Refactor HVideoTool to support project-based workflow: introduced project management features, updated UI for project handling, and enhanced documentation in README and CLAUDE.md. The tool now organizes images and settings into projects, improving usability and detection caching.

hvideotool/core/detection/cache.py

@@ -0,0 +1,77 @@
+"""Persist detection results for a project.
+
+The detection cache is a JSON file (``detections.json`` at the project root) that
+maps each image to its detections so reopening a project doesn't have to re-run
+the detector. The cache file lives apart from the images (which sit in the
+project's ``frames/`` sub-folder), so the file location and the image base
+directory are passed separately.
+
+The cache is tagged with the detector identity (name + model + conf/imgsz); a
+mismatch means the cache was produced by a different detector and is ignored
+(``load_results`` returns ``None``) rather than shown as if current.
+
+Keys are stored as **basenames**, so the cache survives moving/renaming the
+project folder.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from .types import Detection
+
+_VERSION = 1
+
+
+def make_key(detector: str, model_path: str | None, yolo_conf: float, yolo_imgsz: int) -> dict:
+    """Identity of the detector that produced a cache; cache is only valid for a match."""
+    return {
+        "detector": detector,
+        "model_path": model_path or "",
+        "yolo_conf": round(float(yolo_conf), 4),
+        "yolo_imgsz": int(yolo_imgsz),
+    }
+
+
+def save_results(cache_file: Path, key: dict, results: dict[str, list[Detection]]) -> bool:
+    """Write the cache (basename -> detections) to ``cache_file``. False on failure."""
+    payload = {
+        "version": _VERSION,
+        "key": key,
+        "results": {
+            Path(p).name: [d.to_dict() for d in dets]
+            for p, dets in results.items()
+        },
+    }
+    try:
+        cache_file.parent.mkdir(parents=True, exist_ok=True)
+        cache_file.write_text(
+            json.dumps(payload, ensure_ascii=False), encoding="utf-8"
+        )
+        return True
+    except OSError:
+        return False
+
+
+def load_results(cache_file: Path, key: dict, base_dir: Path) -> dict[str, list[Detection]] | None:
+    """Load cached detections from ``cache_file`` if present and the detector matches.
+
+    Returns a dict keyed by **full path** (``base_dir / basename``), or ``None`` if
+    there is no cache, it's unreadable, or it was made by a different detector.
+    """
+    if not cache_file.is_file():
+        return None
+    try:
+        payload = json.loads(cache_file.read_text(encoding="utf-8"))
+    except (OSError, ValueError):
+        return None
+    if payload.get("version") != _VERSION or payload.get("key") != key:
+        return None
+    out: dict[str, list[Detection]] = {}
+    for name, dets in payload.get("results", {}).items():
+        try:
+            out[str(base_dir / name)] = [Detection.from_dict(d) for d in dets]
+        except (KeyError, TypeError, ValueError):
+            continue  # skip a corrupt entry, keep the rest
+    return out

hvideotool/core/project.py

@@ -0,0 +1,148 @@
+"""A HVideoTool *project*: a self-contained folder on disk.
+
+This replaces the old "open a bare folder of images" model. A project is a folder
+that holds:
+
+```
+MyProject/
+├── project.json     # version, name, created, source, settings{detector/model/threshold/restore}
+├── frames/          # the images (what used to be "the folder")
+├── detections.json  # the detection cache (basename -> detections, tagged with detector key)
+└── collections/     # curation sub-folders (created lazily)
+```
+
+The project file carries the **per-project** settings (detector, model, overlay
+threshold, restore engine). Global ``settings.json`` only seeds the defaults for
+*new* projects; once a project exists it remembers how it was last inspected.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from datetime import datetime
+from pathlib import Path
+
+from ..config import AppConfig
+
+PROJECT_FILE = "project.json"
+FRAMES_DIR = "frames"
+CACHE_FILE = "detections.json"
+COLLECTIONS_DIR = "collections"
+FAVORITES_DIR = "Избранное"  # the single default collection ("в избранное")
+_VERSION = 1
+
+# The subset of AppConfig fields a project remembers (mirrored to/from project.json).
+_SETTING_KEYS = (
+    "detector",
+    "model_path",
+    "default_threshold",
+    "restorer",
+    "dm_dir",
+    "dm_model",
+    "dm_python",
+    "dm_gpu",
+)
+
+
+@dataclass
+class Project:
+    """A HVideoTool project rooted at ``root`` — the single source of truth for its
+    on-disk layout and its per-project settings."""
+
+    root: Path
+    name: str
+    settings: dict = field(default_factory=dict)  # subset of AppConfig fields
+    source: str | None = None                     # originating video/folder, if any
+    created: str | None = None
+
+    # ----------------------------------------------------------------- paths
+    @property
+    def project_file(self) -> Path:
+        return self.root / PROJECT_FILE
+
+    @property
+    def frames_dir(self) -> Path:
+        return self.root / FRAMES_DIR
+
+    @property
+    def cache_path(self) -> Path:
+        return self.root / CACHE_FILE
+
+    @property
+    def collections_dir(self) -> Path:
+        return self.root / COLLECTIONS_DIR
+
+    @property
+    def favorites_dir(self) -> Path:
+        """The single default collection — frames moved "to favorites" land here."""
+        return self.collections_dir / FAVORITES_DIR
+
+    # ------------------------------------------------------------- lifecycle
+    @classmethod
+    def create(
+        cls,
+        root: Path | str,
+        name: str | None = None,
+        settings: dict | None = None,
+        source: str | None = None,
+    ) -> "Project":
+        """Create a new project folder (with ``frames/``) and write ``project.json``."""
+        root = Path(root)
+        proj = cls(
+            root=root,
+            name=name or root.name,
+            settings={k: v for k, v in (settings or {}).items() if k in _SETTING_KEYS},
+            source=source,
+            created=datetime.now().isoformat(timespec="seconds"),
+        )
+        proj.frames_dir.mkdir(parents=True, exist_ok=True)
+        proj.save()
+        return proj
+
+    @classmethod
+    def load(cls, path: Path | str) -> "Project":
+        """Load a project from its folder or directly from its ``project.json``."""
+        path = Path(path)
+        root = path.parent if path.name == PROJECT_FILE else path
+        data = json.loads((root / PROJECT_FILE).read_text(encoding="utf-8"))
+        return cls(
+            root=root,
+            name=data.get("name", root.name),
+            settings={k: v for k, v in data.get("settings", {}).items() if k in _SETTING_KEYS},
+            source=data.get("source"),
+            created=data.get("created"),
+        )
+
+    @staticmethod
+    def is_project(path: Path | str) -> bool:
+        """True if ``path`` is a project folder (or a ``project.json``)."""
+        path = Path(path)
+        if path.name == PROJECT_FILE:
+            return path.is_file()
+        return (path / PROJECT_FILE).is_file()
+
+    def save(self) -> None:
+        """Write ``project.json``."""
+        payload = {
+            "version": _VERSION,
+            "name": self.name,
+            "created": self.created,
+            "source": self.source,
+            "settings": self.settings,
+        }
+        self.root.mkdir(parents=True, exist_ok=True)
+        self.project_file.write_text(
+            json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8"
+        )
+
+    # ----------------------------------------------------- settings <-> config
+    def apply_to_config(self, cfg: AppConfig) -> None:
+        """Overlay this project's stored settings onto ``cfg`` (mutates it)."""
+        for key in _SETTING_KEYS:
+            if key in self.settings:
+                setattr(cfg, key, self.settings[key])
+
+    def update_from_config(self, cfg: AppConfig) -> None:
+        """Capture the per-project settings from ``cfg`` into ``self.settings``."""
+        self.settings = {key: getattr(cfg, key) for key in _SETTING_KEYS}

hvideotool/core/restore/_deepmosaics/LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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hvideotool/core/restore/_deepmosaics/NOTICE.md

@@ -0,0 +1,16 @@
+# Vendored DeepMosaics code
+
+`models/` and `util/` here are copied **verbatim** from DeepMosaics
+(https://github.com/HypoX64/DeepMosaics) by HypoX64, licensed **GPL-3.0**
+(see `LICENSE`). We use only the per-image mosaic-clean path
+(`models.loadmodel`, `models.runmodel`, `util.image_processing`) — loaded
+in-process by `hvideotool/core/restore/deepmosaics.py`.
+
+Because this GPL-3.0 code is combined into HVideoTool, the project as a whole is
+distributed under **GPL-3.0**.
+
+Model weights (`clean_*.pth`, `mosaic_position.pth`) are NOT included — the user
+points the app at their own downloaded weights.
+
+These files are unmodified; this directory is added to `sys.path` at import time
+so the original `from models import …` / `import util.…` statements resolve.

hvideotool/core/restore/_deepmosaics/util/clean_cache.py

@@ -0,0 +1,51 @@
+import os
+import shutil
+
+def findalldir(rootdir):
+    dir_list = []
+    for root,dirs,files in os.walk(rootdir): 
+        for dir in dirs:
+            dir_list.append(os.path.join(root,dir))
+    return(dir_list)
+
+def Traversal(filedir):
+    file_list=[]
+    dir_list = []
+    for root,dirs,files in os.walk(filedir): 
+        for file in files:
+            file_list.append(os.path.join(root,file)) 
+        for dir in dirs:
+            dir_list.append(os.path.join(root,dir))
+            Traversal(dir)
+    return file_list,dir_list
+
+def is_img(path):
+    ext = os.path.splitext(path)[1]
+    ext = ext.lower()
+    if ext in ['.jpg','.png','.jpeg','.bmp']:
+        return True
+    else:
+        return False
+
+def is_video(path):
+    ext = os.path.splitext(path)[1]
+    ext = ext.lower()
+    if ext in ['.mp4','.flv','.avi','.mov','.mkv','.wmv','.rmvb']:
+        return True
+    else:
+        return False
+
+def cleanall():
+    file_list,dir_list = Traversal('./')
+    for file in file_list:
+        if ('tmp' in file) | ('pth' in file)|('pycache' in file) | is_video(file) | is_img(file):
+            if os.path.exists(file):
+                if 'imgs' not in file:
+                    os.remove(file)
+                    print('remove file:',file)
+
+    for dir in dir_list:
+        if ('tmp'in dir)|('pycache'in dir):
+            if os.path.exists(dir):
+                shutil.rmtree(dir)
+                print('remove dir:',dir)

hvideotool/core/restore/_deepmosaics/util/data.py

@@ -0,0 +1,160 @@
+import random
+import os
+from util.mosaic import get_random_parameter
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+import cv2
+from . import image_processing as impro
+from . import degradater
+
+def to_tensor(data,gpu_id):
+    data = torch.from_numpy(data)
+    if gpu_id != '-1':
+        data = data.cuda()
+    return data
+
+def normalize(data):
+    '''
+    normalize to -1 ~ 1
+    '''
+    return (data.astype(np.float32)/255.0-0.5)/0.5
+
+def anti_normalize(data):
+    return np.clip((data*0.5+0.5)*255,0,255).astype(np.uint8)
+
+def tensor2im(image_tensor, gray=False, rgb2bgr = True ,is0_1 = False, batch_index=0):
+    image_tensor =image_tensor.data
+    image_numpy = image_tensor[batch_index].cpu().float().numpy()
+    
+    if not is0_1:
+        image_numpy = (image_numpy + 1)/2.0
+    image_numpy = np.clip(image_numpy * 255.0,0,255) 
+
+    # gray -> output 1ch
+    if gray:
+        h, w = image_numpy.shape[1:]
+        image_numpy = image_numpy.reshape(h,w)
+        return image_numpy.astype(np.uint8)
+
+    # output 3ch
+    if image_numpy.shape[0] == 1:
+        image_numpy = np.tile(image_numpy, (3, 1, 1))
+    image_numpy = image_numpy.transpose((1, 2, 0))  
+    if rgb2bgr and not gray:
+        image_numpy = image_numpy[...,::-1]-np.zeros_like(image_numpy)
+    return image_numpy.astype(np.uint8)
+
+
+def im2tensor(image_numpy, gray=False,bgr2rgb = True, reshape = True, gpu_id = '-1',is0_1 = False):
+    if gray:
+        h, w = image_numpy.shape
+        image_numpy = (image_numpy/255.0-0.5)/0.5
+        image_tensor = torch.from_numpy(image_numpy).float()
+        if reshape:
+            image_tensor = image_tensor.reshape(1,1,h,w)
+    else:
+        h, w ,ch = image_numpy.shape
+        if bgr2rgb:
+            image_numpy = image_numpy[...,::-1]-np.zeros_like(image_numpy)
+        if is0_1:
+            image_numpy = image_numpy/255.0
+        else:
+            image_numpy = (image_numpy/255.0-0.5)/0.5
+        image_numpy = image_numpy.transpose((2, 0, 1))
+        image_tensor = torch.from_numpy(image_numpy).float()
+        if reshape:
+            image_tensor = image_tensor.reshape(1,ch,h,w)
+    if gpu_id != '-1':
+        image_tensor = image_tensor.cuda()
+    return image_tensor
+
+def shuffledata(data,target):
+    state = np.random.get_state()
+    np.random.shuffle(data)
+    np.random.set_state(state)
+    np.random.shuffle(target)
+
+def random_transform_single_mask(img,out_shape):
+    out_h,out_w = out_shape
+    img = cv2.resize(img,(int(out_w*random.uniform(1.1, 1.5)),int(out_h*random.uniform(1.1, 1.5))))
+    h,w = img.shape[:2]
+    h_move = int((h-out_h)*random.random())
+    w_move = int((w-out_w)*random.random())
+    img = img[h_move:h_move+out_h,w_move:w_move+out_w]
+    if random.random()<0.5:
+        if random.random()<0.5:
+            img = img[:,::-1]
+        else:
+            img = img[::-1,:]
+    if img.shape[0] != out_h or img.shape[1]!= out_w :
+        img = cv2.resize(img,(out_w,out_h))
+    return img
+
+def get_transform_params():
+    crop_flag  = True
+    rotat_flag = np.random.random()<0.2
+    color_flag = True
+    flip_flag  = np.random.random()<0.2
+    degradate_flag  = np.random.random()<0.5
+    flag_dict = {'crop':crop_flag,'rotat':rotat_flag,'color':color_flag,'flip':flip_flag,'degradate':degradate_flag}
+    
+    crop_rate = [np.random.random(),np.random.random()]
+    rotat_rate = np.random.random()
+    color_rate = [np.random.uniform(-0.05,0.05),np.random.uniform(-0.05,0.05),np.random.uniform(-0.05,0.05),
+        np.random.uniform(-0.05,0.05),np.random.uniform(-0.05,0.05)]
+    flip_rate = np.random.random()
+    degradate_params = degradater.get_random_degenerate_params(mod='weaker_2')
+    rate_dict = {'crop':crop_rate,'rotat':rotat_rate,'color':color_rate,'flip':flip_rate,'degradate':degradate_params}
+
+    return {'flag':flag_dict,'rate':rate_dict}
+
+def random_transform_single_image(img,finesize,params=None,test_flag = False):
+    if params is None:
+        params = get_transform_params()
+    
+    if params['flag']['degradate']:
+        img = degradater.degradate(img,params['rate']['degradate'])
+
+    if params['flag']['crop']:
+        h,w = img.shape[:2]
+        h_move = int((h-finesize)*params['rate']['crop'][0])
+        w_move = int((w-finesize)*params['rate']['crop'][1])
+        img = img[h_move:h_move+finesize,w_move:w_move+finesize]
+    
+    if test_flag:
+        return img
+
+    if params['flag']['rotat']:
+        h,w = img.shape[:2]
+        M = cv2.getRotationMatrix2D((w/2,h/2),90*int(4*params['rate']['rotat']),1)
+        img = cv2.warpAffine(img,M,(w,h))
+
+    if params['flag']['color']:
+        img = impro.color_adjust(img,params['rate']['color'][0],params['rate']['color'][1],
+            params['rate']['color'][2],params['rate']['color'][3],params['rate']['color'][4])
+
+    if params['flag']['flip']:
+        img = img[:,::-1]
+
+    #check shape
+    if img.shape[0]!= finesize or img.shape[1]!= finesize:
+        img = cv2.resize(img,(finesize,finesize))
+        print('warning! shape error.')
+    return img
+
+def random_transform_pair_image(img,mask,finesize,test_flag = False):
+    params = get_transform_params()
+    img = random_transform_single_image(img,finesize,params)
+    params['flag']['degradate'] = False
+    params['flag']['color'] = False
+    mask = random_transform_single_image(mask,finesize,params)
+    return img,mask
+
+def showresult(img1,img2,img3,name,is0_1 = False):
+    size = img1.shape[3]
+    showimg=np.zeros((size,size*3,3))
+    showimg[0:size,0:size] = tensor2im(img1,rgb2bgr = False, is0_1 = is0_1)
+    showimg[0:size,size:size*2] = tensor2im(img2,rgb2bgr = False, is0_1 = is0_1)
+    showimg[0:size,size*2:size*3] = tensor2im(img3,rgb2bgr = False, is0_1 = is0_1)
+    cv2.imwrite(name, showimg)

hvideotool/core/restore/_deepmosaics/util/dataloader.py

@@ -0,0 +1,141 @@
+import os
+import random
+import numpy as np
+from multiprocessing import Process, Queue
+from . import image_processing as impro
+from . import mosaic,data
+
+class VideoLoader(object):
+    """docstring for VideoLoader
+    Load a single video(Converted to images)
+    How to use:
+    1.Init VideoLoader as loader
+    2.Get data by loader.ori_stream
+    3.loader.next()  to get next stream
+    """
+    def __init__(self, opt, video_dir, test_flag=False):
+        super(VideoLoader, self).__init__()
+        self.opt = opt
+        self.test_flag = test_flag
+        self.video_dir = video_dir
+        self.t = 0
+        self.n_iter = self.opt.M -self.opt.S*(self.opt.T+1)
+        self.transform_params = data.get_transform_params()
+        self.ori_load_pool = []
+        self.mosaic_load_pool = []
+        self.previous_pred = None
+        feg_ori =  impro.imread(os.path.join(video_dir,'origin_image','00001.jpg'),loadsize=self.opt.loadsize,rgb=True)
+        feg_mask = impro.imread(os.path.join(video_dir,'mask','00001.png'),mod='gray',loadsize=self.opt.loadsize)
+        self.mosaic_size,self.mod,self.rect_rat,self.feather = mosaic.get_random_parameter(feg_ori,feg_mask)
+        self.startpos = [random.randint(0,self.mosaic_size),random.randint(0,self.mosaic_size)]
+        self.loadsize = self.opt.loadsize
+        #Init load pool
+        for i in range(self.opt.S*self.opt.T):
+            _ori_img = impro.imread(os.path.join(video_dir,'origin_image','%05d' % (i+1)+'.jpg'),loadsize=self.loadsize,rgb=True)
+            _mask = impro.imread(os.path.join(video_dir,'mask','%05d' % (i+1)+'.png' ),mod='gray',loadsize=self.loadsize)
+            _mosaic_img = mosaic.addmosaic_base(_ori_img, _mask, self.mosaic_size,0, self.mod,self.rect_rat,self.feather,self.startpos)
+            _ori_img = data.random_transform_single_image(_ori_img,opt.finesize,self.transform_params)
+            _mosaic_img = data.random_transform_single_image(_mosaic_img,opt.finesize,self.transform_params)
+
+            self.ori_load_pool.append(self.normalize(_ori_img))
+            self.mosaic_load_pool.append(self.normalize(_mosaic_img))
+        self.ori_load_pool = np.array(self.ori_load_pool)
+        self.mosaic_load_pool = np.array(self.mosaic_load_pool)
+
+        #Init frist stream
+        self.ori_stream    = self.ori_load_pool   [np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
+        self.mosaic_stream = self.mosaic_load_pool[np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
+        # stream B,T,H,W,C -> B,C,T,H,W
+        self.ori_stream    = self.ori_stream.reshape   (1,self.opt.T,opt.finesize,opt.finesize,3).transpose((0,4,1,2,3))
+        self.mosaic_stream = self.mosaic_stream.reshape(1,self.opt.T,opt.finesize,opt.finesize,3).transpose((0,4,1,2,3))
+        
+        #Init frist previous frame
+        self.previous_pred = self.ori_load_pool[self.opt.S*self.opt.N-1].copy()
+        # previous B,C,H,W
+        self.previous_pred = self.previous_pred.reshape(1,opt.finesize,opt.finesize,3).transpose((0,3,1,2))
+    
+    def normalize(self,data):
+        '''
+        normalize to -1 ~ 1
+        '''
+        return (data.astype(np.float32)/255.0-0.5)/0.5
+
+    def anti_normalize(self,data):
+        return np.clip((data*0.5+0.5)*255,0,255).astype(np.uint8)
+    
+    def next(self):
+        # random
+        if np.random.random()<0.05:
+            self.startpos = [random.randint(0,self.mosaic_size),random.randint(0,self.mosaic_size)]
+        if np.random.random()<0.02:
+            self.transform_params['rate']['crop'] = [np.random.random(),np.random.random()]
+        if np.random.random()<0.02:
+            self.loadsize = np.random.randint(self.opt.finesize,self.opt.loadsize)
+        
+        if self.t != 0:
+            self.previous_pred = None
+            self.ori_load_pool   [:self.opt.S*self.opt.T-1] = self.ori_load_pool   [1:self.opt.S*self.opt.T]
+            self.mosaic_load_pool[:self.opt.S*self.opt.T-1] = self.mosaic_load_pool[1:self.opt.S*self.opt.T]
+            #print(os.path.join(self.video_dir,'origin_image','%05d' % (self.opt.S*self.opt.T+self.t)+'.jpg'))
+            _ori_img = impro.imread(os.path.join(self.video_dir,'origin_image','%05d' % (self.opt.S*self.opt.T+self.t)+'.jpg'),loadsize=self.loadsize,rgb=True)
+            _mask = impro.imread(os.path.join(self.video_dir,'mask','%05d' % (self.opt.S*self.opt.T+self.t)+'.png' ),mod='gray',loadsize=self.loadsize)
+            _mosaic_img = mosaic.addmosaic_base(_ori_img, _mask, self.mosaic_size,0, self.mod,self.rect_rat,self.feather,self.startpos)
+            _ori_img = data.random_transform_single_image(_ori_img,self.opt.finesize,self.transform_params)
+            _mosaic_img = data.random_transform_single_image(_mosaic_img,self.opt.finesize,self.transform_params)
+            
+            _ori_img,_mosaic_img = self.normalize(_ori_img),self.normalize(_mosaic_img)
+            self.ori_load_pool   [self.opt.S*self.opt.T-1] = _ori_img
+            self.mosaic_load_pool[self.opt.S*self.opt.T-1] = _mosaic_img
+
+            self.ori_stream    = self.ori_load_pool   [np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
+            self.mosaic_stream = self.mosaic_load_pool[np.linspace(0, (self.opt.T-1)*self.opt.S,self.opt.T,dtype=np.int64)].copy()
+
+            # stream B,T,H,W,C -> B,C,T,H,W
+            self.ori_stream    = self.ori_stream.reshape   (1,self.opt.T,self.opt.finesize,self.opt.finesize,3).transpose((0,4,1,2,3))
+            self.mosaic_stream = self.mosaic_stream.reshape(1,self.opt.T,self.opt.finesize,self.opt.finesize,3).transpose((0,4,1,2,3))
+
+        self.t += 1
+
+class VideoDataLoader(object):
+    """VideoDataLoader"""
+    def __init__(self, opt, videolist, test_flag=False):
+        super(VideoDataLoader, self).__init__()
+        self.videolist = []
+        self.opt = opt
+        self.test_flag = test_flag
+        for i in range(self.opt.n_epoch):
+            self.videolist += videolist.copy()
+        random.shuffle(self.videolist)
+        self.each_video_n_iter = self.opt.M -self.opt.S*(self.opt.T+1)
+        self.n_iter = len(self.videolist)//self.opt.load_thread//self.opt.batchsize*self.each_video_n_iter*self.opt.load_thread
+        self.queue = Queue(self.opt.load_thread)
+        self.ori_stream = np.zeros((self.opt.batchsize,3,self.opt.T,self.opt.finesize,self.opt.finesize),dtype=np.float32)# B,C,T,H,W
+        self.mosaic_stream = np.zeros((self.opt.batchsize,3,self.opt.T,self.opt.finesize,self.opt.finesize),dtype=np.float32)# B,C,T,H,W
+        self.previous_pred = np.zeros((self.opt.batchsize,3,self.opt.finesize,self.opt.finesize),dtype=np.float32)
+        self.load_init()
+
+    def load(self,videolist):
+        for load_video_iter in range(len(videolist)//self.opt.batchsize):
+            iter_videolist = videolist[load_video_iter*self.opt.batchsize:(load_video_iter+1)*self.opt.batchsize]
+            videoloaders = [VideoLoader(self.opt,os.path.join(self.opt.dataset,iter_videolist[i]),self.test_flag) for i in range(self.opt.batchsize)]
+            for each_video_iter in range(self.each_video_n_iter):
+                for i in range(self.opt.batchsize):
+                    self.ori_stream[i] = videoloaders[i].ori_stream
+                    self.mosaic_stream[i] = videoloaders[i].mosaic_stream
+                    if each_video_iter == 0:
+                        self.previous_pred[i] = videoloaders[i].previous_pred
+                    videoloaders[i].next()
+                if each_video_iter == 0:
+                    self.queue.put([self.ori_stream.copy(),self.mosaic_stream.copy(),self.previous_pred])
+                else:
+                    self.queue.put([self.ori_stream.copy(),self.mosaic_stream.copy(),None])
+    
+    def load_init(self):
+        ptvn = len(self.videolist)//self.opt.load_thread #pre_thread_video_num
+        for i in range(self.opt.load_thread):
+            p = Process(target=self.load,args=(self.videolist[i*ptvn:(i+1)*ptvn],))
+            p.daemon = True
+            p.start()
+
+    def get_data(self):
+        return self.queue.get()

hvideotool/core/restore/_deepmosaics/util/degradater.py

@@ -0,0 +1,119 @@
+'''
+https://github.com/sonack/GFRNet_pytorch_new
+'''
+import random
+import cv2
+import numpy as np
+
+def gaussian_blur(img, sigma=3, size=13):
+    if sigma > 0:
+        if isinstance(size, int):
+            size = (size, size)
+        img = cv2.GaussianBlur(img, size, sigma)
+    return img
+
+def down(img, scale, shape):
+    if scale > 1:
+        h, w, _ = shape
+        scaled_h, scaled_w = int(h / scale), int(w / scale)
+        img = cv2.resize(img, (scaled_w, scaled_h), interpolation = cv2.INTER_CUBIC)
+    return img
+
+def up(img, scale, shape):
+    if scale > 1:
+        h, w, _ = shape
+        img = cv2.resize(img, (w, h), interpolation = cv2.INTER_CUBIC)
+    return img
+    
+def awgn(img, level):
+    if level > 0:
+        noise = np.random.randn(*img.shape) * level
+        img = (img + noise).clip(0,255).astype(np.uint8)
+    return img
+
+def jpeg_compressor(img,quality):
+    if quality > 0:    # 0 indicating no lossy compression (i.e losslessly compression)
+        encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
+        img = cv2.imdecode(cv2.imencode('.jpg', img, encode_param)[1], 1)
+    return img
+
+def get_random_degenerate_params(mod='strong'):
+    '''
+    mod : strong | only_downsample | only_4x | weaker_1 | weaker_2
+    '''
+    params = {}
+    gaussianBlur_size_list = list(range(3,14,2))
+
+    if mod == 'strong':
+        gaussianBlur_sigma_list = [1 + x for x in range(3)]
+        gaussianBlur_sigma_list += [0]
+        downsample_scale_list = [1 + x * 0.1 for x in range(0,71)]
+        awgn_level_list = list(range(1, 8, 1))        
+        jpeg_quality_list = list(range(10, 41, 1))
+        jpeg_quality_list += int(len(jpeg_quality_list) * 0.33) * [0]
+    
+    elif mod == 'only_downsample':
+        gaussianBlur_sigma_list = [0]
+        downsample_scale_list = [1 + x * 0.1 for x in range(0,71)]
+        awgn_level_list = [0]
+        jpeg_quality_list = [0]
+    
+    elif mod == 'only_4x':
+        gaussianBlur_sigma_list = [0]
+        downsample_scale_list = [4]
+        awgn_level_list = [0]
+        jpeg_quality_list = [0]
+
+    elif mod == 'weaker_1':   # 0.5 trigger prob
+        gaussianBlur_sigma_list = [1 + x for x in range(3)]
+        gaussianBlur_sigma_list += int(len(gaussianBlur_sigma_list)) * [0] # 1/2 trigger this degradation
+        
+        downsample_scale_list = [1 + x * 0.1 for x in range(0,71)]
+        downsample_scale_list += int(len(downsample_scale_list)) * [1]
+        
+        awgn_level_list = list(range(1, 8, 1))
+        awgn_level_list += int(len(awgn_level_list)) * [0]
+        
+        jpeg_quality_list = list(range(10, 41, 1))
+        jpeg_quality_list += int(len(jpeg_quality_list)) * [0]
+
+    elif mod == 'weaker_2':    # weaker than weaker_1, jpeg [20,40]
+        gaussianBlur_sigma_list = [1 + x for x in range(3)]
+        gaussianBlur_sigma_list += int(len(gaussianBlur_sigma_list)) * [0] # 1/2 trigger this degradation
+        
+        downsample_scale_list = [1 + x * 0.1 for x in range(0,71)]
+        downsample_scale_list += int(len(downsample_scale_list)) * [1]
+        
+        awgn_level_list = list(range(1, 8, 1))
+        awgn_level_list += int(len(awgn_level_list)) * [0]
+        
+        jpeg_quality_list = list(range(20, 41, 1))
+        jpeg_quality_list += int(len(jpeg_quality_list)) * [0]
+    
+    params['blur_sigma'] = random.choice(gaussianBlur_sigma_list)
+    params['blur_size'] = random.choice(gaussianBlur_size_list)
+    params['updown_scale'] = random.choice(downsample_scale_list)
+    params['awgn_level'] = random.choice(awgn_level_list)
+    params['jpeg_quality'] = random.choice(jpeg_quality_list)
+
+    return params
+
+def degradate(img,params,jpeg_last = True):
+    shape = img.shape
+    if not params:
+        params = get_random_degenerate_params('original')
+        
+    if jpeg_last:
+        img = gaussian_blur(img,params['blur_sigma'],params['blur_size'])
+        img = down(img,params['updown_scale'],shape)
+        img = awgn(img,params['awgn_level'])
+        img = up(img,params['updown_scale'],shape)
+        img = jpeg_compressor(img,params['jpeg_quality'])
+    else:
+        img = gaussian_blur(img,params['blur_sigma'],params['blur_size'])
+        img = down(img,params['updown_scale'],shape)
+        img = awgn(img,params['awgn_level'])
+        img = jpeg_compressor(img,params['jpeg_quality'])
+        img = up(img,params['updown_scale'],shape)
+
+    return img

hvideotool/core/restore/_deepmosaics/util/ffmpeg.py

@@ -0,0 +1,92 @@
+import os,json
+import subprocess
+# ffmpeg 3.4.6
+
+def args2cmd(args):
+    cmd = ''
+    for arg in args:
+        cmd += (arg+' ')
+    return cmd
+
+def run(args,mode = 0):
+
+    if mode == 0:
+        cmd = args2cmd(args)
+        os.system(cmd)
+
+    elif mode == 1:
+        '''
+        out_string = os.popen(cmd_str).read()
+        For chinese path in Windows
+        https://blog.csdn.net/weixin_43903378/article/details/91979025
+        '''
+        cmd = args2cmd(args)
+        stream = os.popen(cmd)._stream
+        sout = stream.buffer.read().decode(encoding='utf-8')
+        return sout
+
+    elif mode == 2:
+        cmd = args2cmd(args)
+        p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+        sout = p.stdout.readlines()
+        return sout
+
+def video2image(videopath, imagepath, fps=0, start_time='00:00:00', last_time='00:00:00'):
+    args = ['ffmpeg']
+    if last_time != '00:00:00':
+        args += ['-ss', start_time]
+        args += ['-t', last_time]
+    args += ['-i', '"'+videopath+'"']
+    if fps != 0:
+        args += ['-r', str(fps)]
+    args += ['-f', 'image2','-q:v','-0',imagepath]
+    run(args)
+
+def video2voice(videopath, voicepath, start_time='00:00:00', last_time='00:00:00'):
+    args = ['ffmpeg', '-i', '"'+videopath+'"','-async 1 -f mp3','-b:a 320k']
+    if last_time != '00:00:00':
+        args += ['-ss', start_time]
+        args += ['-t', last_time]
+    args += [voicepath]
+    run(args)
+
+def image2video(fps,imagepath,voicepath,videopath):
+    os.system('ffmpeg -y -r '+str(fps)+' -i '+imagepath+' -vcodec libx264 '+os.path.split(voicepath)[0]+'/video_tmp.mp4')
+    if os.path.exists(voicepath):
+        os.system('ffmpeg -i '+os.path.split(voicepath)[0]+'/video_tmp.mp4'+' -i "'+voicepath+'" -vcodec copy -acodec aac '+videopath)
+    else:
+        os.system('ffmpeg -i '+os.path.split(voicepath)[0]+'/video_tmp.mp4 '+videopath)
+
+def get_video_infos(videopath):
+    args =  ['ffprobe -v quiet -print_format json -show_format -show_streams', '-i', '"'+videopath+'"']
+    out_string = run(args,mode=1)
+    infos = json.loads(out_string)
+    try:
+        fps = eval(infos['streams'][0]['avg_frame_rate'])
+        endtime = float(infos['format']['duration'])
+        width = int(infos['streams'][0]['width'])
+        height = int(infos['streams'][0]['height'])
+    except Exception as e:
+        fps = eval(infos['streams'][1]['r_frame_rate'])
+        endtime = float(infos['format']['duration'])
+        width = int(infos['streams'][1]['width'])
+        height = int(infos['streams'][1]['height'])
+
+    return fps,endtime,height,width
+
+def cut_video(in_path,start_time,last_time,out_path,vcodec='h265'):
+    if vcodec == 'copy':
+        os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec copy -acodec copy '+out_path)
+    elif vcodec == 'h264':    
+        os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec libx264 -b 12M '+out_path)
+    elif vcodec == 'h265':
+        os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec libx265 -b 12M '+out_path)
+
+def continuous_screenshot(videopath,savedir,fps):
+    '''
+    videopath: input video path
+    savedir:   images will save here
+    fps:       save how many images per second
+    '''
+    videoname = os.path.splitext(os.path.basename(videopath))[0]
+    os.system('ffmpeg -i "'+videopath+'" -vf fps='+str(fps)+' -q:v -0 '+savedir+'/'+videoname+'_%06d.jpg')

hvideotool/core/restore/_deepmosaics/util/filt.py

@@ -0,0 +1,77 @@
+import numpy as np
+
+def less_zero(arr,num = 7):
+    index = np.linspace(0,len(arr)-1,len(arr),dtype='int')
+    cnt = 0
+    for i in range(2,len(arr)-2):
+        if arr[i] != 0:
+            arr[i] = arr[i]
+            if cnt != 0:
+                if cnt <= num*2:
+                    arr[i-cnt:round(i-cnt/2)] = arr[i-cnt-1-2]
+                    arr[round(i-cnt/2):i] = arr[i+2]
+                    index[i-cnt:round(i-cnt/2)] = i-cnt-1-2
+                    index[round(i-cnt/2):i] = i+2
+                else:
+                    arr[i-cnt:i-cnt+num] = arr[i-cnt-1-2]
+                    arr[i-num:i] = arr[i+2] 
+                    index[i-cnt:i-cnt+num] = i-cnt-1-2
+                    index[i-num:i] = i+2
+                cnt = 0
+        else:
+            cnt += 1
+    return arr,index
+
+def medfilt(data,window):
+    if window%2 == 0 or window < 0:
+        print('Error: the medfilt window must be even number')
+        exit(0)
+    pad = int((window-1)/2)
+    pad_data = np.zeros(len(data)+window-1, dtype = type(data[0]))
+    result = np.zeros(len(data),dtype = type(data[0]))
+    pad_data[pad:pad+len(data)]=data[:]
+    for i in range(len(data)):
+        result[i] = np.median(pad_data[i:i+window])
+    return result
+
+def position_medfilt(positions,window):
+
+    x,mask_index = less_zero(positions[:,0],window)
+    y = less_zero(positions[:,1],window)[0]
+    area = less_zero(positions[:,2],window)[0]
+    x_filt = medfilt(x, window)
+    y_filt = medfilt(y, window)
+    area_filt = medfilt(area, window)
+    cnt = 0
+    for i in range(1,len(x)):
+        if 0.8<x_filt[i]/(x[i]+1)<1.2 and 0.8<y_filt[i]/(y[i]+1)<1.2 and 0.6<area_filt[i]/(area[i]+1)<1.4:
+            mask_index[i] = mask_index[i]
+            if cnt != 0:
+                mask_index[i-cnt:round(i-cnt/2)] = mask_index[i-cnt]
+                mask_index[round(i-cnt/2):i] = mask_index[i] 
+                cnt = 0
+        else:
+            mask_index[i] = mask_index[i-1]
+            cnt += 1
+    return mask_index
+
+# def main():
+#     import matplotlib.pyplot as plt
+#     positions = np.load('../test_pos.npy')
+#     positions_new = np.load('../test_pos.npy')
+#     print(positions.shape)
+#     mask_index = position_medfilt(positions.copy(), 7)
+#     x = positions_new[2]
+
+#     x_new = []
+#     for i in range(len(x)):
+#         x_new.append(x[mask_index[i]])
+
+#     plt.subplot(211)
+#     plt.plot(x)
+#     plt.subplot(212)
+#     plt.plot(x_new)
+#     plt.show()
+
+# if __name__ == '__main__':
+#     main()

hvideotool/core/restore/_deepmosaics/util/image_processing.py

@@ -0,0 +1,253 @@
+import cv2
+import numpy as np
+import random
+from threading import Thread
+
+import platform
+
+system_type = 'Linux'
+if 'Windows' in platform.platform():
+    system_type = 'Windows'
+
+def imread(file_path,mod = 'normal',loadsize = 0, rgb=False):
+    '''
+    mod:  'normal' | 'gray' | 'all'
+    loadsize: 0->original
+    '''
+    if system_type == 'Linux':
+        if mod == 'normal':
+            img = cv2.imread(file_path,1)
+        elif mod == 'gray':
+            img = cv2.imread(file_path,0)
+        elif mod == 'all':
+            img = cv2.imread(file_path,-1)
+    
+    #In windows, for chinese path, use cv2.imdecode insteaded.
+    #It will loss EXIF, I can't fix it
+    else: 
+        if mod == 'normal':
+            img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),1)
+        elif mod == 'gray':
+            img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),0)
+        elif mod == 'all':
+            img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),-1)
+            
+    if loadsize != 0:
+        img = resize(img, loadsize, interpolation=cv2.INTER_CUBIC)
+
+    if rgb and img.ndim==3:
+        img = img[:,:,::-1]
+
+    return img
+
+def imwrite(file_path,img,use_thread=False):
+    '''
+    in other to save chinese path images in windows,
+    this fun just for save final output images
+    '''
+    def subfun(file_path,img):
+        if system_type == 'Linux':
+            cv2.imwrite(file_path, img)
+        else:
+            cv2.imencode('.jpg', img)[1].tofile(file_path)
+    if use_thread:
+        t = Thread(target=subfun,args=(file_path, img,))
+        t.daemon()
+        t.start
+    else:
+        subfun(file_path,img)
+
+def resize(img,size,interpolation=cv2.INTER_LINEAR):
+    '''
+    cv2.INTER_NEAREST      最邻近插值点法
+    cv2.INTER_LINEAR        双线性插值法
+    cv2.INTER_AREA         邻域像素再取样插补
+    cv2.INTER_CUBIC        双立方插补，4*4大小的补点
+    cv2.INTER_LANCZOS4     8x8像素邻域的Lanczos插值
+    '''
+    h, w = img.shape[:2]
+    if np.min((w,h)) ==size:
+        return img
+    if w >= h:
+        res = cv2.resize(img,(int(size*w/h), size),interpolation=interpolation)
+    else:
+        res = cv2.resize(img,(size, int(size*h/w)),interpolation=interpolation)
+    return res
+
+def resize_like(img,img_like):
+    h, w = img_like.shape[:2]
+    img = cv2.resize(img, (w,h))
+    return img
+
+def ch_one2three(img):
+    res = cv2.merge([img, img, img])
+    return res
+
+def color_adjust(img,alpha=0,beta=0,b=0,g=0,r=0,ran = False):
+    '''
+    g(x) = (1+α)g(x)+255*β, 
+    g(x) = g(x[:+b*255,:+g*255,:+r*255])
+    
+    Args:
+        img   : input image
+        alpha : contrast
+        beta  : brightness
+        b     : blue hue
+        g     : green hue
+        r     : red hue
+        ran   : if True, randomly generated color correction parameters
+    Retuens:
+        img   : output image
+    '''
+    img = img.astype('float')
+    if ran:
+        alpha = random.uniform(-0.1,0.1)
+        beta  = random.uniform(-0.1,0.1)
+        b     = random.uniform(-0.05,0.05)
+        g     = random.uniform(-0.05,0.05)
+        r     = random.uniform(-0.05,0.05)
+    img = (1+alpha)*img+255.0*beta
+    bgr = [b*255.0,g*255.0,r*255.0]
+    for i in range(3): img[:,:,i]=img[:,:,i]+bgr[i]
+    
+    return (np.clip(img,0,255)).astype('uint8')
+
+def CAdaIN(src,dst):
+    '''
+    make src has dst's style
+    '''
+    return np.std(dst)*((src-np.mean(src))/np.std(src))+np.mean(dst)
+
+def makedataset(target_image,orgin_image):
+    target_image = resize(target_image,256)
+    orgin_image = resize(orgin_image,256)
+    img = np.zeros((256,512,3), dtype = "uint8")
+    w = orgin_image.shape[1]
+    img[0:256,0:256] = target_image[0:256,int(w/2-256/2):int(w/2+256/2)]
+    img[0:256,256:512] = orgin_image[0:256,int(w/2-256/2):int(w/2+256/2)]
+    return img
+    
+def find_mostlikely_ROI(mask):
+    contours,hierarchy=cv2.findContours(mask, cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
+    if len(contours)>0:
+        areas = []
+        for contour in contours:
+            areas.append(cv2.contourArea(contour))
+        index = areas.index(max(areas))
+        mask = np.zeros_like(mask)
+        mask = cv2.fillPoly(mask,[contours[index]],(255))
+    return mask
+
+def boundingSquare(mask,Ex_mul):
+    # thresh = mask_threshold(mask,10,threshold)
+    area = mask_area(mask)
+    if area == 0 :
+        return 0,0,0,0
+
+    x,y,w,h = cv2.boundingRect(mask)
+    
+    center = np.array([int(x+w/2),int(y+h/2)])
+    size = max(w,h)
+    point0=np.array([x,y])
+    point1=np.array([x+size,y+size])
+
+    h, w = mask.shape[:2]
+    if size*Ex_mul > min(h, w):
+        size = min(h, w)
+        halfsize = int(min(h, w)/2)
+    else:
+        size = Ex_mul*size
+        halfsize = int(size/2)
+        size = halfsize*2
+    point0 = center - halfsize
+    point1 = center + halfsize
+    if point0[0]<0:
+        point0[0]=0
+        point1[0]=size
+    if point0[1]<0:
+        point0[1]=0
+        point1[1]=size
+    if point1[0]>w:
+        point1[0]=w
+        point0[0]=w-size
+    if point1[1]>h:
+        point1[1]=h
+        point0[1]=h-size
+    center = ((point0+point1)/2).astype('int')
+    return center[0],center[1],halfsize,area
+
+def mask_threshold(mask,ex_mun,threshold):
+    mask = cv2.threshold(mask,threshold,255,cv2.THRESH_BINARY)[1]
+    mask = cv2.blur(mask, (ex_mun, ex_mun))
+    mask = cv2.threshold(mask,threshold/5,255,cv2.THRESH_BINARY)[1]
+    return mask
+
+def mask_area(mask):
+    mask = cv2.threshold(mask,127,255,0)[1]
+    # contours= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[1] #for opencv 3.4
+    contours= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)[0]#updata to opencv 4.0
+    try:
+        area = cv2.contourArea(contours[0])
+    except:
+        area = 0
+    return area
+
+def replace_mosaic(img_origin,img_fake,mask,x,y,size,no_feather):
+    img_fake = cv2.resize(img_fake,(size*2,size*2),interpolation=cv2.INTER_CUBIC)
+    if no_feather:
+        img_origin[y-size:y+size,x-size:x+size]=img_fake
+        return img_origin
+    else:
+        # #color correction
+        # RGB_origin = img_origin[y-size:y+size,x-size:x+size].mean(0).mean(0)
+        # RGB_fake = img_fake.mean(0).mean(0)
+        # for i in range(3):img_fake[:,:,i] = np.clip(img_fake[:,:,i]+RGB_origin[i]-RGB_fake[i],0,255)
+        #eclosion
+        eclosion_num = int(size/10)+2
+
+        mask_crop = cv2.resize(mask,(img_origin.shape[1],img_origin.shape[0]))[y-size:y+size,x-size:x+size]
+        mask_crop = ch_one2three(mask_crop)
+
+        mask_crop = (cv2.blur(mask_crop, (eclosion_num, eclosion_num)))
+        mask_crop = mask_crop/255.0
+
+        img_crop = img_origin[y-size:y+size,x-size:x+size]
+        img_origin[y-size:y+size,x-size:x+size] = np.clip((img_crop*(1-mask_crop)+img_fake*mask_crop),0,255).astype('uint8')
+        
+        return img_origin
+
+
+def Q_lapulase(resImg):
+    '''
+    Evaluate image quality
+    score > 20   normal
+    score > 50   clear
+    '''
+    img2gray = cv2.cvtColor(resImg, cv2.COLOR_BGR2GRAY)
+    img2gray = resize(img2gray,512)
+    res = cv2.Laplacian(img2gray, cv2.CV_64F)
+    score = res.var()
+    return score
+
+def psnr(img1,img2):
+    mse = np.mean((img1/255.0-img2/255.0)**2)
+    if mse < 1e-10:
+        return 100
+    psnr_v = 20*np.log10(1/np.sqrt(mse))
+    return psnr_v
+
+def splice(imgs,splice_shape):
+    '''Stitching multiple images, all imgs must have the same size
+    imgs : [img1,img2,img3,img4]
+    splice_shape: (2,2)
+    '''
+    h,w,ch = imgs[0].shape
+    output = np.zeros((h*splice_shape[0],w*splice_shape[1],ch),np.uint8)
+    cnt = 0
+    for i in range(splice_shape[0]):
+        for j in range(splice_shape[1]):
+            if cnt < len(imgs):
+                output[h*i:h*(i+1),w*j:w*(j+1)] = imgs[cnt]
+                cnt += 1
+    return output
+

hvideotool/core/restore/_deepmosaics/util/mosaic.py

@@ -0,0 +1,164 @@
+import cv2
+import numpy as np
+import os
+import random
+from .image_processing import resize,ch_one2three,mask_area
+
+def addmosaic(img,mask,opt):
+    if opt.mosaic_mod == 'random':
+        img = addmosaic_random(img,mask)
+    elif opt.mosaic_size == 0:
+        img = addmosaic_autosize(img, mask, opt.mosaic_mod)
+    else:
+        img = addmosaic_base(img,mask,opt.mosaic_size,opt.output_size,model = opt.mosaic_mod)
+    return img
+
+def addmosaic_base(img,mask,n,out_size = 0,model = 'squa_avg',rect_rat = 1.6,feather=0,start_point=[0,0]):
+    '''
+    img: input image
+    mask: input mask
+    n: mosaic size
+    out_size: output size  0->original
+    model : squa_avg squa_mid squa_random squa_avg_circle_edge rect_avg
+    rect_rat: if model==rect_avg , mosaic w/h=rect_rat
+    feather : feather size, -1->no 0->auto
+    start_point : [0,0], please not input this parameter
+    '''
+    n = int(n)
+    
+    h_start = np.clip(start_point[0], 0, n)
+    w_start = np.clip(start_point[1], 0, n)
+    pix_mid_h = n//2+h_start
+    pix_mid_w = n//2+w_start
+    h, w = img.shape[:2]
+    h_step = (h-h_start)//n
+    w_step = (w-w_start)//n
+    if out_size:
+        img = resize(img,out_size)      
+    if mask.shape[0] != h:
+        mask = cv2.resize(mask,(w,h))
+    img_mosaic = img.copy()
+
+    if model=='squa_avg':
+        for i in range(h_step):
+            for j in range(w_step):
+                if mask[i*n+pix_mid_h,j*n+pix_mid_w]:
+                    img_mosaic[i*n+h_start:(i+1)*n+h_start,j*n+w_start:(j+1)*n+w_start,:]=\
+                           img[i*n+h_start:(i+1)*n+h_start,j*n+w_start:(j+1)*n+w_start,:].mean(axis=(0,1))
+
+    elif model=='squa_mid':
+        for i in range(h_step):
+            for j in range(w_step):
+                if mask[i*n+pix_mid_h,j*n+pix_mid_w]:
+                    img_mosaic[i*n+h_start:(i+1)*n+h_start,j*n+w_start:(j+1)*n+w_start,:]=\
+                           img[i*n+n//2+h_start,j*n+n//2+w_start,:]
+
+    elif model == 'squa_random':
+        for i in range(h_step):
+            for j in range(w_step):
+                if mask[i*n+pix_mid_h,j*n+pix_mid_w]:
+                    img_mosaic[i*n+h_start:(i+1)*n+h_start,j*n+w_start:(j+1)*n+w_start,:]=\
+                    img[h_start+int(i*n-n/2+n*random.random()),w_start+int(j*n-n/2+n*random.random()),:]
+
+    elif model == 'squa_avg_circle_edge':
+        for i in range(h_step):
+            for j in range(w_step):
+                img_mosaic[i*n+h_start:(i+1)*n+h_start,j*n+w_start:(j+1)*n+w_start,:]=\
+                       img[i*n+h_start:(i+1)*n+h_start,j*n+w_start:(j+1)*n+w_start,:].mean(axis=(0,1))
+        mask = cv2.threshold(mask,127,255,cv2.THRESH_BINARY)[1]
+        _mask = ch_one2three(mask)
+        mask_inv = cv2.bitwise_not(_mask)
+        imgroi1 = cv2.bitwise_and(_mask,img_mosaic)
+        imgroi2 = cv2.bitwise_and(mask_inv,img)
+        img_mosaic = cv2.add(imgroi1,imgroi2)
+
+    elif model =='rect_avg':
+        n_h = n
+        n_w = int(n*rect_rat)
+        n_h_half = n_h//2+h_start
+        n_w_half = n_w//2+w_start
+        for i in range((h-h_start)//n_h):
+            for j in range((w-w_start)//n_w):
+                if mask[i*n_h+n_h_half,j*n_w+n_w_half]:
+                    img_mosaic[i*n_h+h_start:(i+1)*n_h+h_start,j*n_w+w_start:(j+1)*n_w+w_start,:]=\
+                           img[i*n_h+h_start:(i+1)*n_h+h_start,j*n_w+w_start:(j+1)*n_w+w_start,:].mean(axis=(0,1))
+    
+    if feather != -1:
+        if feather==0:
+            mask = (cv2.blur(mask, (n, n)))
+        else:
+            mask = (cv2.blur(mask, (feather, feather)))
+        mask = mask/255.0
+        for i in range(3):img_mosaic[:,:,i] = (img[:,:,i]*(1-mask)+img_mosaic[:,:,i]*mask)
+        img_mosaic = img_mosaic.astype(np.uint8)
+    
+    return img_mosaic
+
+def get_autosize(img,mask,area_type = 'normal'):
+    h,w = img.shape[:2]
+    size = np.min([h,w])
+    mask = resize(mask,size)
+    alpha = size/512
+    try:
+        if area_type == 'normal':
+            area = mask_area(mask)
+        elif area_type == 'bounding':
+            w,h = cv2.boundingRect(mask)[2:]
+            area = w*h
+    except:
+        area = 0
+    area = area/(alpha*alpha)
+    if area>50000:
+        size = alpha*((area-50000)/50000+12)
+    elif 20000<area<=50000:
+        size = alpha*((area-20000)/30000+8)
+    elif 5000<area<=20000:
+        size = alpha*((area-5000)/20000+7)
+    elif 0<=area<=5000:
+        size = alpha*((area-0)/5000+6)
+    else:
+        pass
+    return size
+
+def get_random_parameter(img,mask):
+    # mosaic size
+    p = np.array([0.5,0.5])
+    mod = np.random.choice(['normal','bounding'], p = p.ravel())
+    mosaic_size = get_autosize(img,mask,area_type = mod)
+    mosaic_size = int(mosaic_size*random.uniform(0.9,2.5))
+
+    # mosaic mod
+    p = np.array([0.25, 0.3, 0.45])
+    mod = np.random.choice(['squa_mid','squa_avg','rect_avg'], p = p.ravel())
+
+    # rect_rat for rect_avg
+    rect_rat = random.uniform(1.1,1.6)
+    
+    # feather size
+    feather = -1
+    if random.random()<0.7:
+        feather = int(mosaic_size*random.uniform(0,1.5))
+
+    return mosaic_size,mod,rect_rat,feather
+
+
+def addmosaic_autosize(img,mask,model,area_type = 'normal'):
+    mosaic_size = get_autosize(img,mask,area_type = 'normal')
+    img_mosaic = addmosaic_base(img,mask,mosaic_size,model = model)
+    return img_mosaic
+
+def addmosaic_random(img,mask):
+    mosaic_size,mod,rect_rat,feather = get_random_parameter(img,mask)
+    img_mosaic = addmosaic_base(img,mask,mosaic_size,model = mod,rect_rat=rect_rat,feather=feather)
+    return img_mosaic
+
+def get_random_startpos(num,bisa_p,bisa_max,bisa_max_part):
+    pos = np.zeros((num,2), dtype=np.int64)
+    if random.random()<bisa_p:
+        indexs = random.sample((np.linspace(1,num-1,num-1,dtype=np.int64)).tolist(), random.randint(1, bisa_max_part))
+        indexs.append(0)
+        indexs.append(num)
+        indexs.sort()
+        for i in range(len(indexs)-1):
+            pos[indexs[i]:indexs[i+1]] = [random.randint(0,bisa_max),random.randint(0,bisa_max)]
+    return pos

hvideotool/core/restore/_deepmosaics/util/util.py

@@ -0,0 +1,147 @@
+import json
+import os
+import random
+import string
+import shutil
+
+def Traversal(filedir):
+    file_list=[]
+    for root,dirs,files in os.walk(filedir): 
+        for file in files:
+            file_list.append(os.path.join(root,file)) 
+        for dir in dirs:
+            Traversal(dir)
+    return file_list
+
+def randomstr(num):
+    return ''.join(random.sample(string.ascii_letters + string.digits, num))
+
+def is_img(path):
+    ext = os.path.splitext(path)[1]
+    ext = ext.lower()
+    if ext in ['.jpg','.png','.jpeg','.bmp']:
+        return True
+    else:
+        return False
+
+def is_video(path):
+    ext = os.path.splitext(path)[1]
+    ext = ext.lower()
+    if ext in ['.mp4','.flv','.avi','.mov','.mkv','.wmv','.rmvb','.mts']:
+        return True
+    else:
+        return False
+
+def is_imgs(paths):
+    tmp = []
+    for path in paths:
+        if is_img(path):
+            tmp.append(path)
+    return tmp
+
+def is_videos(paths):
+    tmp = []
+    for path in paths:
+        if is_video(path):
+            tmp.append(path)
+    return tmp  
+
+def is_dirs(paths):
+    tmp = []
+    for path in paths:
+        if os.path.isdir(path):
+            tmp.append(path)
+    return tmp  
+
+def writelog(path,log,isprint=False):
+    f = open(path,'a+')
+    f.write(log+'\n')
+    f.close()
+    if isprint:
+        print(log)
+
+def savejson(path,data_dict):
+    json_str = json.dumps(data_dict)
+    f = open(path,'w+')
+    f.write(json_str)
+    f.close()
+
+def loadjson(path):
+    f = open(path, 'r')
+    txt_data = f.read()
+    f.close()
+    return json.loads(txt_data)
+
+def makedirs(path):
+    if os.path.isdir(path):
+        print(path,'existed')
+    else:
+        os.makedirs(path)
+        print('makedir:',path)
+
+def clean_tempfiles(opt,tmp_init=True):
+    tmpdir = opt.temp_dir
+    if os.path.isdir(tmpdir): 
+        print('Clean temp...')  
+        shutil.rmtree(tmpdir)
+    if tmp_init:
+        os.makedirs(tmpdir)
+        os.makedirs(os.path.join(tmpdir, 'video2image'))
+        os.makedirs(os.path.join(tmpdir, 'addmosaic_image'))
+        os.makedirs(os.path.join(tmpdir, 'replace_mosaic'))
+        os.makedirs(os.path.join(tmpdir, 'mosaic_mask'))
+        os.makedirs(os.path.join(tmpdir, 'ROI_mask'))
+        os.makedirs(os.path.join(tmpdir, 'style_transfer'))
+        # make dataset
+        os.makedirs(os.path.join(tmpdir, 'mosaic_crop'))
+        os.makedirs(os.path.join(tmpdir, 'ROI_mask_check'))
+ 
+def file_init(opt):
+    if not os.path.isdir(opt.result_dir):
+        os.makedirs(opt.result_dir)
+        print('makedir:',opt.result_dir)
+    clean_tempfiles(opt,True)
+
+def second2stamp(s):
+    h = int(s/3600)
+    s = int(s%3600)
+    m = int(s/60)
+    s = int(s%60)
+    return "%02d:%02d:%02d" % (h, m, s)
+
+def stamp2second(stamp):
+    substamps = stamp.split(':')
+    return int(substamps[0])*3600 + int(substamps[1])*60 + int(substamps[2])
+
+
+def counttime(start_time,current_time,now_num,all_num):
+    '''
+    start_time,current_time: time.time()
+    '''
+    used_time = int(current_time-start_time)
+    all_time = int(used_time/now_num*all_num)
+    return second2stamp(used_time)+'/'+second2stamp(all_time)
+
+def get_bar(percent,num = 25):
+    bar = '['
+    for i in range(num):
+        if i < round(percent/(100/num)):
+            bar += '#'
+        else:
+            bar += '-'
+    bar += ']'
+    return bar+' '+"%.2f"%percent+'%'
+
+def copyfile(src,dst):
+    try:
+        shutil.copyfile(src, dst)
+    except Exception as e:
+        print(e)
+
+def opt2str(opt):
+    message = ''
+    message += '---------------------- Options --------------------\n'
+    for k, v in sorted(vars(opt).items()):
+        message += '{:>25}: {:<35}\n'.format(str(k), str(v))
+    message += '----------------- End -------------------'
+    return message

hvideotool/core/restore/base.py

@@ -9,11 +9,20 @@ DeepMosaics / LADA later) plug in behind the same interface.
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from collections.abc import Callable
 
 import numpy as np
 
 from ..detection.types import Detection
 
+# Optional cooperative-cancel hook: returns True to abort. Engines that loop or
+# drive a subprocess should poll it; instant engines may ignore it.
+CancelCheck = Callable[[], bool]
+
+
+class Cancelled(Exception):
+    """Raised by a Restorer when ``should_cancel`` asked it to stop."""
+
 
 class Restorer(ABC):
     @property
@@ -21,6 +30,15 @@ class Restorer(ABC):
         return type(self).__name__
 
     @abstractmethod
-    def restore(self, image: np.ndarray, detections: list[Detection]) -> np.ndarray:
-        """Return a copy of ``image`` with the detected regions reconstructed."""
+    def restore(
+        self,
+        image: np.ndarray,
+        detections: list[Detection],
+        should_cancel: CancelCheck | None = None,
+    ) -> np.ndarray:
+        """Return a copy of ``image`` with the detected regions reconstructed.
+
+        ``should_cancel`` (if given) is polled periodically; when it returns
+        True the engine should abort and raise :class:`Cancelled`.
+        """
         raise NotImplementedError

hvideotool/core/restore/deepmosaics.py

@@ -1,107 +1,161 @@
-"""DeepMosaics restorer — real generative mosaic removal.
+"""DeepMosaics restorer — real generative mosaic removal, in-process.
 
-Rather than vendoring DeepMosaics' GPL network code (which must match the exact
-checkpoint), we drive a **user-installed** DeepMosaics (https://github.com/HypoX64/DeepMosaics)
-as a subprocess: write the frame to a temp file, run ``deepmosaic.py --mode clean``,
-read the cleaned image back. This reuses their tested pipeline (incl. their own
-mosaic locator ``mosaic_position.pth``) and respects the GPL boundary.
+The DeepMosaics network code (GPL-3.0) is vendored under ``_deepmosaics/`` (see
+its NOTICE/LICENSE). We load the models **once** and run the per-frame clean path
+in-process — far faster than spawning a subprocess per frame (which reloaded the
+models every time). Only the model *weights* are user-supplied.
 
-Setup the user must do once (see README → Восстановление):
-1. ``git clone https://github.com/HypoX64/DeepMosaics`` and install its deps.
-2. Download clean weights (e.g. ``clean_youknow_video.pth``) AND ``mosaic_position.pth``
-   into one folder.
-3. In the app: Восстановление… → engine "deepmosaics", set the DeepMosaics folder
-   and the clean-model path (a CUDA GPU is strongly recommended).
+Per-frame clean = DeepMosaics' ``cleanmosaic_img_server`` logic, reimplemented
+here (so we don't pull in their video/ffmpeg modules):
+  locate mosaic (BiSeNet ``mosaic_position.pth``) → run the clean generator on the
+  crop → feather it back. DeepMosaics finds the mosaic itself; our detections are
+  used for navigation, not passed to it.
 
-NOTE: DeepMosaics finds the mosaic itself; our detections are used for navigation,
-not passed to it.
+Setup (see README → Восстановление): download the **image** clean weights
+``clean_youknow_resnet_9blocks.pth`` + ``mosaic_position.pth`` into one folder and
+point the app at the clean-model file. The video model ``clean_youknow_video.pth``
+(BVDNet) needs neighbour frames and does NOT work per-frame.
 """
 
 from __future__ import annotations
 
-import subprocess
 import sys
-import tempfile
 from pathlib import Path
+from types import SimpleNamespace
 
 import numpy as np
 
 from ..detection.types import Detection
-from ..imageio import imread_unicode, imwrite_unicode
-from .base import Restorer
+from .base import CancelCheck, Cancelled, Restorer
 
-_IMG_EXTS = {".jpg", ".jpeg", ".png", ".bmp"}
+_VENDOR = Path(__file__).parent / "_deepmosaics"
+# Default place to drop DeepMosaics clean weights (gitignored — see models/).
+DEFAULT_WEIGHTS_DIR = Path(__file__).resolve().parents[3] / "models" / "deepmosaics"
+
+
+def discover_models(extra_dir: str | None = None) -> list[tuple[str, str]]:
+    """Find usable per-frame clean models: (display_name, full_path).
+
+    Scans the bundled ``models/deepmosaics`` folder (plus ``extra_dir`` if given)
+    for ``clean_*.pth``. The video model is skipped — it can't run per-frame.
+    """
+    dirs = [DEFAULT_WEIGHTS_DIR]
+    if extra_dir:
+        dirs.insert(0, Path(extra_dir))
+    out: list[tuple[str, str]] = []
+    seen: set[str] = set()
+    for d in dirs:
+        if not d.is_dir():
+            continue
+        for p in sorted(d.glob("clean_*.pth")):
+            if "video" in p.name.lower() or p.name in seen:
+                continue
+            seen.add(p.name)
+            out.append((p.stem, str(p)))
+    return out
+
+
+def _netg_kind(model_name: str) -> str:
+    """Pick DeepMosaics' netG type from the weights filename (see their options.py)."""
+    n = model_name.lower()
+    if "video" in n:
+        raise ValueError(
+            "Видеомодель (clean_*_video.pth) не работает покадрово — ей нужен соседний "
+            "кадр.\nУкажите картиночную модель clean_youknow_resnet_9blocks.pth."
+        )
+    if "unet_128" in n:
+        return "unet_128"
+    if "hd" in n:
+        return "HD"
+    return "resnet_9blocks"
 
 
 class DeepMosaicsRestorer(Restorer):
     def __init__(
         self,
-        deepmosaics_dir: str | None,
+        deepmosaics_dir: str | None,   # kept for factory/config compatibility (weights hint)
         model_path: str | None,
-        python_exe: str | None = None,
+        python_exe: str | None = None,  # unused now (in-process)
         gpu_id: str = "0",
     ) -> None:
-        if not deepmosaics_dir or not (Path(deepmosaics_dir) / "deepmosaic.py").is_file():
-            raise ValueError(
-                "Не указана папка DeepMosaics (с deepmosaic.py).\n"
-                "Установите DeepMosaics и укажите её в «Восстановление…». См. README."
-            )
         if not model_path or not Path(model_path).is_file():
+            discovered = discover_models()  # fall back to a bundled model
+            if discovered:
+                model_path = discovered[0][1]
+            else:
+                raise ValueError(
+                    "Не найдены веса DeepMosaics (clean_*.pth).\n"
+                    "Положите clean_youknow_resnet_9blocks.pth + mosaic_position.pth в "
+                    "models/deepmosaics (или выберите в «Восстановление…»). См. README."
+                )
+        model = Path(model_path)
+        self._netg = _netg_kind(model.name)  # raises on a video model
+        pos = self._find_mosaic_position(model, deepmosaics_dir)
+        if pos is None:
             raise ValueError(
-                "Не найдены веса DeepMosaics (clean_*.pth).\n"
-                "Скачайте clean_youknow_video.pth + mosaic_position.pth в одну папку. См. README."
+                "Рядом с clean-моделью не найден mosaic_position.pth.\n"
+                "Положите mosaic_position.pth в ту же папку, что и clean_*.pth. См. README."
             )
-        self._dir = Path(deepmosaics_dir)
-        self._model = model_path
-        self._python = python_exe or sys.executable
+        self._model = str(model)
+        self._pos = str(pos)
         self._gpu = gpu_id
+        self._loaded = False  # models loaded lazily on first restore
+
+    @staticmethod
+    def _find_mosaic_position(model: Path, dm_dir: str | None) -> Path | None:
+        candidates = [model.parent / "mosaic_position.pth"]
+        if dm_dir:
+            candidates.append(Path(dm_dir) / "pretrained_models" / "mosaic" / "mosaic_position.pth")
+        return next((p for p in candidates if p.is_file()), None)
 
     @property
     def name(self) -> str:
         return f"DeepMosaics(gpu={self._gpu})"
 
-    def restore(self, image: np.ndarray, detections: list[Detection]) -> np.ndarray:
-        with tempfile.TemporaryDirectory(prefix="hvt_dm_") as tmp:
-            tmpd = Path(tmp)
-            src = tmpd / "frame.jpg"
-            result_dir = tmpd / "result"
-            result_dir.mkdir()
-            imwrite_unicode(str(src), image)
+    # ------------------------------------------------------------------ engine
+    def _ensure_loaded(self) -> None:
+        if self._loaded:
+            return
+        if str(_VENDOR) not in sys.path:
+            sys.path.insert(0, str(_VENDOR))  # so vendored `from models/util import …` resolve
+        from models import loadmodel, runmodel  # type: ignore  # noqa: E402
+        import util.image_processing as impro    # type: ignore  # noqa: E402
 
-            cmd = [
-                self._python, "deepmosaic.py",
-                "--media_path", str(src),
-                "--model_path", str(self._model),
-                "--mode", "clean",
-                "--result_dir", str(result_dir),
-                "--temp_dir", str(tmpd / "dmtmp"),
-                "--gpu_id", str(self._gpu),
-                "--no_preview",
-            ]
-            proc = subprocess.run(
-                cmd, cwd=str(self._dir),
-                stdin=subprocess.DEVNULL,            # so DeepMosaics' error input() can't hang
-                capture_output=True, text=True,
-            )
-            outputs = [p for p in result_dir.iterdir() if p.suffix.lower() in _IMG_EXTS]
-            if outputs:
-                newest = max(outputs, key=lambda p: p.stat().st_mtime)
-                restored = imread_unicode(str(newest))
-                if restored is None:
-                    raise RuntimeError("Не удалось прочитать результат DeepMosaics.")
-                return restored
+        self._runmodel = runmodel
+        self._impro = impro
+        self._opt = SimpleNamespace(
+            gpu_id=self._gpu,
+            netG=self._netg,
+            model_path=self._model,
+            mosaic_position_model_path=self._pos,
+            mask_threshold=64,
+            all_mosaic_area=False,
+            ex_mult=1.5,
+            no_feather=False,
+            traditional=False,
+        )
+        self._netM = loadmodel.bisenet(self._opt, "mosaic")
+        self._netG = loadmodel.pix2pix(self._opt)
+        self._loaded = True
 
-            # No output file — figure out why.
-            log = (proc.stderr or "") + (proc.stdout or "")
-            if "BVDNet.forward()" in log or "argument: 'previous'" in log:
-                raise RuntimeError(
-                    "Видеомодель (clean_*_video.pth) не работает покадрово — ей нужен "
-                    "соседний кадр.\nУкажите картиночную модель clean_youknow_resnet_9blocks.pth."
-                )
-            if proc.returncode == 0:
-                # DeepMosaics ran fine but found no mosaic to clean — keep the frame as is.
-                return image.copy()
-            tail = log.strip().splitlines()[-6:]
-            raise RuntimeError(
-                f"DeepMosaics не вернул результат (код {proc.returncode}).\n" + "\n".join(tail)
-            )
+    def restore(
+        self,
+        image: np.ndarray,
+        detections: list[Detection],
+        should_cancel: CancelCheck | None = None,
+    ) -> np.ndarray:
+        if should_cancel is not None and should_cancel():
+            raise Cancelled("Восстановление отменено")
+        self._ensure_loaded()
+        rm, impro, opt = self._runmodel, self._impro, self._opt
+
+        # DeepMosaics' cleanmosaic_img_server, faithfully reproduced.
+        x, y, size, mask = rm.get_mosaic_position(image, self._netM, opt)
+        if size <= 100:
+            return image.copy()  # no mosaic located — leave the frame untouched
+        if should_cancel is not None and should_cancel():
+            raise Cancelled("Восстановление отменено")
+        work = image.copy()
+        img_mosaic = work[y - size:y + size, x - size:x + size]
+        img_fake = rm.run_pix2pix(img_mosaic, self._netG, opt)
+        return impro.replace_mosaic(work, img_fake, mask, x, y, size, opt.no_feather)

hvideotool/core/restore/factory.py

@@ -1,8 +1,9 @@
 """Restorer factory: build a Restorer from the app config.
 
 - ``inpaint``: cv2 baseline (no weights, no GPU; fills, doesn't reconstruct).
-- ``deepmosaics``: real generative mosaic removal via a user-installed DeepMosaics
-  (subprocess). Needs the DeepMosaics folder + clean weights + (ideally) a CUDA GPU.
+- ``deepmosaics``: real generative mosaic removal. The DeepMosaics network code is
+  vendored (``_deepmosaics/``, GPL-3.0) and run in-process; the user supplies only the
+  clean weights (+ ``mosaic_position.pth`` alongside). A CUDA GPU is recommended.
 """
 
 from __future__ import annotations

hvideotool/core/restore/inpaint.py

@@ -13,7 +13,7 @@ import cv2
 import numpy as np
 
 from ..detection.types import Detection
-from .base import Restorer
+from .base import CancelCheck, Restorer
 from .mask import detections_to_mask
 
 
@@ -27,7 +27,13 @@ class InpaintRestorer(Restorer):
     def name(self) -> str:
         return f"InpaintRestorer({self.method})"
 
-    def restore(self, image: np.ndarray, detections: list[Detection]) -> np.ndarray:
+    def restore(
+        self,
+        image: np.ndarray,
+        detections: list[Detection],
+        should_cancel: CancelCheck | None = None,
+    ) -> np.ndarray:
+        # Single cv2.inpaint call — effectively instant, so cancellation is moot.
         if not detections:
             return image.copy()
         mask = detections_to_mask(image.shape, detections, dilate=self.dilate)