Initial FastAPI Space (code only)

.gitignore

@@ -0,0 +1,5 @@
+.venv/
+__pycache__/
+*.pyc
+models/
+.cache/

Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.10-slim
+
+RUN apt-get update && apt-get install -y \
+    git \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -0,0 +1,11 @@
+---
+title: Interior Fastapi
+emoji: 👀
+colorFrom: yellow
+colorTo: pink
+sdk: docker
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,413 @@
+import os
+import io
+import hashlib
+import numpy as np
+import cv2
+import torch
+from PIL import Image
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import Response, JSONResponse, HTMLResponse, FileResponse
+from skimage.measure import label, regionprops
+from sklearn.decomposition import PCA
+
+from transformers import (
+    OneFormerProcessor,
+    OneFormerForUniversalSegmentation,
+    Mask2FormerForUniversalSegmentation,
+    AutoImageProcessor
+)
+# =========================================================
+# CONFIG
+# =========================================================
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+ALPHA = 0.65
+
+SEMANTIC_MODEL = "shi-labs/oneformer_ade20k_swin_large"
+INSTANCE_MODEL = "facebook/mask2former-swin-large-coco-instance"
+TEXTURE_ROOT = "textures"
+
+OBJECT_CLASSES = {
+    "Wall": {"semantic": ["wall"], "panels": True},
+    "Floor": {"semantic": ["floor"], "panels": False},
+    "Door": {"semantic": ["door"], "panels": False},
+    "Cabinet": {"semantic": ["cabinet", "cupboard", "wardrobe"], "panels": True},
+    "Counter": {"semantic": ["counter"], "panels": False},
+    "Countertop": {"semantic": ["countertop", "worktop"], "panels": False},
+}
+
+REMOVE_FROM_WALL_FLOOR = {
+    "door", "window", "cabinet",
+    "counter", "countertop", "island"
+}
+
+# =========================================================
+# FASTAPI
+# =========================================================
+
+app = FastAPI(title="Interior Texture API")
+
+# =========================================================
+# GLOBAL CACHES (SAFE IF 1 WORKER)
+# =========================================================
+
+DETECTION_CACHE = {}  # image_hash → (image, objects)
+CURRENT_STATE = {      # single user state
+    "image_hash": None,
+    "image": None,
+    "objects": None,
+    "object_textures": {},
+    "panel_textures": {}
+}
+
+# =========================================================
+# LOAD MODELS ONCE
+# =========================================================
+
+print("Loading models...")
+
+sem_proc = OneFormerProcessor.from_pretrained(SEMANTIC_MODEL)
+sem_model = OneFormerForUniversalSegmentation.from_pretrained(
+    SEMANTIC_MODEL
+).to(DEVICE).eval()
+
+inst_proc = AutoImageProcessor.from_pretrained(INSTANCE_MODEL)
+inst_model = Mask2FormerForUniversalSegmentation.from_pretrained(
+    INSTANCE_MODEL
+).to(DEVICE).eval()
+
+print("Models loaded")
+
+# =========================================================
+# UTILITIES
+# =========================================================
+
+def extract_semantic_mask(seg_map, id2label, keywords):
+    mask = np.zeros_like(seg_map, dtype=np.uint8)
+    for cid, name in id2label.items():
+        if any(k in name.lower() for k in keywords):
+            mask[seg_map == cid] = 255
+    return mask
+
+
+def subtract_instances(mask, instances, remove_labels, coco_id2label):
+    cleaned = mask.copy()
+    inst_map = instances["segmentation"].cpu().numpy()
+
+    for seg in instances["segments_info"]:
+        if seg.get("score", 1.0) < 0.7:
+            continue
+        label_name = coco_id2label.get(seg["label_id"], "")
+        if label_name in remove_labels:
+            cleaned[inst_map == seg["id"]] = 0
+
+    return cleaned
+
+
+def edge_cleanup(mask, image_np):
+    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+    edges = cv2.Canny(gray, 80, 160)
+    mask[edges > 0] = 0
+
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    mask = cv2.medianBlur(mask, 7)
+    return mask
+
+
+def extract_panels(mask, min_ratio=0.003):
+    lbl = label(mask)
+    panels = []
+    for r in regionprops(lbl):
+        if r.area > mask.size * min_ratio:
+            p = np.zeros_like(mask)
+            p[lbl == r.label] = 255
+            panels.append(p.astype(bool))
+    return panels
+
+
+def detect_objects(image_np):
+    inputs = sem_proc(
+        images=image_np,
+        task_inputs=["semantic"],
+        return_tensors="pt"
+    ).to(DEVICE)
+
+    with torch.no_grad():
+        sem_out = sem_model(**inputs)
+
+    seg_map = sem_proc.post_process_semantic_segmentation(
+        sem_out,
+        target_sizes=[image_np.shape[:2]]
+    )[0].cpu().numpy()
+
+    inst_inputs = inst_proc(images=image_np, return_tensors="pt").to(DEVICE)
+    with torch.no_grad():
+        inst_out = inst_model(**inst_inputs)
+
+    instances = inst_proc.post_process_instance_segmentation(
+        inst_out,
+        target_sizes=[image_np.shape[:2]]
+    )[0]
+
+    objects = {}
+    for obj, cfg in OBJECT_CLASSES.items():
+        mask = extract_semantic_mask(
+            seg_map, sem_model.config.id2label, cfg["semantic"]
+        )
+
+        if np.count_nonzero(mask) < image_np.size * 0.002:
+            continue
+
+        if obj in {"Wall", "Floor"}:
+            mask = subtract_instances(
+                mask, instances,
+                REMOVE_FROM_WALL_FLOOR,
+                inst_model.config.id2label
+            )
+            mask = edge_cleanup(mask, image_np)
+
+        panels = extract_panels(mask) if cfg["panels"] else [mask.astype(bool)]
+        objects[obj] = panels
+
+    return objects
+
+
+def detect_cached(image_bytes: bytes):
+    image_hash = hashlib.md5(image_bytes).hexdigest()
+
+    if image_hash in DETECTION_CACHE:
+        return image_hash, *DETECTION_CACHE[image_hash]
+
+    image = np.array(Image.open(io.BytesIO(image_bytes)).convert("RGB"))
+    objects = detect_objects(image)
+
+    DETECTION_CACHE[image_hash] = (image, objects)
+    return image_hash, image, objects
+
+
+def apply_texture_panel(image, mask, texture, tile_type):
+    H, W = image.shape[:2]
+    tile_w, tile_h = (280, 560) if "12" in tile_type else (560, 560)
+
+    tile = cv2.resize(texture, (tile_w, tile_h), interpolation=cv2.INTER_NEAREST)
+    canvas = np.zeros((H, W, 3), dtype=np.uint8)
+
+    for y in range(0, H, tile_h):
+        for x in range(0, W, tile_w):
+            canvas[y:y+tile_h, x:x+tile_w] = tile[:H-y, :W-x]
+
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY).astype(np.float32) / 255.0
+    light = cv2.GaussianBlur(gray, (41, 41), 0)
+    light = np.repeat(light[:, :, None], 3, axis=2)
+
+    canvas = canvas.astype(np.float32)
+    canvas *= (0.75 + 0.25 * light)
+
+    out = image.astype(np.float32)
+    out[mask] = (1 - ALPHA) * out[mask] + ALPHA * canvas[mask]
+
+    return out.astype(np.uint8)
+
+# =========================================================
+# API ENDPOINTS
+# =========================================================
+
+
+@app.post("/upload-image")
+async def upload_image(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+    image_hash, image, objects = detect_cached(image_bytes)
+
+    CURRENT_STATE["image_hash"] = image_hash
+    CURRENT_STATE["image"] = image
+    CURRENT_STATE["objects"] = objects
+    CURRENT_STATE["object_textures"].clear()
+    CURRENT_STATE["panel_textures"].clear()
+
+    return {"objects": {k: len(v) for k, v in objects.items()}}
+
+# =========================================================
+# LIST TEXTURES FOR OBJECT
+# =========================================================
+
+@app.get("/textures/{object_name}")
+def list_textures(object_name: str):
+    folder = os.path.join(TEXTURE_ROOT, object_name.lower())
+    if not os.path.isdir(folder):
+        return []
+
+    return [
+        f for f in os.listdir(folder)
+        if f.lower().endswith((".png", ".jpg", ".jpeg"))
+    ]
+
+# =========================================================
+# SERVE TEXTURE FILE
+# =========================================================
+
+@app.get("/texture-file/{object_name}/{filename}")
+def get_texture_file(object_name: str, filename: str):
+    path = os.path.join(TEXTURE_ROOT, object_name.lower(), filename)
+    if not os.path.exists(path):
+        return JSONResponse({"error": "Texture not found"}, status_code=404)
+
+    return FileResponse(path)
+
+# =========================================================
+# APPLY TEXTURE
+# =========================================================
+
+@app.post("/apply-texture")
+async def apply_texture(
+    object_name: str = Form(...),
+    filename: str = Form(...),
+    panel_index: int | None = Form(None),
+    tile_type: str = Form("12 x 24 inches")
+):
+    if CURRENT_STATE["image"] is None:
+        return JSONResponse(
+            {"error": "Upload image first"},
+            status_code=400
+        )
+
+    object_name = object_name.strip().title()
+
+    if object_name not in CURRENT_STATE["objects"]:
+        return JSONResponse(
+            {"error": f"{object_name} not detected in image"},
+            status_code=400
+        )
+
+    # 🔹 LOAD TEXTURE FROM DISK
+    texture_path = os.path.join(
+        TEXTURE_ROOT,
+        object_name.lower(),
+        filename
+    )
+
+    if not os.path.isfile(texture_path):
+        return JSONResponse(
+            {"error": f"Texture not found: {filename}"},
+            status_code=404
+        )
+
+    tex = np.array(
+        Image.open(texture_path).convert("RGB")
+    )
+
+    # 🔹 STORE TEXTURE
+    if panel_index is None:
+        CURRENT_STATE["object_textures"][object_name] = tex
+    else:
+        CURRENT_STATE["panel_textures"][(object_name, panel_index)] = tex
+
+    # 🔹 APPLY TEXTURES
+    output = CURRENT_STATE["image"].copy()
+
+    for obj, panels in CURRENT_STATE["objects"].items():
+        obj_tex = CURRENT_STATE["object_textures"].get(obj)
+        for i, mask in enumerate(panels):
+            tex_use = CURRENT_STATE["panel_textures"].get((obj, i), obj_tex)
+            if tex_use is not None:
+                output = apply_texture_panel(
+                    output, mask, tex_use, tile_type
+                )
+
+    _, buf = cv2.imencode(
+        ".png",
+        cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
+    )
+
+    return Response(buf.tobytes(), media_type="image/png")
+
+
+# =========================================================
+# UI (IMAGE UPLOAD + TEXTURE PREVIEW)
+# =========================================================
+
+@app.get("/", response_class=HTMLResponse)
+def ui():
+    return """
+<!DOCTYPE html>
+<html>
+<head>
+<title>Interior Texture UI</title>
+<style>
+body { display:flex; margin:0; font-family:Arial }
+#left { width:70%; padding:10px }
+#right { width:30%; padding:10px; border-left:1px solid #ccc; overflow-y:auto }
+.texture {
+  width:100%;
+  height:120px;
+  object-fit:cover;
+  margin-bottom:10px;
+  cursor:pointer;
+  border:2px solid transparent;
+}
+.texture:hover { border-color:#007bff }
+</style>
+</head>
+
+<body>
+
+<div id="left">
+  <h3>Upload Image</h3>
+  <input type="file" id="imgInput" />
+  <button onclick="uploadImage()">Upload</button>
+  <hr/>
+  <img id="result" width="100%" />
+</div>
+
+<div id="right">
+  <h3>Textures</h3>
+  <select id="object" onchange="loadTextures()">
+    <option>Wall</option>
+    <option>Floor</option>
+    <option>Door</option>
+    <option>Cabinet</option>
+    <option>Counter</option>
+    <option>Countertop</option>
+  </select>
+  <div id="textures"></div>
+</div>
+
+<script>
+async function loadTextures() {
+  const obj = document.getElementById("object").value;
+  const res = await fetch(`/textures/${obj}`);
+  const files = await res.json();
+
+  const container = document.getElementById("textures");
+  container.innerHTML = "";
+
+  files.forEach(filename => {
+    const img = document.createElement("img");
+    img.src = `/texture-file/${obj}/${filename}`;
+    img.className = "texture";
+
+    img.onclick = () => applyTexture(obj, filename);
+    container.appendChild(img);
+  });
+}
+
+async function applyTexture(objectName, filename) {
+  const form = new FormData();
+  form.append("object_name", objectName);
+  form.append("filename", filename);
+  form.append("tile_type", "12 x 24 inches");
+
+  const res = await fetch("/apply-texture", {
+    method: "POST",
+    body: form
+  });
+
+  const img = document.getElementById("result");
+  img.src = URL.createObjectURL(await res.blob());
+}
+</script>
+
+
+</body>
+</html>
+"""

interior-texture-fastapi/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

interior-texture-fastapi/README.md

@@ -0,0 +1,10 @@
+---
+title: Interior Texture Fastapi
+emoji: 🚀
+colorFrom: yellow
+colorTo: gray
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,16 @@
+fastapi
+uvicorn[standard]
+
+torch
+torchvision
+torchaudio
+
+transformers
+accelerate
+
+opencv-python
+numpy
+Pillow
+
+scikit-image
+scikit-learn