map - depth_min) / (depth_max - depth_min)
+ image = torch.cat([depth_map] * 3, dim=1)
+
+ image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
+ image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
+ return image
+
+ # haven't validated the controlnet part
+ def load_controlnet(self, control_type):
+ if self.current_control != control_type:
+ if control_type == "canny" or control_type is None:
+ self.control_pipe.controlnet = ControlNetModel.from_pretrained(
+ "lllyasviel/sd-controlnet-canny", torch_dtype=weight_dtype, local_files_only=self.local_files_only
+ )
+ elif control_type == "pose":
+ self.control_pipe.controlnet = ControlNetModel.from_pretrained(
+ "lllyasviel/sd-controlnet-openpose",
+ torch_dtype=weight_dtype,
+ local_files_only=self.local_files_only,
+ )
+ elif control_type == "depth":
+ self.control_pipe.controlnet = ControlNetModel.from_pretrained(
+ "lllyasviel/sd-controlnet-depth", torch_dtype=weight_dtype, local_files_only=self.local_files_only
+ )
+ else:
+ self.control_pipe.controlnet = ControlNetModel.from_pretrained(
+ "lllyasviel/sd-controlnet-hed", torch_dtype=weight_dtype, local_files_only=self.local_files_only
+ )
+ self.control_pipe = self.control_pipe.to("cuda")
+ self.current_control = control_type
+
+ # haven't validated the controlnet part
+ def predict_controlnet(
+ self,
+ input_image,
+ input_control_image,
+ control_type,
+ prompt,
+ ddim_steps,
+ scale,
+ seed,
+ negative_prompt,
+ controlnet_conditioning_scale,
+ ):
+ promptA = prompt + " P_obj"
+ promptB = prompt + " P_obj"
+ negative_promptA = negative_prompt
+ negative_promptB = negative_prompt
+ size1, size2 = input_image["image"].convert("RGB").size
+
+ if size1 < size2:
+ input_image["image"] = input_image["image"].convert("RGB").resize((640, int(size2 / size1 * 640)))
+ else:
+ input_image["image"] = input_image["image"].convert("RGB").resize((int(size1 / size2 * 640), 640))
+ img = np.array(input_image["image"].convert("RGB"))
+ W = int(np.shape(img)[0] - np.shape(img)[0] % 8)
+ H = int(np.shape(img)[1] - np.shape(img)[1] % 8)
+ input_image["image"] = input_image["image"].resize((H, W))
+ input_image["mask"] = input_image["mask"].resize((H, W))
+
+ if control_type != self.current_control:
+ self.load_controlnet(control_type)
+ controlnet_image = input_control_image
+ if control_type == "canny":
+ controlnet_image = controlnet_image.resize((H, W))
+ controlnet_image = np.array(controlnet_image)
+ controlnet_image = cv2.Canny(controlnet_image, 100, 200)
+ controlnet_image = controlnet_image[:, :, None]
+ controlnet_image = np.concatenate([controlnet_image, controlnet_image, controlnet_image], axis=2)
+ controlnet_image = Image.fromarray(controlnet_image)
+ elif control_type == "pose":
+ controlnet_image = self.openpose(controlnet_image)
+ elif control_type == "depth":
+ controlnet_image = controlnet_image.resize((H, W))
+ controlnet_image = self.get_depth_map(controlnet_image)
+ else:
+ controlnet_image = self.hed(controlnet_image)
+
+ mask_np = np.array(input_image["mask"].convert("RGB"))
+ controlnet_image = controlnet_image.resize((H, W))
+ set_seed(seed)
+ result = self.control_pipe(
+ promptA=promptB,
+ promptB=promptA,
+ tradeoff=1.0,
+ tradeoff_nag=1.0,
+ negative_promptA=negative_promptA,
+ negative_promptB=negative_promptB,
+ image=input_image["image"].convert("RGB"),
+ mask=input_image["mask"].convert("RGB"),
+ control_image=controlnet_image,
+ width=H,
+ height=W,
+ guidance_scale=scale,
+ controlnet_conditioning_scale=controlnet_conditioning_scale,
+ num_inference_steps=ddim_steps,
+ ).images[0]
+ red = np.array(result).astype("float") * 1
+ red[:, :, 0] = 180.0
+ red[:, :, 2] = 0
+ red[:, :, 1] = 0
+ result_m = np.array(result)
+ result_m = Image.fromarray(
+ (
+ result_m.astype("float") * (1 - mask_np.astype("float") / 512.0)
+ + mask_np.astype("float") / 512.0 * red
+ ).astype("uint8")
+ )
+
+ mask_np = np.array(input_image["mask"].convert("RGB"))
+ m_img = input_image["mask"].convert("RGB").filter(ImageFilter.GaussianBlur(radius=4))
+ m_img = np.asarray(m_img) / 255.0
+ img_np = np.asarray(input_image["image"].convert("RGB")) / 255.0
+ ours_np = np.asarray(result) / 255.0
+ ours_np = ours_np * m_img + (1 - m_img) * img_np
+ result_paste = Image.fromarray(np.uint8(ours_np * 255))
+ return [input_image["image"].convert("RGB"), result_paste], [controlnet_image, result_m]
+
+ def predict(
+ self,
+ task,
+ prompt,
+ negative_prompt,
+ promptA,
+ negative_promptA,
+ promptB,
+ negative_promptB,
+ fitting_degree,
+ input_image,
+ vertical_expansion_ratio=1,
+ horizontal_expansion_ratio=1,
+ ddim_steps=45,
+ scale=7.5,
+ seed=24,
+ ):
+ image, mask = input_image["image"].convert("RGB"), input_image["mask"].convert("RGB")
+
+ # resizing images due to limited memory
+ w, h = image.size
+ new_size = 640 if task != "image-outpainting" else 512
+ image = (
+ image.resize((new_size, int(h / w * new_size)))
+ if w < h
+ else image.resize((int(w / h * new_size), new_size))
+ )
+ mask = mask.resize(image.size, Image.NEAREST)
+ w, h = image.size
+ hole_value = (0, 0, 0)
+
+ # preparing masks for outpainting
+ if task == "image-outpainting":
+ if vertical_expansion_ratio != 1 or horizontal_expansion_ratio != 1:
+ w2, h2 = int(horizontal_expansion_ratio * w), int(vertical_expansion_ratio * h)
+ posw, posh = (w2 - w) // 2, (h2 - h) // 2
+
+ new_image = Image.new("RGB", (w2, h2), hole_value)
+ new_image.paste(image, (posw, posh))
+ image = new_image
+ new_mask = Image.new("RGB", (w2, h2), (255, 255, 255))
+ new_mask.paste(mask, (posw, posh))
+ mask = new_mask
+ w, h = image.size
+
+ # resizing to be divided by 8
+ w, h = w // 8 * 8, h // 8 * 8
+ image = image.resize((w, h))
+ mask = mask.resize((w, h))
+ masked_image = Image.composite(Image.new("RGB", (w, h), hole_value), image, mask.convert("L"))
+
+ # augment mask boundary for better blending results
+ # threshold = 0
+ # aug_mask = mask.filter(ImageFilter.GaussianBlur(radius=5)).convert('L')
+ # aug_mask = aug_mask.point(lambda p: 255 if p > threshold else 0).convert('L')
+ aug_mask = mask
+
+ result = self.pipe(
+ promptA=promptA,
+ promptB=promptB,
+ prompt=prompt,
+ negative_promptA=negative_promptA,
+ negative_promptB=negative_promptB,
+ negative_prompt=negative_prompt,
+ tradeoff=fitting_degree,
+ # input masked_image and augmented mask
+ image=masked_image,
+ mask=aug_mask,
+ # default diffusion parameters
+ num_inference_steps=ddim_steps,
+ generator=torch.Generator("cuda").manual_seed(seed),
+ brushnet_conditioning_scale=1.0,
+ guidance_scale=scale,
+ width=w,
+ height=h,
+ ).images[0]
+
+ # paste the inpainting results into original images
+ result_paste = Image.composite(result, image, aug_mask.convert("L"))
+ dict_out = [masked_image, result_paste]
+ dict_res = [input_image["image"].convert("RGB"), input_image["mask"].convert("RGB"), result]
+ return dict_out, dict_res
+
+
+def parse_args():
+ args = argparse.ArgumentParser()
+ args.add_argument("--pretrained_model_path", type=str, required=True)
+ args.add_argument("--base_model_path", type=str, default=None)
+ args.add_argument("--weight_dtype", type=str, default="float16")
+ args.add_argument("--share", action="store_true")
+ args.add_argument(
+ "--local_files_only", action="store_true", help="enable it to use cached files without requesting from the hub"
+ )
+ args.add_argument("--port", type=int, default=7860)
+ args = args.parse_args()
+
+ if os.path.exists(os.path.join(args.pretrained_model_path, "brushnet")):
+ args.version = "ppt2"
+ else:
+ args.version = "ppt1"
+
+ if args.base_model_path is None:
+ args.base_model_path = "runwayml/stable-diffusion-v1-5"
+ return args
+
+
+if __name__ == "__main__":
+ args = parse_args()
+
+ # initialize the pipeline controller
+ weight_dtype = torch.float16 if args.weight_dtype == "float16" else torch.float32
+ controller = PowerPaintController(
+ pretrained_model_path=args.pretrained_model_path,
+ version=args.version,
+ base_model_path=args.base_model_path,
+ weight_dtype=weight_dtype,
+ local_files_only=args.local_files_only,
+ )
+
+ # ui
+ with gr.Blocks(css="style.css") as demo:
+ with gr.Row():
+ gr.Markdown(
+ "
+
+## Installation and Data Preparation
+
+See [INSTALL.md](INSTALL.md) for the installation of dependencies and dataset preperation required to run this codebase.
+
+## Training
+
+After preparing the training data in ```data/``` directory, use
+```
+python train.py
+```
+to start the training of the model. Use the ```de_type``` argument to choose the combination of degradation types to train on. By default it is set to all the 3 degradation types (noise, rain, and haze).
+
+Example Usage: If we only want to train on deraining and dehazing:
+```
+python train.py --de_type derain dehaze
+```
+
+## Testing
+
+After preparing the testing data in ```test/``` directory, place the mode checkpoint file in the ```ckpt``` directory. The pretrained model can be downloaded [here](https://drive.google.com/file/d/1j-b5Od70pGF7oaCqKAfUzmf-N-xEAjYl/view?usp=sharingg), alternatively, it is also available under the releases tab. To perform the evalaution use
+```
+python test.py --mode {n}
+```
+```n``` is a number that can be used to set the tasks to be evaluated on, 0 for denoising, 1 for deraining, 2 for dehaazing and 3 for all-in-one setting.
+
+Example Usage: To test on all the degradation types at once, run:
+
+```
+python test.py --mode 3
+```
+
+## Demo
+To obtain visual results from the model ```demo.py``` can be used. After placing the saved model file in ```ckpt``` directory, run:
+```
+python demo.py --test_path {path_to_degraded_images} --output_path {save_images_here}
+```
+Example usage to run inference on a directory of images:
+```
+python demo.py --test_path './test/demo/' --output_path './output/demo/'
+```
+Example usage to run inference on an image directly:
+```
+python demo.py --test_path './test/demo/image.png' --output_path './output/demo/'
+```
+To use tiling option while running ```demo.py``` set ```--tile``` option to ```True```. The Tile size and Tile overlap parameters can be adjusted using ```--tile_size``` and ```--tile_overlap``` options respectively.
+
+
+
+
+## Results
+Performance results of the PromptIR framework trained under the all-in-one setting
+
+
+
+
+
+## Installation
+
+See [INSTALL.md](INSTALL.md) for the installation of dependencies required to run Restormer.
+
+## Demo
+
+To test the pre-trained Restormer models of [Deraining](https://drive.google.com/drive/folders/1ZEDDEVW0UgkpWi-N4Lj_JUoVChGXCu_u), [Motion Deblurring](https://drive.google.com/drive/folders/1czMyfRTQDX3j3ErByYeZ1PM4GVLbJeGK), [Defocus Deblurring](https://drive.google.com/drive/folders/1bRBG8DG_72AGA6-eRePvChlT5ZO4cwJ4?usp=sharing), and [Denoising](https://drive.google.com/drive/folders/1Qwsjyny54RZWa7zC4Apg7exixLBo4uF0) on your own images, you can either use Google Colab [](https://colab.research.google.com/drive/1C2818h7KnjNv4R1sabe14_AYL7lWhmu6?usp=sharing), or command line as following
+```
+python demo.py --task Task_Name --input_dir path_to_images --result_dir save_images_here
+```
+Example usage to perform Defocus Deblurring on a directory of images:
+```
+python demo.py --task Single_Image_Defocus_Deblurring --input_dir './demo/degraded/' --result_dir './demo/restored/'
+```
+Example usage to perform Defocus Deblurring on an image directly:
+```
+python demo.py --task Single_Image_Defocus_Deblurring --input_dir './demo/degraded/portrait.jpg' --result_dir './demo/restored/'
+```
+
+## Training and Evaluation
+
+Training and Testing instructions for Deraining, Motion Deblurring, Defocus Deblurring, and Denoising are provided in their respective directories. Here is a summary table containing hyperlinks for easy navigation:
+
+
+
+
+
|
|
|
|
+|
|
|
|
|
|
+
+ - ***Aug. 26, 2021**: See our recent work on [real-world image SR: a pratical degrdation model BSRGAN, ICCV2021](https://github.com/cszn/BSRGAN)
+[](https://github.com/cszn/BSRGAN)*
+ - ***Aug. 26, 2021**: See our recent work on [generative modelling of image SR and image rescaling: normalizing-flow-based HCFlow, ICCV2021](https://github.com/JingyunLiang/HCFlow)
+[](https://github.com/JingyunLiang/HCFlow)[ 
+
+ 
+
+
+
+
+
+
+
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+ width: 50%;
+ box-shadow: 0px 0px 10px #444444;
+ border: 1px solid #000000;
+}
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+ z-index: 1
+}
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+ /* background: rgba(248, 73, 50, 0.4); */
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+}
+
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+ width: 160px;
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+ display: none;
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+ background: rgba(50, 153, 248, 0.4);
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+ width: 65%;
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+ /* width: 1200%; */
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+ color: #FF4B33;
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+ color: #FF4B33;
+ text-decoration: none
+}
+
+/* Some of this has been copied from http://www.mpi-inf.mpg.de/resources/rgbz-camera/ */
+
+/* Sections of the page. */
+
+.section {
+ margin: 2em 2em 0.5em 2em;
+ line-height: 130%;
+}
+
+.section h2 {
+ margin-top: 0.5em;
+ margin-bottom: 0.2em;
+}
+
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+ margin: 1.5em 0;
+ text-align: center;
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+ font-style: italic;
+ font-size: 10.5pt;
+ margin: 1em 0 1em 0;
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+ margin: 1.5em 0;
+ text-align: center;
+}
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+ display: block;
+ float: none;
+ border: 0;
+ margin: 4em auto 1em auto;
+}
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+ line-height: 130%;
+}
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+ margin-top: 0.5em;
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+ margin-bottom: 0;
+ font-family: Consolas, Monaco, monospace;
+ white-space: pre-wrap;
+ /* CSS 3 */
+ white-space: -moz-pre-wrap;
+ /* Mozilla, since 1999 */
+ white-space: -pre-wrap;
+ /* Opera 4-6 */
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+ /* Opera 7 */
+ word-wrap: break-word;
+ /* IE 5.5+ */
+ width: 100%;
+ color: #444;
+ padding: 2px;
+ background: #eee;
+ border: 1px solid #ccc;
+ overflow: auto;
+}
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+ line-height: 130%;
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+ margin-top: 0;
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+ line-height: 130%;
+}
+
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+ margin: 2.5em 0;
+ text-align: center;
+}
+
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+ width: 650px;
+ margin: 3em auto;
+}
+
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+ padding-bottom: 1em;
+}
+
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+ float: left;
+ width: 204px;
+ margin: 5px 10px 15px 0px;
+ text-align: center;
+}
+
+.dataset h2 {
+ display: inline;
+}
+
+.dataset img {
+ border: 2px solid #222;
+ margin: 0.25em auto;
+ display: block;
+}
+
+.backlink {
+ text-align: center;
+}
+
+/* Display lists in a grid of fixed size. This is useful when lists have associated images. */
+
+/* Idea from http://blog.mozilla.org/webdev/2009/02/20/cross-browser-inline-block/ */
+
+li.grid {
+ width: auto;
+ height: auto;
+ /* border: 1px solid #000; */
+ display: -moz-inline-stack;
+ display: inline-block;
+ vertical-align: top;
+ margin: 5px;
+ zoom: 1;
+ *display: inline;
+ _height: 100px;
+}
+
+/* Formatting for each item in a 2D grid. This is used typically in the downloads section. */
+
+.griditem {
+ font-size: 10pt;
+ text-align: center;
+ padding: 5px 5px 5px 5px;
+}
+
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+ text-align: center;
+ height: 120px;
+ width: auto;
+}
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+/* light-box */
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+ font-family: Verdana, sans-serif;
+ margin: 0;
+}
+
+* {
+ box-sizing: border-box;
+}
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+ padding: 0 8px;
+}
+
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+ content: "";
+ display: table;
+ clear: both;
+}
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+ float: left;
+ width: 100%;
+}
+
+/* The Modal (background) */
+
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+ display: none;
+ position: fixed;
+ z-index: 1;
+ padding-top: 100px;
+ left: 0;
+ top: 0;
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+}
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diff --git a/PART2/Zero-DCE/Zero-DCE_files/results.png b/PART2/Zero-DCE/Zero-DCE_files/results.png
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+++ b/PART2/Zero-DCE/Zero-DCE_files/results.png
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diff --git a/PART2/Zero-DCE/Zero-DCE_files/training.png b/PART2/Zero-DCE/Zero-DCE_files/training.png
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diff --git a/PART2/jarvis_agent.py b/PART2/jarvis_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..61df21528d7cfad55f1b475484635c76b82b4734
--- /dev/null
+++ b/PART2/jarvis_agent.py
@@ -0,0 +1,142 @@
+import os
+import json
+import dashscope
+from http import HTTPStatus
+from jarvis_tools_api import JarvisRestorationToolkit
+
+# ================= 核心修复：填入你的 Key =================
+# 1. 这里的引号里，必须填入你刚才复制的 sk- 开头的真实 Key
+# 2. 不要留空格，不要换行
+dashscope.api_key = "sk-409832d72d0c42a6a0380f1552de6950" # <--- 在这里粘贴你的真实 Key！！！
+# ========================================================
+
+class JarvisBrain:
+ def __init__(self):
+ # 初始化工具箱
+ self.toolkit = JarvisRestorationToolkit()
+
+ # 获取工具箱里注册的所有工具，生成给大模型看的菜单
+ self.tools_info = self._get_tools_description()
+
+ def _get_tools_description(self):
+ """将工具库配置转化为自然语言描述，喂给大模型"""
+ desc = "你拥有以下图像修复工具箱 (Toolbox):\n"
+ valid_keys = []
+ for key, cfg in self.toolkit.tools_config.items():
+ desc += f"- 工具名: {key} | 功能: {cfg['desc']}\n"
+ valid_keys.append(key)
+ return desc, valid_keys
+
+ def analyze_image(self, image_path):
+ """调用 Qwen-VL-Max 分析图片并制定计划"""
+ tool_desc_str, valid_keys = self.tools_info
+
+ # 构造系统提示词 (Prompt Engineering)
+ prompt = f"""
+{tool_desc_str}
+
+**任务指令：**
+1. 请作为一个专业的图像处理专家，仔细观察输入的图片。
+2. 分析图片存在哪些画质问题（如：亮度过低/黑暗、噪点、分辨率低/模糊、雨雾遮挡、物体缺失等）。
+3. 根据分析结果，从工具箱中选择**最合适的一个或多个工具**进行修复。
+4. 如果需要多个工具，请按**最佳执行顺序**排列（例如：通常先提亮，再修画质，最后放大）。
+
+**输出要求：**
+必须严格仅输出一段 **JSON** 代码，不要包含任何其他废话或Markdown标记。格式如下：
+{{
+ "reasoning": "简要说明为什么要用这些工具（一句话）",
+ "plan": ["工具名1", "工具名2"]
+}}
+
+**注意：**
+- "plan" 列表中的名字必须严格匹配工具箱中的工具名: {valid_keys}
+- 如果图片质量很好无需修复，"plan" 返回空列表 []。
+"""
+
+ # 处理本地路径 (Windows路径转为 file:// 协议)
+ abs_path = os.path.abspath(image_path)
+ img_url = f"file://{abs_path}"
+
+ print(f"🤖 [Jarvis] 正在思考... (观察图片: {os.path.basename(image_path)})")
+
+ messages = [
+ {
+ "role": "user",
+ "content": [
+ {"image": img_url},
+ {"text": prompt}
+ ]
+ }
+ ]
+
+ try:
+ # 调用通义千问 VL Max 模型
+ response = dashscope.MultiModalConversation.call(
+ model='qwen-vl-max', # 推荐用 max，推理能力更强
+ messages=messages,
+ result_format='message'
+ )
+
+ if response.status_code == HTTPStatus.OK:
+ content = response.output.choices[0].message.content[0]['text']
+ # 清洗数据：有时候模型会加 ```json ... ```，需要去掉
+ content = content.replace("```json", "").replace("```", "").strip()
+ return json.loads(content)
+ else:
+ print(f"❌ API 请求失败: {response.code} - {response.message}")
+ return None
+ except Exception as e:
+ print(f"❌ 大模型调用异常: {e}")
+ return None
+
+ def run(self, image_path):
+ """主入口：分析 -> 决策 -> 执行"""
+ if not os.path.exists(image_path):
+ print(f"❌ 文件不存在: {image_path}")
+ return
+
+ # 1. 获取决策
+ decision = self.analyze_image(image_path)
+
+ if not decision:
+ print("❌ 决策失败，流程终止。")
+ return
+
+ print("\n" + "="*40)
+ print(f"🧠 [思考结果]: {decision['reasoning']}")
+ print(f"📋 [执行计划]: {' -> '.join(decision['plan'])}")
+ print("="*40 + "\n")
+
+ # 2. 执行流水线
+ current_img = image_path
+ pipeline_success = True
+
+ if not decision['plan']:
+ print("🎉 图片无需修复。")
+ return
+
+ for tool_name in decision['plan']:
+ # 调用 jarvis_tools_api.py 里的底层接口
+ # 注意：_run_worker 是内部方法，但在这里我们可以直接调
+ # 或者我们在 api 里封装更好的公开方法，这里直接复用逻辑
+ res = self.toolkit._run_worker(tool_name, current_img)
+
+ if res:
+ current_img = res
+ else:
+ print(f"🚨 工具 {tool_name} 执行失败，流水线中断。")
+ pipeline_success = False
+ break
+
+ if pipeline_success:
+ print(f"\n🎁 [任务完成] 最终结果已保存: {current_img}")
+
+if __name__ == "__main__":
+ brain = JarvisBrain()
+
+ # === 测试图片 ===
+ # 找一张这里面的图，例如低光+模糊的
+ test_image = r"G:\datasets\realblur_dataset_test\075_blur_1.png"
+
+ # 运行
+ brain.run(test_image)
\ No newline at end of file
diff --git a/PART2/jarvis_tools_api.py b/PART2/jarvis_tools_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6ee723e71dd8c4b5d94d03daa601dcf5e6ab7e3
--- /dev/null
+++ b/PART2/jarvis_tools_api.py
@@ -0,0 +1,191 @@
+import os
+import subprocess
+import time
+import sys
+
+class JarvisRestorationToolkit:
+ def __init__(self):
+ # ================= 1. 基础配置 =================
+ self.root_dir = os.path.dirname(os.path.abspath(__file__))
+ self.workspace = os.path.join(self.root_dir, "Jarvis_Workspace")
+ os.makedirs(self.workspace, exist_ok=True)
+
+ # ================= 2. 环境路径注册 =================
+ self.envs = {
+ "ir_final": r"D:\conda\envs\ir_final\python.exe",
+ "swinir_env": r"D:\conda\envs\swinir_env\python.exe",
+ }
+
+ # ================= 3. 工具注册表 (已移除 NAFNet) =================
+ self.tools_config = {
+ # --- ID: 1 ---
+ "DarkIR": {
+ "env": "ir_final",
+ "cwd": os.path.join(self.root_dir, "DarkIR"),
+ "script": "worker_darkir.py",
+ "model": r"models/DarkIR_384.pt",
+ "desc": "低光增强 (旗舰版) - 适合夜景混合降质"
+ },
+ # --- ID: 2 ---
+ "SwinIR": {
+ "env": "swinir_env",
+ "cwd": os.path.join(self.root_dir, "SwinIR"),
+ "script": "worker_swinir.py",
+ "model": r"model_zoo/swinir/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth",
+ "desc": "超分辨率 (x4) - 适合小图/模糊图"
+ },
+ # --- ID: 3 ---
+ "PromptIR": {
+ "env": "ir_final",
+ "cwd": os.path.join(self.root_dir, "PromptIR"),
+ "script": "worker_promptir.py",
+ "model": r"ckpt/model.ckpt",
+ "desc": "去雨去雾 (All-in-One) - 适合恶劣天气"
+ },
+ # --- ID: 4 ---
+ "CodeFormer": {
+ "env": "ir_final",
+ "cwd": os.path.join(self.root_dir, "CodeFormer"),
+ "script": "worker_codeformer.py",
+ "model": "None",
+ "desc": "人脸修复 - 适合模糊人像"
+ },
+ # --- [已移除] NAFNet ---
+
+ # --- ID: 5 (原ID:6) ---
+ "ZeroDCE": {
+ "env": "ir_final",
+ "cwd": os.path.join(self.root_dir, "Zero-DCE", "Zero-DCE_code"),
+ "script": "worker_zerodce.py",
+ "model": r"snapshots/Epoch99.pth",
+ "desc": "低光增强 (极速版) - 适合实时预览"
+ },
+ # --- ID: 6 (原ID:7) ---
+ "PowerPaint": {
+ "env": "ir_final",
+ "cwd": os.path.join(self.root_dir, "PowerPaint"),
+ "script": "worker_powerpaint.py",
+ "model": "runwayml/stable-diffusion-inpainting",
+ "desc": "智能补全 - 适合去水印/修补"
+ },
+ # --- ID: 7 (原ID:8) ---
+ "Restormer": {
+ "env": "ir_final",
+ "cwd": os.path.join(self.root_dir, "Restormer"),
+ "script": "worker_restormer_universal.py",
+ "model": r"Denoising/pretrained_models/gaussian_color_denoising_blind.pth",
+ "desc": "图像去噪 (Denoising) - 消除噪点/颗粒感"
+ }
+ }
+
+ def _run_worker(self, tool_key, input_path):
+ if tool_key not in self.tools_config:
+ print(f"❌ 内部错误：工具KEY '{tool_key}' 未定义")
+ return None
+
+ if not os.path.exists(input_path):
+ print(f"❌ 输入文件不存在: {input_path}")
+ return None
+
+ cfg = self.tools_config[tool_key]
+
+ if not os.path.exists(self.envs[cfg["env"]]):
+ print(f"❌ 环境路径错误: {self.envs[cfg['env']]}")
+ return None
+
+ # 自动生成文件名
+ timestamp = int(time.time() % 10000)
+ filename = os.path.basename(input_path)
+ name_no_ext, ext = os.path.splitext(filename)
+ output_name = f"{name_no_ext}_{tool_key}_{timestamp}{ext}"
+ output_path = os.path.join(self.workspace, output_name)
+
+ cmd = [
+ self.envs[cfg["env"]],
+ cfg["script"],
+ "-i", input_path,
+ "-o", output_path,
+ "-m", cfg["model"]
+ ]
+
+ print(f"\n⚡ [{tool_key}] 执行中...")
+ try:
+ result = subprocess.run(cmd, cwd=cfg["cwd"], capture_output=True, text=True, encoding='utf-8')
+
+ if result.returncode == 0 and os.path.exists(output_path):
+ print(f"✅ 完成 -> {output_name}")
+ return output_path
+ else:
+ print(f"❌ 失败:\n{result.stderr}")
+ if result.stdout: print(f"--- STDOUT ---\n{result.stdout}")
+ return None
+ except Exception as e:
+ print(f"❌ 异常: {e}")
+ return None
+
+if __name__ == "__main__":
+ toolkit = JarvisRestorationToolkit()
+
+ # 默认图片
+ current_img = r"G:\datasets\realblur_dataset_test\075_blur_1.png"
+
+ menu = {
+ "1": "DarkIR",
+ "2": "SwinIR",
+ "3": "PromptIR",
+ "4": "CodeFormer",
+ "5": "ZeroDCE", # 编号已前移
+ "6": "PowerPaint", # 编号已前移
+ "7": "Restormer" # 编号已前移
+ }
+
+ print("="*60)
+ print(" JarvisIR 工具链集成测试 (稳定版)")
+ print("="*60)
+
+ while True:
+ print(f"\n当前处理图片: {current_img}")
+ print("-" * 30)
+ for k, v in menu.items():
+ desc = toolkit.tools_config[v]['desc']
+ print(f"[{k}] {v.ljust(12)} : {desc}")
+ print("-" * 30)
+
+ user_input = input("请输入工具序列 (如 '1 2'，输入 'test_all' 测试全部，'q' 退出): ").strip()
+
+ if user_input.lower() == 'q':
+ break
+
+ # 冒烟测试
+ if user_input.lower() in ['test_all', 'test']:
+ print("\n🚀 启动全工具冒烟测试...")
+ for k, tool_key in menu.items():
+ print(f"\n========== 测试 {k}. {tool_key} ==========")
+ toolkit._run_worker(tool_key, current_img)
+ print("\n✅ 全测试结束！(已移除不稳定工具)")
+ continue
+
+ steps = user_input.split()
+ valid_pipeline = []
+ for s in steps:
+ if s in menu:
+ valid_pipeline.append(menu[s])
+ else:
+ print(f"⚠️ 跳过无效输入: {s}")
+
+ if not valid_pipeline: continue
+
+ print(f"\n🚀 启动流水线: {' -> '.join(valid_pipeline)}")
+ temp_img = current_img
+ for i, tool_key in enumerate(valid_pipeline):
+ print(f"\n>>> Step {i+1}/{len(valid_pipeline)}: 调用 {tool_key}")
+ res = toolkit._run_worker(tool_key, temp_img)
+ if res:
+ temp_img = res
+ else:
+ print("🚨 流水线中断！")
+ break
+
+ print(f"\n🎁 最终结果已保存: {temp_img}")
+ if input("是否将此结果作为下一轮输入? (y/n): ").lower() == 'y':
+ current_img = temp_img
\ No newline at end of file
diff --git a/PART2/prepare_experiment.py b/PART2/prepare_experiment.py
new file mode 100644
index 0000000000000000000000000000000000000000..54f391e31bffabcd013e0addba6f137b1dd755d6
--- /dev/null
+++ b/PART2/prepare_experiment.py
@@ -0,0 +1,65 @@
+import os
+import cv2
+import numpy as np
+import shutil
+import random
+
+# ================= 配置 =================
+# 原始高清数据集路径
+source_root = r"G:\datasets\lolblurtest\test\high_sharp_original"
+# 实验根目录
+exp_root = r"G:\IR_Experiment\Order_Test"
+
+# 输出路径：生成的“坏图”放这里
+target_input_dir = os.path.join(exp_root, "input_dark_noisy")
+os.makedirs(target_input_dir, exist_ok=True)
+
+# ================= 降质函数 =================
+def degradate_image(img):
+ # 1. 变暗 (模拟低光) - 亮度降为 20%
+ img = img.astype(np.float32) * 0.2
+
+ # 2. 加噪 (模拟高感光度噪声)
+ noise = np.random.normal(0, 15, img.shape) # sigma=15
+ img = img + noise
+
+ # 截断并转回 uint8
+ img = np.clip(img, 0, 255).astype(np.uint8)
+ return img
+
+# ================= 执行提取与生成 =================
+print("🚀 开始构建数据集...")
+count = 0
+
+# 遍历源目录
+for root, dirs, files in os.walk(source_root):
+ # 找到图片文件
+ img_files = [f for f in files if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
+
+ if img_files:
+ # 每个文件夹只取第一张，节省时间，够测就行
+ file_name = img_files[0]
+ src_path = os.path.join(root, file_name)
+
+ # 读取
+ img = cv2.imread(src_path)
+ if img is None: continue
+
+ # 制造降质
+ bad_img = degradate_image(img)
+
+ # 保存 (重命名以防冲突)
+ folder_name = os.path.basename(root)
+ save_name = f"{folder_name}_{file_name}"
+ save_path = os.path.join(target_input_dir, save_name)
+
+ cv2.imwrite(save_path, bad_img)
+ count += 1
+ print(f"[{count}] 处理: {save_name}")
+
+ # 限制数量：为了做 demo，生成 5-10 张就足够说明问题了，多了跑得慢
+ if count >= 8:
+ break
+
+print(f"✅ 数据集准备完毕！共 {count} 张。")
+print(f"📂 坏图位置: {target_input_dir}")
\ No newline at end of file
diff --git a/PART2/run_maxim.py b/PART2/run_maxim.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbee78f5aa8030ba81b53bc6dccdf6f6041969e2
--- /dev/null
+++ b/PART2/run_maxim.py
@@ -0,0 +1,38 @@
+import torch
+from transformers import MaximImageProcessor, MaximForImageDeblurring
+from PIL import Image
+import numpy as np
+
+# 1. 设置文件路径 (请修改这里为你真实的路径)
+input_image_path = r"G:\datasets\realblur_dataset_test\075_blur_1.png"
+output_image_path = r"G:\datasets\maxim_result.png"
+
+print(">>> 正在加载模型 (第一次运行会自动下载约 600MB 模型，请耐心等待)...")
+
+# 2. 加载 Google 的 MAXIM 模型 (专用于 GoPro 去模糊任务)
+# 这个库是纯 Python 的，不需要编译 C++，所以一定能跑通
+processor = MaximImageProcessor.from_pretrained("google/maxim-s3-deblurring-gopro")
+model = MaximForImageDeblurring.from_pretrained("google/maxim-s3-deblurring-gopro")
+
+print(">>> 模型加载成功！正在读取图片...")
+
+# 3. 读取并预处理图片
+image = Image.open(input_image_path).convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+
+print(">>> 正在进行去模糊处理 (CPU运行可能需要1-2分钟，请稍候)...")
+
+# 4. 推理 (如果不使用 GPU，这里会自动用 CPU)
+with torch.no_grad():
+ outputs = model(**inputs)
+
+# 5. 后处理并保存
+# MAXIM 输出的是重构后的像素值
+reconstructed_data = outputs.reconstruction.squeeze().permute(1, 2, 0).clamp(0, 1).numpy()
+# 转换为 0-255 格式
+reconstructed_image = (reconstructed_data * 255).astype(np.uint8)
+reconstructed_image = Image.fromarray(reconstructed_image)
+
+# 保存
+reconstructed_image.save(output_image_path)
+print(f">>> 处理完成！结果已保存至: {output_image_path}")
\ No newline at end of file
diff --git a/PART2/tools_interface.py b/PART2/tools_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..8346f9958f596dba2386162eb03811c9c96c2847
--- /dev/null
+++ b/PART2/tools_interface.py
@@ -0,0 +1,123 @@
+import os
+import shutil
+import subprocess
+import glob
+
+class AgentToolbox:
+ def __init__(self):
+ self.root_dir = r"G:\IR_Experiment"
+ self.output_base = os.path.join(self.root_dir, "Agent_Workspace")
+ os.makedirs(self.output_base, exist_ok=True)
+
+ # ================= 环境配置 (关键！) =================
+ # 请根据你的实际情况修改这里的 python.exe 路径
+ # 1. ir_final 环境 (用于 DarkIR, NAFNet, PromptIR)
+ self.env_main = r"D:\conda\envs\ir_final\python.exe"
+ # 2. swinir_env 环境 (用于 SwinIR)
+ self.env_swinir = r"D:\conda\envs\swinir_env\python.exe"
+
+ # 如果找不到路径，尝试用系统默认的 'python' (前提是你激活了对应环境)
+ if not os.path.exists(self.env_main): self.env_main = "python"
+ if not os.path.exists(self.env_swinir): self.env_swinir = "python"
+
+ def _run_cmd(self, cmd, cwd):
+ """执行命令行的通用函数"""
+ print(f"\n[Toolbox] 正在执行: {cmd} ...")
+ try:
+ subprocess.run(cmd, shell=True, check=True, cwd=cwd)
+ return True
+ except subprocess.CalledProcessError as e:
+ print(f"❌ 执行失败: {e}")
+ return False
+
+ # ================= 工具 1: DarkIR (低光增强) =================
+ def call_darkir(self, image_path):
+ print(f"🌙 [调用 DarkIR] 正在处理低光: {os.path.basename(image_path)}")
+ tool_dir = os.path.join(self.root_dir, "DarkIR")
+
+ # 1. 适配输入：DarkIR 也是读文件夹的，我们把图复制到它的 input 目录
+ input_dir = os.path.join(tool_dir, "test_input") # 对应 run_darkir.py 里的路径
+ if os.path.exists(input_dir): shutil.rmtree(input_dir)
+ os.makedirs(input_dir, exist_ok=True)
+ shutil.copy(image_path, os.path.join(input_dir, os.path.basename(image_path)))
+
+ # 2. 调用我们之前写好的脚本
+ # 注意：这里调用的是 run_darkir.py，确保它里面的路径是对的
+ cmd = f'"{self.env_main}" run_darkir.py'
+
+ if self._run_cmd(cmd, cwd=tool_dir):
+ # 3. 提取输出
+ # DarkIR 输出在 results 文件夹
+ result_dir = os.path.join(tool_dir, "results")
+ # 找到生成的文件
+ res_files = glob.glob(os.path.join(result_dir, "*.*"))
+ if res_files:
+ # 把结果移动到 Agent 工作区
+ out_name = f"darkir_{os.path.basename(image_path)}"
+ final_path = os.path.join(self.output_base, out_name)
+ shutil.copy(res_files[0], final_path)
+ return final_path
+ return None
+
+ # ================= 工具 2: SwinIR (超分放大) =================
+ def call_swinir(self, image_path, scale=4):
+ print(f"🔍 [调用 SwinIR] 正在放大 {scale}倍: {os.path.basename(image_path)}")
+ tool_dir = os.path.join(self.root_dir, "SwinIR")
+
+ # 1. 适配输入
+ temp_input = os.path.join(tool_dir, "testsets", "agent_temp")
+ if os.path.exists(temp_input): shutil.rmtree(temp_input)
+ os.makedirs(temp_input, exist_ok=True)
+ shutil.copy(image_path, os.path.join(temp_input, os.path.basename(image_path)))
+
+ # 2. 构建命令
+ # 使用 Real-World x4 模型
+ model_path = r"model_zoo/swinir/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth"
+ cmd = f'"{self.env_swinir}" main_test_swinir.py --task real_sr --scale {scale} --model_path {model_path} --folder_lq testsets/agent_temp --tile 400'
+
+ if self._run_cmd(cmd, cwd=tool_dir):
+ # 3. 提取输出
+ # SwinIR 结果通常在 results/swinir_real_sr_x4 里面
+ result_dir = os.path.join(tool_dir, "results", f"swinir_real_sr_x{scale}")
+ # 找最新生成的文件
+ res_files = glob.glob(os.path.join(result_dir, "*.*"))
+ if res_files:
+ # SwinIR 会给文件名加后缀，我们找包含原名的那个
+ target_file = [f for f in res_files if os.path.basename(image_path).split('.')[0] in f][-1]
+
+ out_name = f"swinir_{os.path.basename(image_path)}"
+ final_path = os.path.join(self.output_base, out_name)
+ shutil.copy(target_file, final_path)
+ return final_path
+ return None
+
+# ================= 模拟 Agent 调度逻辑 =================
+if __name__ == "__main__":
+ toolbox = AgentToolbox()
+
+ # 1. 准备一张测试图 (你可以换成任何存在的图片路径)
+ # 假设我们用之前生成的“低光”测试图
+ original_img = r"G:\datasets\realblur_dataset_test\075_blur_1.png" # 确保这张图存在！
+
+ if not os.path.exists(original_img):
+ print("❌ 测试图不存在，请修改 original_img 路径")
+ exit()
+
+ print(f"🏁 开始处理任务: {original_img}")
+
+ # --- 步骤 1: 先提亮 (DarkIR) ---
+ bright_img = toolbox.call_darkir(original_img)
+
+ if bright_img:
+ print(f"✅ 第一步完成: {bright_img}")
+
+ # --- 步骤 2: 再放大 (SwinIR) ---
+ # 把第一步的结果喂给第二步
+ final_img = toolbox.call_swinir(bright_img)
+
+ if final_img:
+ print(f"🎉 任务全部完成！最终结果: {final_img}")
+ else:
+ print("❌ 第二步 SwinIR 失败")
+ else:
+ print("❌ 第一步 DarkIR 失败")
\ No newline at end of file
+
+ + Zero-Reference Deep Curve Estimation (Zero-DCE) +
+for Low-Light Image Enhancement
+ + + +
+ 1 Tianjin University, Tianjin, China
+ 2 City University of Hong Kong, Hong Kong
+ 3 Nanyang Technological University, Singapore
+ 4 Beijing Jiaotong University, Beijing, China +
+
+ + 2 City University of Hong Kong, Hong Kong
+ 3 Nanyang Technological University, Singapore
+ 4 Beijing Jiaotong University, Beijing, China +
+
+
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new file mode 100644
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+ zoomMaskX = maxScopeX
+ }
+ let maxkScopeY = leftView.offsetHeight - maskHeight / 2
+ if (zoomMaskY >= maxkScopeY) {
+ zoomMaskY = maxkScopeY
+ }
+ mask.style.left = zoomMaskX + leftView.offsetLeft + 'px'
+ mask.style.top = zoomMaskY + leftView.offsetTop + 'px'
+ // rightView position
+ if (fix > 0) {
+ rightView.style.left = container.offsetWidth - 400 * 1.1 + 'px'
+ rightView.style.top = container.offsetHeight + 10 + 'px'
+ } else {
+ rightView.style.left = currentMouseX + 30 + 'px'
+ rightView.style.top = currentMouseY + 30 + 'px'
+ }
+ let zommProportion = bigImg.offsetWidth / leftView.offsetWidth
+ bigImg.style.left = -zommProportion * (zoomMaskX + maskWidth / 2) + rightView.offsetWidth / 2 + 'px'
+ bigImg.style.top = -zommProportion * (zoomMaskY + maskHeight / 2) + rightView.offsetHeight / 2 + 'px'
+
+ }, false)
+}
+// light-box
+function openModal() {
+ document.getElementById('myModal').style.display = "block";
+}
+
+function closeModal() {
+ document.getElementById('myModal').style.display = "none";
+}
+
+function plusSlides(n) {
+ showSlides(slideIndex += n);
+}
+
+function currentSlide(n) {
+ showSlides(slideIndex = n);
+}
+
+function showSlides(n) {
+ var i;
+ var slides = document.getElementsByClassName("mySlides");
+ var dots = document.getElementsByClassName("dot");
+
+ if (n > slides.length) {
+ slideIndex = 1
+ }
+ if (n < 1) {
+ slideIndex = slides.length
+ }
+ for (i = 0; i < slides.length; i++) {
+ slides[i].style.display = "none";
+ }
+ for (i = 0; i < dots.length; i++) {
+ dots[i].className = dots[i].className.replace(" active", "");
+ }
+ slides[slideIndex - 1].style.display = "block";
+ dots[slideIndex - 1].className += " active";
+}
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diff --git a/PART2/Zero-DCE/Zero-DCE_files/face.png b/PART2/Zero-DCE/Zero-DCE_files/face.png
new file mode 100644
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+++ b/PART2/Zero-DCE/Zero-DCE_files/face.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c63f8a934ebd8e686a387e37a7436d25de80e9f411f935ca303942e5534ec8aa
+size 496511
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new file mode 100644
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diff --git a/PART2/Zero-DCE/Zero-DCE_files/js b/PART2/Zero-DCE/Zero-DCE_files/js
new file mode 100644
index 0000000000000000000000000000000000000000..47a4951ede098b770ebe56d939168cd5516acba9
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_files/js
@@ -0,0 +1,216 @@
+
+// Copyright 2012 Google Inc. All rights reserved.
+(function(){
+
+var data = {
+"resource": {
+ "version":"1",
+
+ "macros":[],
+ "tags":[],
+ "predicates":[],
+ "rules":[]
+},
+"runtime":[]
+
+
+
+};
+/*
+
+ Copyright The Closure Library Authors.
+ SPDX-License-Identifier: Apache-2.0
+*/
+var aa,ba="function"==typeof Object.create?Object.create:function(a){var b=function(){};b.prototype=a;return new b},ca;if("function"==typeof Object.setPrototypeOf)ca=Object.setPrototypeOf;else{var da;a:{var ea={Oe:!0},fa={};try{fa.__proto__=ea;da=fa.Oe;break a}catch(a){}da=!1}ca=da?function(a,b){a.__proto__=b;if(a.__proto__!==b)throw new TypeError(a+" is not extensible");return a}:null}var ia=ca,ka=this||self,la=/^[\w+/_-]+[=]{0,2}$/,ma=null;var pa=function(){},qa=function(a){return"function"==typeof a},f=function(a){return"string"==typeof a},ra=function(a){return"number"==typeof a&&!isNaN(a)},ua=function(a){return"[object Array]"==Object.prototype.toString.call(Object(a))},r=function(a,b){if(Array.prototype.indexOf){var c=a.indexOf(b);return"number"==typeof c?c:-1}for(var d=0;db)a=0,b=2147483647;return Math.floor(Math.random()*(b-a+1)+a)},ya=function(a,b){for(var c=new xa,d=0;d"+a+"
";b=b.lastChild;for(var c=[];b.firstChild;)c.push(b.removeChild(b.firstChild));return c},lc=function(a,b,c){c=c||100;for(var d={},e=0;eAbstract
++
+
+
+
+
+ The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits of our Zero-DCE to face detection in the dark are discussed.
+ +
+
+ -
+
Pipeline
+-
+
+
+ 
+
+
+
+
+
+ -
+
The pipeline of our method. (a) The framework of Zero-DCE. A DCE-Net is devised to estimate a set of best-fitting Light-Enhancement curves (LE-curves: LE(I(x);α)=I(x)+αI(x)(1-I(x))) to iteratively enhance a given input image. (b, c) LE-curves with different adjustment parameters α and numbers of iteration n. In (c), α1, α2, and α3 are equal to -1 while n is equal to 4. In each subfigure, the horizontal axis represents the input pixel values while the vertical axis represents the output pixel values.
+
+
+
+ Highlights
+-
+
We propose the first low-light enhancement network that is independent of paired and unpaired training data, thus avoiding the risk of overfitting. As a result, our method generalizes well to various lighting conditions.
+ We design a simple and lightweight deep network that is able to approximate pixel-wise and higher-order curves by iteratively applying itself. Such image-specific curves can effectively perform mapping within a wide dynamic range.
+ We show the potential of training a deep image enhancement model in the absence of reference images through task-specific non-reference loss functions that indirectly evaluate enhancement quality. It is capable of processing images in real-time (about 500 FPS for images of size 640*480*3 on GPU) and takes only 30 minutes for training.
+
+
+ +
+ +
\ No newline at end of file
diff --git a/PART2/Zero-DCE/Zero-DCE_code/.gitignore b/PART2/Zero-DCE/Zero-DCE_code/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..3e6a144e039991b799e69e6353a95b3511d51c5e
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_code/.gitignore
@@ -0,0 +1,2 @@
+__pycache__/
+data/
diff --git a/PART2/Zero-DCE/Zero-DCE_code/Myloss.py b/PART2/Zero-DCE/Zero-DCE_code/Myloss.py
new file mode 100644
index 0000000000000000000000000000000000000000..9faa7df02a80088996b045cdb68a2eaba8ad56bc
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_code/Myloss.py
@@ -0,0 +1,157 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from torchvision.models.vgg import vgg16
+import numpy as np
+
+
+class L_color(nn.Module):
+
+ def __init__(self):
+ super(L_color, self).__init__()
+
+ def forward(self, x ):
+
+ b,c,h,w = x.shape
+
+ mean_rgb = torch.mean(x,[2,3],keepdim=True)
+ mr,mg, mb = torch.split(mean_rgb, 1, dim=1)
+ Drg = torch.pow(mr-mg,2)
+ Drb = torch.pow(mr-mb,2)
+ Dgb = torch.pow(mb-mg,2)
+ k = torch.pow(torch.pow(Drg,2) + torch.pow(Drb,2) + torch.pow(Dgb,2),0.5)
+
+
+ return k
+
+
+class L_spa(nn.Module):
+
+ def __init__(self):
+ super(L_spa, self).__init__()
+ # print(1)kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
+ kernel_left = torch.FloatTensor( [[0,0,0],[-1,1,0],[0,0,0]]).cuda().unsqueeze(0).unsqueeze(0)
+ kernel_right = torch.FloatTensor( [[0,0,0],[0,1,-1],[0,0,0]]).cuda().unsqueeze(0).unsqueeze(0)
+ kernel_up = torch.FloatTensor( [[0,-1,0],[0,1, 0 ],[0,0,0]]).cuda().unsqueeze(0).unsqueeze(0)
+ kernel_down = torch.FloatTensor( [[0,0,0],[0,1, 0],[0,-1,0]]).cuda().unsqueeze(0).unsqueeze(0)
+ self.weight_left = nn.Parameter(data=kernel_left, requires_grad=False)
+ self.weight_right = nn.Parameter(data=kernel_right, requires_grad=False)
+ self.weight_up = nn.Parameter(data=kernel_up, requires_grad=False)
+ self.weight_down = nn.Parameter(data=kernel_down, requires_grad=False)
+ self.pool = nn.AvgPool2d(4)
+ def forward(self, org , enhance ):
+ b,c,h,w = org.shape
+
+ org_mean = torch.mean(org,1,keepdim=True)
+ enhance_mean = torch.mean(enhance,1,keepdim=True)
+
+ org_pool = self.pool(org_mean)
+ enhance_pool = self.pool(enhance_mean)
+
+ weight_diff =torch.max(torch.FloatTensor([1]).cuda() + 10000*torch.min(org_pool - torch.FloatTensor([0.3]).cuda(),torch.FloatTensor([0]).cuda()),torch.FloatTensor([0.5]).cuda())
+ E_1 = torch.mul(torch.sign(enhance_pool - torch.FloatTensor([0.5]).cuda()) ,enhance_pool-org_pool)
+
+
+ D_org_letf = F.conv2d(org_pool , self.weight_left, padding=1)
+ D_org_right = F.conv2d(org_pool , self.weight_right, padding=1)
+ D_org_up = F.conv2d(org_pool , self.weight_up, padding=1)
+ D_org_down = F.conv2d(org_pool , self.weight_down, padding=1)
+
+ D_enhance_letf = F.conv2d(enhance_pool , self.weight_left, padding=1)
+ D_enhance_right = F.conv2d(enhance_pool , self.weight_right, padding=1)
+ D_enhance_up = F.conv2d(enhance_pool , self.weight_up, padding=1)
+ D_enhance_down = F.conv2d(enhance_pool , self.weight_down, padding=1)
+
+ D_left = torch.pow(D_org_letf - D_enhance_letf,2)
+ D_right = torch.pow(D_org_right - D_enhance_right,2)
+ D_up = torch.pow(D_org_up - D_enhance_up,2)
+ D_down = torch.pow(D_org_down - D_enhance_down,2)
+ E = (D_left + D_right + D_up +D_down)
+ # E = 25*(D_left + D_right + D_up +D_down)
+
+ return E
+class L_exp(nn.Module):
+
+ def __init__(self,patch_size,mean_val):
+ super(L_exp, self).__init__()
+ # print(1)
+ self.pool = nn.AvgPool2d(patch_size)
+ self.mean_val = mean_val
+ def forward(self, x ):
+
+ b,c,h,w = x.shape
+ x = torch.mean(x,1,keepdim=True)
+ mean = self.pool(x)
+
+ d = torch.mean(torch.pow(mean- torch.FloatTensor([self.mean_val] ).cuda(),2))
+ return d
+
+class L_TV(nn.Module):
+ def __init__(self,TVLoss_weight=1):
+ super(L_TV,self).__init__()
+ self.TVLoss_weight = TVLoss_weight
+
+ def forward(self,x):
+ batch_size = x.size()[0]
+ h_x = x.size()[2]
+ w_x = x.size()[3]
+ count_h = (x.size()[2]-1) * x.size()[3]
+ count_w = x.size()[2] * (x.size()[3] - 1)
+ h_tv = torch.pow((x[:,:,1:,:]-x[:,:,:h_x-1,:]),2).sum()
+ w_tv = torch.pow((x[:,:,:,1:]-x[:,:,:,:w_x-1]),2).sum()
+ return self.TVLoss_weight*2*(h_tv/count_h+w_tv/count_w)/batch_size
+class Sa_Loss(nn.Module):
+ def __init__(self):
+ super(Sa_Loss, self).__init__()
+ # print(1)
+ def forward(self, x ):
+ # self.grad = np.ones(x.shape,dtype=np.float32)
+ b,c,h,w = x.shape
+ # x_de = x.cpu().detach().numpy()
+ r,g,b = torch.split(x , 1, dim=1)
+ mean_rgb = torch.mean(x,[2,3],keepdim=True)
+ mr,mg, mb = torch.split(mean_rgb, 1, dim=1)
+ Dr = r-mr
+ Dg = g-mg
+ Db = b-mb
+ k =torch.pow( torch.pow(Dr,2) + torch.pow(Db,2) + torch.pow(Dg,2),0.5)
+ # print(k)
+
+
+ k = torch.mean(k)
+ return k
+
+class perception_loss(nn.Module):
+ def __init__(self):
+ super(perception_loss, self).__init__()
+ features = vgg16(pretrained=True).features
+ self.to_relu_1_2 = nn.Sequential()
+ self.to_relu_2_2 = nn.Sequential()
+ self.to_relu_3_3 = nn.Sequential()
+ self.to_relu_4_3 = nn.Sequential()
+
+ for x in range(4):
+ self.to_relu_1_2.add_module(str(x), features[x])
+ for x in range(4, 9):
+ self.to_relu_2_2.add_module(str(x), features[x])
+ for x in range(9, 16):
+ self.to_relu_3_3.add_module(str(x), features[x])
+ for x in range(16, 23):
+ self.to_relu_4_3.add_module(str(x), features[x])
+
+ # don't need the gradients, just want the features
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, x):
+ h = self.to_relu_1_2(x)
+ h_relu_1_2 = h
+ h = self.to_relu_2_2(h)
+ h_relu_2_2 = h
+ h = self.to_relu_3_3(h)
+ h_relu_3_3 = h
+ h = self.to_relu_4_3(h)
+ h_relu_4_3 = h
+ # out = (h_relu_1_2, h_relu_2_2, h_relu_3_3, h_relu_4_3)
+ return h_relu_4_3
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diff --git a/PART2/Zero-DCE/Zero-DCE_code/dataloader.py b/PART2/Zero-DCE/Zero-DCE_code/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a9173d9a7704459c13e19391c8b76e51daa6098
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_code/dataloader.py
@@ -0,0 +1,59 @@
+import os
+import sys
+
+import torch
+import torch.utils.data as data
+
+import numpy as np
+from PIL import Image
+import glob
+import random
+import cv2
+
+random.seed(1143)
+
+
+def populate_train_list(lowlight_images_path):
+
+
+
+
+ image_list_lowlight = glob.glob(lowlight_images_path + "*.jpg")
+
+ train_list = image_list_lowlight
+
+ random.shuffle(train_list)
+
+ return train_list
+
+
+
+class lowlight_loader(data.Dataset):
+
+ def __init__(self, lowlight_images_path):
+
+ self.train_list = populate_train_list(lowlight_images_path)
+ self.size = 256
+
+ self.data_list = self.train_list
+ print("Total training examples:", len(self.train_list))
+
+
+
+
+ def __getitem__(self, index):
+
+ data_lowlight_path = self.data_list[index]
+
+ data_lowlight = Image.open(data_lowlight_path)
+
+ data_lowlight = data_lowlight.resize((self.size,self.size), Image.ANTIALIAS)
+
+ data_lowlight = (np.asarray(data_lowlight)/255.0)
+ data_lowlight = torch.from_numpy(data_lowlight).float()
+
+ return data_lowlight.permute(2,0,1)
+
+ def __len__(self):
+ return len(self.data_list)
+
diff --git a/PART2/Zero-DCE/Zero-DCE_code/lowlight_test.py b/PART2/Zero-DCE/Zero-DCE_code/lowlight_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..63863262fcc77b6bafd4a883c355ef7dc0af5a1b
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_code/lowlight_test.py
@@ -0,0 +1,65 @@
+import torch
+import torch.nn as nn
+import torchvision
+import torch.backends.cudnn as cudnn
+import torch.optim
+import os
+import sys
+import argparse
+import time
+import dataloader
+import model
+import numpy as np
+from torchvision import transforms
+from PIL import Image
+import glob
+import time
+
+
+
+def lowlight(image_path):
+ os.environ['CUDA_VISIBLE_DEVICES']='0'
+ data_lowlight = Image.open(image_path)
+
+
+
+ data_lowlight = (np.asarray(data_lowlight)/255.0)
+
+
+ data_lowlight = torch.from_numpy(data_lowlight).float()
+ data_lowlight = data_lowlight.permute(2,0,1)
+ data_lowlight = data_lowlight.cuda().unsqueeze(0)
+
+ DCE_net = model.enhance_net_nopool().cuda()
+ DCE_net.load_state_dict(torch.load('snapshots/Epoch99.pth'))
+ start = time.time()
+ _,enhanced_image,_ = DCE_net(data_lowlight)
+
+ end_time = (time.time() - start)
+ print(end_time)
+ image_path = image_path.replace('test_data','result')
+
+ result_path = image_path
+ if not os.path.exists(os.path.dirname(result_path)):
+ os.makedirs(os.path.dirname(result_path))
+ if not os.path.exists(image_path.replace('/'+image_path.split("/")[-1],'')):
+ os.makedirs(image_path.replace('/'+image_path.split("/")[-1],''))
+
+ torchvision.utils.save_image(enhanced_image, result_path)
+
+if __name__ == '__main__':
+# test_images
+ with torch.no_grad():
+ filePath = 'data/test_data/'
+
+ file_list = os.listdir(filePath)
+
+ for file_name in file_list:
+ test_list = glob.glob(filePath+file_name+"/*")
+ for image in test_list:
+ # image = image
+ print(image)
+ lowlight(image)
+
+
+
diff --git a/PART2/Zero-DCE/Zero-DCE_code/lowlight_train.py b/PART2/Zero-DCE/Zero-DCE_code/lowlight_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..49dfcd3dff1317f6d2801c3310eb91c7f9b0a29b
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_code/lowlight_train.py
@@ -0,0 +1,124 @@
+import torch
+import torch.nn as nn
+import torchvision
+import torch.backends.cudnn as cudnn
+import torch.optim
+import os
+import sys
+import argparse
+import time
+import dataloader
+import model
+import Myloss
+import numpy as np
+from torchvision import transforms
+
+
+def weights_init(m):
+ classname = m.__class__.__name__
+ if classname.find('Conv') != -1:
+ m.weight.data.normal_(0.0, 0.02)
+ elif classname.find('BatchNorm') != -1:
+ m.weight.data.normal_(1.0, 0.02)
+ m.bias.data.fill_(0)
+
+
+
+
+
+def train(config):
+
+ os.environ['CUDA_VISIBLE_DEVICES']='0'
+
+ DCE_net = model.enhance_net_nopool().cuda()
+
+ DCE_net.apply(weights_init)
+ if config.load_pretrain == True:
+ DCE_net.load_state_dict(torch.load(config.pretrain_dir))
+ train_dataset = dataloader.lowlight_loader(config.lowlight_images_path)
+
+ train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=config.train_batch_size, shuffle=True, num_workers=config.num_workers, pin_memory=True)
+
+
+
+ L_color = Myloss.L_color()
+ L_spa = Myloss.L_spa()
+
+ L_exp = Myloss.L_exp(16,0.6)
+ L_TV = Myloss.L_TV()
+
+
+ optimizer = torch.optim.Adam(DCE_net.parameters(), lr=config.lr, weight_decay=config.weight_decay)
+
+ DCE_net.train()
+
+ for epoch in range(config.num_epochs):
+ for iteration, img_lowlight in enumerate(train_loader):
+
+ img_lowlight = img_lowlight.cuda()
+
+ enhanced_image_1,enhanced_image,A = DCE_net(img_lowlight)
+
+ Loss_TV = 200*L_TV(A)
+
+ loss_spa = torch.mean(L_spa(enhanced_image, img_lowlight))
+
+ loss_col = 5*torch.mean(L_color(enhanced_image))
+
+ loss_exp = 10*torch.mean(L_exp(enhanced_image))
+
+
+ # best_loss
+ loss = Loss_TV + loss_spa + loss_col + loss_exp
+ #
+
+
+ optimizer.zero_grad()
+ loss.backward()
+ torch.nn.utils.clip_grad_norm(DCE_net.parameters(),config.grad_clip_norm)
+ optimizer.step()
+
+ if ((iteration+1) % config.display_iter) == 0:
+ print("Loss at iteration", iteration+1, ":", loss.item())
+ if ((iteration+1) % config.snapshot_iter) == 0:
+
+ torch.save(DCE_net.state_dict(), config.snapshots_folder + "Epoch" + str(epoch) + '.pth')
+
+
+
+
+if __name__ == "__main__":
+
+ parser = argparse.ArgumentParser()
+
+ # Input Parameters
+ parser.add_argument('--lowlight_images_path', type=str, default="data/train_data/")
+ parser.add_argument('--lr', type=float, default=0.0001)
+ parser.add_argument('--weight_decay', type=float, default=0.0001)
+ parser.add_argument('--grad_clip_norm', type=float, default=0.1)
+ parser.add_argument('--num_epochs', type=int, default=200)
+ parser.add_argument('--train_batch_size', type=int, default=8)
+ parser.add_argument('--val_batch_size', type=int, default=4)
+ parser.add_argument('--num_workers', type=int, default=4)
+ parser.add_argument('--display_iter', type=int, default=10)
+ parser.add_argument('--snapshot_iter', type=int, default=10)
+ parser.add_argument('--snapshots_folder', type=str, default="snapshots/")
+ parser.add_argument('--load_pretrain', type=bool, default= False)
+ parser.add_argument('--pretrain_dir', type=str, default= "snapshots/Epoch99.pth")
+
+ config = parser.parse_args()
+
+ if not os.path.exists(config.snapshots_folder):
+ os.mkdir(config.snapshots_folder)
+
+
+ train(config)
+
+
+
+
+
+
+
+
+
diff --git a/PART2/Zero-DCE/Zero-DCE_code/model.py b/PART2/Zero-DCE/Zero-DCE_code/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..91abb9b4bf241e619f61244484d1ddb5cbef4437
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_code/model.py
@@ -0,0 +1,59 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+#import pytorch_colors as colors
+import numpy as np
+
+class enhance_net_nopool(nn.Module):
+
+ def __init__(self):
+ super(enhance_net_nopool, self).__init__()
+
+ self.relu = nn.ReLU(inplace=True)
+
+ number_f = 32
+ self.e_conv1 = nn.Conv2d(3,number_f,3,1,1,bias=True)
+ self.e_conv2 = nn.Conv2d(number_f,number_f,3,1,1,bias=True)
+ self.e_conv3 = nn.Conv2d(number_f,number_f,3,1,1,bias=True)
+ self.e_conv4 = nn.Conv2d(number_f,number_f,3,1,1,bias=True)
+ self.e_conv5 = nn.Conv2d(number_f*2,number_f,3,1,1,bias=True)
+ self.e_conv6 = nn.Conv2d(number_f*2,number_f,3,1,1,bias=True)
+ self.e_conv7 = nn.Conv2d(number_f*2,24,3,1,1,bias=True)
+
+ self.maxpool = nn.MaxPool2d(2, stride=2, return_indices=False, ceil_mode=False)
+ self.upsample = nn.UpsamplingBilinear2d(scale_factor=2)
+
+
+
+ def forward(self, x):
+
+ x1 = self.relu(self.e_conv1(x))
+ # p1 = self.maxpool(x1)
+ x2 = self.relu(self.e_conv2(x1))
+ # p2 = self.maxpool(x2)
+ x3 = self.relu(self.e_conv3(x2))
+ # p3 = self.maxpool(x3)
+ x4 = self.relu(self.e_conv4(x3))
+
+ x5 = self.relu(self.e_conv5(torch.cat([x3,x4],1)))
+ # x5 = self.upsample(x5)
+ x6 = self.relu(self.e_conv6(torch.cat([x2,x5],1)))
+
+ x_r = F.tanh(self.e_conv7(torch.cat([x1,x6],1)))
+ r1,r2,r3,r4,r5,r6,r7,r8 = torch.split(x_r, 3, dim=1)
+
+
+ x = x + r1*(torch.pow(x,2)-x)
+ x = x + r2*(torch.pow(x,2)-x)
+ x = x + r3*(torch.pow(x,2)-x)
+ enhance_image_1 = x + r4*(torch.pow(x,2)-x)
+ x = enhance_image_1 + r5*(torch.pow(enhance_image_1,2)-enhance_image_1)
+ x = x + r6*(torch.pow(x,2)-x)
+ x = x + r7*(torch.pow(x,2)-x)
+ enhance_image = x + r8*(torch.pow(x,2)-x)
+ r = torch.cat([r1,r2,r3,r4,r5,r6,r7,r8],1)
+ return enhance_image_1,enhance_image,r
+
+
+
diff --git a/PART2/Zero-DCE/Zero-DCE_code/snapshots/Epoch99.pth b/PART2/Zero-DCE/Zero-DCE_code/snapshots/Epoch99.pth
new file mode 100644
index 0000000000000000000000000000000000000000..fe06003f32b620f8544953a9cd7ec397080c3d3e
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_code/snapshots/Epoch99.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a4395acb874f320375d9704997cef874eaaaaa26a1777ceb29a92b70f74c3612
+size 320017
diff --git a/PART2/Zero-DCE/Zero-DCE_code/worker_zerodce.py b/PART2/Zero-DCE/Zero-DCE_code/worker_zerodce.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c604a66ff17084c33cc9e3910949b4605691130
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_code/worker_zerodce.py
@@ -0,0 +1,52 @@
+import torch
+import torch.nn as nn
+import torchvision
+import torch.optim
+import os
+import sys
+import argparse
+import cv2
+import numpy as np
+import model # 引用同目录下的 model.py
+
+def run_inference(input_path, output_path, model_path):
+ # 强制单卡
+ os.environ['CUDA_VISIBLE_DEVICES']='0'
+
+ # ================= 核心修复 =================
+ # 根据你提供的 model.py，类名是 enhance_net_nopool，且不需要参数
+ DCE_net = model.enhance_net_nopool().cuda()
+ # ==========================================
+
+ # 加载权重
+ DCE_net.load_state_dict(torch.load(model_path))
+ DCE_net.eval()
+
+ # 读取图片
+ if not os.path.exists(input_path):
+ print(f"❌ 错误: 找不到输入图片 {input_path}")
+ return
+
+ data_lowlight = cv2.imread(input_path)
+ data_lowlight = cv2.cvtColor(data_lowlight, cv2.COLOR_BGR2RGB)
+ data_lowlight = torch.from_numpy(data_lowlight).float() / 255.0
+ data_lowlight = data_lowlight.permute(2,0,1).cuda().unsqueeze(0)
+
+ # 推理
+ with torch.no_grad():
+ # model.py 的 forward 返回三个值: enhance_image_1, enhance_image, r
+ # 我们通常要第二个 (enhance_image) 或者第一个，看效果，通常取最后一个作为最终结果
+ _, enhanced_image, _ = DCE_net(data_lowlight)
+
+ # 保存
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
+ torchvision.utils.save_image(enhanced_image, output_path)
+ print(f"ZeroDCE_Success: {output_path}")
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('-i', '--input', required=True)
+ parser.add_argument('-o', '--output', required=True)
+ parser.add_argument('-m', '--model', required=True)
+ args = parser.parse_args()
+ run_inference(args.input, args.output, args.model)
\ No newline at end of file
diff --git a/PART2/Zero-DCE/Zero-DCE_files/MathJax.js.download b/PART2/Zero-DCE/Zero-DCE_files/MathJax.js.download
new file mode 100644
index 0000000000000000000000000000000000000000..3f468cb985062174ce893648fdc6ee2632385054
--- /dev/null
+++ b/PART2/Zero-DCE/Zero-DCE_files/MathJax.js.download
@@ -0,0 +1,19 @@
+/*
+ * /MathJax.js
+ *
+ * Copyright (c) 2009-2018 The MathJax Consortium
+ *
+ * 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.
+ */
+
+if(document.getElementById&&document.childNodes&&document.createElement){if(!(window.MathJax&&MathJax.Hub)){if(window.MathJax){window.MathJax={AuthorConfig:window.MathJax}}else{window.MathJax={}}MathJax.isPacked=true;MathJax.version="2.7.7";MathJax.fileversion="2.7.7";MathJax.cdnVersion="2.7.7";MathJax.cdnFileVersions={};(function(d){var b=window[d];if(!b){b=window[d]={}}var e=[];var c=function(f){var g=f.constructor;if(!g){g=function(){}}for(var h in f){if(h!=="constructor"&&f.hasOwnProperty(h)){g[h]=f[h]}}return g};var a=function(){return function(){return arguments.callee.Init.call(this,arguments)}};b.Object=c({constructor:a(),Subclass:function(f,h){var g=a();g.SUPER=this;g.Init=this.Init;g.Subclass=this.Subclass;g.Augment=this.Augment;g.protoFunction=this.protoFunction;g.can=this.can;g.has=this.has;g.isa=this.isa;g.prototype=new this(e);g.prototype.constructor=g;g.Augment(f,h);return g},Init:function(f){var g=this;if(f.length===1&&f[0]===e){return g}if(!(g instanceof 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+ +1. Visual Comparisons on Typical Low-light Images
+
+
+
+
+
+
+ 
+
+
+ + +
2. Visual Face Detection Results Before and After Enanced by Zero-DCE
+
+
+
+
+
+
+ 
+
+
+ + +
3. Real Low-light Video with Variational Illumination Enanced by Zero-DCE
+
+
+
+
+
+ +
4. Self-training (taking first 100 frames as training data) for Low-light Video Enhancement
+
+
+
+
+
+ +
+
+
+
+
+
Ablation Studies
+1. Contribution of Each Loss
+
+
+
+
+
+
+ 
+
+
+ +
+Ablation study of the contribution of each loss (spatial consistency loss Lspa, exposure control loss Lexp, color constancy loss Lcol, illumination smoothness loss LtvA). +
+ +2. Effect of Parameter Settings
+
+
+
+
+
+
+ 
+
+
+ +
+ Ablation study of the effect of parameter settings. l-f-n represents the proposed Zero-DCE with l convolutional layers, f feature maps of each layer (except the last layer), and n iterations. +
+ +3. Impact of Training Data
+
+
+
+
+
+
+ 
+
+
+ +
+To test the impact of training data, we retrain the Zero-DCE on different datasets: 1) only 900 low-light images out of 2,422 images in the original training set (Zero-DCELow), 2) 9,000 unlabeled low-light images provided in the DARK FACE dataset (Zero-DCELargeL), and 3) 4800 multi-exposure images from the data augmented combination of Part1 and Part2 subsets in the SICE dataset (Zero-DCELargeLH). +
+ +4. Advantage of Three-channel Adjustment
+
+
+
+
+
+
+ 
+
+
+ +
+Ablation study of the advantage of three-channel adjustment (RGB, CIE Lab, YCbCr color spaces). +
+ + +
+
+
+
+
+ Materials
+
+  + Paper + |
+
+ + Supplementary Material + |
+
+  + Code and Model + |
+
+
+
+ Citation
+
+
+ @Article{Zero-DCE,
+ author = {Guo, Chunle and Li, Chongyi and Guo, Jichang and Loy, Chen Change and Hou, Junhui and Kwong, Sam and Cong Runmin},
+ title = {Zero-reference deep curve estimation for low-light image enhancement},
+ journal = {arXiv preprint arXiv:2001.06826},
+ year = {2020}
+ }
+
+
+
+
+
+Contact
+If you have any questions, please contact Chongyi Li at lichongyi25@gmail.com or Chunle Guo at guochunle@tju.edu.cn.
+