feat: consolidate backend and docker-compose setup
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Rafhan Mazaya Fathurrahman committed
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#!/usr/bin/env python3
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"""
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Ultralytics YOLO Classification Training Script
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Trains a product-packaging classifier from class folders in `foto-kemasan-v2`.
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Each subfolder under `foto-kemasan-v2/` is one product class; images live directly
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inside that folder.
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Usage (from repo root or this directory):
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# 1) Train the model (defaults to foto-kemasan-v2, 100 epochs)
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uv run python pfm-web-app/public/produk-pfm/train_classifier.py train --imgsz 224
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# 2) Run prediction on an image using the trained weights
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uv run python pfm-web-app/public/produk-pfm/train_classifier.py predict \\
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--image "pfm-web-app/public/produk-pfm/foto-kemasan-v2/15030101 FIESTA CRINKLE CUT 500 GR/WhatsApp Image 2026-05-28 at 11.46.31.jpeg"
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"""
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import os
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import re
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import sys
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import shutil
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import random
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import argparse
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from datetime import date
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from pathlib import Path
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import torch
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try:
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from ultralytics import YOLO
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except ImportError:
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print("Error: 'ultralytics' library not found. Please install it using: uv add ultralytics")
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sys.exit(1)
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SCRIPT_DIR = Path(__file__).resolve().parent
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DEFAULT_DATASET_DIR = SCRIPT_DIR / "foto-kemasan-v2"
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DEFAULT_SPLIT_DIR = SCRIPT_DIR / "yolo_dataset"
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DEFAULT_MODEL = SCRIPT_DIR / "yolo26n-cls.pt"
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DEFAULT_MODELS_DIR = SCRIPT_DIR / "models"
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DEFAULT_PROJECT = SCRIPT_DIR / "runs" / "classify"
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DEFAULT_EPOCHS = 100
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def classifier_output_path(epochs: int = DEFAULT_EPOCHS, run_date: date | None = None) -> Path:
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"""Build the dated classifier artifact path under models/."""
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run_date = run_date or date.today()
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return DEFAULT_MODELS_DIR / f"produk-pfm-classifier-26n-{epochs}e-{run_date:%Y-%m-%d}.pt"
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def _classifier_date_from_name(path: Path) -> date | None:
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match = re.search(
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r"produk-pfm-classifier-26n-\d+e-(\d{4}-\d{2}-\d{2})\.pt$",
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path.name,
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)
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if not match:
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return None
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year, month, day = (int(part) for part in match.group(1).split("-"))
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return date(year, month, day)
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def latest_classifier_weights(models_dir: Path = DEFAULT_MODELS_DIR) -> Path:
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"""Return the newest produk-pfm-classifier weights in models/, if any."""
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if not models_dir.is_dir():
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return classifier_output_path()
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candidates = list(models_dir.glob("produk-pfm-classifier-26n-*e-*.pt"))
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if not candidates:
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return classifier_output_path()
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def sort_key(path: Path) -> tuple[date, float]:
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name_date = _classifier_date_from_name(path) or date.min
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return (name_date, path.stat().st_mtime)
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return max(candidates, key=sort_key)
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VALID_IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".webp", ".bmp"}
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def is_image_file(path: Path) -> bool:
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return path.is_file() and path.suffix.lower() in VALID_IMAGE_EXTENSIONS
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def split_dataset(src_dir: Path, dest_dir: Path, split_ratio: float = 0.8, seed: int = 42):
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"""
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Split class folders from src_dir into train/val folders in dest_dir.
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Ensures every class with 2+ images keeps at least one image in validation.
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"""
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random.seed(seed)
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train_dir = dest_dir / "train"
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val_dir = dest_dir / "val"
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if dest_dir.exists():
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print(f"Cleaning existing split directory: {dest_dir}")
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shutil.rmtree(dest_dir)
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train_dir.mkdir(parents=True, exist_ok=True)
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val_dir.mkdir(parents=True, exist_ok=True)
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exclude_dirs = {dest_dir.name, "train", "val"}
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class_dirs = [d for d in src_dir.iterdir() if d.is_dir() and d.name not in exclude_dirs]
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class_dirs.sort()
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print(f"Found {len(class_dirs)} product classes in {src_dir}")
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total_train = 0
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total_val = 0
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for c_dir in class_dirs:
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class_name = c_dir.name
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images = sorted(
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[f for f in c_dir.iterdir() if is_image_file(f)],
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key=lambda p: p.name,
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)
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random.shuffle(images)
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num_images = len(images)
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if num_images == 0:
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print(f"Warning: Class '{class_name}' has 0 images. Skipping.")
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continue
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class_train_dir = train_dir / class_name
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class_val_dir = val_dir / class_name
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class_train_dir.mkdir(parents=True, exist_ok=True)
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class_val_dir.mkdir(parents=True, exist_ok=True)
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if num_images == 1:
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train_images = images
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val_images = images
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elif num_images == 2:
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train_images = [images[0]]
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val_images = [images[1]]
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else:
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split_idx = max(1, int(num_images * split_ratio))
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split_idx = min(split_idx, num_images - 1)
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train_images = images[:split_idx]
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val_images = images[split_idx:]
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for img in train_images:
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shutil.copy(img, class_train_dir / img.name)
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total_train += 1
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for img in val_images:
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shutil.copy(img, class_val_dir / img.name)
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total_val += 1
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print(
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f" Class '{class_name}': {len(train_images)} train, "
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f"{len(val_images)} val (total: {num_images})"
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)
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print(f"Dataset split completed: {total_train} train images, {total_val} validation images.")
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print(f"Split dataset located at: {dest_dir.absolute()}")
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def train_model(args):
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"""Handles training the YOLO classification model."""
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src_path = Path(args.src_dir).resolve()
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dest_path = Path(args.split_dir).resolve()
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if not src_path.is_dir():
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print(f"Error: Source dataset directory not found: {src_path}")
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sys.exit(1)
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print(f"--- Preparing Dataset from {src_path} ---")
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split_dataset(src_path, dest_path, split_ratio=args.split_ratio)
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model_path = Path(args.model).resolve()
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print(f"\n--- Initializing YOLO Model ({model_path}) ---")
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model = YOLO(str(model_path))
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if args.device:
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device = args.device
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else:
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device = "0" if torch.cuda.is_available() else "cpu"
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print(f"Using device: {device}")
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print("\n--- Starting Training ---")
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results = model.train(
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data=str(dest_path),
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epochs=args.epochs,
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imgsz=args.imgsz,
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batch=args.batch,
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device=device,
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project=str(Path(args.project).resolve()),
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name=args.name,
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exist_ok=True,
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workers=args.workers,
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lr0=args.lr,
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optimizer=args.optimizer,
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seed=42,
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)
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best_weights = Path(results.save_dir) / "weights" / "best.pt"
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output_path = Path(args.output).resolve() if args.output else classifier_output_path(args.epochs)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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shutil.copy2(best_weights, output_path)
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print("\nTraining completed successfully!")
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print(f"Run weights saved at: {best_weights}")
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print(f"Published model saved at: {output_path}")
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if args.export:
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print("\n--- Exporting model to ONNX format ---")
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try:
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export_model = YOLO(str(output_path))
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onnx_path = Path(export_model.export(format="onnx"))
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dated_onnx = output_path.with_suffix(".onnx")
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if onnx_path.resolve() != dated_onnx.resolve():
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shutil.copy2(onnx_path, dated_onnx)
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print(f"Model exported successfully to: {dated_onnx}")
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except Exception as e:
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print(f"Warning: ONNX export failed: {e}")
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print("\nYou can run predictions with:")
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print(f" uv run python {Path(__file__).name} predict --image <image_path> --model {output_path}")
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def predict_image(args):
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"""Runs classification inference on a single image."""
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model_path = Path(args.model).resolve()
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image_path = Path(args.image).resolve()
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if not model_path.exists():
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print(f"Error: Model weights not found at {model_path}")
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sys.exit(1)
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if not image_path.exists():
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print(f"Error: Target image file not found at {image_path}")
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sys.exit(1)
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print(f"Loading model from {model_path}...")
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model = YOLO(str(model_path))
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print(f"Running prediction on {image_path}...")
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results = model(str(image_path))
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for result in results:
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probs = result.probs
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top1_idx = probs.top1
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top1_conf = float(probs.top1conf)
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top1_name = result.names[top1_idx]
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print("\n=== Classification Results ===")
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print(f"Top-1 Prediction: {top1_name} (Confidence: {top1_conf:.4f})")
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print("\nAll Probabilities:")
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sorted_probs = sorted(
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[(result.names[i], float(val)) for i, val in enumerate(probs.data)],
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key=lambda x: x[1],
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reverse=True,
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)
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for name, score in sorted_probs:
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print(f" {name}: {score:.4f}")
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def main():
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parser = argparse.ArgumentParser(
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description="Ultralytics YOLO classification utility for produk-pfm packaging photos."
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)
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subparsers = parser.add_subparsers(dest="command", required=True, help="Command to run")
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train_parser = subparsers.add_parser("train", help="Train a classification model")
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train_parser.add_argument(
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"--src-dir",
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type=str,
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default=str(DEFAULT_DATASET_DIR),
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help=f"Source dataset directory with one class folder per product (default: {DEFAULT_DATASET_DIR.name})",
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)
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train_parser.add_argument(
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"--split-dir",
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type=str,
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default=str(DEFAULT_SPLIT_DIR),
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help="Output split dataset directory",
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)
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train_parser.add_argument(
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"--split-ratio",
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type=float,
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default=0.8,
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help="Train/val split ratio for classes with 3+ images (default: 0.8)",
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)
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train_parser.add_argument(
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"--model",
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type=str,
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default=str(DEFAULT_MODEL),
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help="Pretrained model (e.g. yolo26n-cls.pt, yolo11n-cls.pt, yolov8n-cls.pt)",
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)
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train_parser.add_argument(
|
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"--epochs",
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type=int,
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default=DEFAULT_EPOCHS,
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help=f"Number of training epochs (default: {DEFAULT_EPOCHS})",
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)
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train_parser.add_argument(
|
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"--output",
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type=str,
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default=None,
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help=(
|
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"Published .pt output path (default: "
|
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"models/produk-pfm-classifier-26n-{epochs}e-{YYYY-MM-DD}.pt)"
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),
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)
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train_parser.add_argument("--imgsz", type=int, default=224, help="Target image size for classification")
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train_parser.add_argument("--batch", type=int, default=8, help="Batch size for training")
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train_parser.add_argument(
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"--device",
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type=str,
|
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default=None,
|
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help="Device to run on (e.g. 0 or 'cpu'). Default is GPU if available.",
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)
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train_parser.add_argument(
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"--project",
|
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type=str,
|
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default=str(DEFAULT_PROJECT),
|
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help="Project output folder name",
|
||||
)
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train_parser.add_argument("--name", type=str, default="train", help="Experiment name")
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train_parser.add_argument("--workers", type=int, default=4, help="Number of data loading workers")
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train_parser.add_argument("--lr", type=float, default=0.01, help="Initial learning rate")
|
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train_parser.add_argument(
|
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"--optimizer",
|
||||
type=str,
|
||||
default="auto",
|
||||
choices=["SGD", "Adam", "AdamW", "RMSProp", "auto"],
|
||||
help="Optimizer to use",
|
||||
)
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train_parser.add_argument(
|
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"--export",
|
||||
action="store_true",
|
||||
default=True,
|
||||
help="Export model to ONNX after training",
|
||||
)
|
||||
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||||
predict_parser = subparsers.add_parser("predict", help="Predict class of an image")
|
||||
predict_parser.add_argument("--image", type=str, required=True, help="Path to image file")
|
||||
predict_parser.add_argument(
|
||||
"--model",
|
||||
type=str,
|
||||
default=str(latest_classifier_weights()),
|
||||
help="Path to trained YOLO .pt model weights (default: newest models/produk-pfm-classifier-*.pt)",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.command == "train":
|
||||
train_model(args)
|
||||
elif args.command == "predict":
|
||||
predict_image(args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1 @@
|
||||
<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1155 1000"><path d="m577.3 0 577.4 1000H0z" fill="#fff"/></svg>
|
||||
|
After Width: | Height: | Size: 128 B |
@@ -0,0 +1 @@
|
||||
<svg fill="none" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16"><path fill-rule="evenodd" clip-rule="evenodd" d="M1.5 2.5h13v10a1 1 0 0 1-1 1h-11a1 1 0 0 1-1-1zM0 1h16v11.5a2.5 2.5 0 0 1-2.5 2.5h-11A2.5 2.5 0 0 1 0 12.5zm3.75 4.5a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5M7 4.75a.75.75 0 1 1-1.5 0 .75.75 0 0 1 1.5 0m1.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5" fill="#666"/></svg>
|
||||
|
After Width: | Height: | Size: 385 B |
Reference in new issue
Block a user