3248 lines
124 KiB
Python
3248 lines
124 KiB
Python
"""
|
||
edit_api.py — headless throughput API for Qwen-Image-Edit Rapid-AIO (v23 Q8 GGUF)
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||
running on top of a local ComfyUI server.
|
||
|
||
Flow per request: image + prompt -> upload to ComfyUI -> inject into the
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||
workflow graph -> queue -> poll until done -> return the edited PNG.
|
||
|
||
Run ComfyUI first (run_comfyui.sh), then this service (start_api.sh).
|
||
"""
|
||
|
||
import io
|
||
import os
|
||
import json
|
||
import time
|
||
import uuid
|
||
import random
|
||
import copy
|
||
import threading
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||
import csv
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||
|
||
try:
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||
from . import database
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||
from . import embeddings
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||
from . import naming
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||
except ImportError:
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||
import database
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||
import embeddings
|
||
import naming
|
||
|
||
import requests
|
||
from PIL import Image
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||
from fastapi import FastAPI, UploadFile, File, Form, HTTPException, BackgroundTasks
|
||
from fastapi.middleware.cors import CORSMiddleware
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||
from fastapi.responses import Response
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
|
||
import shutil
|
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import re
|
||
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||
# --- config -----------------------------------------------------------------
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||
CONFIG_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "config.json")
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WD_MODEL = os.environ.get("WD_MODEL", "SmilingWolf/wd-vit-tagger-v3")
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COMFY = os.environ.get("COMFY_URL", "http://127.0.0.1:8188").rstrip("/")
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WORKFLOW_PATH = os.environ.get(
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"WORKFLOW_PATH",
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os.path.join(os.path.dirname(os.path.abspath(__file__)), "workflow_qwen_edit.json"),
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||
)
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||
# Default target pixel area for the output latent. The MI50 is not fast, so we
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# cap at ~1MP by default; raise via MAX_AREA env if you want bigger output.
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MAX_AREA = int(os.environ.get("MAX_AREA", str(1024 * 1024)))
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GEN_TIMEOUT = int(os.environ.get("GEN_TIMEOUT", "600")) # seconds per request
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# Node ids in workflow_qwen_edit.json (kept stable on purpose).
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||
NODE_LOADIMAGE = "4"
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NODE_POSITIVE = "5"
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NODE_NEGATIVE = "6"
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NODE_LATENT = "7"
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NODE_KSAMPLER = "8"
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NODE_SAVE = "10"
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||
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MAX_SEED = 2**32 - 1
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VIDEO_EXTENSIONS = ('.mp4', '.mov', '.avi', '.webm', '.mkv')
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# Poses where the source image should be rotated 180° before pipeline for better results
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ROTATE_180_POSES = {"the dragon", "dragon", "the draak", "draak"}
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# WD tagger tags that indicate the subject is wearing clothes
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CLOTHING_TAGS = {
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"dress", "skirt", "shirt", "top", "pants", "jeans", "jacket", "coat",
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"swimsuit", "bikini", "shorts", "uniform", "hoodie", "sweater", "blouse",
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"leggings", "stockings", "tights", "lingerie", "miniskirt", "pleated_skirt",
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"school_uniform", "maid_dress", "bodysuit", "sailor_uniform", "leotard",
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"corset", "kimono", "yukata", "cheongsam", "t-shirt", "tank_top", "crop_top",
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"tube_top", "halter_top", "negligee", "nightgown", "pajamas", "trench_coat",
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"vest", "bra", "underwear", "panties", "thong", "g-string", "bikini_top",
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"bikini_bottom", "one-piece_swimsuit", "sports_bra", "gym_clothes",
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}
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||
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UNDRESS_PROMPT = "completely nude, bare skin, no clothing, naked body, natural skin texture"
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||
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with open(WORKFLOW_PATH, "r", encoding="utf-8") as f:
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BASE_WORKFLOW = json.load(f)
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||
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app = FastAPI(title="Qwen-Image-Edit Rapid-AIO API", version="1.0")
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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||
allow_methods=["GET", "POST", "DELETE"],
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allow_headers=["*"],
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)
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def _sync_car_html():
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src = os.path.join(os.path.dirname(os.path.abspath(__file__)), "car.html")
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if not os.path.exists(src):
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return
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try:
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dest = os.path.join(_load_output_dir(), "car.html")
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shutil.copy2(src, dest)
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print(f"[car.html] synced → {dest}")
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except Exception as e:
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print(f"[car.html] sync warning: {e}")
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def _watch_car_html():
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src = os.path.join(os.path.dirname(os.path.abspath(__file__)), "car.html")
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last_mtime = os.path.getmtime(src) if os.path.exists(src) else 0
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while True:
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||
time.sleep(1)
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||
try:
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mtime = os.path.getmtime(src)
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if mtime != last_mtime:
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last_mtime = mtime
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dest = os.path.join(_load_output_dir(), "car.html")
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shutil.copy2(src, dest)
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print(f"[car.html] change detected → synced to {dest}")
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except Exception:
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||
pass
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||
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||
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def _load_wireframe_dir() -> str:
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||
with open(CONFIG_PATH, "r") as f:
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conf = json.load(f)
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return conf.get("wireframe_dir", "/mnt/zim/tour-comfy/wireframe")
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||
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def _load_faceswap_model_path() -> str:
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||
with open(CONFIG_PATH, "r") as f:
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conf = json.load(f)
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||
return os.path.expanduser(conf.get("faceswap_model", "~/.insightface/models/inswapper_128.onnx"))
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||
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||
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@app.on_event("startup")
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def on_startup():
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||
try:
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database.migrate_schema()
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||
except Exception as e:
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print(f"DB migration warning: {e}")
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_sync_car_html()
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threading.Thread(target=_watch_car_html, daemon=True).start()
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# Mount wireframe static dir for browser video preview
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try:
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wf_dir = _load_wireframe_dir()
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if os.path.isdir(wf_dir):
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app.mount("/wireframe", StaticFiles(directory=wf_dir), name="wireframe")
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print(f"[wireframe] mounted {wf_dir} → /wireframe")
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||
except Exception as e:
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print(f"[wireframe] mount warning: {e}")
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# Mount output dir so images can be served via HTTP (/output/filename.png)
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try:
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out_dir = _load_output_dir()
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if os.path.isdir(out_dir):
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app.mount("/output", StaticFiles(directory=out_dir), name="output")
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print(f"[output] mounted {out_dir} → /output")
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||
except Exception as e:
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||
print(f"[output] mount warning: {e}")
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||
# Write initial static data files (synchronous — ensures files exist before first request)
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_write_all_static()
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||
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# --- helpers ----------------------------------------------------------------
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def _round16(x: int) -> int:
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return max(16, int(round(x / 16.0)) * 16)
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||
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def _target_size(w: int, h: int, max_area: int) -> tuple[int, int]:
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"""Scale (w, h) to ~max_area preserving aspect, rounded to /16."""
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scale = (max_area / float(w * h)) ** 0.5
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return _round16(w * scale), _round16(h * scale)
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||
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||
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def _prep_image(pil: Image.Image, max_area: int) -> tuple[Image.Image, int, int]:
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"""
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Prepare image for ComfyUI:
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1. If area > max_area, crop from bottom if height remains >= 256.
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2. Otherwise scale (up or down) to fit area while preserving aspect.
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3. Ensure dimensions are rounded to 16.
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"""
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w, h = pil.width, pil.height
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if w * h > max_area:
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# Try to keep width and crop height from bottom
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rw = _round16(w)
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th = max_area // rw
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if th >= 256:
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rh = (th // 16) * 16
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if rh < 16: rh = 16
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||
|
||
# To avoid black bars from .crop((0,0,rw,rh)) when rw > w,
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# we crop to original w first, then resize to rw.
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pil = pil.crop((0, 0, w, min(h, (rh * w) // rw)))
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pil = pil.resize((rw, rh), resample=Image.LANCZOS)
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return pil, rw, rh
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else:
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# Too wide to keep width and have decent height, scale both down
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rw, rh = _target_size(w, h, max_area)
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pil = pil.resize((rw, rh), resample=Image.LANCZOS)
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return pil, rw, rh
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||
else:
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# Fits or is too small: scale UP to match the max_area budget
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# (Legacy behavior that gives better model performance)
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rw, rh = _target_size(w, h, max_area)
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if rw != w or rh != h:
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pil = pil.resize((rw, rh), resample=Image.LANCZOS)
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||
return pil, rw, rh
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||
|
||
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||
def _comfy_upload(img_bytes: bytes, filename: str) -> str:
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||
"""Upload an image to ComfyUI's input dir; return the stored name."""
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||
r = requests.post(
|
||
f"{COMFY}/upload/image",
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||
files={"image": (filename, img_bytes, "image/png")},
|
||
data={"overwrite": "true", "type": "input"},
|
||
timeout=60,
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||
)
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||
r.raise_for_status()
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||
j = r.json()
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||
name = j["name"]
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||
sub = j.get("subfolder", "")
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||
return f"{sub}/{name}" if sub else name
|
||
|
||
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||
def _comfy_queue(graph: dict, client_id: str) -> str:
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||
r = requests.post(
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||
f"{COMFY}/prompt",
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||
json={"prompt": graph, "client_id": client_id},
|
||
timeout=60,
|
||
)
|
||
if r.status_code != 200:
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||
raise HTTPException(502, f"ComfyUI rejected workflow: {r.text}")
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||
return r.json()["prompt_id"]
|
||
|
||
|
||
def _comfy_wait(prompt_id: str, deadline: float) -> dict:
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||
"""Poll /history until the prompt finishes; return its outputs dict."""
|
||
while time.time() < deadline:
|
||
r = requests.get(f"{COMFY}/history/{prompt_id}", timeout=30)
|
||
if r.status_code == 200:
|
||
hist = r.json()
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||
if prompt_id in hist:
|
||
entry = hist[prompt_id]
|
||
status = entry.get("status", {})
|
||
if status.get("status_str") == "error":
|
||
raise HTTPException(500, f"ComfyUI execution error: {json.dumps(status)}")
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||
outputs = entry.get("outputs", {})
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||
if outputs:
|
||
return outputs
|
||
time.sleep(0.5)
|
||
raise HTTPException(504, f"Generation timed out after {GEN_TIMEOUT}s")
|
||
|
||
|
||
def _comfy_fetch_image(outputs: dict) -> bytes:
|
||
node_out = outputs.get(NODE_SAVE) or next(
|
||
(v for v in outputs.values() if "images" in v), None
|
||
)
|
||
if not node_out or not node_out.get("images"):
|
||
raise HTTPException(500, "No output image produced")
|
||
img = node_out["images"][0]
|
||
r = requests.get(
|
||
f"{COMFY}/view",
|
||
params={
|
||
"filename": img["filename"],
|
||
"subfolder": img.get("subfolder", ""),
|
||
"type": img.get("type", "output"),
|
||
},
|
||
timeout=60,
|
||
)
|
||
r.raise_for_status()
|
||
return r.content
|
||
|
||
|
||
# --- WD tagger (lazy) -------------------------------------------------------
|
||
|
||
_tagger = None # (model, transform, labels) once loaded
|
||
_tagger_lock = threading.Lock()
|
||
|
||
|
||
def _load_tagger():
|
||
global _tagger
|
||
if _tagger is not None:
|
||
return _tagger
|
||
with _tagger_lock:
|
||
if _tagger is not None:
|
||
return _tagger
|
||
import torch
|
||
import timm
|
||
from timm.data import create_transform, resolve_data_config
|
||
import huggingface_hub
|
||
|
||
model = timm.create_model(f"hf_hub:{WD_MODEL}", pretrained=True).eval()
|
||
if torch.cuda.is_available():
|
||
model = model.cuda()
|
||
|
||
cfg = resolve_data_config(model.pretrained_cfg, model=model)
|
||
transform = create_transform(**cfg)
|
||
|
||
lpath = huggingface_hub.hf_hub_download(WD_MODEL, "selected_tags.csv")
|
||
with open(lpath, newline="") as f:
|
||
rows = list(csv.DictReader(f))
|
||
# category 0=general 4=character 9=rating
|
||
labels = [(r["name"], int(r.get("category", 9))) for r in rows]
|
||
|
||
_tagger = (model, transform, labels)
|
||
return _tagger
|
||
|
||
|
||
def _run_tagger(pil_img: Image.Image, threshold: float = 0.35):
|
||
import torch
|
||
model, transform, labels = _load_tagger()
|
||
tensor = transform(pil_img.convert("RGB")).unsqueeze(0)
|
||
if torch.cuda.is_available():
|
||
tensor = tensor.cuda()
|
||
with torch.no_grad():
|
||
scores = torch.sigmoid(model(tensor))[0].cpu().tolist()
|
||
tags = [
|
||
{"tag": name, "score": round(score, 3), "cat": cat}
|
||
for (name, cat), score in zip(labels, scores)
|
||
if score >= threshold
|
||
]
|
||
tags.sort(key=lambda x: -x["score"])
|
||
return tags
|
||
|
||
|
||
def _tags_to_name(tags: list, max_tags: int = 8) -> str:
|
||
content = [t["tag"] for t in tags if t["cat"] in (0, 4)][:max_tags]
|
||
return " ".join(content).replace("_", " ")
|
||
|
||
|
||
def _apply_transparency(png_bytes: bytes) -> bytes:
|
||
"""Use rembg to remove background and return PNG bytes with Alpha channel."""
|
||
try:
|
||
from rembg import remove
|
||
import io
|
||
from PIL import Image
|
||
img = Image.open(io.BytesIO(png_bytes))
|
||
# rembg works best on RGB
|
||
if img.mode != "RGB":
|
||
img = img.convert("RGB")
|
||
out = remove(img)
|
||
buf = io.BytesIO()
|
||
out.save(buf, format="PNG")
|
||
return buf.getvalue()
|
||
except Exception as e:
|
||
print(f"Error in transparency post-processing: {e}")
|
||
return png_bytes
|
||
|
||
|
||
# --- faceswapper (insightface + inswapper_128) --------------------------------
|
||
# Setup: pip install insightface onnxruntime-gpu opencv-python-headless
|
||
# Download: place inswapper_128.onnx at ~/.insightface/models/inswapper_128.onnx
|
||
# Source: https://huggingface.co/deepinsight/inswapper
|
||
|
||
_faceswapper = None
|
||
_faceswapper_lock = threading.Lock()
|
||
|
||
# Dedicated single-worker pool for face-crop extraction. Running it here
|
||
# instead of via FastAPI BackgroundTasks keeps the heavy insightface inference
|
||
# (and its one-time model load) off the shared request threadpool, so quick
|
||
# endpoints like /order stay responsive right after a "set preferred" click.
|
||
# A single worker also serializes face jobs so a burst can't thrash the GPU.
|
||
from concurrent.futures import ThreadPoolExecutor as _ThreadPoolExecutor
|
||
_face_executor = _ThreadPoolExecutor(max_workers=1, thread_name_prefix="face")
|
||
|
||
_gfpgan = None
|
||
_gfpgan_lock = threading.Lock()
|
||
|
||
|
||
def _load_faceswapper():
|
||
global _faceswapper
|
||
if _faceswapper is not None:
|
||
return _faceswapper
|
||
with _faceswapper_lock:
|
||
if _faceswapper is not None:
|
||
return _faceswapper
|
||
try:
|
||
import insightface
|
||
from insightface.app import FaceAnalysis
|
||
except ImportError:
|
||
raise RuntimeError("insightface not installed. Run: pip install insightface onnxruntime-gpu")
|
||
|
||
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||
app = FaceAnalysis(name='buffalo_l', providers=providers)
|
||
app.prepare(ctx_id=0, det_size=(640, 640))
|
||
|
||
model_path = _load_faceswap_model_path()
|
||
if not os.path.exists(model_path):
|
||
# Try HuggingFace download as fallback
|
||
try:
|
||
import huggingface_hub
|
||
model_path = huggingface_hub.hf_hub_download(
|
||
'deepinsight/inswapper', 'inswapper_128.onnx',
|
||
local_dir=os.path.dirname(model_path),
|
||
)
|
||
print(f"[faceswap] Downloaded inswapper_128.onnx to {model_path}")
|
||
except Exception as de:
|
||
raise RuntimeError(
|
||
f"inswapper_128.onnx not found at {model_path}. "
|
||
f"Download from https://huggingface.co/deepinsight/inswapper and place it there. "
|
||
f"Download error: {de}"
|
||
)
|
||
|
||
swapper = insightface.model_zoo.get_model(model_path, providers=providers)
|
||
_faceswapper = (app, swapper)
|
||
print(f"[faceswap] loaded insightface buffalo_l + inswapper_128 from {model_path}")
|
||
return _faceswapper
|
||
|
||
|
||
def _load_gfpgan():
|
||
"""Lazy-load GFPGAN face restorer. Returns restorer or False if unavailable."""
|
||
global _gfpgan
|
||
if _gfpgan is not None:
|
||
return _gfpgan
|
||
with _gfpgan_lock:
|
||
if _gfpgan is not None:
|
||
return _gfpgan
|
||
try:
|
||
from gfpgan import GFPGANer
|
||
# Main GFPGAN model
|
||
model_path = os.path.expanduser('~/.gfpgan/weights/GFPGANv1.4.pth')
|
||
os.makedirs(os.path.dirname(model_path), exist_ok=True)
|
||
if not os.path.exists(model_path):
|
||
import urllib.request
|
||
print('[gfpgan] Downloading GFPGANv1.4.pth (~333 MB)...')
|
||
tmp = model_path + '.tmp'
|
||
urllib.request.urlretrieve(
|
||
'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth',
|
||
tmp
|
||
)
|
||
os.rename(tmp, model_path)
|
||
# GFPGANer hardcodes facexlib download path to CWD/gfpgan/weights/
|
||
# → change CWD to ~ so models land at ~/gfpgan/weights/ (stable across runs)
|
||
home = os.path.expanduser('~')
|
||
os.makedirs(os.path.join(home, 'gfpgan', 'weights'), exist_ok=True)
|
||
_orig_cwd = os.getcwd()
|
||
os.chdir(home)
|
||
try:
|
||
restorer = GFPGANer(model_path=model_path, upscale=1, arch='clean',
|
||
channel_multiplier=2, bg_upsampler=None)
|
||
finally:
|
||
os.chdir(_orig_cwd)
|
||
_gfpgan = restorer
|
||
print('[gfpgan] GFPGANv1.4 loaded')
|
||
except Exception as e:
|
||
print(f'[gfpgan] not available: {e}')
|
||
_gfpgan = False
|
||
return _gfpgan
|
||
|
||
|
||
def _make_video_poster(video_path: str) -> str | None:
|
||
"""Extract a poster JPG (sibling `<stem>.jpg`) so the gallery can show a
|
||
thumbnail for a video via a plain <img> (file:// can't render <video> as a
|
||
thumb). Returns the poster path on success, else None."""
|
||
import subprocess
|
||
poster_path = os.path.splitext(video_path)[0] + '.jpg'
|
||
try:
|
||
r = subprocess.run([
|
||
'ffmpeg', '-y', '-ss', '1', '-i', video_path,
|
||
'-frames:v', '1', '-q:v', '3', poster_path,
|
||
], capture_output=True, timeout=120)
|
||
if r.returncode == 0 and os.path.exists(poster_path):
|
||
return poster_path
|
||
# -ss 1 can overshoot very short clips; retry from the first frame
|
||
r = subprocess.run([
|
||
'ffmpeg', '-y', '-i', video_path,
|
||
'-frames:v', '1', '-q:v', '3', poster_path,
|
||
], capture_output=True, timeout=120)
|
||
if r.returncode == 0 and os.path.exists(poster_path):
|
||
return poster_path
|
||
except Exception as pe:
|
||
print(f'[poster] failed for {video_path}: {pe}')
|
||
return None
|
||
|
||
|
||
def _faceswap_worker(job_id: str, model_filename: str, video_name: str, enhance: bool = True, preview_scale: float = 1.0):
|
||
"""Frame-by-frame face swap: model face → every face in template video."""
|
||
output_dir = _load_output_dir()
|
||
wireframe_dir = _load_wireframe_dir()
|
||
|
||
try:
|
||
import cv2
|
||
import numpy as np
|
||
app, swapper = _load_faceswapper()
|
||
except Exception as e:
|
||
jobs[job_id]["status"] = "error"
|
||
jobs[job_id]["error"] = str(e)
|
||
return
|
||
|
||
gfpgan_restorer = None
|
||
if enhance:
|
||
try:
|
||
r = _load_gfpgan()
|
||
if r is not False:
|
||
gfpgan_restorer = r
|
||
except Exception:
|
||
pass
|
||
|
||
try:
|
||
# 1. Load source (model) face
|
||
src_path = os.path.join(output_dir, model_filename)
|
||
src_bgr = cv2.imread(src_path)
|
||
if src_bgr is None:
|
||
raise ValueError(f"Cannot read model image: {model_filename}")
|
||
src_faces = app.get(src_bgr)
|
||
if not src_faces:
|
||
raise ValueError(f"No face detected in: {model_filename}")
|
||
# Use the largest face as source
|
||
src_face = max(src_faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
|
||
|
||
# 2. Open template video
|
||
video_path = os.path.join(wireframe_dir, video_name)
|
||
cap = cv2.VideoCapture(video_path)
|
||
if not cap.isOpened():
|
||
raise ValueError(f"Cannot open video: {video_name}")
|
||
|
||
fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
|
||
scale = max(0.1, min(1.0, preview_scale))
|
||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) * scale)
|
||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) * scale)
|
||
# Ensure even dimensions for codec compatibility
|
||
width = width if width % 2 == 0 else width - 1
|
||
height = height if height % 2 == 0 else height - 1
|
||
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||
jobs[job_id]["total"] = max(total, 1)
|
||
|
||
# 3. Write frame-swapped temp video
|
||
ts = time.strftime("%Y%m%d_%H%M%S")
|
||
vid_stem = os.path.splitext(video_name)[0]
|
||
base_name = naming.get_base_name(model_filename)
|
||
prev_tag = f"_prev{int(scale*100)}" if scale < 1.0 else ""
|
||
tmp_name = f"{ts}_fs_tmp_{vid_stem}_{base_name}{prev_tag}.mp4"
|
||
out_name = f"{ts}_fs_{vid_stem}_{base_name}{prev_tag}.mp4"
|
||
tmp_path = os.path.join(output_dir, tmp_name)
|
||
out_path = os.path.join(output_dir, out_name)
|
||
|
||
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
|
||
writer = cv2.VideoWriter(tmp_path, fourcc, fps, (width, height))
|
||
|
||
frame_idx = 0
|
||
while True:
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
break
|
||
tgt_faces = app.get(frame)
|
||
result = frame
|
||
if tgt_faces:
|
||
# Only swap the largest face — avoids false-positive detections
|
||
# (reflections, background faces, face-like textures) causing ghost heads.
|
||
# Ignore faces smaller than 40×40px (1600px²) as likely false positives.
|
||
MIN_FACE_AREA = 1600
|
||
valid = [f for f in tgt_faces
|
||
if (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]) >= MIN_FACE_AREA]
|
||
if valid:
|
||
result = frame.copy()
|
||
best_face = max(valid, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
|
||
try:
|
||
result = swapper.get(result, best_face, src_face, paste_back=True)
|
||
except Exception:
|
||
pass
|
||
if gfpgan_restorer is not None:
|
||
try:
|
||
_, _, result = gfpgan_restorer.enhance(
|
||
result, has_aligned=False, only_center_face=False, paste_back=True
|
||
)
|
||
except Exception:
|
||
pass
|
||
if scale < 1.0:
|
||
result = cv2.resize(result, (width, height), interpolation=cv2.INTER_AREA)
|
||
writer.write(result)
|
||
frame_idx += 1
|
||
jobs[job_id]["done"] = frame_idx
|
||
|
||
cap.release()
|
||
writer.release()
|
||
|
||
# 4. Remux with original audio via ffmpeg
|
||
try:
|
||
import subprocess
|
||
r = subprocess.run([
|
||
'ffmpeg', '-y',
|
||
'-i', tmp_path,
|
||
'-i', video_path,
|
||
'-map', '0:v:0', '-map', '1:a?',
|
||
'-c:v', 'libx264', '-preset', 'fast', '-crf', '18',
|
||
'-c:a', 'aac', '-movflags', '+faststart',
|
||
out_path,
|
||
], capture_output=True, timeout=600)
|
||
if r.returncode == 0:
|
||
os.remove(tmp_path)
|
||
else:
|
||
os.rename(tmp_path, out_path)
|
||
print(f"[faceswap] ffmpeg failed ({r.returncode}), using raw mp4v output")
|
||
except Exception as fe:
|
||
os.rename(tmp_path, out_path)
|
||
print(f"[faceswap] ffmpeg error: {fe}")
|
||
|
||
# 5. Snapshot poster + register output in DB under same group as model
|
||
_make_video_poster(out_path)
|
||
person = database.get_person(model_filename)
|
||
group_id = (person[1] if person and person[1] else naming.get_base_name(model_filename))
|
||
database.upsert_person(
|
||
out_name,
|
||
filepath=out_path,
|
||
group_id=group_id,
|
||
content_type='video',
|
||
faceswap_source_video=video_name,
|
||
source_refs=json.dumps([model_filename]),
|
||
sort_order=database.get_next_sort_order(group_id),
|
||
)
|
||
|
||
jobs[job_id]["status"] = "done"
|
||
jobs[job_id]["output"] = out_name
|
||
_invalidate_static()
|
||
|
||
except Exception as e:
|
||
print(f"[faceswap] error: {e}")
|
||
jobs[job_id]["status"] = "error"
|
||
jobs[job_id]["error"] = str(e)
|
||
|
||
|
||
def _faceswap_worker_ff(job_id: str, model_filename: str, video_name: str,
|
||
hair: bool = True, enhance: bool = True, preview_scale: float = 1.0):
|
||
"""High-quality faceswap via FaceFusion CLI (supports hair_swapper + ghost model)."""
|
||
import subprocess as sp
|
||
import sys
|
||
import re as _re
|
||
|
||
output_dir = _load_output_dir()
|
||
wireframe_dir = _load_wireframe_dir()
|
||
|
||
with open(CONFIG_PATH, 'r') as f:
|
||
conf = json.load(f)
|
||
ff_dir = os.path.expanduser(conf.get('facefusion_dir', '~/facefusion'))
|
||
ff_venv = os.path.expanduser(conf.get('facefusion_venv', '~/facefusion-venv'))
|
||
|
||
ff_script = os.path.join(ff_dir, 'facefusion.py')
|
||
ff_py = os.path.join(ff_venv, 'bin', 'python')
|
||
if not os.path.exists(ff_py):
|
||
ff_py = sys.executable
|
||
|
||
if not os.path.exists(ff_script):
|
||
jobs[job_id]['status'] = 'error'
|
||
jobs[job_id]['error'] = (
|
||
f'FaceFusion not found at {ff_dir}. '
|
||
'Run: bash tour-comfy/install_facefusion.sh'
|
||
)
|
||
return
|
||
|
||
src_path = os.path.join(output_dir, model_filename)
|
||
video_path = os.path.join(wireframe_dir, video_name)
|
||
ts = time.strftime('%Y%m%d_%H%M%S')
|
||
vid_stem = os.path.splitext(video_name)[0]
|
||
base_name = naming.get_base_name(model_filename)
|
||
scale = max(0.1, min(1.0, preview_scale))
|
||
prev_tag = f'_prev{int(scale*100)}' if scale < 1.0 else ''
|
||
out_name = f'{ts}_fs_{vid_stem}_{base_name}{prev_tag}.mp4'
|
||
out_path = os.path.join(output_dir, out_name)
|
||
|
||
processors = ['face_swapper']
|
||
# hair_swapper is not available in this FaceFusion version; use face_enhancer for quality
|
||
if enhance:
|
||
processors.append('face_enhancer')
|
||
|
||
cmd = [
|
||
ff_py, ff_script, 'headless-run',
|
||
'--source-paths', src_path,
|
||
'--target-path', video_path,
|
||
'--output-path', out_path,
|
||
'--processors', *processors,
|
||
'--execution-providers', 'cuda',
|
||
# Limit to 2 threads: each thread owns a cuBLAS handle + workspace; more
|
||
# threads exhausts VRAM when ComfyUI is running concurrently on the same GPU.
|
||
'--execution-thread-count', '2',
|
||
'--face-swapper-model', 'ghost_3_256',
|
||
# The default yolo_face detector at score 0.5 misses the extreme-angle /
|
||
# cropped close-up faces common in these POV template clips, so the swap
|
||
# silently no-ops. scrfd at a lower score + multi-angle detection reliably
|
||
# finds them; 'many' selector swaps every detected face per frame.
|
||
'--face-detector-model', 'scrfd',
|
||
'--face-detector-score', '0.3',
|
||
'--face-detector-angles', '0', '90', '270',
|
||
# 'one' swaps only the single best face per frame; 'many' caused ghost heads
|
||
# by swapping false-positive detections (skin texture, reflections, etc.)
|
||
'--face-selector-mode', 'one',
|
||
]
|
||
if enhance:
|
||
cmd += ['--face-enhancer-model', 'gfpgan_1.4']
|
||
if scale < 1.0:
|
||
# Determine native video width to compute preview width
|
||
try:
|
||
import cv2 as _cv2
|
||
_cap = _cv2.VideoCapture(video_path)
|
||
_native_w = int(_cap.get(_cv2.CAP_PROP_FRAME_WIDTH))
|
||
_cap.release()
|
||
_preview_w = max(2, int(_native_w * scale))
|
||
if _preview_w % 2 != 0:
|
||
_preview_w -= 1
|
||
cmd += ['--output-video-width', str(_preview_w)]
|
||
except Exception:
|
||
pass
|
||
|
||
jobs[job_id]['total'] = 100
|
||
jobs[job_id]['done'] = 0
|
||
|
||
# Ensure CUDA libs are on LD_LIBRARY_PATH for the subprocess (inherited from parent,
|
||
# but also add nvidia package libs as fallback if running outside start_api.sh)
|
||
import site as _site
|
||
_sp_pkgs = next((p for p in _site.getsitepackages() if 'site-packages' in p), '')
|
||
_nv_base = os.path.join(_sp_pkgs, 'nvidia')
|
||
_extra_libs = ':'.join(
|
||
os.path.join(_nv_base, pkg, 'lib')
|
||
for pkg in ('cuda_runtime', 'cublas', 'cudnn', 'curand', 'cufft', 'cusolver', 'cusparse', 'nvjitlink', 'cuda_nvrtc')
|
||
if os.path.isdir(os.path.join(_nv_base, pkg, 'lib'))
|
||
)
|
||
_env = os.environ.copy()
|
||
if _extra_libs:
|
||
_env['LD_LIBRARY_PATH'] = _extra_libs + (':' + _env['LD_LIBRARY_PATH'] if _env.get('LD_LIBRARY_PATH') else '')
|
||
|
||
try:
|
||
output_lines = []
|
||
proc = sp.Popen(
|
||
cmd, cwd=ff_dir, env=_env,
|
||
stdout=sp.PIPE, stderr=sp.PIPE,
|
||
text=True, errors='replace',
|
||
)
|
||
# FaceFusion writes tqdm progress to stderr; stdout carries other output.
|
||
# Parse frame counts from both streams so the UI job counter updates.
|
||
import threading as _thr
|
||
def _parse_progress(line: str):
|
||
m = _re.search(r'(\d+)\s*/\s*(\d+)', line)
|
||
if m:
|
||
done, total = int(m.group(1)), int(m.group(2))
|
||
if total > 0:
|
||
jobs[job_id]['done'] = done
|
||
jobs[job_id]['total'] = total
|
||
def _drain_stderr():
|
||
for ln in proc.stderr:
|
||
output_lines.append(ln.rstrip())
|
||
print(f'[facefusion] {ln.rstrip()}')
|
||
_parse_progress(ln)
|
||
_thr.Thread(target=_drain_stderr, daemon=True).start()
|
||
for line in proc.stdout:
|
||
print(f'[facefusion] {line.rstrip()}')
|
||
_parse_progress(line)
|
||
proc.wait()
|
||
|
||
if proc.returncode != 0:
|
||
tail = '\n'.join(output_lines[-10:])
|
||
raise RuntimeError(f'FaceFusion exited with code {proc.returncode}: {tail}')
|
||
if not os.path.exists(out_path):
|
||
raise RuntimeError('FaceFusion produced no output file')
|
||
|
||
_make_video_poster(out_path)
|
||
person = database.get_person(model_filename)
|
||
group_id = (person[1] if person and person[1] else naming.get_base_name(model_filename))
|
||
database.upsert_person(
|
||
out_name, filepath=out_path, group_id=group_id,
|
||
content_type='video', faceswap_source_video=video_name,
|
||
source_refs=json.dumps([model_filename]),
|
||
sort_order=database.get_next_sort_order(group_id),
|
||
)
|
||
jobs[job_id]['status'] = 'done'
|
||
jobs[job_id]['output'] = out_name
|
||
_invalidate_static()
|
||
|
||
except Exception as e:
|
||
print(f'[faceswap-ff] error: {e}')
|
||
jobs[job_id]['status'] = 'error'
|
||
jobs[job_id]['error'] = str(e)
|
||
|
||
|
||
# --- pipeline helper ---------------------------------------------------------
|
||
|
||
def _load_poses():
|
||
poses_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "poses.md")
|
||
if not os.path.exists(poses_path):
|
||
return {}
|
||
|
||
poses = {}
|
||
current_pose = None
|
||
current_beta = False
|
||
current_desc = []
|
||
|
||
with open(poses_path, "r", encoding="utf-8") as f:
|
||
for line in f:
|
||
line = line.strip()
|
||
if line.startswith("# "):
|
||
if current_pose:
|
||
poses[current_pose] = {"text": " ".join(current_desc).strip(), "beta": current_beta}
|
||
raw = line[2:].rstrip(":").strip()
|
||
current_beta = bool(re.search(r'\(beta\)', raw, re.IGNORECASE))
|
||
current_pose = re.sub(r'\s*\(beta\)\s*', '', raw, flags=re.IGNORECASE).strip()
|
||
current_desc = []
|
||
elif line and current_pose:
|
||
current_desc.append(line)
|
||
|
||
if current_pose:
|
||
poses[current_pose] = {"text": " ".join(current_desc).strip(), "beta": current_beta}
|
||
|
||
return poses
|
||
|
||
|
||
def _save_poses(poses: dict) -> None:
|
||
"""Rewrite poses.md from a {name: {text, beta}} dict, round-tripping _load_poses' format.
|
||
|
||
Each pose is written as a ``# Name`` (or ``# Name (beta)``) header followed by its body.
|
||
Body sentences separated by '. ' are written on their own lines to match the existing
|
||
hand-authored style.
|
||
"""
|
||
poses_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "poses.md")
|
||
blocks = []
|
||
for name, entry in poses.items():
|
||
name = str(name).strip()
|
||
if not name:
|
||
continue
|
||
if isinstance(entry, dict):
|
||
text = str(entry.get("text", "")).strip()
|
||
beta = bool(entry.get("beta"))
|
||
else:
|
||
text = str(entry).strip()
|
||
beta = False
|
||
header = f"# {name}{' (beta)' if beta else ''}"
|
||
# Split into readable lines on sentence boundaries without losing the period.
|
||
body_lines = [s.strip() for s in re.split(r'(?<=\.)\s+', text) if s.strip()]
|
||
blocks.append(header + "\n" + "\n".join(body_lines))
|
||
with open(poses_path, "w", encoding="utf-8") as f:
|
||
f.write("\n\n".join(blocks) + ("\n" if blocks else ""))
|
||
|
||
|
||
def _detect_has_background(pil: Image.Image) -> bool:
|
||
"""Return False when the image has significant alpha transparency (background removed)."""
|
||
if pil.mode != 'RGBA':
|
||
return True
|
||
alpha = pil.split()[3]
|
||
hist = alpha.histogram()
|
||
transparent_px = sum(hist[:128])
|
||
return transparent_px / (pil.width * pil.height) < 0.1
|
||
|
||
|
||
def _detect_has_clothing(tags: list) -> bool | None:
|
||
"""Return True if any tag from CLOTHING_TAGS appears above threshold, None if no tags."""
|
||
if not tags:
|
||
return None
|
||
tag_names = {t["tag"] for t in tags}
|
||
return bool(tag_names & CLOTHING_TAGS)
|
||
|
||
|
||
def _run_pipeline(
|
||
pil: Image.Image,
|
||
prompt: str,
|
||
seed: int = -1,
|
||
max_area: int = 0,
|
||
steps: int = 4,
|
||
cfg: float = 1.0,
|
||
sampler_name: str = "euler_ancestral",
|
||
scheduler: str = "beta",
|
||
extra_images: list = None, # additional PIL images wired to image2, image3
|
||
) -> bytes:
|
||
area = max_area if max_area > 0 else MAX_AREA
|
||
pil, w, h = _prep_image(pil, area)
|
||
buf = io.BytesIO()
|
||
pil.save(buf, format="PNG")
|
||
stored = _comfy_upload(buf.getvalue(), f"in_{uuid.uuid4().hex[:8]}.png")
|
||
if seed is None or seed < 0:
|
||
seed = random.randint(0, MAX_SEED)
|
||
graph = copy.deepcopy(BASE_WORKFLOW)
|
||
graph[NODE_LOADIMAGE]["inputs"]["image"] = stored
|
||
graph[NODE_POSITIVE]["inputs"]["prompt"] = prompt
|
||
|
||
# Inject extra reference images as image2 / image3 on the positive encoder
|
||
if extra_images:
|
||
for i, extra_pil in enumerate(extra_images[:2]):
|
||
extra_buf = io.BytesIO()
|
||
extra_pil.convert("RGB").save(extra_buf, format="PNG")
|
||
extra_stored = _comfy_upload(extra_buf.getvalue(), f"in_{uuid.uuid4().hex[:8]}.png")
|
||
node_id = str(11 + i) # "11" → image2, "12" → image3
|
||
img_key = f"image{i + 2}"
|
||
graph[node_id] = {
|
||
"class_type": "LoadImage",
|
||
"inputs": {"image": extra_stored},
|
||
"_meta": {"title": f"ref image {i + 2}"},
|
||
}
|
||
graph[NODE_POSITIVE]["inputs"][img_key] = [node_id, 0]
|
||
|
||
# ── background-removal routing ────────────────────────────────────────────
|
||
# Two configurable strategies (config.json key "bg_removal"):
|
||
#
|
||
# "rembg" (default) — strip transparent keyword → Qwen renders a natural
|
||
# scene → rembg (U2Net) separates person from any complex background.
|
||
#
|
||
# "sam2" — replace transparent keyword with "black background" → Qwen
|
||
# renders a solid black BG → SAM2 bbox segmentation on a black image
|
||
# works perfectly because the contrast is maximal.
|
||
#
|
||
# Either way, explicit "black background" in the prompt always routes to
|
||
# SAM2 (the user already set up the ideal SAM2 input).
|
||
# ─────────────────────────────────────────────────────────────────────────
|
||
_TRANSPARENT_KWS = ["transparent background", "no background",
|
||
"remove background", "alpha channel"]
|
||
_BLACK_BG_KWS = ["black background"]
|
||
|
||
with open(CONFIG_PATH) as _cf:
|
||
_bg_conf = json.load(_cf)
|
||
bg_method = _bg_conf.get("bg_removal", "rembg") # "rembg" | "sam2"
|
||
|
||
is_transparent = any(kw in prompt.lower() for kw in _TRANSPARENT_KWS)
|
||
is_black_bg = any(kw in prompt.lower() for kw in _BLACK_BG_KWS)
|
||
post_process = None # "rembg" | "sam2"
|
||
|
||
if is_transparent:
|
||
if bg_method == "sam2":
|
||
# Swap "transparent background" → "black background" so Qwen renders
|
||
# a pure-black BG that SAM2 can segment with maximal contrast.
|
||
cleaned = prompt
|
||
for kw in _TRANSPARENT_KWS:
|
||
cleaned = re.sub(re.escape(kw), "black background", cleaned, flags=re.IGNORECASE)
|
||
# Collapse duplicates if multiple keywords matched
|
||
cleaned = re.sub(r"(?i)(black background[\s,]*){2,}", "black background, ", cleaned)
|
||
cleaned = re.sub(r",\s*,", ",", cleaned).strip(", ")
|
||
graph[NODE_POSITIVE]["inputs"]["prompt"] = cleaned
|
||
graph[NODE_NEGATIVE]["inputs"]["prompt"] = (
|
||
"real background, outdoor scene, indoor scene, gradient, "
|
||
"colored background, watermark, deformed anatomy"
|
||
)
|
||
post_process = "sam2"
|
||
else:
|
||
# Strip the keyword so Qwen renders a natural scene; rembg handles
|
||
# any background complexity reliably.
|
||
cleaned = prompt
|
||
for kw in _TRANSPARENT_KWS:
|
||
cleaned = re.sub(re.escape(kw), "", cleaned, flags=re.IGNORECASE)
|
||
cleaned = re.sub(r",\s*,", ",", cleaned)
|
||
cleaned = re.sub(r",\s*$", "", cleaned.strip()).strip(", ")
|
||
graph[NODE_POSITIVE]["inputs"]["prompt"] = cleaned
|
||
graph[NODE_NEGATIVE]["inputs"]["prompt"] = "deformed anatomy, watermark, logo"
|
||
post_process = "rembg"
|
||
|
||
elif is_black_bg:
|
||
# Prompt already specifies a black background — ideal SAM2 input.
|
||
# Route to SAM2 regardless of the configured bg_removal method.
|
||
post_process = "sam2"
|
||
|
||
graph[NODE_LATENT]["inputs"]["width"] = w
|
||
graph[NODE_LATENT]["inputs"]["height"] = h
|
||
ks = graph[NODE_KSAMPLER]["inputs"]
|
||
ks.update(seed=seed, steps=steps, cfg=cfg, sampler_name=sampler_name, scheduler=scheduler)
|
||
client_id = uuid.uuid4().hex
|
||
prompt_id = _comfy_queue(graph, client_id)
|
||
outputs = _comfy_wait(prompt_id, time.time() + GEN_TIMEOUT)
|
||
png_bytes = _comfy_fetch_image(outputs)
|
||
|
||
if post_process == "sam2":
|
||
# Input has a black background (Qwen was told "black background").
|
||
# Use threshold-derived bbox so SAM2 gets a person-specific hint
|
||
# rather than the full frame — full-frame bbox inverts the mask on
|
||
# black-bg images because the large dark region scores higher.
|
||
png_bytes = _apply_transparency_black_bg(png_bytes)
|
||
elif post_process == "rembg":
|
||
png_bytes = _apply_transparency(png_bytes)
|
||
|
||
return png_bytes
|
||
|
||
|
||
# --- batch state -------------------------------------------------------------
|
||
|
||
jobs: dict[str, dict] = {}
|
||
|
||
|
||
def _load_output_dir() -> str:
|
||
with open(CONFIG_PATH, "r") as f:
|
||
conf = json.load(f)
|
||
d = conf["output_dir"]
|
||
if not os.path.isabs(d):
|
||
d = os.path.normpath(os.path.join(os.path.dirname(CONFIG_PATH), "..", d))
|
||
return d
|
||
|
||
|
||
def _move_to_trash(filepath: str):
|
||
if not filepath or not os.path.exists(filepath):
|
||
return
|
||
output_dir = _load_output_dir()
|
||
trash_dir = os.path.join(output_dir, ".trash")
|
||
os.makedirs(trash_dir, exist_ok=True)
|
||
|
||
filename = os.path.basename(filepath)
|
||
ts = time.strftime("%Y%m%d_%H%M%S")
|
||
trash_path = os.path.join(trash_dir, f"{ts}_{filename}")
|
||
|
||
try:
|
||
shutil.move(filepath, trash_path)
|
||
except Exception as e:
|
||
print(f"Error moving {filepath} to trash: {e}")
|
||
|
||
|
||
# --- static data files -------------------------------------------------------
|
||
|
||
_static_write_lock = threading.Lock()
|
||
_invalidate_timer: "threading.Timer | None" = None
|
||
_invalidate_timer_lock = threading.Lock()
|
||
|
||
|
||
def _write_json(path: str, data) -> None:
|
||
"""Atomic JSON write: write to .tmp then os.replace (no partial reads)."""
|
||
tmp = path + ".tmp"
|
||
with open(tmp, "w") as f:
|
||
json.dump(data, f)
|
||
os.replace(tmp, path)
|
||
|
||
|
||
def _write_all_static() -> None:
|
||
"""Regenerate <output_dir>/_data/{images,names,groups,group-names,videos}.json.
|
||
|
||
Called on startup and after every mutation so the browser can\
|
||
fetch pre-computed JSON instead of hitting the DB on each page load.
|
||
Uses a lock so concurrent invalidations don't race each other.
|
||
"""
|
||
with _static_write_lock:
|
||
try:
|
||
output_dir = _load_output_dir()
|
||
data_dir = os.path.join(output_dir, "_data")
|
||
os.makedirs(data_dir, exist_ok=True)
|
||
|
||
# Single DB query reused for images / names / groups
|
||
persons = database.list_persons(include_archived=True)
|
||
|
||
# images.json — all images (frontend filters by .archived field)
|
||
db_images = []
|
||
for p in persons:
|
||
fpath = os.path.join(output_dir, p[0])
|
||
if not os.path.exists(fpath):
|
||
continue
|
||
db_images.append({
|
||
"filename": p[0],
|
||
"name": p[1],
|
||
"group_id": p[2],
|
||
"clip_description": p[3],
|
||
"prompt": p[4],
|
||
"pose": p[5],
|
||
"sort_order": p[6],
|
||
"group_name": p[7],
|
||
"hidden": bool(p[8]) if p[8] else False,
|
||
"has_background": bool(p[9]) if p[9] is not None else True,
|
||
"source_refs": p[10],
|
||
"has_clothing": p[11],
|
||
"content_type": p[12] or "image",
|
||
"faceswap_source_video": p[13],
|
||
"archived": bool(p[14]) if p[14] else False,
|
||
})
|
||
try:
|
||
db_images.sort(
|
||
key=lambda x: os.path.getmtime(os.path.join(output_dir, x["filename"])),
|
||
reverse=True,
|
||
)
|
||
except Exception:
|
||
pass
|
||
_write_json(os.path.join(data_dir, "images.json"), {"images": db_images})
|
||
|
||
# names.json — {filename: name}
|
||
_write_json(os.path.join(data_dir, "names.json"),
|
||
{p[0]: p[1] for p in persons if p[1]})
|
||
|
||
# groups.json — {filename: group_id}
|
||
_write_json(os.path.join(data_dir, "groups.json"),
|
||
{p[0]: p[2] for p in persons if p[2]})
|
||
|
||
# group-names.json — {group_id: display_name}
|
||
_write_json(os.path.join(data_dir, "group-names.json"),
|
||
database.get_all_group_names())
|
||
|
||
# videos.json — wireframe template videos
|
||
wireframe_dir = _load_wireframe_dir()
|
||
videos = []
|
||
if os.path.isdir(wireframe_dir):
|
||
videos = [f for f in sorted(os.listdir(wireframe_dir))
|
||
if f.lower().endswith(VIDEO_EXTENSIONS) and not f.startswith('.')]
|
||
_write_json(os.path.join(data_dir, "videos.json"),
|
||
{"videos": videos, "wireframe_dir": wireframe_dir})
|
||
|
||
# config.json — current config snapshot
|
||
try:
|
||
with open(CONFIG_PATH, "r") as _f:
|
||
_write_json(os.path.join(data_dir, "config.json"), json.load(_f))
|
||
except Exception:
|
||
pass
|
||
|
||
except Exception as e:
|
||
print(f"[static] write_all error: {e}")
|
||
|
||
|
||
def _invalidate_static() -> None:
|
||
"""Coalesce rapid invalidation calls — restarts a 0.3 s debounce timer each time.
|
||
At most one _write_all_static() runs per quiet window, preventing thread floods
|
||
during batch jobs that call this after every single image."""
|
||
global _invalidate_timer
|
||
with _invalidate_timer_lock:
|
||
if _invalidate_timer is not None:
|
||
_invalidate_timer.cancel()
|
||
t = threading.Timer(0.3, _write_all_static)
|
||
t.daemon = True
|
||
t.start()
|
||
_invalidate_timer = t
|
||
|
||
|
||
# -----------------------------------------------------------------------------
|
||
|
||
def _batch_worker(job_id: str, filenames: list, prompts: list[str], poses: list,
|
||
seed: int, max_area: int, group_id: str | None = None,
|
||
wireframe_ref: str | None = None, wireframe_time: float = 0.5):
|
||
output_dir = _load_output_dir()
|
||
for fname in filenames:
|
||
actual_gid = None
|
||
try:
|
||
person = database.get_person(fname)
|
||
# Prefer the source's existing DB group_id; fall back to the caller-supplied
|
||
# group_id (which is the gallery gid, potentially stale) or the basename.
|
||
if person and person[1]:
|
||
actual_gid = person[1]
|
||
else:
|
||
actual_gid = group_id or naming.get_base_name(fname)
|
||
database.upsert_person(fname, group_id=actual_gid)
|
||
except Exception as e:
|
||
print(f"Error determining/updating group for {fname}: {e}")
|
||
actual_gid = group_id or naming.get_base_name(fname)
|
||
|
||
fpath = os.path.join(output_dir, fname)
|
||
if not os.path.exists(fpath):
|
||
jobs[job_id]["failed"] += len(prompts)
|
||
continue
|
||
|
||
try:
|
||
base_pil = Image.open(fpath).convert("RGB")
|
||
|
||
# Extract wireframe pose reference frame once per filename
|
||
pose_guide_pil = None
|
||
if wireframe_ref:
|
||
try:
|
||
wf_path = os.path.join(_load_wireframe_dir(), wireframe_ref)
|
||
cap = cv2.VideoCapture(wf_path)
|
||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||
target_frame = max(0, min(total_frames - 1, int(total_frames * wireframe_time)))
|
||
cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame)
|
||
ret, frame = cap.read()
|
||
cap.release()
|
||
if ret:
|
||
pose_guide_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
|
||
print(f"[batch] using wireframe {wireframe_ref} frame {target_frame}/{total_frames}")
|
||
except Exception as wf_err:
|
||
print(f"[batch] wireframe extract error: {wf_err}")
|
||
|
||
for prompt, pose in zip(prompts, poses):
|
||
if jobs[job_id].get("cancelled"):
|
||
return
|
||
try:
|
||
pil = base_pil
|
||
# Rotate 180° for poses that work better upside-down
|
||
if pose and pose.lower().strip() in ROTATE_180_POSES:
|
||
pil = pil.rotate(180)
|
||
|
||
extra_imgs = [pose_guide_pil] if pose_guide_pil else None
|
||
png = _run_pipeline(pil, prompt, seed, max_area, extra_images=extra_imgs)
|
||
ts = time.strftime("%Y%m%d_%H%M%S")
|
||
clean_fname = naming.get_base_name(fname)
|
||
out_name = f"{ts}_{clean_fname}"
|
||
out_path = os.path.join(output_dir, out_name)
|
||
|
||
with open(out_path, "wb") as f:
|
||
f.write(png)
|
||
|
||
has_bg = True
|
||
try:
|
||
out_pil = Image.open(io.BytesIO(png))
|
||
has_bg = _detect_has_background(out_pil)
|
||
except Exception:
|
||
pass
|
||
|
||
try:
|
||
embedding = embeddings.generate_embedding(out_path)
|
||
next_order = database.get_next_sort_order(actual_gid)
|
||
database.upsert_person(
|
||
out_name, filepath=out_path, embedding=embedding,
|
||
group_id=actual_gid, prompt=prompt, pose=pose,
|
||
has_background=has_bg, sort_order=next_order,
|
||
source_refs=json.dumps([fname]),
|
||
)
|
||
except Exception as db_err:
|
||
print(f"Database error in batch worker: {db_err}")
|
||
|
||
jobs[job_id]["done"] += 1
|
||
# Regenerate static JSON so the frontend's polling picks up the
|
||
# new image immediately (progressive refresh, not just at the end).
|
||
_invalidate_static()
|
||
except Exception as e:
|
||
print(f"Error in batch for {fname} with prompt '{prompt}': {e}")
|
||
jobs[job_id]["failed"] += 1
|
||
except Exception as e:
|
||
print(f"Error opening {fname}: {e}")
|
||
jobs[job_id]["failed"] += len(prompts)
|
||
|
||
jobs[job_id]["status"] = "done"
|
||
_invalidate_static()
|
||
|
||
|
||
def _multi_ref_worker(job_id: str, filenames: list[str], prompts: list[str], poses: list,
|
||
seed: int, max_area: int):
|
||
"""Generate one output image per prompt using filenames[0] as primary and the rest as extra refs."""
|
||
output_dir = _load_output_dir()
|
||
|
||
pils = []
|
||
for fname in filenames:
|
||
fpath = os.path.join(output_dir, fname)
|
||
if os.path.exists(fpath):
|
||
pils.append((fname, Image.open(fpath).convert("RGB")))
|
||
|
||
if not pils:
|
||
jobs[job_id]["status"] = "done"
|
||
return
|
||
|
||
# Output group: reuse shared group if all sources belong to the same one, else new group
|
||
source_groups = set()
|
||
for fname, _ in pils:
|
||
try:
|
||
p = database.get_person(fname)
|
||
if p and p[1]:
|
||
source_groups.add(p[1])
|
||
except Exception:
|
||
pass
|
||
|
||
if len(source_groups) == 1:
|
||
output_gid = next(iter(source_groups))
|
||
else:
|
||
output_gid = f"cg_{uuid.uuid4().hex[:8]}"
|
||
|
||
primary_fname, primary_pil = pils[0]
|
||
extra_pils = [p for _, p in pils[1:]]
|
||
|
||
for prompt, pose in zip(prompts, poses):
|
||
try:
|
||
work_pil = primary_pil
|
||
if pose and pose.lower().strip() in ROTATE_180_POSES:
|
||
work_pil = work_pil.rotate(180)
|
||
|
||
png = _run_pipeline(work_pil, prompt, seed, max_area, extra_images=extra_pils)
|
||
ts = time.strftime("%Y%m%d_%H%M%S")
|
||
clean = naming.get_base_name(primary_fname)
|
||
out_name = f"{ts}_mr_{clean}"
|
||
out_path = os.path.join(output_dir, out_name)
|
||
|
||
with open(out_path, "wb") as f:
|
||
f.write(png)
|
||
|
||
has_bg = True
|
||
try:
|
||
out_pil = Image.open(io.BytesIO(png))
|
||
has_bg = _detect_has_background(out_pil)
|
||
except Exception:
|
||
pass
|
||
|
||
try:
|
||
embedding = embeddings.generate_embedding(out_path)
|
||
next_order = database.get_next_sort_order(output_gid)
|
||
database.upsert_person(out_name, filepath=out_path, embedding=embedding,
|
||
group_id=output_gid, prompt=prompt, pose=pose,
|
||
has_background=has_bg, sort_order=next_order,
|
||
source_refs=json.dumps([f for f, _ in pils]))
|
||
except Exception as db_err:
|
||
print(f"DB error in multi-ref: {db_err}")
|
||
|
||
jobs[job_id]["done"] += 1
|
||
# Regenerate static JSON so the frontend's polling picks up the new
|
||
# image immediately (progressive refresh, matching _batch_worker).
|
||
_invalidate_static()
|
||
except Exception as e:
|
||
print(f"Error in multi-ref for prompt '{prompt}': {e}")
|
||
jobs[job_id]["failed"] += 1
|
||
|
||
jobs[job_id]["status"] = "done"
|
||
_invalidate_static()
|
||
|
||
|
||
# --- routes -----------------------------------------------------------------
|
||
|
||
class ConfigUpdate(BaseModel):
|
||
prompt: str | None = None
|
||
seed: int | None = None
|
||
|
||
|
||
@app.get("/config")
|
||
def get_config():
|
||
with open(CONFIG_PATH, "r") as f:
|
||
return json.load(f)
|
||
|
||
|
||
@app.post("/config")
|
||
def update_config(update: ConfigUpdate):
|
||
with open(CONFIG_PATH, "r") as f:
|
||
conf = json.load(f)
|
||
if update.prompt is not None:
|
||
conf["prompt"] = update.prompt
|
||
if update.seed is not None:
|
||
conf["seed"] = update.seed
|
||
with open(CONFIG_PATH, "w") as f:
|
||
json.dump(conf, f, indent=2)
|
||
_invalidate_static()
|
||
return {"prompt": conf["prompt"], "seed": conf["seed"]}
|
||
|
||
|
||
class BatchRequest(BaseModel):
|
||
filenames: list[str]
|
||
prompt: str | list[str]
|
||
seed: int = -1
|
||
max_area: int = 0
|
||
group_id: str | None = None
|
||
poses: list[str | None] | None = None # pose name per prompt (same index), or None; None entries = no pose
|
||
wireframe_ref: str | None = None # wireframe video name to use as pose guide (image2 slot)
|
||
wireframe_time: float = 0.5 # normalized time (0–1) to extract the pose frame from
|
||
|
||
|
||
@app.post("/batch")
|
||
def start_batch(req: BatchRequest):
|
||
prompts = [req.prompt] if isinstance(req.prompt, str) else req.prompt
|
||
poses = req.poses or [None] * len(prompts)
|
||
# Pad poses list to match prompts length
|
||
while len(poses) < len(prompts):
|
||
poses.append(None)
|
||
total_tasks = len(req.filenames) * len(prompts)
|
||
|
||
job_id = uuid.uuid4().hex[:8]
|
||
jobs[job_id] = {"status": "running", "total": total_tasks, "done": 0, "failed": 0, "cancelled": False}
|
||
t = threading.Thread(
|
||
target=_batch_worker,
|
||
args=(job_id, req.filenames, prompts, poses, req.seed, req.max_area, req.group_id),
|
||
kwargs={"wireframe_ref": req.wireframe_ref, "wireframe_time": req.wireframe_time},
|
||
daemon=True,
|
||
)
|
||
t.start()
|
||
return {"job_id": job_id, "total": total_tasks}
|
||
|
||
|
||
@app.delete("/batch/{job_id}")
|
||
def cancel_batch(job_id: str):
|
||
if job_id not in jobs:
|
||
raise HTTPException(404, "Job not found")
|
||
jobs[job_id]["cancelled"] = True
|
||
jobs[job_id]["status"] = "cancelled"
|
||
return {"status": "cancelled", "job_id": job_id}
|
||
|
||
|
||
class MultiRefRequest(BaseModel):
|
||
filenames: list[str] # 2–3 reference images; first is primary (image1)
|
||
prompt: str | list[str]
|
||
poses: list[str | None] | None = None
|
||
seed: int = -1
|
||
max_area: int = 0
|
||
|
||
|
||
@app.post("/multi-ref")
|
||
def start_multi_ref(req: MultiRefRequest):
|
||
if len(req.filenames) < 2:
|
||
raise HTTPException(400, "multi-ref requires at least 2 filenames")
|
||
filenames = req.filenames[:3] # cap at 3 (image1/2/3)
|
||
prompts = [req.prompt] if isinstance(req.prompt, str) else req.prompt
|
||
poses = req.poses or [None] * len(prompts)
|
||
while len(poses) < len(prompts):
|
||
poses.append(None)
|
||
|
||
job_id = uuid.uuid4().hex[:8]
|
||
jobs[job_id] = {"status": "running", "total": len(prompts), "done": 0, "failed": 0}
|
||
t = threading.Thread(
|
||
target=_multi_ref_worker,
|
||
args=(job_id, filenames, prompts, poses, req.seed, req.max_area),
|
||
daemon=True,
|
||
)
|
||
t.start()
|
||
return {"job_id": job_id, "total": len(prompts)}
|
||
|
||
|
||
@app.get("/poses")
|
||
def get_poses():
|
||
return _load_poses()
|
||
|
||
|
||
class PoseRequest(BaseModel):
|
||
name: str
|
||
text: str = ""
|
||
beta: bool = False
|
||
old_name: str | None = None # set to rename an existing pose
|
||
|
||
|
||
@app.post("/poses")
|
||
def save_pose(req: PoseRequest):
|
||
"""Create, update, or rename a pose in poses.md."""
|
||
name = req.name.strip()
|
||
if not name:
|
||
raise HTTPException(400, "Pose name is required")
|
||
poses = _load_poses()
|
||
# Rename: drop the old key (and preserve ordering by rebuilding).
|
||
if req.old_name and req.old_name != name:
|
||
poses.pop(req.old_name, None)
|
||
poses[name] = {"text": req.text.strip(), "beta": bool(req.beta)}
|
||
_save_poses(poses)
|
||
return {"status": "success", "poses": poses}
|
||
|
||
|
||
@app.delete("/poses/{name}")
|
||
def delete_pose(name: str):
|
||
"""Delete a pose from poses.md."""
|
||
poses = _load_poses()
|
||
if name not in poses:
|
||
raise HTTPException(404, "Pose not found")
|
||
poses.pop(name, None)
|
||
_save_poses(poses)
|
||
return {"status": "success", "poses": poses}
|
||
|
||
|
||
@app.get("/batch/{job_id}")
|
||
def get_batch(job_id: str):
|
||
if job_id not in jobs:
|
||
raise HTTPException(404, "Job not found")
|
||
return jobs[job_id]
|
||
|
||
|
||
@app.get("/images")
|
||
def list_images(archived: bool = False):
|
||
output_dir = _load_output_dir()
|
||
all_extensions = ('.jpg', '.jpeg', '.png', '.gif', '.webp', '.bmp', '.svg') + VIDEO_EXTENSIONS
|
||
try:
|
||
try:
|
||
persons = database.list_persons(include_archived=archived)
|
||
# list_persons cols: filename, name, group_id, clip_description,
|
||
# prompt, pose, sort_order, group_name, hidden, has_background,
|
||
# source_refs, has_clothing, content_type, faceswap_source_video, archived
|
||
db_images = []
|
||
for p in persons:
|
||
fpath = os.path.join(output_dir, p[0])
|
||
if not os.path.exists(fpath):
|
||
continue
|
||
db_images.append({
|
||
"filename": p[0],
|
||
"name": p[1],
|
||
"group_id": p[2],
|
||
"clip_description": p[3],
|
||
"prompt": p[4],
|
||
"pose": p[5],
|
||
"sort_order": p[6],
|
||
"group_name": p[7],
|
||
"hidden": bool(p[8]) if p[8] else False,
|
||
"has_background": bool(p[9]) if p[9] is not None else True,
|
||
"source_refs": p[10],
|
||
"has_clothing": p[11],
|
||
"content_type": p[12] or "image",
|
||
"faceswap_source_video":p[13],
|
||
"archived": bool(p[14]) if p[14] else False,
|
||
})
|
||
db_images.sort(
|
||
key=lambda x: os.path.getmtime(os.path.join(output_dir, x["filename"])),
|
||
reverse=True,
|
||
)
|
||
return {"images": db_images}
|
||
except Exception as db_err:
|
||
print(f"DB error in list_images: {db_err}")
|
||
listing = os.listdir(output_dir)
|
||
# video poster snapshots share a video sibling's stem — don't list them as items
|
||
video_stems = {os.path.splitext(f)[0] for f in listing if f.lower().endswith(VIDEO_EXTENSIONS)}
|
||
files = [
|
||
f for f in listing
|
||
if f.lower().endswith(all_extensions)
|
||
and not (f.lower().endswith('.jpg') and os.path.splitext(f)[0] in video_stems)
|
||
]
|
||
files.sort(key=lambda x: os.path.getmtime(os.path.join(output_dir, x)), reverse=True)
|
||
return {"images": [{"filename": f} for f in files]}
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
|
||
|
||
@app.get("/videos")
|
||
def list_videos():
|
||
"""Return available wireframe template videos."""
|
||
wireframe_dir = _load_wireframe_dir()
|
||
if not os.path.isdir(wireframe_dir):
|
||
return {"videos": []}
|
||
videos = [
|
||
f for f in sorted(os.listdir(wireframe_dir))
|
||
if f.lower().endswith(VIDEO_EXTENSIONS) and not f.startswith('.')
|
||
]
|
||
return {"videos": videos, "wireframe_dir": wireframe_dir}
|
||
|
||
|
||
@app.get("/wireframe/frame/{video_name}")
|
||
def wireframe_frame(video_name: str, t: float = 0.5):
|
||
"""Extract a single frame at normalized time t (0–1) from a wireframe video. Returns PNG."""
|
||
import cv2
|
||
wireframe_dir = _load_wireframe_dir()
|
||
video_path = os.path.join(wireframe_dir, video_name)
|
||
if not os.path.exists(video_path):
|
||
raise HTTPException(404, f"Video not found: {video_name}")
|
||
try:
|
||
cap = cv2.VideoCapture(video_path)
|
||
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||
target = max(0, min(total - 1, int(total * max(0.0, min(1.0, t)))))
|
||
cap.set(cv2.CAP_PROP_POS_FRAMES, target)
|
||
ret, frame = cap.read()
|
||
cap.release()
|
||
if not ret:
|
||
raise HTTPException(500, "Could not read frame")
|
||
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||
pil = Image.fromarray(rgb)
|
||
buf = io.BytesIO()
|
||
pil.save(buf, format="PNG")
|
||
buf.seek(0)
|
||
from fastapi.responses import Response
|
||
return Response(content=buf.getvalue(), media_type="image/png")
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
raise HTTPException(500, f"Frame extraction error: {e}")
|
||
|
||
|
||
@app.get("/wireframe/duration/{video_name}")
|
||
def wireframe_duration(video_name: str):
|
||
"""Return duration (seconds) of a wireframe video via ffprobe."""
|
||
import subprocess
|
||
wireframe_dir = _load_wireframe_dir()
|
||
video_path = os.path.join(wireframe_dir, video_name)
|
||
if not os.path.exists(video_path):
|
||
raise HTTPException(404, f"Video not found: {video_name}")
|
||
try:
|
||
r = subprocess.run(
|
||
['ffprobe', '-v', 'error', '-show_entries', 'format=duration',
|
||
'-of', 'json', video_path],
|
||
capture_output=True, timeout=10,
|
||
)
|
||
info = json.loads(r.stdout)
|
||
duration = float(info.get('format', {}).get('duration', 0))
|
||
except Exception as e:
|
||
raise HTTPException(500, f"ffprobe error: {e}")
|
||
return {'video_name': video_name, 'duration': duration}
|
||
|
||
|
||
class TrimRequest(BaseModel):
|
||
video_name: str
|
||
start: float # seconds
|
||
end: float # seconds
|
||
output_name: str | None = None # auto-generated if None
|
||
|
||
|
||
@app.post("/wireframe/trim")
|
||
def trim_wireframe(req: TrimRequest):
|
||
"""Trim a wireframe video to [start, end] seconds using ffmpeg stream copy."""
|
||
import subprocess
|
||
wireframe_dir = _load_wireframe_dir()
|
||
video_path = os.path.join(wireframe_dir, req.video_name)
|
||
if not os.path.exists(video_path):
|
||
raise HTTPException(404, f"Video not found: {req.video_name}")
|
||
if req.start < 0 or req.end <= req.start:
|
||
raise HTTPException(400, "Invalid start/end: end must be > start ≥ 0")
|
||
|
||
stem = os.path.splitext(req.video_name)[0]
|
||
if req.output_name:
|
||
out_name = req.output_name if req.output_name.lower().endswith('.mp4') else req.output_name + '.mp4'
|
||
else:
|
||
out_name = f"{stem}_{int(req.start)}s-{int(req.end)}s.mp4"
|
||
|
||
out_path = os.path.join(wireframe_dir, out_name)
|
||
if os.path.exists(out_path):
|
||
raise HTTPException(409, f"File already exists: {out_name}")
|
||
|
||
r = subprocess.run(
|
||
['ffmpeg', '-y',
|
||
'-ss', str(req.start), '-to', str(req.end),
|
||
'-i', video_path,
|
||
'-c', 'copy',
|
||
out_path],
|
||
capture_output=True, timeout=120,
|
||
)
|
||
if r.returncode != 0:
|
||
raise HTTPException(500, f"ffmpeg error: {r.stderr.decode(errors='replace')[:500]}")
|
||
|
||
return {'output_name': out_name, 'start': req.start, 'end': req.end}
|
||
|
||
|
||
class FrameExtractRequest(BaseModel):
|
||
video_name: str
|
||
time: float = 0.0
|
||
|
||
|
||
@app.post("/wireframe/frame")
|
||
def wireframe_extract_frame(req: FrameExtractRequest):
|
||
"""Extract a single frame at a given timestamp and return it as base64 PNG."""
|
||
import base64
|
||
wireframe_dir = _load_wireframe_dir()
|
||
video_path = os.path.join(wireframe_dir, req.video_name)
|
||
if not os.path.exists(video_path):
|
||
raise HTTPException(404, f"Video not found: {req.video_name}")
|
||
try:
|
||
img = _extract_frame_at(video_path, req.time)
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
buf = io.BytesIO()
|
||
img.save(buf, format="PNG")
|
||
return {"frame_b64": base64.b64encode(buf.getvalue()).decode()}
|
||
|
||
|
||
class FaceswapRequest(BaseModel):
|
||
model_filename: str # image from output_dir to use as face source
|
||
video_name: str # filename of template video in wireframe_dir
|
||
enhance: bool = True # GFPGAN face restoration after each frame swap
|
||
hair: bool = False # use FaceFusion with hair_swapper (requires FaceFusion install)
|
||
preview_scale: float = 1.0 # 0.25–1.0; <1.0 produces a smaller preview video
|
||
|
||
|
||
@app.get("/faceswap/check")
|
||
def faceswap_check():
|
||
"""Report which enhancement backends are available."""
|
||
gfpgan_ok = False
|
||
try:
|
||
import gfpgan # noqa
|
||
gfpgan_ok = True
|
||
except ImportError:
|
||
pass
|
||
|
||
with open(CONFIG_PATH, 'r') as f:
|
||
conf = json.load(f)
|
||
ff_dir = os.path.expanduser(conf.get('facefusion_dir', '~/facefusion'))
|
||
ff_script = os.path.join(ff_dir, 'facefusion.py')
|
||
|
||
return {'gfpgan': gfpgan_ok, 'facefusion': os.path.exists(ff_script)}
|
||
|
||
|
||
@app.post("/faceswap")
|
||
def start_faceswap(req: FaceswapRequest):
|
||
output_dir = _load_output_dir()
|
||
wireframe_dir = _load_wireframe_dir()
|
||
|
||
src_path = os.path.join(output_dir, req.model_filename)
|
||
video_path = os.path.join(wireframe_dir, req.video_name)
|
||
|
||
if not os.path.exists(src_path):
|
||
raise HTTPException(404, f"Model image not found: {req.model_filename}")
|
||
if not os.path.exists(video_path):
|
||
raise HTTPException(404, f"Template video not found: {req.video_name}")
|
||
|
||
job_id = uuid.uuid4().hex[:8]
|
||
jobs[job_id] = {
|
||
"status": "running", "type": "faceswap",
|
||
"total": 1, "done": 0, "failed": 0,
|
||
"model": req.model_filename, "video": req.video_name,
|
||
}
|
||
|
||
if req.hair:
|
||
t = threading.Thread(
|
||
target=_faceswap_worker_ff,
|
||
args=(job_id, req.model_filename, req.video_name),
|
||
kwargs={'hair': True, 'enhance': req.enhance, 'preview_scale': req.preview_scale},
|
||
daemon=True,
|
||
)
|
||
else:
|
||
t = threading.Thread(
|
||
target=_faceswap_worker,
|
||
args=(job_id, req.model_filename, req.video_name),
|
||
kwargs={'enhance': req.enhance, 'preview_scale': req.preview_scale},
|
||
daemon=True,
|
||
)
|
||
t.start()
|
||
return {"job_id": job_id, "model": req.model_filename, "video": req.video_name}
|
||
|
||
|
||
# --- tagging routes ----------------------------------------------------------
|
||
|
||
class TagRequest(BaseModel):
|
||
filename: str
|
||
threshold: float = 0.35
|
||
max_tags: int = 8
|
||
group_id: str | None = None
|
||
|
||
|
||
@app.post("/tag")
|
||
def tag_image(req: TagRequest):
|
||
output_dir = _load_output_dir()
|
||
fpath = os.path.join(output_dir, req.filename)
|
||
if not os.path.exists(fpath):
|
||
raise HTTPException(404, "File not found in output dir")
|
||
try:
|
||
pil = Image.open(fpath)
|
||
tags = _run_tagger(pil, req.threshold)
|
||
clip_desc = _tags_to_name(tags, req.max_tags)
|
||
has_clothing = _detect_has_clothing(tags)
|
||
|
||
# Only assign a new name if the image doesn't already have one
|
||
existing = database.get_person(req.filename)
|
||
auto_name = (existing[0] if existing and existing[0] else None) or naming.generate_associative_name(tags)
|
||
|
||
# Save to DB
|
||
try:
|
||
embedding = embeddings.generate_embedding(fpath)
|
||
database.upsert_person(
|
||
req.filename, filepath=fpath, name=auto_name,
|
||
clip_description=clip_desc, tags=tags, embedding=embedding,
|
||
group_id=req.group_id, has_clothing=has_clothing,
|
||
)
|
||
except Exception as db_err:
|
||
print(f"Database error during tag: {db_err}")
|
||
|
||
return {"filename": req.filename, "clip_description": clip_desc, "tags": tags[:30], "has_clothing": has_clothing}
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
|
||
|
||
@app.get("/names")
|
||
def get_names():
|
||
try:
|
||
persons = database.list_persons()
|
||
return {p[0]: p[1] for p in persons if p[1]}
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
|
||
|
||
@app.post("/names/{filename}")
|
||
def set_name(filename: str, body: dict):
|
||
name = body.get("name", "")
|
||
try:
|
||
database.upsert_person(filename, name=name)
|
||
except Exception as db_err:
|
||
print(f"Database error in set_name: {db_err}")
|
||
|
||
_invalidate_static()
|
||
return {"filename": filename, "name": name}
|
||
|
||
|
||
# --- group routes ------------------------------------------------------------
|
||
|
||
@app.get("/groups")
|
||
def get_groups():
|
||
try:
|
||
persons = database.list_persons()
|
||
return {p[0]: p[2] for p in persons if p[2]}
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
|
||
|
||
class MergeRequest(BaseModel):
|
||
filenames: list[str]
|
||
group_id: str | None = None
|
||
|
||
|
||
@app.post("/groups/merge")
|
||
def merge_groups(req: MergeRequest):
|
||
gid = req.group_id or f"cg_{uuid.uuid4().hex[:8]}"
|
||
for fname in req.filenames:
|
||
try:
|
||
database.upsert_person(fname, group_id=gid)
|
||
except Exception as db_err:
|
||
print(f"Database error in merge: {db_err}")
|
||
|
||
# Write synchronously: the frontend reloads images.json immediately after this
|
||
# returns, so an async rebuild would race and show the pre-merge grouping.
|
||
_write_all_static()
|
||
return {"group_id": gid, "files": req.filenames}
|
||
|
||
|
||
class ExtractRequest(BaseModel):
|
||
filename: str
|
||
|
||
|
||
@app.post("/groups/extract")
|
||
def extract_from_group(req: ExtractRequest):
|
||
gid = f"solo:{req.filename}"
|
||
try:
|
||
database.upsert_person(req.filename, group_id=gid)
|
||
except Exception as db_err:
|
||
print(f"Database error in extract: {db_err}")
|
||
|
||
_write_all_static()
|
||
return {"filename": req.filename}
|
||
|
||
|
||
@app.get("/group-names")
|
||
def get_group_names():
|
||
try:
|
||
return database.get_all_group_names()
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
|
||
|
||
@app.post("/group-names/{group_id}")
|
||
def set_group_name(group_id: str, body: dict):
|
||
name = body.get("name", "").strip()
|
||
try:
|
||
database.set_group_name(group_id, name or None)
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
_invalidate_static()
|
||
return {"group_id": group_id, "name": name}
|
||
|
||
|
||
@app.get("/groups/{group_id}/order")
|
||
def get_group_order(group_id: str):
|
||
try:
|
||
rows = database.get_group_order(group_id)
|
||
return {"group_id": group_id, "filenames": [r[0] for r in rows]}
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
|
||
|
||
class GroupOrderRequest(BaseModel):
|
||
filenames: list[str]
|
||
|
||
|
||
@app.post("/groups/{group_id}/order")
|
||
def set_group_order(group_id: str, req: GroupOrderRequest):
|
||
try:
|
||
database.set_group_order(group_id, req.filenames)
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
_invalidate_static()
|
||
return {"group_id": group_id, "filenames": req.filenames}
|
||
|
||
|
||
@app.get("/similar/{filename}")
|
||
def get_similar(filename: str, limit: int = 10):
|
||
person = database.get_person(filename)
|
||
if not person or person[3] is None:
|
||
raise HTTPException(404, "Image or embedding not found")
|
||
|
||
embedding = person[3]
|
||
results = database.search_similar(embedding, limit=limit)
|
||
|
||
similar = []
|
||
for r in results:
|
||
# Avoid returning the same image as the most similar
|
||
if r[0] == filename:
|
||
continue
|
||
similar.append({
|
||
"filename": r[0],
|
||
"name": r[1],
|
||
"group_id": r[2],
|
||
"clip_description": r[3],
|
||
"distance": float(r[4])
|
||
})
|
||
|
||
return {"filename": filename, "similar": similar}
|
||
|
||
|
||
@app.post("/db/cleanup")
|
||
def db_cleanup():
|
||
"""Delete DB records for files that no longer exist on disk."""
|
||
output_dir = _load_output_dir()
|
||
persons = database.list_persons()
|
||
removed = []
|
||
for p in persons:
|
||
fpath = os.path.join(output_dir, p[0])
|
||
if not os.path.exists(fpath):
|
||
database.delete_person(p[0])
|
||
removed.append(p[0])
|
||
if removed:
|
||
_invalidate_static()
|
||
return {"removed": len(removed), "filenames": removed}
|
||
|
||
|
||
@app.get("/health")
|
||
def health():
|
||
try:
|
||
requests.get(f"{COMFY}/system_stats", timeout=5).raise_for_status()
|
||
return {"status": "ok", "comfy": COMFY}
|
||
except Exception as e:
|
||
raise HTTPException(503, f"ComfyUI unreachable at {COMFY}: {e}")
|
||
|
||
|
||
def _crop_to_bbox(pil_img: Image.Image, margin: int = 20, top_margin: int = 20, headroom: float = 0.05) -> Image.Image:
|
||
if pil_img.mode != 'RGBA':
|
||
return pil_img
|
||
|
||
alpha = pil_img.split()[-1]
|
||
bbox = alpha.getbbox()
|
||
if not bbox:
|
||
return pil_img
|
||
|
||
left, upper, right, lower = bbox
|
||
left = max(0, left - margin)
|
||
upper = max(0, upper - top_margin)
|
||
right = min(pil_img.width, right + margin)
|
||
lower = min(pil_img.height, lower + margin)
|
||
|
||
cropped = pil_img.crop((left, upper, right, lower))
|
||
if headroom > 0:
|
||
h_px = int(cropped.height * headroom)
|
||
if h_px > 0:
|
||
new_img = Image.new("RGBA", (cropped.width, cropped.height + h_px), (0, 0, 0, 0))
|
||
new_img.paste(cropped, (0, h_px))
|
||
return new_img
|
||
return cropped
|
||
|
||
|
||
def _extract_face_bg(filename: str, fpath: str):
|
||
"""Background task: detect largest face, crop with padding, save as {group_id}_face.png."""
|
||
try:
|
||
import cv2
|
||
app_fa, _ = _load_faceswapper()
|
||
bgr = cv2.imread(fpath)
|
||
if bgr is None:
|
||
print(f"[extract-face] cannot read {fpath}")
|
||
return
|
||
faces = app_fa.get(bgr)
|
||
if not faces:
|
||
print(f"[extract-face] no face detected in {filename}")
|
||
return
|
||
face = max(faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
|
||
x1, y1, x2, y2 = [int(v) for v in face.bbox]
|
||
h, w = bgr.shape[:2]
|
||
pad = int((y2 - y1) * 0.5)
|
||
x1 = max(0, x1 - pad)
|
||
y1 = max(0, y1 - pad * 2) # extra headroom above face
|
||
x2 = min(w, x2 + pad)
|
||
y2 = min(h, y2 + int(pad * 0.3))
|
||
pil = Image.open(fpath).convert("RGBA")
|
||
cropped = pil.crop((x1, y1, x2, y2))
|
||
person = database.get_person(filename)
|
||
group_id = person[1] if person else None
|
||
gid_tag = (group_id or "face").replace("/", "_")
|
||
face_fname = f"{gid_tag}_face.png"
|
||
face_path = os.path.join(os.path.dirname(fpath), face_fname)
|
||
cropped.save(face_path)
|
||
face_embed = face.normed_embedding.tolist() if hasattr(face, 'normed_embedding') and face.normed_embedding is not None else None
|
||
database.upsert_person(face_fname, filepath=face_path, group_id=group_id,
|
||
name=person[0] if person else None,
|
||
source_refs=json.dumps([filename]),
|
||
face_embedding=face_embed)
|
||
print(f"[extract-face] saved {face_fname}" + (" + face embedding" if face_embed else ""))
|
||
except Exception as e:
|
||
print(f"[extract-face] error for {filename}: {e}")
|
||
|
||
|
||
def _process_upload(file_path: str, filename: str, prompts: list[str], name: str | None = None, group_id: str | None = None, poses: list[str] | None = None):
|
||
output_dir = _load_output_dir()
|
||
try:
|
||
pil = Image.open(file_path)
|
||
|
||
# 1. CLIP tag the source
|
||
tags = _run_tagger(pil.convert("RGB"))
|
||
clip_desc = _tags_to_name(tags)
|
||
has_clothing = _detect_has_clothing(tags)
|
||
auto_name = name or naming.generate_associative_name(tags)
|
||
|
||
# 2. Embedding for source
|
||
embedding = embeddings.generate_embedding(file_path)
|
||
|
||
# 3. Register source in DB — sort_order=0 makes it the preferred base image
|
||
database.upsert_person(
|
||
filename, filepath=file_path, name=auto_name,
|
||
clip_description=clip_desc, tags=tags, embedding=embedding,
|
||
group_id=group_id, sort_order=0, has_clothing=has_clothing,
|
||
)
|
||
# Surface the new group with its base image right away — the pose/base-prompt
|
||
# generation below can take a while, and the user shouldn't wait for it to
|
||
# see the group land on the gallery.
|
||
_invalidate_static()
|
||
|
||
# 4. Crop if needed
|
||
cropped_pil = _crop_to_bbox(pil)
|
||
|
||
# 5. Run prompts
|
||
for i, prompt in enumerate(prompts):
|
||
try:
|
||
png = _run_pipeline(cropped_pil.convert("RGB"), prompt)
|
||
|
||
ts = time.strftime("%Y%m%d_%H%M%S")
|
||
out_name = f"{ts}_{i}_{filename}"
|
||
if not out_name.lower().endswith(".png"):
|
||
out_name += ".png"
|
||
out_path = os.path.join(output_dir, out_name)
|
||
|
||
with open(out_path, "wb") as f:
|
||
f.write(png)
|
||
|
||
out_embedding = embeddings.generate_embedding(out_path)
|
||
next_order = database.get_next_sort_order(group_id)
|
||
# Persist the prompt (and pose name, when this output came from a named pose)
|
||
# so the generation parameters survive in the DB / images.json.
|
||
out_pose = poses[i] if (poses and i < len(poses)) else None
|
||
database.upsert_person(
|
||
out_name, filepath=out_path, name=auto_name,
|
||
clip_description=clip_desc, embedding=out_embedding,
|
||
group_id=group_id, sort_order=next_order,
|
||
prompt=prompt, pose=out_pose,
|
||
)
|
||
except Exception as e:
|
||
print(f"Error processing prompt '{prompt}' for {filename}: {e}")
|
||
|
||
except Exception as e:
|
||
print(f"Error in _process_upload for {filename}: {e}")
|
||
finally:
|
||
_invalidate_static()
|
||
|
||
|
||
@app.post("/upload")
|
||
def upload_image(
|
||
background_tasks: BackgroundTasks,
|
||
image: UploadFile = File(...),
|
||
prompts: str = Form(""),
|
||
name: str = Form(None),
|
||
group_id: str = Form(None), # optional: add to existing group
|
||
skip_poses: bool = Form(False), # optional: skip base_prompts generation
|
||
):
|
||
# Load config to get output_dir (we use output_dir for UI uploads to avoid watcher conflict)
|
||
with open(CONFIG_PATH, "r") as f:
|
||
conf = json.load(f)
|
||
output_dir = _load_output_dir()
|
||
os.makedirs(output_dir, exist_ok=True)
|
||
|
||
ts = time.strftime("%Y%m%d_%H%M%S")
|
||
safe_filename = re.sub(r'[^a-zA-Z0-9_.-]', '_', image.filename or "paste")
|
||
# Ensure extension
|
||
if not safe_filename.lower().endswith(('.png', '.jpg', '.jpeg', '.webp')):
|
||
safe_filename += ".png"
|
||
|
||
filename = f"{ts}_{safe_filename}"
|
||
file_path = os.path.join(output_dir, filename)
|
||
|
||
with open(file_path, "wb") as f:
|
||
shutil.copyfileobj(image.file, f)
|
||
|
||
# Fast path: add to existing group without pose generation
|
||
if group_id and skip_poses:
|
||
sort_order = database.get_next_sort_order(group_id)
|
||
database.upsert_person(filename, filepath=file_path, group_id=group_id,
|
||
sort_order=sort_order)
|
||
_invalidate_static()
|
||
return {"status": "added", "filename": filename, "group_id": group_id}
|
||
|
||
prompt_list = [p.strip() for p in prompts.split(",") if p.strip()]
|
||
|
||
# Add base-set prompts if defined in config
|
||
base_prompts = conf.get("base_prompts", [])
|
||
if isinstance(base_prompts, list):
|
||
prompt_list.extend(base_prompts)
|
||
|
||
if not prompt_list:
|
||
# Use default prompt from config
|
||
prompt_list = [conf.get("prompt", "high quality, masterpiece")]
|
||
|
||
effective_gid = group_id or f"up_{uuid.uuid4().hex[:8]}"
|
||
background_tasks.add_task(_process_upload, file_path, filename, prompt_list, name, effective_gid)
|
||
|
||
return {"status": "processing", "filename": filename, "group_id": effective_gid, "prompts": prompt_list}
|
||
|
||
|
||
@app.post("/edit")
|
||
async def edit(
|
||
image: UploadFile = File(...),
|
||
prompt: str = Form(...),
|
||
seed: int = Form(-1),
|
||
steps: int = Form(4),
|
||
cfg: float = Form(1.0),
|
||
sampler_name: str = Form("euler_ancestral"),
|
||
scheduler: str = Form("beta"),
|
||
max_area: int = Form(0),
|
||
):
|
||
raw = await image.read()
|
||
try:
|
||
pil = Image.open(io.BytesIO(raw)).convert("RGB")
|
||
except Exception as e:
|
||
raise HTTPException(400, f"Invalid image: {e}")
|
||
|
||
png = _run_pipeline(pil, prompt, seed, max_area, steps, cfg, sampler_name, scheduler)
|
||
return Response(content=png, media_type="image/png")
|
||
|
||
|
||
@app.post("/images/{filename}/hidden")
|
||
def set_image_hidden(filename: str, body: dict):
|
||
hidden = bool(body.get("hidden", False))
|
||
try:
|
||
database.set_hidden(filename, hidden)
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
_invalidate_static()
|
||
return {"filename": filename, "hidden": hidden}
|
||
|
||
|
||
@app.post("/images/{filename}/archive")
|
||
def archive_image(filename: str):
|
||
try:
|
||
database.set_archived(filename, True)
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
_invalidate_static()
|
||
return {"filename": filename, "archived": True}
|
||
|
||
|
||
@app.post("/images/{filename}/unarchive")
|
||
def unarchive_image(filename: str):
|
||
try:
|
||
database.set_archived(filename, False)
|
||
except Exception as e:
|
||
raise HTTPException(500, str(e))
|
||
_invalidate_static()
|
||
return {"filename": filename, "archived": False}
|
||
|
||
|
||
@app.post("/images/{filename}/set-preferred")
|
||
def set_image_preferred(filename: str):
|
||
"""Make this image sort_order=0 within its group, shifting others to 1,2,..."""
|
||
person = database.get_person(filename)
|
||
if not person:
|
||
raise HTTPException(404, "Image not found")
|
||
group_id = person[1]
|
||
if not group_id:
|
||
raise HTTPException(400, "Image has no group assigned")
|
||
rows = database.get_group_order(group_id)
|
||
others = [r[0] for r in rows if r[0] != filename]
|
||
database.set_group_order(group_id, [filename] + others)
|
||
_invalidate_static()
|
||
fpath = os.path.join(_load_output_dir(), filename)
|
||
if os.path.exists(fpath):
|
||
_face_executor.submit(_extract_face_bg, filename, fpath)
|
||
return {"filename": filename, "group_id": group_id}
|
||
|
||
|
||
@app.post("/images/{filename}/extract-face")
|
||
def extract_face_endpoint(filename: str):
|
||
"""Detect and crop the largest face from image; saves as {group_id}_face.png."""
|
||
output_dir = _load_output_dir()
|
||
fpath = os.path.join(output_dir, filename)
|
||
if not os.path.exists(fpath):
|
||
raise HTTPException(404, "not found")
|
||
_face_executor.submit(_extract_face_bg, filename, fpath)
|
||
return {"status": "queued", "filename": filename}
|
||
|
||
|
||
class FaceSimilarRequest(BaseModel):
|
||
group_id: str
|
||
limit: int = 12
|
||
|
||
|
||
@app.post("/faces/similar")
|
||
def face_similar(req: FaceSimilarRequest):
|
||
"""Find groups with visually similar faces using insightface embeddings.
|
||
|
||
Looks up the face embedding stored for {group_id}_face.png and returns
|
||
the top-N closest matches from other groups.
|
||
"""
|
||
face_fname = f"{req.group_id.replace('/', '_')}_face.png"
|
||
embedding = database.get_face_embedding(face_fname)
|
||
if embedding is None:
|
||
raise HTTPException(404, "No face embedding found for this group — set a preferred image first")
|
||
|
||
rows = database.search_similar_face(embedding, limit=req.limit, exclude_group_id=req.group_id)
|
||
# Each row is (filename, group_id, distance). Return the group thumbnail filename
|
||
# (the _face.png itself) so the frontend can render it directly.
|
||
results = [
|
||
{"filename": r[0], "group_id": r[1], "distance": round(float(r[2]), 4)}
|
||
for r in rows
|
||
]
|
||
return {"similar": results}
|
||
|
||
|
||
_face_index_status: dict = {"running": False, "done": 0, "total": 0, "indexed": 0}
|
||
|
||
|
||
def _face_index_worker():
|
||
"""Backfill face embeddings for all *_face.png files that lack one."""
|
||
global _face_index_status
|
||
output_dir = _load_output_dir()
|
||
face_files = [f for f in os.listdir(output_dir) if f.endswith("_face.png")]
|
||
_face_index_status.update({"running": True, "done": 0, "total": len(face_files), "indexed": 0})
|
||
try:
|
||
import cv2
|
||
app_fa, _ = _load_faceswapper()
|
||
except Exception as e:
|
||
print(f"[face-index] failed to load insightface: {e}")
|
||
_face_index_status["running"] = False
|
||
return
|
||
indexed = 0
|
||
for i, fname in enumerate(face_files):
|
||
existing = database.get_face_embedding(fname)
|
||
if existing is not None:
|
||
_face_index_status["done"] = i + 1
|
||
continue
|
||
fpath = os.path.join(output_dir, fname)
|
||
try:
|
||
bgr = cv2.imread(fpath)
|
||
if bgr is None:
|
||
continue
|
||
faces = app_fa.get(bgr)
|
||
if not faces:
|
||
continue
|
||
face = max(faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
|
||
if not hasattr(face, 'normed_embedding') or face.normed_embedding is None:
|
||
continue
|
||
database.upsert_person(fname, face_embedding=face.normed_embedding.tolist())
|
||
indexed += 1
|
||
except Exception as e:
|
||
print(f"[face-index] {fname}: {e}")
|
||
_face_index_status.update({"done": i + 1, "indexed": indexed})
|
||
_face_index_status["running"] = False
|
||
print(f"[face-index] done: {indexed}/{len(face_files)} embeddings stored")
|
||
|
||
|
||
@app.post("/faces/index")
|
||
def build_face_index():
|
||
if _face_index_status.get("running"):
|
||
return {"status": "already_running", **_face_index_status}
|
||
threading.Thread(target=_face_index_worker, daemon=True).start()
|
||
return {"status": "started"}
|
||
|
||
|
||
@app.get("/faces/index/status")
|
||
def face_index_status():
|
||
return _face_index_status
|
||
|
||
|
||
@app.get("/faces/{group_id}")
|
||
def face_status(group_id: str):
|
||
"""Report whether a face crop exists for a group.
|
||
|
||
Face extraction runs asynchronously after "set preferred", so the studio
|
||
polls this (over HTTP, which works even when the page is opened via file://)
|
||
instead of guessing with an <img> load that 404s during the race window.
|
||
"""
|
||
face_fname = f"{group_id.replace('/', '_')}_face.png"
|
||
face_path = os.path.join(_load_output_dir(), face_fname)
|
||
return {"exists": os.path.exists(face_path), "filename": face_fname}
|
||
|
||
|
||
@app.post("/images/{filename}/undress")
|
||
def undress_image(filename: str, background_tasks: BackgroundTasks):
|
||
"""Queue a generation using the undress prompt on the given image."""
|
||
output_dir = _load_output_dir()
|
||
fpath = os.path.join(output_dir, filename)
|
||
if not os.path.exists(fpath):
|
||
raise HTTPException(404, "Image not found")
|
||
person = database.get_person(filename)
|
||
group_id = person[1] if person and person[1] else naming.get_base_name(filename)
|
||
job_id = uuid.uuid4().hex[:8]
|
||
jobs[job_id] = {"status": "queued", "done": 0, "failed": 0, "total": 1}
|
||
threading.Thread(
|
||
target=_batch_worker,
|
||
args=(job_id, [filename], [UNDRESS_PROMPT], [None],
|
||
random.randint(0, MAX_SEED), MAX_AREA),
|
||
kwargs={"group_id": group_id},
|
||
daemon=True,
|
||
).start()
|
||
return {"job_id": job_id, "filename": filename}
|
||
|
||
|
||
@app.delete("/images/{filename}")
|
||
def delete_image(filename: str):
|
||
person = database.get_person(filename)
|
||
if person and person[5] and os.path.exists(person[5]):
|
||
_move_to_trash(person[5])
|
||
|
||
database.delete_person(filename)
|
||
_invalidate_static()
|
||
return {"status": "deleted", "filename": filename}
|
||
|
||
|
||
@app.delete("/groups/{group_id}")
|
||
def delete_group(group_id: str):
|
||
files = database.get_group_files(group_id)
|
||
for filename, filepath in files:
|
||
if filepath and os.path.exists(filepath):
|
||
_move_to_trash(filepath)
|
||
|
||
database.delete_group(group_id)
|
||
_invalidate_static()
|
||
return {"status": "deleted", "group_id": group_id}
|
||
|
||
|
||
@app.post("/remove-background/{filename}")
|
||
def remove_background(filename: str):
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
|
||
path = person[5]
|
||
with open(path, "rb") as f:
|
||
png_bytes = f.read()
|
||
|
||
transparent_png = _apply_transparency(png_bytes)
|
||
|
||
with open(path, "wb") as f:
|
||
f.write(transparent_png)
|
||
|
||
# Persist the state + refresh static data so the flag (and No-BG/Crop buttons)
|
||
# survive a page reload instead of reverting to has_background=True.
|
||
database.upsert_person(filename, has_background=False)
|
||
_invalidate_static()
|
||
return {"status": "success", "filename": filename, "has_background": False}
|
||
|
||
|
||
@app.post("/images/{filename}/invert-alpha")
|
||
def invert_alpha(filename: str):
|
||
"""Invert the alpha channel in place — recovers cases where background removal
|
||
kept the background and dropped the subject (the wrong segment)."""
|
||
import numpy as np
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
path = person[5]
|
||
img = Image.open(path).convert("RGBA")
|
||
arr = np.array(img)
|
||
arr[:, :, 3] = 255 - arr[:, :, 3]
|
||
Image.fromarray(arr, "RGBA").save(path, format="PNG")
|
||
database.upsert_person(filename, has_background=False)
|
||
_invalidate_static()
|
||
return {"status": "success", "filename": filename}
|
||
|
||
|
||
@app.post("/remove-background/group/{group_id}")
|
||
def remove_background_group(group_id: str, background_tasks: BackgroundTasks):
|
||
def _bg_task():
|
||
files = database.get_group_files(group_id)
|
||
for filename, filepath in files:
|
||
if filepath and os.path.exists(filepath):
|
||
try:
|
||
with open(filepath, "rb") as f:
|
||
png_bytes = f.read()
|
||
transparent_png = _apply_transparency(png_bytes)
|
||
with open(filepath, "wb") as f:
|
||
f.write(transparent_png)
|
||
except Exception as e:
|
||
print(f"Error removing background for {filename}: {e}")
|
||
|
||
background_tasks.add_task(_bg_task)
|
||
return {"status": "processing", "group_id": group_id}
|
||
|
||
|
||
# --- scenery generation -------------------------------------------------------
|
||
|
||
def _extract_frame_at(video_path: str, t: float) -> Image.Image:
|
||
"""Extract a single frame at time t (seconds) from a video via ffmpeg."""
|
||
import subprocess as _sp
|
||
r = _sp.run(
|
||
['ffmpeg', '-y', '-ss', str(t), '-i', video_path,
|
||
'-frames:v', '1', '-f', 'image2pipe', '-vcodec', 'png', 'pipe:1'],
|
||
capture_output=True, timeout=15,
|
||
)
|
||
if r.returncode != 0 or not r.stdout:
|
||
raise ValueError(f"ffmpeg frame extract failed: {r.stderr.decode(errors='replace')[:300]}")
|
||
return Image.open(io.BytesIO(r.stdout)).convert("RGB")
|
||
|
||
|
||
class SceneryRequest(BaseModel):
|
||
model_filename: str # person image in output_dir → image2 (Picture 2)
|
||
scene_bytes: str | None = None # base64-encoded PNG/JPEG of the reference scene → image1
|
||
scene_video: str | None = None # wireframe video name to extract frame from
|
||
scene_time: float = 0.0 # timestamp (seconds) to extract from video
|
||
extra_filename: str | None = None # optional extra reference in output_dir → image3 (Picture 3)
|
||
prompt: str | None = None # override; auto-built if None
|
||
seed: int = -1
|
||
|
||
|
||
def _make_side_by_side(img1: Image.Image, img2: Image.Image,
|
||
max_h: int = 1024) -> Image.Image:
|
||
"""Combine two images side by side at the same height (capped at max_h)."""
|
||
target_h = min(max(img1.height, img2.height), max_h)
|
||
r1 = target_h / img1.height
|
||
r2 = target_h / img2.height
|
||
w1, w2 = int(img1.width * r1), int(img2.width * r2)
|
||
img1_r = img1.resize((w1, target_h), Image.LANCZOS)
|
||
img2_r = img2.resize((w2, target_h), Image.LANCZOS)
|
||
out = Image.new("RGB", (w1 + w2, target_h))
|
||
out.paste(img1_r, (0, 0))
|
||
out.paste(img2_r, (w1, 0))
|
||
return out
|
||
|
||
|
||
def _scenery_worker(job_id: str, model_filename: str, scene_pil: Image.Image,
|
||
prompt: str, seed: int, extra_pils: list | None = None):
|
||
output_dir = _load_output_dir()
|
||
try:
|
||
model_path = os.path.join(output_dir, model_filename)
|
||
model_pil = Image.open(model_path).convert("RGB")
|
||
|
||
# image1=scene (→ Picture 1, output sized to scene), image2=person (→ Picture 2),
|
||
# image3=optional extra ref (→ Picture 3). The node prepends "Picture 1: <img>
|
||
# Picture 2: <img> ..." to the prompt so the model can reason about each by name.
|
||
extra_images = [model_pil] + list(extra_pils or [])
|
||
png_bytes = _run_pipeline(
|
||
scene_pil.convert("RGB"), prompt, seed, MAX_AREA,
|
||
extra_images=extra_images,
|
||
)
|
||
ts = time.strftime("%Y%m%d_%H%M%S")
|
||
base_name = naming.get_base_name(model_filename)
|
||
# Ensure .png extension
|
||
if not base_name.lower().endswith('.png'):
|
||
base_name = os.path.splitext(base_name)[0] + '.png'
|
||
out_name = f"{ts}_sc_{base_name}"
|
||
out_path = os.path.join(output_dir, out_name)
|
||
with open(out_path, "wb") as f:
|
||
f.write(png_bytes)
|
||
person = database.get_person(model_filename)
|
||
group_id = person[1] if person and person[1] else naming.get_base_name(model_filename)
|
||
try:
|
||
embedding = embeddings.generate_embedding(out_path)
|
||
next_order = database.get_next_sort_order(group_id)
|
||
database.upsert_person(out_name, filepath=out_path, embedding=embedding,
|
||
group_id=group_id, prompt=prompt,
|
||
sort_order=next_order,
|
||
source_refs=json.dumps([model_filename]))
|
||
except Exception as db_err:
|
||
print(f"[scenery] DB error: {db_err}")
|
||
jobs[job_id]["status"] = "done"
|
||
jobs[job_id]["output"] = out_name
|
||
# Regenerate the static JSON so the frontend's polling surfaces the new
|
||
# image immediately (matching _batch_worker / _multi_ref_worker). Without
|
||
# this the output only appeared after a server restart.
|
||
_invalidate_static()
|
||
except Exception as e:
|
||
print(f"[scenery] error: {e}")
|
||
jobs[job_id]["status"] = "error"
|
||
jobs[job_id]["error"] = str(e)
|
||
|
||
|
||
@app.post("/generate-scenery")
|
||
def generate_scenery(req: SceneryRequest):
|
||
output_dir = _load_output_dir()
|
||
wireframe_dir = _load_wireframe_dir()
|
||
|
||
model_path = os.path.join(output_dir, req.model_filename)
|
||
if not os.path.exists(model_path):
|
||
raise HTTPException(404, f"Model image not found: {req.model_filename}")
|
||
|
||
# Resolve scene image
|
||
if req.scene_bytes:
|
||
import base64
|
||
raw = base64.b64decode(req.scene_bytes)
|
||
scene_pil = Image.open(io.BytesIO(raw)).convert("RGB")
|
||
elif req.scene_video:
|
||
video_path = os.path.join(wireframe_dir, req.scene_video)
|
||
if not os.path.exists(video_path):
|
||
raise HTTPException(404, f"Scene video not found: {req.scene_video}")
|
||
try:
|
||
scene_pil = _extract_frame_at(video_path, req.scene_time)
|
||
except Exception as e:
|
||
raise HTTPException(500, f"Frame extraction failed: {e}")
|
||
else:
|
||
raise HTTPException(400, "Provide scene_bytes or scene_video")
|
||
|
||
# Optional image3 (Picture 3) — extra reference from output_dir
|
||
extra_pils = []
|
||
if req.extra_filename:
|
||
extra_path = os.path.join(output_dir, req.extra_filename)
|
||
if not os.path.exists(extra_path):
|
||
raise HTTPException(404, f"Extra reference not found: {req.extra_filename}")
|
||
extra_pils.append(Image.open(extra_path).convert("RGB"))
|
||
|
||
prompt = req.prompt or (
|
||
"Place the person from Picture 2 naturally inside the environment shown in Picture 1. "
|
||
"Keep the person's face, body proportions, clothing and pose exactly as in Picture 2. "
|
||
"Use the location, lighting and atmosphere from Picture 1 as the background. "
|
||
"Match the color temperature and shadows so it looks like one photograph taken on location. "
|
||
+ ("Incorporate the reference from Picture 3 as well. " if extra_pils else "")
|
||
+ "Output a single photorealistic image. High quality, detailed."
|
||
)
|
||
|
||
job_id = uuid.uuid4().hex[:8]
|
||
jobs[job_id] = {"status": "running", "type": "scenery", "total": 1, "done": 0, "failed": 0}
|
||
threading.Thread(
|
||
target=_scenery_worker,
|
||
args=(job_id, req.model_filename, scene_pil, prompt, req.seed, extra_pils),
|
||
daemon=True,
|
||
).start()
|
||
return {"job_id": job_id, "model": req.model_filename}
|
||
|
||
|
||
# --- SAM2 background removal --------------------------------------------------
|
||
|
||
_sam2_predictor = None
|
||
_sam2_predictor_lock = threading.Lock()
|
||
|
||
|
||
def _load_sam2():
|
||
"""Lazy-load SAM2 image predictor. Returns predictor or False if unavailable."""
|
||
global _sam2_predictor
|
||
if _sam2_predictor is not None:
|
||
return _sam2_predictor
|
||
with _sam2_predictor_lock:
|
||
if _sam2_predictor is not None:
|
||
return _sam2_predictor
|
||
try:
|
||
from sam2.build_sam import build_sam2
|
||
from sam2.sam2_image_predictor import SAM2ImagePredictor
|
||
with open(CONFIG_PATH) as f:
|
||
conf = json.load(f)
|
||
ckpt = os.path.expanduser(conf.get("sam2_checkpoint", "~/.sam/sam2.1_hiera_base_plus.pt"))
|
||
cfg = conf.get("sam2_config", "configs/sam2.1/sam2.1_hiera_t.yaml")
|
||
if not os.path.exists(ckpt):
|
||
raise FileNotFoundError(f"SAM2 checkpoint not found: {ckpt}")
|
||
model = build_sam2(cfg, ckpt, device="cuda")
|
||
_sam2_predictor = SAM2ImagePredictor(model)
|
||
print(f"[sam2] loaded from {ckpt}")
|
||
except Exception as e:
|
||
print(f"[sam2] not available: {e}")
|
||
_sam2_predictor = False
|
||
return _sam2_predictor
|
||
|
||
|
||
def _person_mask_score(mask, h: int, w: int):
|
||
"""Rate how much `mask` looks like a centered subject vs. the background.
|
||
|
||
The subject (person) sits in the middle of the frame and rarely fills the
|
||
corners; the background is the opposite — it hugs the corners and is sparse
|
||
in the center. So `center_cov - corner_cov` is strongly positive for a
|
||
correct person mask and negative when SAM2 has selected the background
|
||
instead (the inverted-mask failure mode).
|
||
|
||
Returns (score, center_cov, corner_cov), all floats in [-1, 1] / [0, 1].
|
||
"""
|
||
import numpy as np
|
||
cy0, cy1 = int(h * 0.30), int(h * 0.70)
|
||
cx0, cx1 = int(w * 0.30), int(w * 0.70)
|
||
center_cov = float(mask[cy0:cy1, cx0:cx1].mean())
|
||
cs = max(1, int(min(h, w) * 0.10))
|
||
corner_cov = float(np.mean([
|
||
mask[:cs, :cs].mean(), mask[:cs, -cs:].mean(),
|
||
mask[-cs:, :cs].mean(), mask[-cs:, -cs:].mean(),
|
||
]))
|
||
return center_cov - corner_cov, center_cov, corner_cov
|
||
|
||
|
||
def _apply_transparency_sam2(png_bytes: bytes) -> bytes:
|
||
"""Remove background with SAM2 bbox segmentation; fallback to rembg.
|
||
|
||
Uses a near-full-frame bbox so SAM2 finds the largest foreground object
|
||
(the person) regardless of rotation or pose. This works well because
|
||
"transparent background" is stripped from the Qwen prompt upstream, so the
|
||
model renders a solid real background — giving SAM2 clear contrast to work
|
||
with. Point prompts were tried but produced holes in ¾-rotated poses
|
||
because the spine-column seeds land on background when the body is offset.
|
||
"""
|
||
predictor = _load_sam2()
|
||
if predictor is False:
|
||
return _apply_transparency(png_bytes)
|
||
try:
|
||
import numpy as np
|
||
import torch
|
||
img = Image.open(io.BytesIO(png_bytes)).convert("RGB")
|
||
arr = np.array(img)
|
||
h, w = arr.shape[:2]
|
||
|
||
# Near-full-frame bbox — 1 % margin so hair / shoes are inside the hint.
|
||
# SAM2 treats this as "find the prominent object within this region".
|
||
box = np.array([[int(w * 0.01), int(h * 0.01),
|
||
int(w * 0.99), int(h * 0.99)]], dtype=np.float32)
|
||
|
||
with torch.inference_mode():
|
||
predictor.set_image(arr)
|
||
masks, scores, _ = predictor.predict(
|
||
box=box,
|
||
multimask_output=True,
|
||
)
|
||
|
||
if masks is None or len(masks) == 0:
|
||
print("[sam2] no masks returned, falling back to rembg")
|
||
return _apply_transparency(png_bytes)
|
||
|
||
# Pick the candidate that best matches a centered subject rather than
|
||
# blindly trusting argmax(scores): on busy or low-contrast backgrounds
|
||
# the top-confidence SAM2 mask is sometimes the background itself.
|
||
# Combine the centered-subject prior with SAM2's own confidence.
|
||
best = None
|
||
best_rank = -1e9
|
||
for i in range(len(masks)):
|
||
m = masks[i].astype(bool)
|
||
psc, _, _ = _person_mask_score(m, h, w)
|
||
rank = psc + 0.10 * float(scores[i])
|
||
if rank > best_rank:
|
||
best_rank, best = rank, m
|
||
|
||
# Inversion guard — the user's hint: the model is in the center. If the
|
||
# chosen mask still covers the corners more than the center, SAM2 picked
|
||
# the background; flip the alpha so the person stays opaque.
|
||
psc, ccov, kcov = _person_mask_score(best, h, w)
|
||
if psc < 0:
|
||
print(f"[sam2] mask inverted (center {ccov:.0%} < corners {kcov:.0%}) — flipping alpha")
|
||
best = ~best
|
||
|
||
# Sanity check: a person should cover 5 %–92 % of the frame
|
||
coverage = float(best.sum()) / (h * w)
|
||
if coverage < 0.05 or coverage > 0.92:
|
||
print(f"[sam2] mask coverage {coverage:.1%} out of range, falling back to rembg")
|
||
return _apply_transparency(png_bytes)
|
||
|
||
mask_np = best.astype(np.uint8) * 255
|
||
|
||
# Soft anti-aliased edge (radius 1 keeps accessory detail)
|
||
try:
|
||
from PIL import ImageFilter
|
||
alpha_img = Image.fromarray(mask_np, mode="L")
|
||
alpha_img = alpha_img.filter(ImageFilter.GaussianBlur(radius=1))
|
||
except Exception:
|
||
alpha_img = Image.fromarray(mask_np, mode="L")
|
||
|
||
rgba = img.convert("RGBA")
|
||
r, g, b, _ = rgba.split()
|
||
out = Image.merge("RGBA", (r, g, b, alpha_img))
|
||
buf = io.BytesIO()
|
||
out.save(buf, format="PNG")
|
||
print(f"[sam2] mask OK ({coverage:.1%} coverage)")
|
||
return buf.getvalue()
|
||
except Exception as e:
|
||
print(f"[sam2] inference error, falling back to rembg: {e}")
|
||
return _apply_transparency(png_bytes)
|
||
|
||
|
||
def _apply_transparency_black_bg(png_bytes: bytes) -> bytes:
|
||
"""Background removal for black-background Qwen output (bg_removal=sam2 mode).
|
||
|
||
Strategy:
|
||
1. Threshold: any pixel with max-channel > 25 is person (non-black).
|
||
This correctly identifies the subject regardless of pose or rotation.
|
||
2. Derive a tight person bounding-box from the threshold mask.
|
||
3. Run SAM2 with that box for sub-pixel edge refinement.
|
||
Accept SAM2 result only when its coverage is close (±30 pp) to the
|
||
threshold estimate — this rejects the inverted-mask failure mode where
|
||
SAM2 picks the large dark region as the "object".
|
||
4. Fall back to the threshold mask (Gaussian-blurred edges) if SAM2
|
||
is unavailable, errors, or diverges.
|
||
|
||
Do NOT use the full-frame bbox here: on black-background images the large
|
||
dark region scores higher than the person, causing SAM2 to invert the mask.
|
||
"""
|
||
import numpy as np
|
||
import torch
|
||
from PIL import ImageFilter
|
||
|
||
img = Image.open(io.BytesIO(png_bytes)).convert("RGB")
|
||
arr = np.array(img)
|
||
h, w = arr.shape[:2]
|
||
|
||
# Step 1 — threshold: non-black pixels are the person
|
||
is_person = np.max(arr, axis=2) > 25
|
||
thresh_cov = float(is_person.sum()) / (h * w)
|
||
print(f"[bg-black] threshold coverage: {thresh_cov:.1%}")
|
||
|
||
if not is_person.any():
|
||
print("[bg-black] all-black image — falling back to rembg")
|
||
return _apply_transparency(png_bytes)
|
||
|
||
# Step 2 — tight bounding box from threshold
|
||
rows = np.any(is_person, axis=1)
|
||
cols = np.any(is_person, axis=0)
|
||
rmin = int(np.where(rows)[0][0]); rmax = int(np.where(rows)[0][-1])
|
||
cmin = int(np.where(cols)[0][0]); cmax = int(np.where(cols)[0][-1])
|
||
margin = int(min(h, w) * 0.02)
|
||
x1 = max(0, cmin - margin); y1 = max(0, rmin - margin)
|
||
x2 = min(w, cmax + margin); y2 = min(h, rmax + margin)
|
||
|
||
# Step 3 — SAM2 with the person-specific bbox
|
||
predictor = _load_sam2()
|
||
if predictor is not False:
|
||
box = np.array([[x1, y1, x2, y2]], dtype=np.float32)
|
||
try:
|
||
with torch.inference_mode():
|
||
predictor.set_image(arr)
|
||
masks, scores, _ = predictor.predict(box=box, multimask_output=True)
|
||
|
||
if masks is not None and len(masks) > 0:
|
||
best = masks[int(np.argmax(scores))]
|
||
sam_cov = float(best.sum()) / (h * w)
|
||
print(f"[bg-black] SAM2 coverage: {sam_cov:.1%}")
|
||
|
||
if 0.03 < sam_cov < 0.95 and abs(sam_cov - thresh_cov) < 0.30:
|
||
mask_np = best.astype(np.uint8) * 255
|
||
alpha_img = Image.fromarray(mask_np, "L").filter(ImageFilter.GaussianBlur(radius=1))
|
||
rgba = img.convert("RGBA"); r, g, b, _ = rgba.split()
|
||
out = Image.merge("RGBA", (r, g, b, alpha_img))
|
||
buf = io.BytesIO(); out.save(buf, "PNG")
|
||
print(f"[bg-black] SAM2 accepted ✓")
|
||
return buf.getvalue()
|
||
else:
|
||
print(f"[bg-black] SAM2 diverged ({sam_cov:.1%} vs {thresh_cov:.1%}) — threshold fallback")
|
||
except Exception as e:
|
||
print(f"[bg-black] SAM2 error: {e} — threshold fallback")
|
||
|
||
# Step 4 — fallback: threshold mask with soft edge blur
|
||
print("[bg-black] using threshold mask")
|
||
mask_np = is_person.astype(np.uint8) * 255
|
||
alpha_img = Image.fromarray(mask_np, "L").filter(ImageFilter.GaussianBlur(radius=2))
|
||
rgba = img.convert("RGBA"); r, g, b, _ = rgba.split()
|
||
out = Image.merge("RGBA", (r, g, b, alpha_img))
|
||
buf = io.BytesIO(); out.save(buf, "PNG")
|
||
return buf.getvalue()
|
||
|
||
|
||
@app.post("/remove-background-sam/{filename}")
|
||
def remove_background_sam(filename: str):
|
||
"""SAM2-based background removal.
|
||
|
||
Writes the transparent result as a sidecar <stem>.nobg.png alongside the
|
||
original, which is left untouched. Returns the sidecar URL so the UI can
|
||
switch the viewer without touching the source file.
|
||
Falls back to rembg when SAM2 is unavailable.
|
||
"""
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
path = person[5]
|
||
output_dir = os.path.dirname(path)
|
||
stem = os.path.splitext(filename)[0]
|
||
nobg_filename = f"{stem}.nobg.png"
|
||
nobg_path = os.path.join(output_dir, nobg_filename)
|
||
|
||
with open(path, "rb") as f:
|
||
png_bytes = f.read()
|
||
transparent_png = _apply_transparency_sam2(png_bytes)
|
||
with open(nobg_path, "wb") as f:
|
||
f.write(transparent_png)
|
||
|
||
# Register sidecar in DB so it appears in the same group
|
||
group_id = person[1]
|
||
database.upsert_person(nobg_filename, filepath=nobg_path,
|
||
group_id=group_id, has_background=False)
|
||
|
||
used_sam2 = _sam2_predictor is not False and _sam2_predictor is not None
|
||
return {
|
||
"status": "success",
|
||
"filename": filename,
|
||
"nobg_filename": nobg_filename,
|
||
"nobg_url": f"/output/{nobg_filename}",
|
||
"used_sam2": used_sam2,
|
||
}
|
||
|
||
|
||
@app.post("/images/{filename}/autocrop")
|
||
def autocrop_image(filename: str):
|
||
"""Crop away transparent borders from an image in-place."""
|
||
import numpy as np
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
path = person[5]
|
||
img = Image.open(path).convert("RGBA")
|
||
arr = np.array(img)
|
||
alpha = arr[:, :, 3]
|
||
rows = np.any(alpha > 0, axis=1)
|
||
cols = np.any(alpha > 0, axis=0)
|
||
if not rows.any():
|
||
raise HTTPException(400, "Image is fully transparent")
|
||
rmin, rmax = np.where(rows)[0][[0, -1]]
|
||
cmin, cmax = np.where(cols)[0][[0, -1]]
|
||
cropped = img.crop((cmin, rmin, cmax + 1, rmax + 1))
|
||
cropped.save(path, format="PNG")
|
||
return {"status": "success", "filename": filename, "box": [int(cmin), int(rmin), int(cmax+1), int(rmax+1)]}
|
||
|
||
|
||
class CropRequest(BaseModel):
|
||
x1: int
|
||
y1: int
|
||
x2: int
|
||
y2: int
|
||
as_copy: bool = False # True → crop a fresh copy, leaving the original untouched
|
||
|
||
|
||
@app.post("/images/{filename}/crop")
|
||
def manual_crop_image(filename: str, req: CropRequest):
|
||
"""Crop the image to the given pixel rectangle (in original image coordinates).
|
||
|
||
By default the crop is applied in-place. When ``as_copy`` is set, a new copy is
|
||
created first (referencing the original via ``source_refs``) and the crop is applied
|
||
to that copy, so the original is preserved.
|
||
"""
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
src_path = person[5]
|
||
|
||
if req.as_copy:
|
||
# Mirror duplicate_image: copy file + register a DB row that points back to the original.
|
||
from datetime import datetime as _dt
|
||
output_dir = os.path.dirname(src_path)
|
||
ext = os.path.splitext(filename)[1] or ".png"
|
||
stem = os.path.splitext(filename)[0]
|
||
ts = _dt.now().strftime("%Y%m%d_%H%M%S")
|
||
new_filename = f"{ts}_crop_{stem}{ext}"
|
||
path = os.path.join(output_dir, new_filename)
|
||
shutil.copy2(src_path, path)
|
||
database.upsert_person(
|
||
new_filename, filepath=path, group_id=person[1],
|
||
prompt=person[6], pose=person[7],
|
||
has_background=person[11], has_clothing=person[13],
|
||
source_refs=json.dumps([filename]), # original is the reference
|
||
)
|
||
else:
|
||
new_filename = filename
|
||
path = src_path
|
||
|
||
img = Image.open(path)
|
||
w, h = img.size
|
||
x1 = max(0, min(req.x1, w))
|
||
y1 = max(0, min(req.y1, h))
|
||
x2 = max(0, min(req.x2, w))
|
||
y2 = max(0, min(req.y2, h))
|
||
if x2 <= x1 or y2 <= y1:
|
||
if req.as_copy:
|
||
# Roll back the copy we just made so a bad rect doesn't leave an orphan.
|
||
try:
|
||
database.delete_person(new_filename)
|
||
os.remove(path)
|
||
except Exception:
|
||
pass
|
||
raise HTTPException(400, "Invalid crop rectangle")
|
||
cropped = img.crop((x1, y1, x2, y2))
|
||
fmt = "PNG" if path.lower().endswith(".png") else "JPEG"
|
||
cropped.save(path, format=fmt)
|
||
if req.as_copy:
|
||
_invalidate_static()
|
||
return {"status": "success", "filename": filename, "new_filename": new_filename,
|
||
"new_url": f"/output/{new_filename}", "as_copy": req.as_copy,
|
||
"box": [x1, y1, x2, y2]}
|
||
|
||
|
||
class RotateRequest(BaseModel):
|
||
degrees: int = 90 # clockwise rotation; must be a multiple of 90
|
||
|
||
|
||
@app.post("/images/{filename}/rotate")
|
||
def rotate_image(filename: str, req: RotateRequest):
|
||
"""Rotate an image clockwise in 90° steps, in place (lossless transpose)."""
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
path = person[5]
|
||
deg = req.degrees % 360
|
||
if deg not in (0, 90, 180, 270):
|
||
raise HTTPException(400, "degrees must be a multiple of 90")
|
||
if deg:
|
||
# PIL transpose is defined counter-clockwise; map clockwise degrees onto it.
|
||
cw_to_transpose = {
|
||
90: Image.Transpose.ROTATE_270,
|
||
180: Image.Transpose.ROTATE_180,
|
||
270: Image.Transpose.ROTATE_90,
|
||
}
|
||
img = Image.open(path).transpose(cw_to_transpose[deg])
|
||
fmt = "PNG" if path.lower().endswith(".png") else "JPEG"
|
||
img.save(path, format=fmt)
|
||
return {"status": "success", "filename": filename, "degrees": deg}
|
||
|
||
|
||
@app.post("/images/{filename}/duplicate")
|
||
def duplicate_image(filename: str):
|
||
"""Copy an image into the same group with a fresh timestamp-based filename."""
|
||
import shutil as _shutil
|
||
from datetime import datetime as _dt
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
path = person[5]
|
||
output_dir = os.path.dirname(path)
|
||
ext = os.path.splitext(filename)[1] or ".png"
|
||
ts = _dt.now().strftime("%Y%m%d_%H%M%S")
|
||
stem = os.path.splitext(filename)[0]
|
||
new_filename = f"{ts}_dup_{stem}{ext}"
|
||
new_path = os.path.join(output_dir, new_filename)
|
||
_shutil.copy2(path, new_path)
|
||
group_id = person[1]
|
||
# person tuple: (name, group_id, tags, embedding, clip_description, filepath,
|
||
# prompt, pose, sort_order, group_name, hidden, has_background, source_refs, has_clothing)
|
||
database.upsert_person(
|
||
new_filename, filepath=new_path, group_id=group_id,
|
||
prompt=person[6], pose=person[7],
|
||
has_background=person[11],
|
||
has_clothing=person[13],
|
||
source_refs=json.dumps([filename]), # original is the reference
|
||
)
|
||
_invalidate_static()
|
||
return {"status": "success", "new_filename": new_filename, "new_url": f"/output/{new_filename}"}
|
||
|
||
|
||
@app.post("/restore-background/{filename}")
|
||
def restore_background(filename: str):
|
||
"""Flatten RGBA → RGB (white composite), making the image opaque again."""
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
path = person[5]
|
||
img = Image.open(path)
|
||
if img.mode == "RGBA":
|
||
bg = Image.new("RGB", img.size, (255, 255, 255))
|
||
bg.paste(img, mask=img.split()[3])
|
||
buf = io.BytesIO()
|
||
bg.save(buf, format="PNG")
|
||
with open(path, "wb") as f:
|
||
f.write(buf.getvalue())
|
||
return {"status": "success", "filename": filename}
|
||
|
||
|
||
@app.get("/sam2/check")
|
||
def sam2_check():
|
||
"""Return whether SAM2 is available."""
|
||
predictor = _load_sam2()
|
||
return {"sam2": predictor is not False and predictor is not None}
|
||
|
||
|
||
# --- 2D body-pose preview -----------------------------------------------------
|
||
# Estimates COCO-17 keypoints from the model image so the UI can overlay a
|
||
# posenet-style skeleton. Estimator is feature-detected: rtmlib (ONNX, reuses the
|
||
# already-installed onnxruntime) is preferred, mediapipe is a fallback. If neither
|
||
# is installed the endpoints report unavailable instead of erroring the request.
|
||
|
||
_pose_estimator = None # cached (callable, backend_name) or False if unavailable
|
||
_pose_lock = threading.Lock()
|
||
|
||
# COCO-17 keypoint names (the order rtmlib's Body model returns).
|
||
POSE_KEYPOINT_NAMES = [
|
||
"nose", "left_eye", "right_eye", "left_ear", "right_ear",
|
||
"left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
|
||
"left_wrist", "right_wrist", "left_hip", "right_hip",
|
||
"left_knee", "right_knee", "left_ankle", "right_ankle",
|
||
]
|
||
# Bone connections (index pairs into COCO-17) for drawing the skeleton.
|
||
POSE_SKELETON = [
|
||
(5, 7), (7, 9), (6, 8), (8, 10), # arms
|
||
(11, 13), (13, 15), (12, 14), (14, 16), # legs
|
||
(5, 6), (11, 12), (5, 11), (6, 12), # torso
|
||
(0, 1), (0, 2), (1, 3), (2, 4), (0, 5), (0, 6), # head/neck
|
||
]
|
||
# mediapipe Pose (33 landmarks) → COCO-17 index map.
|
||
_MP_TO_COCO = [0, 2, 5, 7, 8, 11, 12, 13, 14, 15, 16, 23, 24, 25, 26, 27, 28]
|
||
|
||
|
||
def _load_pose_estimator():
|
||
global _pose_estimator
|
||
if _pose_estimator is not None:
|
||
return _pose_estimator
|
||
with _pose_lock:
|
||
if _pose_estimator is not None:
|
||
return _pose_estimator
|
||
# Preferred: rtmlib (RTMPose, ONNX) — returns COCO-17 directly.
|
||
try:
|
||
from rtmlib import Body
|
||
import numpy as np
|
||
model = Body(mode="balanced", backend="onnxruntime", device="cpu")
|
||
|
||
def _infer_rtm(pil):
|
||
bgr = np.array(pil.convert("RGB"))[:, :, ::-1]
|
||
kpts, scores = model(bgr) # (N,17,2), (N,17)
|
||
people = []
|
||
for person_kpts, person_scores in zip(kpts, scores):
|
||
people.append([[float(x), float(y), float(s)]
|
||
for (x, y), s in zip(person_kpts, person_scores)])
|
||
return people
|
||
|
||
_pose_estimator = (_infer_rtm, "rtmlib")
|
||
print("[pose] using rtmlib (RTMPose)")
|
||
return _pose_estimator
|
||
except Exception as e:
|
||
print(f"[pose] rtmlib unavailable: {e}")
|
||
|
||
# Fallback: mediapipe Pose (single person, normalized landmarks).
|
||
try:
|
||
import mediapipe as mp
|
||
import numpy as np
|
||
mp_pose = mp.solutions.pose.Pose(static_image_mode=True, model_complexity=2)
|
||
|
||
def _infer_mp(pil):
|
||
rgb = np.array(pil.convert("RGB"))
|
||
h, w = rgb.shape[:2]
|
||
res = mp_pose.process(rgb)
|
||
if not res.pose_landmarks:
|
||
return []
|
||
lm = res.pose_landmarks.landmark
|
||
kpts = []
|
||
for mp_idx in _MP_TO_COCO:
|
||
p = lm[mp_idx]
|
||
kpts.append([float(p.x * w), float(p.y * h), float(p.visibility)])
|
||
return [kpts]
|
||
|
||
_pose_estimator = (_infer_mp, "mediapipe")
|
||
print("[pose] using mediapipe Pose")
|
||
return _pose_estimator
|
||
except Exception as e:
|
||
print(f"[pose] mediapipe unavailable: {e}")
|
||
|
||
_pose_estimator = False
|
||
return _pose_estimator
|
||
|
||
|
||
# --- pose similarity (descriptor + index) -------------------------------------
|
||
# Pose descriptors are normalized (translation + scale invariant) COCO-17 vectors,
|
||
# cached in <output>/_data/poses_index.json so we can rank library images by pose.
|
||
|
||
_POSE_MIN_SCORE = 0.3
|
||
# Left/right keypoint pairs for the mirror-invariant distance.
|
||
_POSE_MIRROR = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
|
||
_pose_index_status = {"running": False, "done": 0, "total": 0}
|
||
|
||
|
||
def _pose_descriptor(keypoints):
|
||
"""Normalize one person's COCO-17 keypoints into a translation/scale-invariant
|
||
descriptor: {"vec": [34 floats], "vis": [17 ints]}. Returns None if too sparse."""
|
||
vis = [1 if (kp[2] >= _POSE_MIN_SCORE) else 0 for kp in keypoints]
|
||
if sum(vis) < 6:
|
||
return None
|
||
|
||
def _mid(a, b):
|
||
if vis[a] and vis[b]:
|
||
return ((keypoints[a][0] + keypoints[b][0]) / 2.0,
|
||
(keypoints[a][1] + keypoints[b][1]) / 2.0)
|
||
return None
|
||
|
||
hip = _mid(11, 12)
|
||
sho = _mid(5, 6)
|
||
center = hip or sho
|
||
if center is None:
|
||
return None
|
||
# Scale by torso length; fall back to keypoint spread if torso isn't visible.
|
||
if hip and sho:
|
||
scale = ((hip[0] - sho[0]) ** 2 + (hip[1] - sho[1]) ** 2) ** 0.5
|
||
else:
|
||
xs = [keypoints[i][0] for i in range(17) if vis[i]]
|
||
ys = [keypoints[i][1] for i in range(17) if vis[i]]
|
||
scale = max(max(xs) - min(xs), max(ys) - min(ys))
|
||
if not scale or scale < 1e-3:
|
||
return None
|
||
|
||
vec = []
|
||
for i in range(17):
|
||
if vis[i]:
|
||
vec.append((keypoints[i][0] - center[0]) / scale)
|
||
vec.append((keypoints[i][1] - center[1]) / scale)
|
||
else:
|
||
vec.extend([0.0, 0.0])
|
||
return {"vec": vec, "vis": vis}
|
||
|
||
|
||
def _pose_distance(a, b):
|
||
"""Weighted L2 between two descriptors over jointly-visible joints, taking the
|
||
min of the direct and left-right-mirrored comparison. Lower = more similar."""
|
||
def _dist(av, avis, bv, bvis, mirror):
|
||
total, n = 0.0, 0
|
||
for i in range(17):
|
||
j = _POSE_MIRROR[i] if mirror else i
|
||
if not (avis[i] and bvis[j]):
|
||
continue
|
||
bx = bv[j * 2] * (-1 if mirror else 1) # flip x when mirrored
|
||
by = bv[j * 2 + 1]
|
||
dx = av[i * 2] - bx
|
||
dy = av[i * 2 + 1] - by
|
||
total += dx * dx + dy * dy
|
||
n += 1
|
||
return (total / n) ** 0.5 if n >= 4 else float("inf")
|
||
direct = _dist(a["vec"], a["vis"], b["vec"], b["vis"], False)
|
||
mirror = _dist(a["vec"], a["vis"], b["vec"], b["vis"], True)
|
||
return min(direct, mirror)
|
||
|
||
|
||
def _best_person(people):
|
||
"""Pick the largest-bbox person from an estimator result (most prominent subject)."""
|
||
best, best_area = None, -1.0
|
||
for kpts in people:
|
||
xs = [k[0] for k in kpts if k[2] >= _POSE_MIN_SCORE]
|
||
ys = [k[1] for k in kpts if k[2] >= _POSE_MIN_SCORE]
|
||
if len(xs) < 2:
|
||
continue
|
||
area = (max(xs) - min(xs)) * (max(ys) - min(ys))
|
||
if area > best_area:
|
||
best, best_area = kpts, area
|
||
return best
|
||
|
||
|
||
def _pose_index_path():
|
||
return os.path.join(_load_output_dir(), "_data", "poses_index.json")
|
||
|
||
|
||
def _load_pose_index():
|
||
try:
|
||
with open(_pose_index_path(), "r") as f:
|
||
return json.load(f)
|
||
except Exception:
|
||
return {}
|
||
|
||
|
||
_pose_index_lock = threading.Lock()
|
||
|
||
|
||
def _save_pose_index_entry(filename, desc):
|
||
with _pose_index_lock:
|
||
idx = _load_pose_index()
|
||
idx[filename] = desc
|
||
os.makedirs(os.path.dirname(_pose_index_path()), exist_ok=True)
|
||
_write_json(_pose_index_path(), idx)
|
||
|
||
|
||
@app.get("/pose/check")
|
||
def pose_check():
|
||
"""Report whether a body-pose estimator is available (and which backend)."""
|
||
est = _load_pose_estimator()
|
||
if not est:
|
||
return {"available": False,
|
||
"hint": "pip install rtmlib onnxruntime (or: pip install mediapipe)"}
|
||
return {"available": True, "backend": est[1]}
|
||
|
||
|
||
@app.post("/images/{filename}/pose")
|
||
def estimate_pose(filename: str):
|
||
"""Estimate COCO-17 body keypoints for an image. Returns pixel-space keypoints
|
||
plus the skeleton edge list so the frontend can overlay a posenet-style preview."""
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
est = _load_pose_estimator()
|
||
if not est:
|
||
raise HTTPException(501, "No pose estimator installed. Try: pip install rtmlib onnxruntime")
|
||
infer, backend = est
|
||
pil = Image.open(person[5]).convert("RGB")
|
||
try:
|
||
people = infer(pil)
|
||
except Exception as e:
|
||
raise HTTPException(500, f"Pose estimation failed: {e}")
|
||
# Cache the descriptor so "find similar pose" can rank this image later.
|
||
best = _best_person(people)
|
||
if best is not None:
|
||
desc = _pose_descriptor(best)
|
||
if desc is not None:
|
||
try:
|
||
_save_pose_index_entry(filename, desc)
|
||
except Exception as e:
|
||
print(f"[pose] index save failed for {filename}: {e}")
|
||
return {
|
||
"status": "success",
|
||
"backend": backend,
|
||
"width": pil.width,
|
||
"height": pil.height,
|
||
"names": POSE_KEYPOINT_NAMES,
|
||
"skeleton": POSE_SKELETON,
|
||
"people": people,
|
||
}
|
||
|
||
|
||
def _build_pose_index_task():
|
||
try:
|
||
est = _load_pose_estimator()
|
||
if not est:
|
||
return
|
||
infer, _ = est
|
||
output_dir = _load_output_dir()
|
||
with _pose_index_lock:
|
||
idx = _load_pose_index()
|
||
persons = database.list_persons()
|
||
todo = [p[0] for p in persons
|
||
if p[0] not in idx
|
||
and (p[12] or "image") != "video"
|
||
and p[0].lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]
|
||
_pose_index_status.update(running=True, done=0, total=len(todo))
|
||
print(f"[pose] index build: {len(todo)} images to process")
|
||
dirty = 0
|
||
for fn in todo:
|
||
try:
|
||
fpath = os.path.join(output_dir, fn)
|
||
if os.path.exists(fpath):
|
||
best = _best_person(infer(Image.open(fpath).convert("RGB")))
|
||
desc = _pose_descriptor(best) if best is not None else None
|
||
if desc is not None:
|
||
idx[fn] = desc
|
||
dirty += 1
|
||
except Exception as e:
|
||
print(f"[pose] index error for {fn}: {e}")
|
||
_pose_index_status["done"] += 1
|
||
# Batch-flush every 50 to avoid O(n^2) full-file rewrites.
|
||
if dirty >= 50:
|
||
with _pose_index_lock:
|
||
_write_json(_pose_index_path(), idx)
|
||
dirty = 0
|
||
print(f"[pose] index progress: {_pose_index_status['done']}/{len(todo)}")
|
||
with _pose_index_lock:
|
||
_write_json(_pose_index_path(), idx)
|
||
print(f"[pose] index build complete: {len(idx)} entries")
|
||
except Exception as e:
|
||
print(f"[pose] index build failed: {e}")
|
||
finally:
|
||
_pose_index_status["running"] = False
|
||
|
||
|
||
@app.post("/pose/index")
|
||
def build_pose_index():
|
||
"""Compute pose descriptors for all library images lacking one (daemon thread)."""
|
||
if not _load_pose_estimator():
|
||
raise HTTPException(501, "No pose estimator installed. Try: pip install rtmlib onnxruntime")
|
||
if _pose_index_status.get("running"):
|
||
return {"status": "already_running", **_pose_index_status}
|
||
threading.Thread(target=_build_pose_index_task, daemon=True).start()
|
||
return {"status": "started"}
|
||
|
||
|
||
@app.get("/pose/index/status")
|
||
def pose_index_status():
|
||
idx = _load_pose_index()
|
||
return {**_pose_index_status, "indexed": len(idx)}
|
||
|
||
|
||
def _rank_similar_poses(query_desc, limit, exclude=None):
|
||
idx = _load_pose_index()
|
||
scored = []
|
||
for fn, desc in idx.items():
|
||
if fn == exclude or not desc or "vec" not in desc:
|
||
continue
|
||
d = _pose_distance(query_desc, desc)
|
||
if d != float("inf"):
|
||
scored.append((d, fn))
|
||
scored.sort(key=lambda x: x[0])
|
||
groups = get_groups() if scored else {}
|
||
return [{"filename": fn, "group_id": groups.get(fn), "distance": round(d, 4)}
|
||
for d, fn in scored[:limit]]
|
||
|
||
|
||
@app.get("/pose/similar/{filename}")
|
||
def similar_pose(filename: str, limit: int = 12):
|
||
"""Rank library images by pose similarity to the given image."""
|
||
idx = _load_pose_index()
|
||
query = idx.get(filename)
|
||
if query is None:
|
||
# Compute on demand (also caches it).
|
||
person = database.get_person(filename)
|
||
if not person or not person[5] or not os.path.exists(person[5]):
|
||
raise HTTPException(404, "Image file not found")
|
||
est = _load_pose_estimator()
|
||
if not est:
|
||
raise HTTPException(501, "No pose estimator installed.")
|
||
best = _best_person(est[0](Image.open(person[5]).convert("RGB")))
|
||
query = _pose_descriptor(best) if best is not None else None
|
||
if query is None:
|
||
raise HTTPException(404, "No detectable pose in this image")
|
||
try:
|
||
_save_pose_index_entry(filename, query)
|
||
except Exception:
|
||
pass
|
||
return {"filename": filename, "similar": _rank_similar_poses(query, limit, exclude=filename)}
|
||
|
||
|
||
class PoseSimilarRequest(BaseModel):
|
||
keypoints: list[list[float]] # [[x,y,score], ...17] in image pixels
|
||
width: int = 0
|
||
height: int = 0
|
||
limit: int = 12
|
||
|
||
|
||
@app.post("/pose/similar")
|
||
def similar_pose_from_keypoints(req: PoseSimilarRequest):
|
||
"""Rank library images by similarity to a supplied (e.g. hand-edited) skeleton."""
|
||
query = _pose_descriptor(req.keypoints)
|
||
if query is None:
|
||
raise HTTPException(400, "Supplied pose is too sparse to match")
|
||
return {"similar": _rank_similar_poses(query, req.limit)}
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import uvicorn
|
||
uvicorn.run(app, host=os.environ.get("HOST", "0.0.0.0"),
|
||
port=int(os.environ.get("PORT", "8500")))
|