Show SV metadata for postprocess results

This commit is contained in:
admin
2026-06-10 17:57:54 +08:00
parent ec5b25d2d0
commit 75ab895ba2
5 changed files with 160 additions and 14 deletions

View File

@@ -99,6 +99,25 @@ def post_result_path(filename: str, source: str, processor: str, params: dict[st
return image_result_dir(filename) / "postprocess" / f"{_safe_slug(source)}__{_safe_slug(processor)}{suffix}.png"
def post_metadata_path(output_path: Path) -> Path:
return output_path.with_suffix(".json")
def read_post_metadata(output_path: Path) -> dict[str, Any]:
metadata_path = post_metadata_path(output_path)
if not metadata_path.exists():
return {}
try:
return json.loads(metadata_path.read_text(encoding="utf-8"))
except Exception:
return {}
def write_post_metadata(output_path: Path, payload: dict[str, Any]) -> None:
metadata_path = post_metadata_path(output_path)
metadata_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
def relpath(path: Path) -> str:
return path.resolve().relative_to(ROOT).as_posix()
@@ -206,7 +225,20 @@ def get_results(filename: str) -> dict[str, Any]:
post_dir = image_result_dir(filename) / "postprocess"
if post_dir.exists():
for path in sorted(post_dir.glob("*.png"), key=lambda p: p.name.lower()):
post.append({"name": path.stem, "exists": True, "path": relpath(path)})
metadata = read_post_metadata(path)
post.append(
{
"name": path.stem,
"exists": True,
"path": relpath(path),
"meta": metadata.get("meta", {}),
"processor": metadata.get("processor", ""),
"source": metadata.get("source", ""),
"reference": metadata.get("reference", ""),
"params": metadata.get("params", {}),
"metadata_path": relpath(post_metadata_path(path)) if post_metadata_path(path).exists() else "",
}
)
return {
"image": filename,
@@ -249,6 +281,7 @@ def collect_download_items(filenames: list[str] | None = None, include_original:
if post_dir.exists():
for result in sorted(post_dir.glob("*.png"), key=lambda p: p.name.lower()):
add_file(result, f"{folder}/postprocess/{result.name}")
add_file(post_metadata_path(result), f"{folder}/postprocess/{post_metadata_path(result).name}")
return items
@@ -523,6 +556,19 @@ def run_postprocessors_for_source(
proc_params = params.get(processor, params)
output = post_result_path(filename, source_name, processor, proc_params)
meta = run_postprocess(processor, source_path, output, reference_path=reference_path, params=proc_params)
write_post_metadata(
output,
{
"image": filename,
"source": source_name,
"processor": processor,
"reference": reference_path.name,
"params": proc_params,
"meta": meta,
"output": relpath(output),
"created_at": time.strftime("%Y-%m-%d %H:%M:%S"),
},
)
outputs.append(output)
log(f"后处理完成 {source_name} / {processor}: {json.dumps(meta, ensure_ascii=False)}")
return outputs

View File

@@ -130,9 +130,12 @@ def _run_default_batch_job(log, payload: dict[str, Any]) -> dict[str, Any]:
baidu_output = pipeline.run_dehaze_method(filename, "Baidu_API", {}, log)
auto_output = pipeline.post_result_path(filename, "Baidu_API", "auto_sv", {})
if skip_existing and auto_output.exists():
auto_meta = pipeline.post_metadata_path(auto_output)
if skip_existing and auto_output.exists() and auto_meta.exists():
log(f"[自动 S/V] 已存在,跳过: {pipeline.relpath(auto_output)}")
else:
if auto_output.exists() and not auto_meta.exists():
log("[自动 S/V] 已有图片但缺少 S/V 元数据,重新计算")
outputs = pipeline.run_postprocessors_for_source(
filename,
"Baidu_API",

View File

@@ -28,6 +28,56 @@ function formatBytes(bytes) {
return `${(bytes / 1024 / 1024).toFixed(1)} MB`;
}
function formatGain(value) {
const number = Number(value);
return Number.isFinite(number) ? number.toFixed(3) : "";
}
function svSummary(meta = {}) {
const s = formatGain(meta.s_gain);
const v = formatGain(meta.v_gain);
if (!s || !v) return "";
return `S ${s} · V ${v}`;
}
function updateGainLabels() {
$("sGainValue").textContent = formatGain($("sGain").value);
$("vGainValue").textContent = formatGain($("vGain").value);
}
function setOnlyPostprocessor(processor) {
[...$("postList").querySelectorAll("input[type=checkbox]")].forEach((input) => {
input.checked = input.value === processor;
});
}
function setSelectIfOption(selectId, value) {
const select = $(selectId);
if ([...select.options].some((option) => option.value === value)) {
select.value = value;
}
}
function applySvFromResult(item) {
const meta = item.meta || {};
const sGain = Number(meta.s_gain);
const vGain = Number(meta.v_gain);
if (!Number.isFinite(sGain) || !Number.isFinite(vGain)) return;
$("sGain").value = Math.min(2.5, Math.max(0, sGain));
$("vGain").value = Math.min(2.5, Math.max(0, vGain));
updateGainLabels();
setOnlyPostprocessor("manual_sv");
if (item.source) {
setSelectIfOption("postSource", item.source);
}
if (item.reference) {
setSelectIfOption("referenceImage", item.reference);
}
$("logBox").textContent = `已载入 ${item.name} 的参数S=${formatGain(sGain)}V=${formatGain(vGain)}\n可在左侧手动微调后点击“生成后处理”。`;
setJobState("SV Ready", "done");
}
function renderImages() {
const box = $("imageList");
box.innerHTML = "";
@@ -104,14 +154,26 @@ async function selectImage(name) {
await refreshResults();
}
function resultCard(title, path, exists = true) {
function resultCard(title, path, exists = true, item = null) {
const card = document.createElement("article");
card.className = "result-card";
const summary = item ? svSummary(item.meta) : "";
card.className = `result-card ${summary ? "clickable" : ""}`;
const badge = exists ? '<span class="badge ok">ready</span>' : '<span class="badge pending">未生成</span>';
const body = exists
? `<div class="image-frame"><img src="${assetUrl(path)}" alt="${title}" loading="lazy" /></div>`
? `<div class="image-frame"><img src="${assetUrl(path)}" alt="${title}" loading="lazy" /></div>${summary ? `<div class="sv-strip"><span>${summary}</span><small>点击载入</small></div>` : ""}`
: '<div class="image-frame"><span class="missing">暂无结果</span></div>';
card.innerHTML = `<header><h3>${title}</h3>${badge}</header>${body}`;
if (summary && item) {
card.tabIndex = 0;
card.title = "点击将这组 S/V 载入左侧手动调整";
card.addEventListener("click", () => applySvFromResult(item));
card.addEventListener("keydown", (event) => {
if (event.key === "Enter" || event.key === " ") {
event.preventDefault();
applySvFromResult(item);
}
});
}
return card;
}
@@ -148,7 +210,7 @@ async function refreshResults() {
grid.appendChild(resultCard(item.label, item.path, item.exists));
});
results.postprocess.forEach((item) => {
grid.appendChild(resultCard(item.name, item.path, true));
grid.appendChild(resultCard(item.name, item.path, true, item));
});
updatePostSourceOptions(results);
}
@@ -253,12 +315,9 @@ function bindControls() {
$("downloadCurrentBtn").addEventListener("click", downloadCurrent);
$("downloadAllBtn").addEventListener("click", downloadAll);
$("refreshBtn").addEventListener("click", refreshResults);
$("sGain").addEventListener("input", () => {
$("sGainValue").textContent = `${Math.round(Number($("sGain").value) * 100)}%`;
});
$("vGain").addEventListener("input", () => {
$("vGainValue").textContent = `${Math.round(Number($("vGain").value) * 100)}%`;
});
$("sGain").addEventListener("input", updateGainLabels);
$("vGain").addEventListener("input", updateGainLabels);
updateGainLabels();
}
async function init() {

View File

@@ -318,6 +318,17 @@ h1 {
background: rgba(255, 250, 241, 0.82);
}
.result-card.clickable {
cursor: pointer;
}
.result-card.clickable:hover,
.result-card.clickable:focus {
outline: 2px solid rgba(31, 107, 87, 0.36);
outline-offset: 2px;
border-color: rgba(31, 107, 87, 0.55);
}
.result-card header {
display: flex;
align-items: center;
@@ -372,6 +383,32 @@ h1 {
background: #fff;
}
.sv-strip {
display: flex;
align-items: center;
justify-content: space-between;
gap: 10px;
min-height: 36px;
padding: 7px 10px;
border-top: 1px solid var(--line);
background: rgba(31, 107, 87, 0.09);
color: var(--green-dark);
font-size: 12px;
font-variant-numeric: tabular-nums;
}
.sv-strip span {
min-width: 0;
overflow-wrap: anywhere;
font-weight: 700;
}
.sv-strip small {
flex: 0 0 auto;
color: var(--muted);
font-size: 11px;
}
.missing {
color: var(--muted);
font-size: 13px;

View File

@@ -67,8 +67,9 @@ BAIDU_SECRET_KEY=...
4. 勾选需要运行的模型,点击“运行选中模型”。
5. 在后处理区域选择源图、参考图和后处理方法,点击“生成后处理”。
6. 需要批量处理时,点击“批量默认流程”,会对 `待去雾图片/` 中所有图片执行 `Baidu_API -> 自动 S/V`。已有的百度结果和自动 S/V 结果会跳过,避免重复消耗 API。
7. 需要导出结果时,在“下载”区域点击“下载当前图片”或“批量下载全部”,网页会生成 ZIP 包
8. 结果会保存到 `web_results/`,网页会自动刷新显示
7. 自动 S/V、手动 S/V 等后处理卡片会显示对应的 S/V 参数;点击带有 S/V 的结果卡片,会把参数载入左侧手动 S/V 滑杆,方便继续人工微调
8. 需要导出结果时,在“下载”区域点击“下载当前图片”或“批量下载全部”,网页会生成 ZIP 包
9. 结果会保存到 `web_results/`,网页会自动刷新显示。
页面中“未生成”表示对应结果文件还不存在,并不是任务卡住。