diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..552299e --- /dev/null +++ b/.env.example @@ -0,0 +1,16 @@ +CADDY_DOMAIN=localhost +DJANGO_DEBUG=0 +DJANGO_SECRET_KEY=replace-with-a-long-random-value +DJANGO_ALLOWED_HOSTS=localhost,127.0.0.1,api +DJANGO_CSRF_TRUSTED_ORIGINS=http://localhost,https://localhost +CORS_ALLOWED_ORIGINS=http://localhost,http://localhost:5173 +DJANGO_SECURE_SSL_REDIRECT=0 +DJANGO_SESSION_COOKIE_SECURE=0 +DJANGO_CSRF_COOKIE_SECURE=0 +DJANGO_SECURE_HSTS_SECONDS=0 +DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS=0 +DJANGO_SECURE_HSTS_PRELOAD=0 +POSTGRES_DB=enhancer +POSTGRES_USER=enhancer +POSTGRES_PASSWORD=enhancer +IMAGE_SESSION_TTL_HOURS=6 diff --git a/.env.sample b/.env.sample new file mode 100644 index 0000000..b4672d9 --- /dev/null +++ b/.env.sample @@ -0,0 +1,16 @@ +CADDY_DOMAIN=example.com +DJANGO_DEBUG=0 +DJANGO_SECRET_KEY=replace-with-a-long-random-secret +DJANGO_ALLOWED_HOSTS=example.com,www.example.com,api +DJANGO_CSRF_TRUSTED_ORIGINS=https://example.com,https://www.example.com +CORS_ALLOWED_ORIGINS=https://example.com,https://www.example.com +DJANGO_SECURE_SSL_REDIRECT=1 +DJANGO_SESSION_COOKIE_SECURE=1 +DJANGO_CSRF_COOKIE_SECURE=1 +DJANGO_SECURE_HSTS_SECONDS=31536000 +DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS=1 +DJANGO_SECURE_HSTS_PRELOAD=0 +POSTGRES_DB=enhancer +POSTGRES_USER=enhancer +POSTGRES_PASSWORD=replace-with-a-strong-password +IMAGE_SESSION_TTL_HOURS=6 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f262369 --- /dev/null +++ b/.gitignore @@ -0,0 +1,15 @@ +__pycache__/ +*.py[cod] +.venv/ +.pytest_cache/ +.coverage +htmlcov/ +db.sqlite3 +backend/media/ +backend/staticfiles/ +.env +node_modules/ +dist/ +coverage/ +.vite/ +*.log diff --git a/Caddyfile b/Caddyfile new file mode 100644 index 0000000..3795f2c --- /dev/null +++ b/Caddyfile @@ -0,0 +1,15 @@ +{$CADDY_DOMAIN:localhost} { + encode zstd gzip + + handle /api/* { + reverse_proxy api:8000 + } + + handle /media/* { + reverse_proxy api:8000 + } + + handle { + reverse_proxy web:80 + } +} diff --git a/README.md b/README.md new file mode 100644 index 0000000..00f9f31 --- /dev/null +++ b/README.md @@ -0,0 +1,58 @@ +# Spatial Image Enhancer Pro + +Production-grade public SPA for spatial-domain image enhancement using Django REST Framework, OpenCV, NumPy, React, Tailwind CSS, Celery, Redis, PostgreSQL, Docker Compose, and Caddy. + +## Local Development + +Backend: + +```bash +cd backend +python -m venv .venv +.venv\Scripts\activate +copy .env.sample .env +pip install -r requirements.txt +python manage.py migrate +python manage.py runserver +``` + +Frontend: + +```bash +cd frontend +copy .env.sample .env +npm install +npm run dev +``` + +Open `http://localhost:5173`. The frontend `.env` uses `VITE_API_BASE=http://localhost:8000`, and the backend `.env` allows that origin via CORS/CSRF settings. + +## Docker + +```bash +copy .env.example .env +copy backend\.env.sample backend\.env +docker compose up --build +``` + +Caddy serves the SPA and proxies `/api/*` and `/media/*`. Set `CADDY_DOMAIN`, `DJANGO_ALLOWED_HOSTS`, `DJANGO_CSRF_TRUSTED_ORIGINS`, `CORS_ALLOWED_ORIGINS`, and a strong `DJANGO_SECRET_KEY` before production deployment. + +For production, use `.env.sample` as the root Compose template and `backend/.env.production.sample` as the backend-only template. + +## Backend Structure + +The Django app follows the HackSoftware Django Styleguide pattern: + +- API views validate request input and return responses. +- `processing/services.py` contains business workflows and writes. +- `processing/selectors.py` contains database fetch helpers. +- Settings are environment-driven through `backend/.env`. + +## API + +- `POST /api/images/` +- `POST /api/process/` +- `POST /api/batch/` +- `GET /api/jobs/{job_id}/` + +Images are stored as ephemeral sessions and removed by the cleanup task after the configured TTL. diff --git a/backend/.dockerignore b/backend/.dockerignore new file mode 100644 index 0000000..c93af90 --- /dev/null +++ b/backend/.dockerignore @@ -0,0 +1,8 @@ +__pycache__/ +*.py[cod] +.pytest_cache/ +db.sqlite3 +media/ +staticfiles/ +.env +.env.local diff --git a/backend/.env.production.sample b/backend/.env.production.sample new file mode 100644 index 0000000..866cfa9 --- /dev/null +++ b/backend/.env.production.sample @@ -0,0 +1,23 @@ +DJANGO_DEBUG=0 +DJANGO_SECRET_KEY=replace-with-a-long-random-secret +DJANGO_ALLOWED_HOSTS=example.com,www.example.com,api +DJANGO_CSRF_TRUSTED_ORIGINS=https://example.com,https://www.example.com +CORS_ALLOWED_ORIGINS=https://example.com,https://www.example.com +DJANGO_SECURE_SSL_REDIRECT=1 +DJANGO_SESSION_COOKIE_SECURE=1 +DJANGO_CSRF_COOKIE_SECURE=1 +DJANGO_SECURE_HSTS_SECONDS=31536000 +DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS=1 +DJANGO_SECURE_HSTS_PRELOAD=0 +DJANGO_LOG_LEVEL=INFO +POSTGRES_DB=enhancer +POSTGRES_USER=enhancer +POSTGRES_PASSWORD=replace-with-a-strong-password +POSTGRES_HOST=db +POSTGRES_PORT=5432 +POSTGRES_CONN_MAX_AGE=60 +CELERY_BROKER_URL=redis://redis:6379/0 +CELERY_RESULT_BACKEND=redis://redis:6379/0 +CELERY_TASK_TIME_LIMIT=600 +IMAGE_SESSION_TTL_HOURS=6 +MAX_UPLOAD_MB=20 diff --git a/backend/.env.sample b/backend/.env.sample new file mode 100644 index 0000000..ac34b7e --- /dev/null +++ b/backend/.env.sample @@ -0,0 +1,23 @@ +DJANGO_DEBUG=1 +DJANGO_SECRET_KEY=dev-spatial-image-enhancer-change-before-production +DJANGO_ALLOWED_HOSTS=localhost,127.0.0.1,api +DJANGO_CSRF_TRUSTED_ORIGINS=http://localhost:5173,http://127.0.0.1:5173,http://localhost:8000,http://127.0.0.1:8000 +CORS_ALLOWED_ORIGINS=http://localhost:5173,http://127.0.0.1:5173 +DJANGO_SECURE_SSL_REDIRECT=0 +DJANGO_SESSION_COOKIE_SECURE=0 +DJANGO_CSRF_COOKIE_SECURE=0 +DJANGO_SECURE_HSTS_SECONDS=0 +DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS=0 +DJANGO_SECURE_HSTS_PRELOAD=0 +DJANGO_LOG_LEVEL=INFO +POSTGRES_DB=enhancer +POSTGRES_USER=enhancer +POSTGRES_PASSWORD=enhancer +POSTGRES_HOST= +POSTGRES_PORT=5432 +POSTGRES_CONN_MAX_AGE=60 +CELERY_BROKER_URL=redis://localhost:6379/0 +CELERY_RESULT_BACKEND=redis://localhost:6379/0 +CELERY_TASK_TIME_LIMIT=600 +IMAGE_SESSION_TTL_HOURS=6 +MAX_UPLOAD_MB=20 diff --git a/backend/Dockerfile b/backend/Dockerfile new file mode 100644 index 0000000..9474dd2 --- /dev/null +++ b/backend/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.12-slim + +ENV PYTHONDONTWRITEBYTECODE=1 +ENV PYTHONUNBUFFERED=1 + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y --no-install-recommends libglib2.0-0 libgl1 \ + && rm -rf /var/lib/apt/lists/* + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY . . + +CMD ["gunicorn", "enhancer_project.wsgi:application", "--bind", "0.0.0.0:8000"] diff --git a/backend/enhancer_project/__init__.py b/backend/enhancer_project/__init__.py new file mode 100644 index 0000000..53f4ccb --- /dev/null +++ b/backend/enhancer_project/__init__.py @@ -0,0 +1,3 @@ +from .celery import app as celery_app + +__all__ = ("celery_app",) diff --git a/backend/enhancer_project/asgi.py b/backend/enhancer_project/asgi.py new file mode 100644 index 0000000..73d8ab3 --- /dev/null +++ b/backend/enhancer_project/asgi.py @@ -0,0 +1,7 @@ +import os + +from django.core.asgi import get_asgi_application + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "enhancer_project.settings") + +application = get_asgi_application() diff --git a/backend/enhancer_project/celery.py b/backend/enhancer_project/celery.py new file mode 100644 index 0000000..1f5c229 --- /dev/null +++ b/backend/enhancer_project/celery.py @@ -0,0 +1,9 @@ +import os + +from celery import Celery + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "enhancer_project.settings") + +app = Celery("enhancer_project") +app.config_from_object("django.conf:settings", namespace="CELERY") +app.autodiscover_tasks() diff --git a/backend/enhancer_project/env.py b/backend/enhancer_project/env.py new file mode 100644 index 0000000..83d3e81 --- /dev/null +++ b/backend/enhancer_project/env.py @@ -0,0 +1,34 @@ +import os +from pathlib import Path + +from dotenv import load_dotenv + + +BASE_DIR = Path(__file__).resolve().parent.parent + +load_dotenv(BASE_DIR / ".env") + + +def env(name, default=None): + return os.environ.get(name, default) + + +def env_bool(name, default=False): + value = env(name) + if value is None: + return default + return value.lower() in {"1", "true", "yes", "on"} + + +def env_int(name, default): + value = env(name) + if value is None or value == "": + return default + return int(value) + + +def env_list(name, default=""): + value = env(name, default) + if not value: + return [] + return [item.strip() for item in value.split(",") if item.strip()] diff --git a/backend/enhancer_project/settings.py b/backend/enhancer_project/settings.py new file mode 100644 index 0000000..9b557e2 --- /dev/null +++ b/backend/enhancer_project/settings.py @@ -0,0 +1,145 @@ +from pathlib import Path + +from .env import env, env_bool, env_int, env_list + +BASE_DIR = Path(__file__).resolve().parent.parent + + +DEBUG = env_bool("DJANGO_DEBUG", False) +SECRET_KEY = env("DJANGO_SECRET_KEY") +if not SECRET_KEY: + if DEBUG: + SECRET_KEY = "dev-only-spatial-image-enhancer" + else: + raise RuntimeError("DJANGO_SECRET_KEY must be set when DJANGO_DEBUG is false.") + +ALLOWED_HOSTS = env_list("DJANGO_ALLOWED_HOSTS", "localhost,127.0.0.1,api") +CSRF_TRUSTED_ORIGINS = env_list("DJANGO_CSRF_TRUSTED_ORIGINS", "") + +INSTALLED_APPS = [ + "django.contrib.admin", + "django.contrib.auth", + "django.contrib.contenttypes", + "django.contrib.sessions", + "django.contrib.messages", + "django.contrib.staticfiles", + "corsheaders", + "rest_framework", + "processing", +] + +MIDDLEWARE = [ + "corsheaders.middleware.CorsMiddleware", + "django.middleware.security.SecurityMiddleware", + "whitenoise.middleware.WhiteNoiseMiddleware", + "django.contrib.sessions.middleware.SessionMiddleware", + "django.middleware.common.CommonMiddleware", + "django.middleware.csrf.CsrfViewMiddleware", + "django.contrib.auth.middleware.AuthenticationMiddleware", + "django.contrib.messages.middleware.MessageMiddleware", + "django.middleware.clickjacking.XFrameOptionsMiddleware", +] + +ROOT_URLCONF = "enhancer_project.urls" + +TEMPLATES = [ + { + "BACKEND": "django.template.backends.django.DjangoTemplates", + "DIRS": [], + "APP_DIRS": True, + "OPTIONS": { + "context_processors": [ + "django.template.context_processors.debug", + "django.template.context_processors.request", + "django.contrib.auth.context_processors.auth", + "django.contrib.messages.context_processors.messages", + ], + }, + }, +] + +WSGI_APPLICATION = "enhancer_project.wsgi.application" + +if env("POSTGRES_HOST"): + DATABASES = { + "default": { + "ENGINE": "django.db.backends.postgresql", + "NAME": env("POSTGRES_DB", "enhancer"), + "USER": env("POSTGRES_USER", "enhancer"), + "PASSWORD": env("POSTGRES_PASSWORD", "enhancer"), + "HOST": env("POSTGRES_HOST", "db"), + "PORT": env("POSTGRES_PORT", "5432"), + "CONN_MAX_AGE": env_int("POSTGRES_CONN_MAX_AGE", 60), + } + } +else: + DATABASES = { + "default": { + "ENGINE": "django.db.backends.sqlite3", + "NAME": BASE_DIR / "db.sqlite3", + } + } + +LANGUAGE_CODE = "en-us" +TIME_ZONE = "UTC" +USE_I18N = True +USE_TZ = True + +STATIC_URL = "static/" +STATIC_ROOT = BASE_DIR / "staticfiles" +MEDIA_URL = "/media/" +MEDIA_ROOT = BASE_DIR / "media" +DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField" + +STORAGES = { + "default": { + "BACKEND": "django.core.files.storage.FileSystemStorage", + }, + "staticfiles": { + "BACKEND": "whitenoise.storage.CompressedManifestStaticFilesStorage", + }, +} + +REST_FRAMEWORK = { + "DEFAULT_RENDERER_CLASSES": ["rest_framework.renderers.JSONRenderer"], + "DEFAULT_PARSER_CLASSES": [ + "rest_framework.parsers.JSONParser", + "rest_framework.parsers.MultiPartParser", + "rest_framework.parsers.FormParser", + ], +} + +CORS_ALLOWED_ORIGINS = env_list("CORS_ALLOWED_ORIGINS", "http://localhost:5173,http://127.0.0.1:5173") +CORS_ALLOW_CREDENTIALS = False + +CELERY_BROKER_URL = env("CELERY_BROKER_URL", "redis://redis:6379/0") +CELERY_RESULT_BACKEND = env("CELERY_RESULT_BACKEND", CELERY_BROKER_URL) +CELERY_TASK_TRACK_STARTED = True +CELERY_TASK_TIME_LIMIT = env_int("CELERY_TASK_TIME_LIMIT", 600) + +IMAGE_SESSION_TTL_HOURS = env_int("IMAGE_SESSION_TTL_HOURS", 6) +MAX_UPLOAD_MB = env_int("MAX_UPLOAD_MB", 20) + +SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") +SECURE_SSL_REDIRECT = env_bool("DJANGO_SECURE_SSL_REDIRECT", not DEBUG) +SESSION_COOKIE_SECURE = env_bool("DJANGO_SESSION_COOKIE_SECURE", not DEBUG) +CSRF_COOKIE_SECURE = env_bool("DJANGO_CSRF_COOKIE_SECURE", not DEBUG) +SECURE_HSTS_SECONDS = env_int("DJANGO_SECURE_HSTS_SECONDS", 0 if DEBUG else 31536000) +SECURE_HSTS_INCLUDE_SUBDOMAINS = env_bool("DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS", not DEBUG) +SECURE_HSTS_PRELOAD = env_bool("DJANGO_SECURE_HSTS_PRELOAD", False) +SECURE_CONTENT_TYPE_NOSNIFF = True +X_FRAME_OPTIONS = "DENY" + +LOGGING = { + "version": 1, + "disable_existing_loggers": False, + "handlers": { + "console": { + "class": "logging.StreamHandler", + } + }, + "root": { + "handlers": ["console"], + "level": env("DJANGO_LOG_LEVEL", "INFO"), + }, +} diff --git a/backend/enhancer_project/urls.py b/backend/enhancer_project/urls.py new file mode 100644 index 0000000..f27a422 --- /dev/null +++ b/backend/enhancer_project/urls.py @@ -0,0 +1,13 @@ +from django.conf import settings +from django.conf.urls.static import static +from django.contrib import admin +from django.urls import include, path + + +urlpatterns = [ + path("admin/", admin.site.urls), + path("api/", include("processing.urls")), +] + +if settings.DEBUG: + urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) diff --git a/backend/enhancer_project/wsgi.py b/backend/enhancer_project/wsgi.py new file mode 100644 index 0000000..0fb3329 --- /dev/null +++ b/backend/enhancer_project/wsgi.py @@ -0,0 +1,7 @@ +import os + +from django.core.wsgi import get_wsgi_application + +os.environ.setdefault("DJANGO_SETTINGS_MODULE", "enhancer_project.settings") + +application = get_wsgi_application() diff --git a/backend/manage.py b/backend/manage.py new file mode 100644 index 0000000..9af2bf1 --- /dev/null +++ b/backend/manage.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python +import os +import sys + + +def main(): + os.environ.setdefault("DJANGO_SETTINGS_MODULE", "enhancer_project.settings") + from django.core.management import execute_from_command_line + + execute_from_command_line(sys.argv) + + +if __name__ == "__main__": + main() diff --git a/backend/processing/__init__.py b/backend/processing/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/processing/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/processing/algorithms.py b/backend/processing/algorithms.py new file mode 100644 index 0000000..0969ed7 --- /dev/null +++ b/backend/processing/algorithms.py @@ -0,0 +1,436 @@ +import base64 +import math +from io import BytesIO + +import cv2 +import numpy as np +from numpy.lib.stride_tricks import sliding_window_view +from PIL import Image + + +LAPLACIAN_MASK = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]], dtype=np.float32) +SOBEL_GX = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=np.float32) +SOBEL_GY = SOBEL_GX.T +ROBERTS_GX = np.array([[1, 0], [0, -1]], dtype=np.float32) +ROBERTS_GY = np.array([[0, 1], [-1, 0]], dtype=np.float32) + + +class ProcessingError(ValueError): + pass + + +def ensure_uint8(image): + return np.clip(image, 0, 255).astype(np.uint8) + + +def normalize_to_uint8(image): + arr = image.astype(np.float32) + min_value = float(np.min(arr)) + max_value = float(np.max(arr)) + if math.isclose(min_value, max_value): + return np.zeros(arr.shape, dtype=np.uint8) + return np.round((arr - min_value) * 255.0 / (max_value - min_value)).astype(np.uint8) + + +def require_odd(value, name="size", minimum=3): + try: + value = int(value) + except (TypeError, ValueError) as exc: + raise ProcessingError(f"{name} must be an odd integer.") from exc + if value < minimum or value % 2 == 0: + raise ProcessingError(f"{name} must be an odd integer >= {minimum}.") + return value + + +def require_finite_positive(value, name): + try: + value = float(value) + except (TypeError, ValueError) as exc: + raise ProcessingError(f"{name} must be a finite positive number.") from exc + if not np.isfinite(value) or value <= 0: + raise ProcessingError(f"{name} must be a finite positive number.") + return value + + +def to_gray(image): + if image.ndim == 2: + return image + return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) + + +def gray_to_rgb(gray): + return cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB) + + +def histogram(image): + gray = to_gray(image) + counts = np.bincount(gray.ravel(), minlength=256).astype(np.float64) + probabilities = counts / max(gray.size, 1) + return probabilities.round(8).tolist() + + +def image_to_data_url(image): + pil_image = Image.fromarray(ensure_uint8(image)) + buffer = BytesIO() + pil_image.save(buffer, format="PNG") + payload = base64.b64encode(buffer.getvalue()).decode("ascii") + return f"data:image/png;base64,{payload}" + + +def data_url_to_bytes(value): + if "," in value: + value = value.split(",", 1)[1] + return base64.b64decode(value) + + +def decode_image(uploaded_file=None, base64_image=None): + if uploaded_file is None and not base64_image: + raise ProcessingError("Provide an image file or base64 image payload.") + if uploaded_file is not None: + raw = uploaded_file.read() + else: + raw = data_url_to_bytes(base64_image) + + image = Image.open(BytesIO(raw)) + image = image.convert("RGB") + return np.array(image, dtype=np.uint8) + + +def negative(image, params): + return 255 - image + + +def logarithmic(image, params): + c = require_finite_positive(params.get("c", 1.0 / math.log(2.0)), "c") + normalized = image.astype(np.float32) / 255.0 + transformed = c * np.log1p(normalized) + return ensure_uint8(np.round(np.clip(transformed, 0.0, 1.0) * 255.0)) + + +def gamma(image, params): + gamma_value = require_finite_positive(params.get("gamma", 1.0), "gamma") + c = require_finite_positive(params.get("c", 1.0), "c") + normalized = image.astype(np.float32) / 255.0 + transformed = c * np.power(normalized, gamma_value) + return ensure_uint8(np.round(np.clip(transformed, 0.0, 1.0) * 255.0)) + + +def contrast_stretch(image, params): + low = int(params.get("low", 0)) + high = int(params.get("high", 255)) + if low < 0 or high > 255 or low >= high: + raise ProcessingError("Contrast stretch requires 0 <= low < high <= 255.") + stretched = (image.astype(np.float32) - low) * (255.0 / (high - low)) + return ensure_uint8(np.round(stretched)) + + +def gray_slice(image, params): + start = int(params.get("start", 96)) + end = int(params.get("end", 160)) + if start < 0 or end > 255 or start > end: + raise ProcessingError("Gray-level slicing requires 0 <= start <= end <= 255.") + preserve = bool(params.get("preserve_background", True)) + highlight = np.array(params.get("highlight", [255, 64, 64]), dtype=np.uint8) + if highlight.shape != (3,): + raise ProcessingError("highlight must be an RGB triplet.") + gray = to_gray(image) + mask = (gray >= start) & (gray <= end) + base = image.copy() if image.ndim == 3 else gray_to_rgb(gray if preserve else np.zeros_like(gray)) + if not preserve: + base = np.zeros((*gray.shape, 3), dtype=np.uint8) + base[mask] = highlight + return base + + +def bit_plane(image, params): + bit = int(params.get("bit", 7)) + if bit < 0 or bit > 7: + raise ProcessingError("bit must be between 0 and 7.") + plane = ((to_gray(image) >> bit) & 1) * 255 + return gray_to_rgb(plane.astype(np.uint8)) + + +def histogram_equalization(image, params): + gray = to_gray(image) + counts = np.bincount(gray.ravel(), minlength=256) + cdf = counts.cumsum().astype(np.float64) + nonzero = cdf[cdf > 0] + if nonzero.size == 0: + return gray_to_rgb(gray) + cdf_min = nonzero[0] + denom = gray.size - cdf_min + if denom <= 0: + equalized = np.zeros_like(gray) + else: + lut = np.round((cdf - cdf_min) / denom * 255.0).clip(0, 255).astype(np.uint8) + equalized = lut[gray] + return gray_to_rgb(equalized) + + +def target_cdf_from_params(params): + if "cdf" in params: + cdf = np.array(params["cdf"], dtype=np.float64) + if cdf.shape != (256,) or np.any(np.diff(cdf) < 0): + raise ProcessingError("cdf must contain 256 non-decreasing values.") + if cdf[-1] <= 0: + raise ProcessingError("cdf must end with a positive value.") + return cdf / cdf[-1] + + mode = params.get("target", "uniform") + levels = np.arange(256, dtype=np.float64) + if mode == "dark": + pdf = np.exp(-levels / 64.0) + elif mode == "bright": + pdf = np.exp(-(255.0 - levels) / 64.0) + elif mode == "bimodal": + pdf = np.exp(-((levels - 72.0) ** 2) / (2 * 22.0**2)) + np.exp(-((levels - 190.0) ** 2) / (2 * 28.0**2)) + else: + pdf = np.ones(256, dtype=np.float64) + cdf = np.cumsum(pdf) + return cdf / cdf[-1] + + +def histogram_matching(image, params): + gray = to_gray(image) + source_counts = np.bincount(gray.ravel(), minlength=256).astype(np.float64) + source_cdf = np.cumsum(source_counts) + source_cdf /= source_cdf[-1] + target_cdf = target_cdf_from_params(params) + target_levels = np.arange(256) + mapping = np.interp(source_cdf, target_cdf, target_levels).round().clip(0, 255).astype(np.uint8) + return gray_to_rgb(mapping[gray]) + + +def local_equalization(image, params): + size = require_odd(params.get("size", 7), "size") + gray = to_gray(image) + radius = size // 2 + padded = np.pad(gray, radius, mode="edge") + windows = sliding_window_view(padded, (size, size)) + centers = gray[..., None, None] + ranks = np.count_nonzero(windows <= centers, axis=(-1, -2)) + equalized = np.round(ranks * 255.0 / (size * size)).astype(np.uint8) + return gray_to_rgb(equalized) + + +def apply_kernel(image, kernel, normalize_derivative=False): + source = image.astype(np.float32) + if image.ndim == 2: + filtered = cv2.filter2D(source, cv2.CV_32F, kernel, borderType=cv2.BORDER_REFLECT) + else: + channels = [cv2.filter2D(source[:, :, idx], cv2.CV_32F, kernel, borderType=cv2.BORDER_REFLECT) for idx in range(source.shape[2])] + filtered = np.stack(channels, axis=2) + if normalize_derivative: + return normalize_to_uint8(filtered) + return ensure_uint8(np.round(filtered)) + + +def filter_float(image, kernel): + source = image.astype(np.float32) + if image.ndim == 2: + return cv2.filter2D(source, cv2.CV_32F, kernel, borderType=cv2.BORDER_REFLECT) + channels = [cv2.filter2D(source[:, :, idx], cv2.CV_32F, kernel, borderType=cv2.BORDER_REFLECT) for idx in range(source.shape[2])] + return np.stack(channels, axis=2) + + +def box_filter(image, params): + size = require_odd(params.get("size", 3), "size") + return cv2.blur(image, (size, size), borderType=cv2.BORDER_REFLECT) + + +def weighted_average(image, params): + size = require_odd(params.get("size", 3), "size") + if "kernel" in params: + kernel = np.array(params["kernel"], dtype=np.float32) + if kernel.shape != (size, size): + raise ProcessingError("kernel dimensions must match size.") + elif size == 3: + kernel = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=np.float32) + else: + sigma = max(size / 6.0, 0.1) + ax = np.arange(-(size // 2), size // 2 + 1, dtype=np.float32) + xx, yy = np.meshgrid(ax, ax) + kernel = np.exp(-(xx**2 + yy**2) / (2.0 * sigma**2)) + total = float(np.sum(kernel)) + if math.isclose(total, 0.0): + raise ProcessingError("weighted average kernel sum must not be zero.") + return apply_kernel(image, kernel / total) + + +def median_filter(image, params): + size = require_odd(params.get("size", 3), "size") + return cv2.medianBlur(image, size) + + +def laplacian(image, params): + mode = params.get("mode", "sharpen") + lap = filter_float(image, LAPLACIAN_MASK) + if mode == "edge": + return normalize_to_uint8(lap) + sign = params.get("sign", "add") + source = image.astype(np.float32) + sharpened = source + lap if sign == "add" else source - lap + return ensure_uint8(np.round(sharpened)) + + +def high_boost(image, params): + amplification = float(params.get("amplification", 1.5)) + if not np.isfinite(amplification) or amplification < 1.0: + raise ProcessingError("amplification must be >= 1.") + size = require_odd(params.get("size", 3), "size") + blurred = cv2.blur(image, (size, size), borderType=cv2.BORDER_REFLECT).astype(np.float32) + boosted = amplification * image.astype(np.float32) - blurred + return ensure_uint8(np.round(boosted)) + + +def gradient_magnitude(image, gx_kernel, gy_kernel): + gray = to_gray(image).astype(np.float32) + gx = cv2.filter2D(gray, cv2.CV_32F, gx_kernel, borderType=cv2.BORDER_REFLECT) + gy = cv2.filter2D(gray, cv2.CV_32F, gy_kernel, borderType=cv2.BORDER_REFLECT) + magnitude = np.sqrt(gx**2 + gy**2) + return gray_to_rgb(normalize_to_uint8(magnitude)) + + +def sobel(image, params): + return gradient_magnitude(image, SOBEL_GX, SOBEL_GY) + + +def roberts(image, params): + return gradient_magnitude(image, ROBERTS_GX, ROBERTS_GY) + + +def rgb_to_hsi(image): + rgb = image.astype(np.float32) / 255.0 + r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2] + numerator = 0.5 * ((r - g) + (r - b)) + denominator = np.sqrt((r - g) ** 2 + (r - b) * (g - b)) + 1e-8 + theta = np.arccos(np.clip(numerator / denominator, -1.0, 1.0)) + h = np.where(b <= g, theta, 2.0 * np.pi - theta) / (2.0 * np.pi) + total = r + g + b + s = np.where(total <= 1e-8, 0.0, 1.0 - 3.0 * np.minimum(np.minimum(r, g), b) / total) + i = total / 3.0 + return np.stack([h, s, i], axis=-1) + + +def hsi_to_rgb(hsi): + h = (hsi[..., 0] % 1.0) * 2.0 * np.pi + s = np.clip(hsi[..., 1], 0.0, 1.0) + i = np.clip(hsi[..., 2], 0.0, 1.0) + r = np.zeros_like(h) + g = np.zeros_like(h) + b = np.zeros_like(h) + + sector0 = h < 2.0 * np.pi / 3.0 + sector1 = (h >= 2.0 * np.pi / 3.0) & (h < 4.0 * np.pi / 3.0) + sector2 = ~sector0 & ~sector1 + + h0 = h[sector0] + b[sector0] = i[sector0] * (1.0 - s[sector0]) + r[sector0] = i[sector0] * (1.0 + s[sector0] * np.cos(h0) / (np.cos(np.pi / 3.0 - h0) + 1e-8)) + g[sector0] = 3.0 * i[sector0] - (r[sector0] + b[sector0]) + + h1 = h[sector1] - 2.0 * np.pi / 3.0 + r[sector1] = i[sector1] * (1.0 - s[sector1]) + g[sector1] = i[sector1] * (1.0 + s[sector1] * np.cos(h1) / (np.cos(np.pi / 3.0 - h1) + 1e-8)) + b[sector1] = 3.0 * i[sector1] - (r[sector1] + g[sector1]) + + h2 = h[sector2] - 4.0 * np.pi / 3.0 + g[sector2] = i[sector2] * (1.0 - s[sector2]) + b[sector2] = i[sector2] * (1.0 + s[sector2] * np.cos(h2) / (np.cos(np.pi / 3.0 - h2) + 1e-8)) + r[sector2] = 3.0 * i[sector2] - (g[sector2] + b[sector2]) + + return ensure_uint8(np.round(np.clip(np.stack([r, g, b], axis=-1), 0.0, 1.0) * 255.0)) + + +def hsi_intensity_filter(image, params): + method = params.get("method", "smooth") + hsi = rgb_to_hsi(image) + intensity = np.round(hsi[..., 2] * 255.0).astype(np.uint8) + if method == "sharpen": + filtered = laplacian(intensity, {"mode": "sharpen", "sign": params.get("sign", "add")}) + else: + filtered = box_filter(intensity, {"size": params.get("size", 3)}) + hsi[..., 2] = filtered.astype(np.float32) / 255.0 + return hsi_to_rgb(hsi) + + +def pseudo_color_slices(image, params): + gray = to_gray(image) + slices = params.get( + "slices", + [ + {"start": 0, "end": 85, "color": [59, 130, 246]}, + {"start": 86, "end": 170, "color": [34, 197, 94]}, + {"start": 171, "end": 255, "color": [239, 68, 68]}, + ], + ) + output = np.zeros((*gray.shape, 3), dtype=np.uint8) + for item in slices: + start = int(item["start"]) + end = int(item["end"]) + color = np.array(item["color"], dtype=np.uint8) + if start < 0 or end > 255 or start > end or color.shape != (3,): + raise ProcessingError("Each pseudo-color slice requires start/end in 0..255 and an RGB color.") + output[(gray >= start) & (gray <= end)] = color + return output + + +def gray_to_color_sinusoidal(image, params): + gray = to_gray(image).astype(np.float32) / 255.0 + hue_frequency = float(params.get("hue_frequency", 1.0)) + saturation_frequency = float(params.get("saturation_frequency", 0.5)) + intensity_frequency = float(params.get("intensity_frequency", 0.25)) + h = (0.5 + 0.5 * np.sin(2.0 * np.pi * hue_frequency * gray)) % 1.0 + s = 0.55 + 0.4 * np.sin(2.0 * np.pi * saturation_frequency * gray + np.pi / 3.0) + i = 0.5 + 0.45 * np.sin(2.0 * np.pi * intensity_frequency * gray - np.pi / 2.0) + return hsi_to_rgb(np.stack([h, np.clip(s, 0, 1), np.clip(i, 0, 1)], axis=-1)) + + +OPERATIONS = { + "negative": negative, + "log": logarithmic, + "gamma": gamma, + "contrast_stretch": contrast_stretch, + "gray_slice": gray_slice, + "bit_plane": bit_plane, + "hist_equalization": histogram_equalization, + "hist_match": histogram_matching, + "local_equalization": local_equalization, + "box_filter": box_filter, + "weighted_average": weighted_average, + "median_filter": median_filter, + "laplacian": laplacian, + "high_boost": high_boost, + "sobel": sobel, + "roberts": roberts, + "hsi_intensity_filter": hsi_intensity_filter, + "pseudo_color_slices": pseudo_color_slices, + "gray_to_color_sinusoidal": gray_to_color_sinusoidal, +} + + +def process_image(image, operation, params=None): + params = params or {} + if operation not in OPERATIONS: + raise ProcessingError(f"Unsupported operation '{operation}'.") + return ensure_uint8(OPERATIONS[operation](ensure_uint8(image), params)) + + +def subtract_images(left, right): + verify_registration([left, right]) + diff = left.astype(np.float32) - right.astype(np.float32) + return normalize_to_uint8(np.abs(diff)) + + +def average_images(images): + verify_registration(images) + stack = np.stack([image.astype(np.float32) for image in images], axis=0) + return ensure_uint8(np.round(np.mean(stack, axis=0))) + + +def verify_registration(images): + if len(images) < 2: + raise ProcessingError("At least two registered images are required.") + shape = images[0].shape + if any(image.shape != shape for image in images[1:]): + raise ProcessingError("Images must have identical width, height, and channel count.") diff --git a/backend/processing/apps.py b/backend/processing/apps.py new file mode 100644 index 0000000..eac519d --- /dev/null +++ b/backend/processing/apps.py @@ -0,0 +1,6 @@ +from django.apps import AppConfig + + +class ProcessingConfig(AppConfig): + default_auto_field = "django.db.models.BigAutoField" + name = "processing" diff --git a/backend/processing/migrations/0001_initial.py b/backend/processing/migrations/0001_initial.py new file mode 100644 index 0000000..960a994 --- /dev/null +++ b/backend/processing/migrations/0001_initial.py @@ -0,0 +1,43 @@ +import uuid + +from django.db import migrations, models + + +class Migration(migrations.Migration): + initial = True + + dependencies = [] + + operations = [ + migrations.CreateModel( + name="ImageSession", + fields=[ + ("id", models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), + ("original_image", models.CharField(max_length=255)), + ("processed_image", models.CharField(blank=True, max_length=255)), + ("width", models.PositiveIntegerField()), + ("height", models.PositiveIntegerField()), + ("channels", models.PositiveSmallIntegerField()), + ("color_mode", models.CharField(max_length=16)), + ("original_histogram", models.JSONField(default=list)), + ("processed_histogram", models.JSONField(blank=True, default=list)), + ("created_at", models.DateTimeField(auto_now_add=True)), + ("expires_at", models.DateTimeField()), + ], + ), + migrations.CreateModel( + name="ProcessingJob", + fields=[ + ("id", models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), + ("operation", models.CharField(max_length=64)), + ("params", models.JSONField(blank=True, default=dict)), + ("status", models.CharField(choices=[("pending", "Pending"), ("running", "Running"), ("complete", "Complete"), ("failed", "Failed")], default="pending", max_length=16)), + ("progress", models.PositiveSmallIntegerField(default=0)), + ("result_image", models.CharField(blank=True, max_length=255)), + ("result_histogram", models.JSONField(blank=True, default=list)), + ("error", models.TextField(blank=True)), + ("created_at", models.DateTimeField(auto_now_add=True)), + ("updated_at", models.DateTimeField(auto_now=True)), + ], + ), + ] diff --git a/backend/processing/migrations/__init__.py b/backend/processing/migrations/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/backend/processing/migrations/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/processing/models.py b/backend/processing/models.py new file mode 100644 index 0000000..de22d07 --- /dev/null +++ b/backend/processing/models.py @@ -0,0 +1,53 @@ +import uuid + +from django.conf import settings +from django.db import models +from django.utils import timezone + + +class ImageSession(models.Model): + id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False) + original_image = models.CharField(max_length=255) + processed_image = models.CharField(max_length=255, blank=True) + width = models.PositiveIntegerField() + height = models.PositiveIntegerField() + channels = models.PositiveSmallIntegerField() + color_mode = models.CharField(max_length=16) + original_histogram = models.JSONField(default=list) + processed_histogram = models.JSONField(default=list, blank=True) + created_at = models.DateTimeField(auto_now_add=True) + expires_at = models.DateTimeField() + + def save(self, *args, **kwargs): + if not self.expires_at: + self.expires_at = timezone.now() + timezone.timedelta(hours=settings.IMAGE_SESSION_TTL_HOURS) + super().save(*args, **kwargs) + + @property + def expired(self): + return timezone.now() >= self.expires_at + + +class ProcessingJob(models.Model): + STATUS_PENDING = "pending" + STATUS_RUNNING = "running" + STATUS_COMPLETE = "complete" + STATUS_FAILED = "failed" + + STATUS_CHOICES = [ + (STATUS_PENDING, "Pending"), + (STATUS_RUNNING, "Running"), + (STATUS_COMPLETE, "Complete"), + (STATUS_FAILED, "Failed"), + ] + + id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False) + operation = models.CharField(max_length=64) + params = models.JSONField(default=dict, blank=True) + status = models.CharField(max_length=16, choices=STATUS_CHOICES, default=STATUS_PENDING) + progress = models.PositiveSmallIntegerField(default=0) + result_image = models.CharField(max_length=255, blank=True) + result_histogram = models.JSONField(default=list, blank=True) + error = models.TextField(blank=True) + created_at = models.DateTimeField(auto_now_add=True) + updated_at = models.DateTimeField(auto_now=True) diff --git a/backend/processing/selectors.py b/backend/processing/selectors.py new file mode 100644 index 0000000..a4b4ce0 --- /dev/null +++ b/backend/processing/selectors.py @@ -0,0 +1,9 @@ +from .models import ImageSession, ProcessingJob + + +def image_session_get(*, session_id): + return ImageSession.objects.filter(id=session_id).first() + + +def processing_job_get(*, job_id): + return ProcessingJob.objects.filter(id=job_id).first() diff --git a/backend/processing/services.py b/backend/processing/services.py new file mode 100644 index 0000000..5206b51 --- /dev/null +++ b/backend/processing/services.py @@ -0,0 +1,84 @@ +import time + +from django.conf import settings +from django.utils import timezone + +from .algorithms import ProcessingError, decode_image, histogram, process_image +from .models import ImageSession, ProcessingJob +from .storage import load_image_array, payload_for_image, save_image_array +from .tasks import run_batch_job + + +def image_session_create(*, uploaded_file=None, image_base64=None): + if uploaded_file and uploaded_file.size > settings.MAX_UPLOAD_MB * 1024 * 1024: + raise ProcessingError(f"Upload exceeds {settings.MAX_UPLOAD_MB} MB.") + + image = decode_image(uploaded_file=uploaded_file, base64_image=image_base64) + relative_path = save_image_array(image, "original") + hist = histogram(image) + session = ImageSession.objects.create( + original_image=relative_path, + width=image.shape[1], + height=image.shape[0], + channels=image.shape[2] if image.ndim == 3 else 1, + color_mode="RGB" if image.ndim == 3 else "L", + original_histogram=hist, + expires_at=timezone.now() + timezone.timedelta(hours=settings.IMAGE_SESSION_TTL_HOURS), + ) + payload = { + "session_id": str(session.id), + "width": session.width, + "height": session.height, + "channels": session.channels, + "color_mode": session.color_mode, + "original_histogram": hist, + "expires_at": session.expires_at.isoformat(), + } + payload.update(payload_for_image(image, relative_path)) + return payload + + +def image_session_process(*, session, operation, params): + if session.expired: + raise ProcessingError("Image session has expired.") + + started_at = time.perf_counter() + source = load_image_array(session.original_image) + result = process_image(source, operation, params) + relative_path = save_image_array(result, f"processed-{operation}") + hist = histogram(result) + + session.processed_image = relative_path + session.processed_histogram = hist + session.save(update_fields=["processed_image", "processed_histogram"]) + + payload = { + "session_id": str(session.id), + "operation": operation, + "params": params, + "processed_histogram": hist, + "elapsed_ms": round((time.perf_counter() - started_at) * 1000, 2), + } + payload.update(payload_for_image(result, relative_path)) + return payload + + +def batch_job_create(*, operation, session_ids, params=None): + job = ProcessingJob.objects.create(operation=operation, params=params or {}) + run_batch_job.delay(str(job.id), operation, [str(session_id) for session_id in session_ids]) + return job + + +def processing_job_payload(*, job): + payload = { + "job_id": str(job.id), + "operation": job.operation, + "status": job.status, + "progress": job.progress, + "error": job.error, + "result_histogram": job.result_histogram, + } + if job.result_image: + image = load_image_array(job.result_image) + payload.update(payload_for_image(image, job.result_image)) + return payload diff --git a/backend/processing/storage.py b/backend/processing/storage.py new file mode 100644 index 0000000..e4b5538 --- /dev/null +++ b/backend/processing/storage.py @@ -0,0 +1,57 @@ +from pathlib import Path +from uuid import uuid4 + +import cv2 +import numpy as np +from django.conf import settings + +from .algorithms import ProcessingError, ensure_uint8, image_to_data_url + + +def session_dir(): + path = Path(settings.MEDIA_ROOT) / "sessions" + path.mkdir(parents=True, exist_ok=True) + return path + + +def save_image_array(image, prefix="image"): + filename = f"sessions/{prefix}-{uuid4().hex}.png" + path = Path(settings.MEDIA_ROOT) / filename + path.parent.mkdir(parents=True, exist_ok=True) + rgb = ensure_uint8(image) + bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR) + ok, encoded = cv2.imencode(".png", bgr) + if not ok: + raise ProcessingError("Unable to encode image for temporary storage.") + path.write_bytes(encoded.tobytes()) + return filename + + +def load_image_array(relative_path): + path = Path(settings.MEDIA_ROOT) / relative_path + if not path.exists(): + raise ProcessingError("Temporary image file is missing or unreadable.") + raw = np.frombuffer(path.read_bytes(), dtype=np.uint8) + image = cv2.imdecode(raw, cv2.IMREAD_COLOR) + if image is None: + raise ProcessingError("Temporary image file is missing or unreadable.") + return cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.uint8) + + +def delete_relative_file(relative_path): + if not relative_path: + return + path = (Path(settings.MEDIA_ROOT) / relative_path).resolve() + media_root = Path(settings.MEDIA_ROOT).resolve() + if media_root not in path.parents and path != media_root: + return + if path.exists(): + path.unlink() + + +def payload_for_image(image, relative_path): + return { + "image_path": relative_path, + "image_url": f"{settings.MEDIA_URL}{relative_path}", + "image_data": image_to_data_url(image), + } diff --git a/backend/processing/tasks.py b/backend/processing/tasks.py new file mode 100644 index 0000000..f2474d8 --- /dev/null +++ b/backend/processing/tasks.py @@ -0,0 +1,56 @@ +from pathlib import Path + +from celery import shared_task +from django.utils import timezone + +from .algorithms import ProcessingError, average_images, histogram, subtract_images +from .models import ImageSession, ProcessingJob +from .storage import delete_relative_file, load_image_array, save_image_array + + +@shared_task(bind=True) +def run_batch_job(self, job_id, operation, session_ids): + job = ProcessingJob.objects.get(id=job_id) + job.status = ProcessingJob.STATUS_RUNNING + job.progress = 10 + job.save(update_fields=["status", "progress", "updated_at"]) + try: + sessions = list(ImageSession.objects.filter(id__in=session_ids)) + if len(sessions) != len(session_ids): + raise ProcessingError("One or more sessions do not exist.") + images = [load_image_array(session.processed_image or session.original_image) for session in sessions] + job.progress = 45 + job.save(update_fields=["progress", "updated_at"]) + if operation == "average": + result = average_images(images) + elif operation == "subtract": + result = subtract_images(images[0], images[1]) + else: + raise ProcessingError(f"Unsupported batch operation '{operation}'.") + relative_path = save_image_array(result, f"batch-{operation}") + job.status = ProcessingJob.STATUS_COMPLETE + job.progress = 100 + job.result_image = relative_path + job.result_histogram = histogram(result) + job.error = "" + job.save(update_fields=["status", "progress", "result_image", "result_histogram", "error", "updated_at"]) + except Exception as exc: + job.status = ProcessingJob.STATUS_FAILED + job.error = str(exc) + job.progress = 100 + job.save(update_fields=["status", "error", "progress", "updated_at"]) + raise + + +@shared_task +def cleanup_expired_sessions(): + expired = ImageSession.objects.filter(expires_at__lt=timezone.now()) + for session in expired: + delete_relative_file(session.original_image) + delete_relative_file(session.processed_image) + expired.delete() + + old_jobs = ProcessingJob.objects.filter(created_at__lt=timezone.now() - timezone.timedelta(hours=24)) + for job in old_jobs: + delete_relative_file(job.result_image) + old_jobs.delete() diff --git a/backend/processing/tests/test_algorithms.py b/backend/processing/tests/test_algorithms.py new file mode 100644 index 0000000..c0a0da9 --- /dev/null +++ b/backend/processing/tests/test_algorithms.py @@ -0,0 +1,61 @@ +import numpy as np +from django.test import SimpleTestCase + +from processing.algorithms import ( + LAPLACIAN_MASK, + ProcessingError, + average_images, + gamma, + histogram_equalization, + median_filter, + negative, + roberts, + sobel, + subtract_images, + verify_registration, +) + + +class AlgorithmTests(SimpleTestCase): + def test_negative_transform_uses_l_minus_one(self): + image = np.array([[[0, 127, 255]]], dtype=np.uint8) + result = negative(image, {}) + np.testing.assert_array_equal(result, np.array([[[255, 128, 0]]], dtype=np.uint8)) + + def test_gamma_identity(self): + image = np.array([[[0, 128, 255]]], dtype=np.uint8) + result = gamma(image, {"gamma": 1, "c": 1}) + np.testing.assert_array_equal(result, image) + + def test_laplacian_mask_sums_to_zero(self): + self.assertEqual(int(LAPLACIAN_MASK.sum()), 0) + + def test_histogram_equalization_spreads_two_levels(self): + image = np.array([[0, 0], [255, 255]], dtype=np.uint8) + result = histogram_equalization(image, {}) + expected = np.dstack([image, image, image]) + np.testing.assert_array_equal(result, expected) + + def test_median_removes_impulse_noise(self): + image = np.full((3, 3, 3), 100, dtype=np.uint8) + image[1, 1] = 255 + result = median_filter(image, {"size": 3}) + self.assertEqual(int(result[1, 1, 0]), 100) + + def test_gradient_outputs_are_display_normalized(self): + image = np.zeros((5, 5, 3), dtype=np.uint8) + image[:, 3:] = 255 + self.assertEqual(sobel(image, {}).dtype, np.uint8) + self.assertEqual(roberts(image, {}).dtype, np.uint8) + + def test_arithmetic_requires_registered_shapes(self): + left = np.zeros((2, 2, 3), dtype=np.uint8) + right = np.zeros((3, 2, 3), dtype=np.uint8) + with self.assertRaises(ProcessingError): + verify_registration([left, right]) + + def test_average_and_subtraction(self): + left = np.zeros((2, 2, 3), dtype=np.uint8) + right = np.full((2, 2, 3), 100, dtype=np.uint8) + self.assertEqual(int(average_images([left, right])[0, 0, 0]), 50) + self.assertEqual(int(subtract_images(left, right)[0, 0, 0]), 0) diff --git a/backend/processing/tests/test_api.py b/backend/processing/tests/test_api.py new file mode 100644 index 0000000..53b9db3 --- /dev/null +++ b/backend/processing/tests/test_api.py @@ -0,0 +1,64 @@ +import tempfile +from io import BytesIO +from pathlib import Path +from unittest.mock import patch + +from django.core.files.uploadedfile import SimpleUploadedFile +from django.test import TestCase, override_settings +from PIL import Image +from rest_framework.test import APIClient + + +def png_upload(color=(32, 64, 128), size=(4, 4), name="sample.png"): + buffer = BytesIO() + Image.new("RGB", size, color).save(buffer, format="PNG") + return SimpleUploadedFile(name, buffer.getvalue(), content_type="image/png") + + +class ApiTests(TestCase): + def setUp(self): + self.tmp = tempfile.TemporaryDirectory() + self.override = override_settings(MEDIA_ROOT=Path(self.tmp.name), IMAGE_SESSION_TTL_HOURS=1) + self.override.enable() + self.client = APIClient() + + def tearDown(self): + self.override.disable() + self.tmp.cleanup() + + def test_upload_and_process(self): + upload = self.client.post("/api/images/", {"image": png_upload()}, format="multipart") + self.assertEqual(upload.status_code, 201) + session_id = upload.data["session_id"] + self.assertEqual(len(upload.data["original_histogram"]), 256) + + processed = self.client.post( + "/api/process/", + {"session_id": session_id, "operation": "gamma", "params": {"gamma": 1, "c": 1}}, + format="json", + ) + self.assertEqual(processed.status_code, 200) + self.assertEqual(len(processed.data["processed_histogram"]), 256) + self.assertTrue(processed.data["image_data"].startswith("data:image/png;base64,")) + + def test_invalid_kernel_rejected(self): + upload = self.client.post("/api/images/", {"image": png_upload()}, format="multipart") + processed = self.client.post( + "/api/process/", + {"session_id": upload.data["session_id"], "operation": "median_filter", "params": {"size": 4}}, + format="json", + ) + self.assertEqual(processed.status_code, 400) + + @patch("processing.services.run_batch_job.delay") + def test_batch_returns_job_id(self, delay): + first = self.client.post("/api/images/", {"image": png_upload(name="a.png")}, format="multipart") + second = self.client.post("/api/images/", {"image": png_upload(color=(96, 96, 96), name="b.png")}, format="multipart") + response = self.client.post( + "/api/batch/", + {"operation": "average", "session_ids": [first.data["session_id"], second.data["session_id"]]}, + format="json", + ) + self.assertEqual(response.status_code, 202) + self.assertIn("job_id", response.data) + delay.assert_called_once() diff --git a/backend/processing/urls.py b/backend/processing/urls.py new file mode 100644 index 0000000..43e61fb --- /dev/null +++ b/backend/processing/urls.py @@ -0,0 +1,12 @@ +from django.urls import path + +from .views import BatchView, HealthView, ImageUploadView, JobDetailView, ProcessView + + +urlpatterns = [ + path("health/", HealthView.as_view(), name="health"), + path("images/", ImageUploadView.as_view(), name="image-upload"), + path("process/", ProcessView.as_view(), name="process"), + path("batch/", BatchView.as_view(), name="batch"), + path("jobs//", JobDetailView.as_view(), name="job-detail"), +] diff --git a/backend/processing/views.py b/backend/processing/views.py new file mode 100644 index 0000000..2aa9504 --- /dev/null +++ b/backend/processing/views.py @@ -0,0 +1,89 @@ +from rest_framework import serializers, status +from rest_framework.response import Response +from rest_framework.views import APIView + +from .algorithms import ProcessingError +from .selectors import image_session_get, processing_job_get +from .services import batch_job_create, image_session_create, image_session_process, processing_job_payload + + +def error_response(message, code=status.HTTP_400_BAD_REQUEST): + return Response({"detail": message}, status=code) + + +class ImageUploadView(APIView): + class InputSerializer(serializers.Serializer): + image = serializers.ImageField(required=False) + image_base64 = serializers.CharField(required=False, allow_blank=False) + + def validate(self, attrs): + if not attrs.get("image") and not attrs.get("image_base64"): + raise serializers.ValidationError("Provide image or image_base64.") + return attrs + + def post(self, request): + serializer = self.InputSerializer(data=request.data) + serializer.is_valid(raise_exception=True) + try: + payload = image_session_create( + uploaded_file=serializer.validated_data.get("image"), + image_base64=serializer.validated_data.get("image_base64"), + ) + return Response(payload, status=status.HTTP_201_CREATED) + except ProcessingError as exc: + return error_response(str(exc)) + + +class ProcessView(APIView): + class InputSerializer(serializers.Serializer): + session_id = serializers.UUIDField() + operation = serializers.CharField() + params = serializers.DictField(required=False, default=dict) + + def post(self, request): + serializer = self.InputSerializer(data=request.data) + serializer.is_valid(raise_exception=True) + session = image_session_get(session_id=serializer.validated_data["session_id"]) + if session is None: + return error_response("Image session does not exist.", status.HTTP_404_NOT_FOUND) + + try: + payload = image_session_process( + session=session, + operation=serializer.validated_data["operation"], + params=serializer.validated_data.get("params", {}), + ) + return Response(payload) + except ProcessingError as exc: + code = status.HTTP_410_GONE if str(exc) == "Image session has expired." else status.HTTP_400_BAD_REQUEST + return error_response(str(exc), code) + + +class BatchView(APIView): + class InputSerializer(serializers.Serializer): + operation = serializers.ChoiceField(choices=["average", "subtract"]) + session_ids = serializers.ListField(child=serializers.UUIDField(), min_length=2) + params = serializers.DictField(required=False, default=dict) + + def post(self, request): + serializer = self.InputSerializer(data=request.data) + serializer.is_valid(raise_exception=True) + job = batch_job_create( + operation=serializer.validated_data["operation"], + session_ids=serializer.validated_data["session_ids"], + params=serializer.validated_data.get("params", {}), + ) + return Response({"job_id": str(job.id), "status": job.status}, status=status.HTTP_202_ACCEPTED) + + +class JobDetailView(APIView): + def get(self, request, job_id): + job = processing_job_get(job_id=job_id) + if job is None: + return error_response("Job does not exist.", status.HTTP_404_NOT_FOUND) + return Response(processing_job_payload(job=job)) + + +class HealthView(APIView): + def get(self, request): + return Response({"status": "ok"}) diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000..a53c86f --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,12 @@ +Django==5.0.7 +djangorestframework==3.15.2 +django-cors-headers==4.4.0 +python-dotenv==1.0.1 +whitenoise==6.7.0 +celery==5.4.0 +redis==5.0.7 +psycopg2-binary==2.9.9 +opencv-python-headless==4.10.0.84 +numpy==1.26.4 +Pillow==10.4.0 +gunicorn==22.0.0 diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..96d60b9 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,116 @@ +services: + db: + image: postgres:16-alpine + environment: + POSTGRES_DB: ${POSTGRES_DB:-enhancer} + POSTGRES_USER: ${POSTGRES_USER:-enhancer} + POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-enhancer} + volumes: + - postgres_data:/var/lib/postgresql/data + healthcheck: + test: ["CMD-SHELL", "pg_isready -U $${POSTGRES_USER} -d $${POSTGRES_DB}"] + interval: 10s + timeout: 5s + retries: 5 + + redis: + image: redis:7-alpine + command: ["redis-server", "--appendonly", "yes"] + volumes: + - redis_data:/data + healthcheck: + test: ["CMD", "redis-cli", "ping"] + interval: 10s + timeout: 5s + retries: 5 + + api: + build: ./backend + command: sh -c "python manage.py migrate && python manage.py collectstatic --noinput && gunicorn enhancer_project.wsgi:application --bind 0.0.0.0:8000" + env_file: + - ./backend/.env + environment: + DJANGO_DEBUG: ${DJANGO_DEBUG:-0} + DJANGO_SECRET_KEY: ${DJANGO_SECRET_KEY:-change-me} + DJANGO_ALLOWED_HOSTS: ${DJANGO_ALLOWED_HOSTS:-localhost,127.0.0.1,api} + DJANGO_CSRF_TRUSTED_ORIGINS: ${DJANGO_CSRF_TRUSTED_ORIGINS:-http://localhost,https://localhost} + DJANGO_SECURE_SSL_REDIRECT: ${DJANGO_SECURE_SSL_REDIRECT:-0} + DJANGO_SESSION_COOKIE_SECURE: ${DJANGO_SESSION_COOKIE_SECURE:-0} + DJANGO_CSRF_COOKIE_SECURE: ${DJANGO_CSRF_COOKIE_SECURE:-0} + DJANGO_SECURE_HSTS_SECONDS: ${DJANGO_SECURE_HSTS_SECONDS:-0} + DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS: ${DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS:-0} + DJANGO_SECURE_HSTS_PRELOAD: ${DJANGO_SECURE_HSTS_PRELOAD:-0} + CORS_ALLOWED_ORIGINS: ${CORS_ALLOWED_ORIGINS:-http://localhost:5173,http://localhost} + POSTGRES_HOST: db + POSTGRES_DB: ${POSTGRES_DB:-enhancer} + POSTGRES_USER: ${POSTGRES_USER:-enhancer} + POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-enhancer} + CELERY_BROKER_URL: redis://redis:6379/0 + CELERY_RESULT_BACKEND: redis://redis:6379/0 + IMAGE_SESSION_TTL_HOURS: ${IMAGE_SESSION_TTL_HOURS:-6} + volumes: + - media_data:/app/media + depends_on: + db: + condition: service_healthy + redis: + condition: service_healthy + + worker: + build: ./backend + command: celery -A enhancer_project worker --loglevel=info + env_file: + - ./backend/.env + environment: + DJANGO_DEBUG: ${DJANGO_DEBUG:-0} + DJANGO_SECRET_KEY: ${DJANGO_SECRET_KEY:-change-me} + DJANGO_ALLOWED_HOSTS: ${DJANGO_ALLOWED_HOSTS:-localhost,127.0.0.1,api} + DJANGO_CSRF_TRUSTED_ORIGINS: ${DJANGO_CSRF_TRUSTED_ORIGINS:-http://localhost,https://localhost} + DJANGO_SECURE_SSL_REDIRECT: ${DJANGO_SECURE_SSL_REDIRECT:-0} + DJANGO_SESSION_COOKIE_SECURE: ${DJANGO_SESSION_COOKIE_SECURE:-0} + DJANGO_CSRF_COOKIE_SECURE: ${DJANGO_CSRF_COOKIE_SECURE:-0} + DJANGO_SECURE_HSTS_SECONDS: ${DJANGO_SECURE_HSTS_SECONDS:-0} + DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS: ${DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS:-0} + DJANGO_SECURE_HSTS_PRELOAD: ${DJANGO_SECURE_HSTS_PRELOAD:-0} + CORS_ALLOWED_ORIGINS: ${CORS_ALLOWED_ORIGINS:-http://localhost:5173,http://localhost} + POSTGRES_HOST: db + POSTGRES_DB: ${POSTGRES_DB:-enhancer} + POSTGRES_USER: ${POSTGRES_USER:-enhancer} + POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-enhancer} + CELERY_BROKER_URL: redis://redis:6379/0 + CELERY_RESULT_BACKEND: redis://redis:6379/0 + IMAGE_SESSION_TTL_HOURS: ${IMAGE_SESSION_TTL_HOURS:-6} + volumes: + - media_data:/app/media + depends_on: + db: + condition: service_healthy + redis: + condition: service_healthy + + web: + build: ./frontend + depends_on: + - api + + caddy: + image: caddy:2-alpine + ports: + - "80:80" + - "443:443" + environment: + CADDY_DOMAIN: ${CADDY_DOMAIN:-localhost} + volumes: + - ./Caddyfile:/etc/caddy/Caddyfile:ro + - caddy_data:/data + - caddy_config:/config + depends_on: + - web + - api + +volumes: + postgres_data: + redis_data: + media_data: + caddy_data: + caddy_config: diff --git a/frontend/.dockerignore b/frontend/.dockerignore new file mode 100644 index 0000000..5124ac2 --- /dev/null +++ b/frontend/.dockerignore @@ -0,0 +1,7 @@ +node_modules/ +dist/ +coverage/ +.vite/ +.env +.env.local +*.log diff --git a/frontend/.env.sample b/frontend/.env.sample new file mode 100644 index 0000000..27786df --- /dev/null +++ b/frontend/.env.sample @@ -0,0 +1 @@ +VITE_API_BASE=http://localhost:8000 diff --git a/frontend/Dockerfile b/frontend/Dockerfile new file mode 100644 index 0000000..a265a4f --- /dev/null +++ b/frontend/Dockerfile @@ -0,0 +1,11 @@ +FROM node:22-alpine AS build + +WORKDIR /app +COPY package.json package-lock.json* ./ +RUN npm install +COPY . . +RUN npm run build + +FROM nginx:1.27-alpine +COPY --from=build /app/dist /usr/share/nginx/html +COPY nginx.conf /etc/nginx/conf.d/default.conf diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000..8fd5e2a --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,12 @@ + + + + + + Spatial Image Enhancer Pro + + +
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b/frontend/postcss.config.js new file mode 100644 index 0000000..ba80730 --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {} + } +}; diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx new file mode 100644 index 0000000..7f3a374 --- /dev/null +++ b/frontend/src/App.jsx @@ -0,0 +1,152 @@ +import { useEffect, useMemo, useState } from "react"; +import CanvasPane from "./components/CanvasPane.jsx"; +import Controls from "./components/Controls.jsx"; +import HistogramPanel from "./components/HistogramPanel.jsx"; +import { createBatch, getJob, processImage, uploadImage } from "./lib/api.js"; +import { useDebouncedEffect } from "./lib/debounce.js"; + +export default function App() { + const [session, setSession] = useState(null); + const [processed, setProcessed] = useState(null); + const [batchSessions, setBatchSessions] = useState([]); + const [operation, setOperation] = useState("gamma"); + const [params, setParams] = useState({ gamma: 1 }); + const [status, setStatus] = useState("Upload an image to begin."); + const [busy, setBusy] = useState(false); + const [transform, setTransform] = useState({ x: 0, y: 0, scale: 1 }); + + const originalHistogram = session?.original_histogram; + const processedHistogram = processed?.processed_histogram || processed?.result_histogram; + + async function handleUpload(file) { + if (!file) return; + setBusy(true); + setStatus("Uploading image..."); + try { + const payload = await uploadImage(file); + setSession(payload); + setProcessed(null); + setBatchSessions([payload.session_id]); + setTransform({ x: 0, y: 0, scale: 1 }); + setStatus(`${payload.width} x ${payload.height} ${payload.color_mode} image loaded.`); + } catch (error) { + setStatus(error.message); + } finally { + setBusy(false); + } + } + + async function handleBatchUpload(file) { + if (!file) return; + setBusy(true); + setStatus("Uploading batch image..."); + try { + const payload = await uploadImage(file); + setBatchSessions((current) => [...current, payload.session_id]); + setStatus("Batch image added."); + } catch (error) { + setStatus(error.message); + } finally { + setBusy(false); + } + } + + async function runBatch(kind) { + setBusy(true); + setStatus(`Starting ${kind} job...`); + try { + const job = await createBatch(kind, batchSessions); + const result = await pollJob(job.job_id); + setProcessed(result); + setStatus(`${kind} complete.`); + } catch (error) { + setStatus(error.message); + } finally { + setBusy(false); + } + } + + async function pollJob(jobId) { + for (let attempt = 0; attempt < 80; attempt += 1) { + const job = await getJob(jobId); + setStatus(`Job ${job.status}: ${job.progress}%`); + if (job.status === "complete") return job; + if (job.status === "failed") throw new Error(job.error || "Batch job failed"); + await new Promise((resolve) => window.setTimeout(resolve, 1000)); + } + throw new Error("Batch job timed out."); + } + + useDebouncedEffect( + () => { + if (!session?.session_id || !operation) return; + let cancelled = false; + async function run() { + setBusy(true); + setStatus(`Processing ${operation}...`); + try { + const payload = await processImage(session.session_id, operation, params); + if (!cancelled) { + setProcessed(payload); + setStatus(`${operation} complete in ${payload.elapsed_ms} ms.`); + } + } catch (error) { + if (!cancelled) setStatus(error.message); + } finally { + if (!cancelled) setBusy(false); + } + } + run(); + return () => { + cancelled = true; + }; + }, + [session?.session_id, operation, JSON.stringify(params)], + 300 + ); + + const processedImage = processed?.image_data || session?.image_data; + const originalImage = session?.image_data; + + const viewportTitle = useMemo(() => { + if (!session) return "No image"; + return `${session.width} x ${session.height} ${session.color_mode}`; + }, [session]); + + return ( +
+ { + setOperation(nextOperation); + setParams(nextParams); + }} + onParamChange={(key, value) => setParams((current) => ({ ...current, [key]: value }))} + onUpload={handleUpload} + onBatchUpload={handleBatchUpload} + batchCount={batchSessions.length} + onBatchRun={runBatch} + disabled={!session || busy} + busy={busy} + /> + +
+
+
+

Professional Dark Studio

+

{viewportTitle}

+
+
{busy ? "Working..." : status}
+
+ +
+ + +
+ + +
+
+ ); +} diff --git a/frontend/src/App.real-render.test.jsx b/frontend/src/App.real-render.test.jsx new file mode 100644 index 0000000..1ee3abd --- /dev/null +++ b/frontend/src/App.real-render.test.jsx @@ -0,0 +1,9 @@ +import { render, screen } from "@testing-library/react"; +import App from "./App.jsx"; + +describe("App real render", () => { + it("mounts without mocking third-party components", () => { + render(); + expect(screen.getByText("Spatial Image Enhancer Pro")).toBeInTheDocument(); + }); +}); diff --git a/frontend/src/App.test.jsx b/frontend/src/App.test.jsx new file mode 100644 index 0000000..edd2157 --- /dev/null +++ b/frontend/src/App.test.jsx @@ -0,0 +1,15 @@ +import { render, screen } from "@testing-library/react"; +import App from "./App.jsx"; + +vi.mock("react-quick-pinch-zoom", () => ({ + default: ({ children }) =>
{children}
+})); + +describe("App", () => { + it("renders the processing studio immediately", () => { + render(); + expect(screen.getByText("Spatial Image Enhancer Pro")).toBeInTheDocument(); + expect(screen.getAllByText("Original").length).toBeGreaterThan(0); + expect(screen.getAllByText("Processed").length).toBeGreaterThan(0); + }); +}); diff --git a/frontend/src/components/CanvasPane.jsx b/frontend/src/components/CanvasPane.jsx new file mode 100644 index 0000000..6819d61 --- /dev/null +++ b/frontend/src/components/CanvasPane.jsx @@ -0,0 +1,49 @@ +import { useEffect, useRef } from "react"; +import QuickPinchZoom from "react-quick-pinch-zoom"; + +function drawToCanvas(canvas, imageData) { + if (!canvas || !imageData) return; + const context = canvas.getContext("2d"); + const image = new Image(); + image.onload = () => { + canvas.width = image.naturalWidth; + canvas.height = image.naturalHeight; + context.clearRect(0, 0, canvas.width, canvas.height); + context.drawImage(image, 0, 0); + }; + image.src = imageData; +} + +export default function CanvasPane({ title, imageData, histogram, transform, onTransform }) { + const canvasRef = useRef(null); + const holderRef = useRef(null); + + useEffect(() => { + drawToCanvas(canvasRef.current, imageData); + }, [imageData]); + + useEffect(() => { + if (!holderRef.current) return; + holderRef.current.style.transform = `translate3d(${transform.x}px, ${transform.y}px, 0) scale(${transform.scale})`; + }, [transform]); + + return ( +
+
+

{title}

+ {histogram ? "p(r_k) ready" : "No histogram"} +
+
+ {imageData ? ( + +
+ +
+ + ) : ( +
Upload an image to start processing.
+ )} +
+
+ ); +} diff --git a/frontend/src/components/Controls.jsx b/frontend/src/components/Controls.jsx new file mode 100644 index 0000000..4bc17f9 --- /dev/null +++ b/frontend/src/components/Controls.jsx @@ -0,0 +1,167 @@ +import { Activity, Aperture, Blend, Layers, SlidersHorizontal, Upload } from "lucide-react"; + +const groups = [ + { + title: "Intensity", + icon: SlidersHorizontal, + operations: [ + { id: "negative", label: "Negative", params: [] }, + { id: "log", label: "Log", params: [{ key: "c", label: "c", min: 0.1, max: 3, step: 0.05, default: 1.44 }] }, + { id: "gamma", label: "Gamma", params: [{ key: "gamma", label: "Gamma", min: 0.1, max: 4, step: 0.05, default: 1 }] }, + { + id: "contrast_stretch", + label: "Contrast Stretch", + params: [ + { key: "low", label: "Low", min: 0, max: 254, step: 1, default: 30 }, + { key: "high", label: "High", min: 1, max: 255, step: 1, default: 220 } + ] + }, + { + id: "gray_slice", + label: "Gray Slice", + params: [ + { key: "start", label: "Start", min: 0, max: 255, step: 1, default: 96 }, + { key: "end", label: "End", min: 0, max: 255, step: 1, default: 160 } + ] + }, + { id: "bit_plane", label: "Bit Plane", params: [{ key: "bit", label: "Bit", min: 0, max: 7, step: 1, default: 7 }] } + ] + }, + { + title: "Histogram", + icon: Activity, + operations: [ + { id: "hist_equalization", label: "Global Equalization", params: [] }, + { id: "hist_match", label: "Match Uniform", params: [] }, + { id: "local_equalization", label: "Local Equalization", params: [{ key: "size", label: "Window", min: 3, max: 31, step: 2, default: 7 }] } + ] + }, + { + title: "Spatial Filters", + icon: Aperture, + operations: [ + { id: "box_filter", label: "Box", params: [{ key: "size", label: "Size", min: 3, max: 35, step: 2, default: 3 }] }, + { id: "weighted_average", label: "Weighted Avg", params: [{ key: "size", label: "Size", min: 3, max: 35, step: 2, default: 3 }] }, + { id: "median_filter", label: "Median", params: [{ key: "size", label: "Size", min: 3, max: 15, step: 2, default: 3 }] }, + { id: "laplacian", label: "Laplacian", params: [] }, + { + id: "high_boost", + label: "High Boost", + params: [ + { key: "amplification", label: "A", min: 1, max: 5, step: 0.1, default: 1.5 }, + { key: "size", label: "Size", min: 3, max: 35, step: 2, default: 3 } + ] + }, + { id: "sobel", label: "Sobel", params: [] }, + { id: "roberts", label: "Roberts", params: [] } + ] + }, + { + title: "Color", + icon: Blend, + operations: [ + { id: "pseudo_color_slices", label: "Intensity Slices", params: [] }, + { id: "gray_to_color_sinusoidal", label: "HSI Sinusoids", params: [{ key: "hue_frequency", label: "Hue Freq", min: 0.2, max: 4, step: 0.1, default: 1 }] }, + { id: "hsi_intensity_filter", label: "HSI Smooth I", params: [{ key: "size", label: "Size", min: 3, max: 15, step: 2, default: 3 }] } + ] + } +]; + +function initialParams(operation) { + return Object.fromEntries(operation.params.map((param) => [param.key, param.default])); +} + +export default function Controls({ + selected, + params, + onOperationChange, + onParamChange, + onUpload, + onBatchUpload, + batchCount, + onBatchRun, + disabled, + busy +}) { + return ( + + ); +} diff --git a/frontend/src/components/HistogramPanel.jsx b/frontend/src/components/HistogramPanel.jsx new file mode 100644 index 0000000..2ad1802 --- /dev/null +++ b/frontend/src/components/HistogramPanel.jsx @@ -0,0 +1,36 @@ +import { Area, AreaChart, CartesianGrid, ResponsiveContainer, Tooltip, XAxis, YAxis } from "recharts"; + +function toChartData(original, processed) { + return Array.from({ length: 256 }, (_, level) => ({ + level, + original: original?.[level] ?? 0, + processed: processed?.[level] ?? 0 + })); +} + +export default function HistogramPanel({ original, processed }) { + const data = toChartData(original, processed); + return ( +
+
+

Histogram Analytics

+
+ Original + Processed +
+
+
+ + + + + + + + + + +
+
+ ); +} diff --git a/frontend/src/lib/api.js b/frontend/src/lib/api.js new file mode 100644 index 0000000..6536aa0 --- /dev/null +++ b/frontend/src/lib/api.js @@ -0,0 +1,42 @@ +const API_BASE = import.meta.env.VITE_API_BASE || ""; + +async function parseResponse(response) { + const payload = await response.json().catch(() => ({})); + if (!response.ok) { + throw new Error(payload.detail || "Request failed"); + } + return payload; +} + +export async function uploadImage(file) { + const body = new FormData(); + body.append("image", file); + const response = await fetch(`${API_BASE}/api/images/`, { + method: "POST", + body + }); + return parseResponse(response); +} + +export async function processImage(sessionId, operation, params) { + const response = await fetch(`${API_BASE}/api/process/`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ session_id: sessionId, operation, params }) + }); + return parseResponse(response); +} + +export async function createBatch(operation, sessionIds, params = {}) { + const response = await fetch(`${API_BASE}/api/batch/`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ operation, session_ids: sessionIds, params }) + }); + return parseResponse(response); +} + +export async function getJob(jobId) { + const response = await fetch(`${API_BASE}/api/jobs/${jobId}/`); + return parseResponse(response); +} diff --git a/frontend/src/lib/debounce.js b/frontend/src/lib/debounce.js new file mode 100644 index 0000000..a1a4afc --- /dev/null +++ b/frontend/src/lib/debounce.js @@ -0,0 +1,14 @@ +import { useEffect } from "react"; + +export function useDebouncedEffect(effect, deps, delay = 300) { + useEffect(() => { + let cleanup; + const handle = window.setTimeout(() => { + cleanup = effect(); + }, delay); + return () => { + window.clearTimeout(handle); + if (typeof cleanup === "function") cleanup(); + }; + }, deps); +} diff --git a/frontend/src/main.jsx b/frontend/src/main.jsx new file mode 100644 index 0000000..201a174 --- /dev/null +++ b/frontend/src/main.jsx @@ -0,0 +1,10 @@ +import React from "react"; +import { createRoot } from "react-dom/client"; +import App from "./App.jsx"; +import "./styles/app.css"; + +createRoot(document.getElementById("root")).render( + + + +); diff --git a/frontend/src/styles/app.css b/frontend/src/styles/app.css new file mode 100644 index 0000000..19ffdab --- /dev/null +++ b/frontend/src/styles/app.css @@ -0,0 +1,29 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + color-scheme: dark; + background: #09090b; +} + +body { + margin: 0; + min-width: 320px; + min-height: 100vh; + background: #09090b; + color: #e4e4e7; +} + +canvas { + max-width: 100%; + height: auto; + image-rendering: auto; +} + +.studio-grid { + background-image: + linear-gradient(rgba(148, 163, 184, 0.08) 1px, transparent 1px), + linear-gradient(90deg, rgba(148, 163, 184, 0.08) 1px, transparent 1px); + background-size: 24px 24px; +} diff --git a/frontend/src/test-setup.js b/frontend/src/test-setup.js new file mode 100644 index 0000000..d0de870 --- /dev/null +++ b/frontend/src/test-setup.js @@ -0,0 +1 @@ +import "@testing-library/jest-dom"; diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js new file mode 100644 index 0000000..a28e615 --- /dev/null +++ b/frontend/tailwind.config.js @@ -0,0 +1,11 @@ +export default { + content: ["./index.html", "./src/**/*.{js,jsx}"], + theme: { + extend: { + fontFamily: { + sans: ["Inter", "ui-sans-serif", "system-ui", "sans-serif"] + } + } + }, + plugins: [] +}; diff --git a/frontend/vite.config.js b/frontend/vite.config.js new file mode 100644 index 0000000..fc1c20a --- /dev/null +++ b/frontend/vite.config.js @@ -0,0 +1,18 @@ +import { defineConfig } from "vite"; +import react from "@vitejs/plugin-react"; + +export default defineConfig({ + plugins: [react()], + server: { + port: 5173, + proxy: { + "/api": "http://localhost:8000", + "/media": "http://localhost:8000" + } + }, + test: { + environment: "jsdom", + globals: true, + setupFiles: "./src/test-setup.js" + } +});