1377 lines
56 KiB
Python
1377 lines
56 KiB
Python
"""
|
||
Simple customizable annotation GUI with auto-labeling support.
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Built with Gradio - easy to modify and extend.
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Run: python annotation_gui.py
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To set default paths, edit config.py
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"""
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||
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from __future__ import annotations
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||
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import argparse
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import json
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import subprocess
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import threading
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from pathlib import Path
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from typing import Any
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import gradio as gr
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from PIL import Image, ImageDraw
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# Try to load config, use fallbacks if not available
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try:
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from config import (
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DEFAULT_IMAGES_DIR, DEFAULT_MODEL_WEIGHTS, DEFAULT_PORT,
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DEFAULT_DETECTION_THRESHOLD, DEFAULT_TRAIN_EPOCHS,
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DEFAULT_BATCH_SIZE, DEFAULT_LEARNING_RATE, DEFAULT_MODEL_SIZE
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||
)
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except ImportError:
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DEFAULT_IMAGES_DIR = None
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DEFAULT_MODEL_WEIGHTS = None
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||
DEFAULT_PORT = 7860
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DEFAULT_DETECTION_THRESHOLD = 0.5
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DEFAULT_TRAIN_EPOCHS = 20
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DEFAULT_BATCH_SIZE = 4
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DEFAULT_LEARNING_RATE = 1e-4
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DEFAULT_MODEL_SIZE = "small"
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class AnnotationApp:
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def __init__(self, images_dir: Path | None = None, model_weights: Path | None = None):
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self.images_dir = images_dir if images_dir else Path.cwd()
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self.current_model_path = model_weights
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self.current_model_type = None # Track model type: 'rf-detr', 'rt-detr', 'yolov6', 'yolox'
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self.available_models = [] # List of discovered models for quick switching
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self.image_paths = []
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self.current_idx = 0
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self.annotations = {} # image_name -> list of boxes
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self.model = None
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self.training_process = None
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self.training_thread = None
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self.training_status = "Not training"
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# Load images if directory provided
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if images_dir and images_dir.exists():
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self._load_images(images_dir)
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if model_weights and model_weights.exists():
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self._load_model(model_weights)
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def _load_images(self, images_dir: Path):
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"""Load images from directory."""
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self.images_dir = images_dir
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self.image_paths = sorted(
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list(images_dir.glob("*.jpg")) + list(images_dir.glob("*.png"))
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)
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self.current_idx = 0
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# Load existing annotations if present
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self.ann_file = images_dir / "annotations.json"
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if self.ann_file.exists():
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with self.ann_file.open("r") as f:
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self.annotations = json.load(f)
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else:
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self.annotations = {}
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return f"✓ Loaded {len(self.image_paths)} images from {images_dir}"
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def find_best_weights(self, directory: Path) -> tuple[Path | None, str | None]:
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"""Find the best weights file in a directory based on model type detection."""
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if not directory.exists():
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return None, None
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# Check for RF-DETR weights (checkpoint_best_total.pth)
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rf_detr_weights = directory / "checkpoint_best_total.pth"
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if rf_detr_weights.exists():
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return rf_detr_weights, "rf-detr"
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# Check for Ultralytics weights (best.pt) in weights/ subdirectory
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ultralytics_weights = directory / "weights" / "best.pt"
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if ultralytics_weights.exists():
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# Try to determine specific type from directory name or other clues
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dir_name = directory.name.lower()
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if "rtdetr" in dir_name:
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return ultralytics_weights, "rt-detr"
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elif "yolov6" in dir_name:
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return ultralytics_weights, "yolov6"
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elif "yolox" in dir_name:
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return ultralytics_weights, "yolox"
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else:
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# Default to rt-detr for ultralytics models
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return ultralytics_weights, "rt-detr"
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# Check for Ultralytics weights in training/weights/ subdirectory (YOLOv6/YOLOX format)
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training_weights = directory / "training" / "weights" / "best.pt"
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if training_weights.exists():
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dir_name = directory.name.lower()
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if "rtdetr" in dir_name:
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return training_weights, "rt-detr"
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elif "yolov6" in dir_name:
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return training_weights, "yolov6"
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elif "yolox" in dir_name:
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return training_weights, "yolox"
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else:
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# Default to yolox for training/weights structure
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return training_weights, "yolox"
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# Check for direct best.pt in directory
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direct_best = directory / "best.pt"
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if direct_best.exists():
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return direct_best, "rt-detr" # Default assumption
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||
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# Check for any .pth or .pt files as fallback
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pth_files = list(directory.glob("*.pth")) + list(directory.glob("*.pt"))
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if pth_files:
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||
# Prefer files with "best" in name
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best_files = [f for f in pth_files if "best" in f.name.lower()]
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if best_files:
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return best_files[0], self._guess_model_type_from_path(best_files[0])
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||
else:
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return pth_files[0], self._guess_model_type_from_path(pth_files[0])
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||
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return None, None
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||
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def _guess_model_type_from_path(self, path: Path) -> str:
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"""Guess model type from file path."""
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path_str = str(path).lower()
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if "rf" in path_str or "checkpoint" in path_str:
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||
return "rf-detr"
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||
elif "rtdetr" in path_str:
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return "rt-detr"
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elif "yolov6" in path_str:
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return "yolov6"
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elif "yolox" in path_str:
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return "yolox"
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else:
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return "rt-detr" # Default
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def _load_model(self, weights_path: Path, model_type: str = None):
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"""Load model for auto-labeling based on type."""
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try:
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import torch
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if model_type is None:
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model_type = self._guess_model_type_from_path(weights_path)
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print(f"Loading {model_type} model from {weights_path}...")
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# Check for GPU availability
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(f"Using device: {device}")
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if model_type == "rf-detr":
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# RF-DETR uses custom loader - try different model sizes
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from rfdetr import RFDETRBase, RFDETRMedium, RFDETRNano, RFDETRSmall
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# Try to determine model size from checkpoint or use nano as default
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checkpoint = torch.load(weights_path, map_location='cpu', weights_only=False)
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if 'model' in checkpoint:
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||
# Training checkpoint - check the model size from the state dict
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state_dict = checkpoint['model']
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# Look for clues about model size in the state dict keys
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if any('backbone.0.encoder.encoder.embeddings.position_embeddings' in key for key in state_dict.keys()):
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# Try different model sizes to find the right one
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models_to_try = [
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("nano", RFDETRNano),
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("small", RFDETRSmall),
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("medium", RFDETRMedium),
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("base", RFDETRBase)
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||
]
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||
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for size_name, model_class in models_to_try:
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||
try:
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||
self.model = model_class()
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# Try loading with strict=False to handle mismatches
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missing_keys, unexpected_keys = self.model.load_state_dict(state_dict, strict=False)
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if len(missing_keys) < len(self.model.state_dict()): # Some keys matched
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print(f"✓ Loaded RF-DETR {size_name} model (with {len(missing_keys)} missing keys)")
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# Move to GPU if available
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||
self.model = self.model.to(device)
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||
break
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||
except Exception as e:
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||
continue
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else:
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raise Exception("Could not load checkpoint with any RF-DETR model size")
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||
else:
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||
# Direct weights file
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||
self.model = RFDETRNano(pretrain_weights=str(weights_path))
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self.model = self.model.to(device)
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else:
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# Direct weights file
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self.model = RFDETRNano(pretrain_weights=str(weights_path))
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||
self.model = self.model.to(device)
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||
else:
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# RT-DETR, YOLOv6, YOLOX all use Ultralytics
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if model_type == "rt-detr":
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from ultralytics import RTDETR
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self.model = RTDETR(str(weights_path))
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else:
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from ultralytics import YOLO
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self.model = YOLO(str(weights_path))
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# Ultralytics models should automatically use GPU if available
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# but let's ensure they're on the right device
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if hasattr(self.model, 'to'):
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self.model = self.model.to(device)
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self.current_model_path = weights_path
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self.current_model_type = model_type
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# Add to available models for quick switching
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model_display = f"Custom: {weights_path.name} ({model_type.upper()})"
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existing_model = next((m for m in self.available_models if m['path'] == weights_path), None)
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if not existing_model:
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self.available_models.append({
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"path": weights_path,
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"type": model_type,
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"dir": weights_path.parent.name,
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"display": model_display
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})
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print("✓ Model loaded")
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||
return f"✓ {model_type.upper()} model loaded from {weights_path.name}"
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||
except Exception as e:
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error_msg = f"⚠ Could not load {model_type or 'model'}: {e}"
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||
print(error_msg)
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||
self.model = None
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||
self.current_model_type = None
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||
return error_msg
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||
|
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def load_new_model(self, weights_path: str, model_type: str = "Auto-detect") -> str:
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"""Load a new model from the GUI."""
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path = Path(weights_path)
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if not path.exists():
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return f"❌ File not found: {weights_path}"
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||
|
||
# Convert dropdown value to internal type
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||
if model_type == "Auto-detect":
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model_type = None
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elif model_type == "rf-detr":
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model_type = "rf-detr"
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||
elif model_type == "rt-detr":
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||
model_type = "rt-detr"
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||
elif model_type == "yolov6":
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model_type = "yolov6"
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||
elif model_type == "yolox":
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model_type = "yolox"
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||
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return self._load_model(path, model_type)
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def load_model_from_directory(self, directory_path: str) -> str:
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"""Load the best model found in a directory."""
|
||
path = Path(directory_path)
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if not path.exists():
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||
return f"❌ Directory not found: {directory_path}"
|
||
|
||
if not path.is_dir():
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||
return f"❌ Not a directory: {directory_path}"
|
||
|
||
weights_path, detected_type = self.find_best_weights(path)
|
||
if weights_path is None:
|
||
return f"❌ No model weights found in {directory_path}"
|
||
|
||
return self._load_model(weights_path, detected_type)
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||
|
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def scan_for_models(self, return_info: bool = True) -> str:
|
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"""Scan for available trained models in common directories."""
|
||
runs_dir = Path("runs")
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available_models = []
|
||
|
||
if runs_dir.exists():
|
||
for subdir in runs_dir.iterdir():
|
||
if subdir.is_dir():
|
||
weights_path, model_type = self.find_best_weights(subdir)
|
||
if weights_path:
|
||
available_models.append({
|
||
"path": weights_path,
|
||
"type": model_type,
|
||
"dir": subdir.name,
|
||
"display": f"{subdir.name} ({model_type.upper()})"
|
||
})
|
||
|
||
# Store available models for quick access
|
||
self.available_models = available_models
|
||
|
||
if not return_info:
|
||
return ""
|
||
|
||
if not available_models:
|
||
return "❌ No trained models found in 'runs/' directory"
|
||
|
||
# Format as readable list
|
||
lines = ["📂 Available Models:"]
|
||
for i, model in enumerate(available_models, 1):
|
||
lines.append(f"{i}. {model['dir']} → {model['path'].name} ({model['type'].upper()})")
|
||
|
||
lines.append("\n💡 Use the Model Selector dropdown above to quickly switch models")
|
||
return "\n".join(lines)
|
||
|
||
def get_available_models_list(self) -> list:
|
||
"""Get list of available models for dropdown."""
|
||
if not self.available_models:
|
||
self.scan_for_models(return_info=False) # This will populate self.available_models
|
||
|
||
if not self.available_models:
|
||
return ["No models found - click '🔍 Scan for Models'"]
|
||
|
||
return [model['display'] for model in self.available_models]
|
||
|
||
def load_model_by_index(self, model_display: str) -> str:
|
||
"""Load a model by its display name from the available models list."""
|
||
if not hasattr(self, 'available_models') or not self.available_models:
|
||
return "❌ No models available. Click '🔍 Scan for Models' first."
|
||
|
||
for model in self.available_models:
|
||
if model['display'] == model_display:
|
||
return self._load_model(model['path'], model['type'])
|
||
|
||
return f"❌ Model '{model_display}' not found"
|
||
|
||
def load_new_images_dir(self, images_dir: str) -> tuple[Image.Image | None, str, str]:
|
||
"""Load a new images directory from the GUI."""
|
||
path = Path(images_dir)
|
||
if not path.exists():
|
||
return None, "", f"❌ Directory not found: {images_dir}"
|
||
|
||
if not path.is_dir():
|
||
return None, "", f"❌ Not a directory: {images_dir}"
|
||
|
||
result = self._load_images(path)
|
||
|
||
# Load first image
|
||
if self.image_paths:
|
||
img, filename = self.get_current_image()
|
||
boxes = self.annotations.get(filename, [])
|
||
img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img
|
||
boxes_text = self._format_boxes_text(boxes)
|
||
info = f"{result}\nImage 1/{len(self.image_paths)}: {filename}"
|
||
return img_with_boxes, boxes_text, info
|
||
else:
|
||
return None, "", f"{result}\n⚠️ No .jpg or .png images found in directory"
|
||
|
||
def get_current_model_info(self) -> str:
|
||
"""Get info about currently loaded model."""
|
||
if self.model and self.current_model_path:
|
||
type_info = f" ({self.current_model_type.upper()})" if self.current_model_type else ""
|
||
return f"📦 Loaded: {self.current_model_path}{type_info}"
|
||
elif self.model:
|
||
return "📦 Model loaded (pretrained)"
|
||
else:
|
||
return "⚠️ No model loaded"
|
||
|
||
def get_current_dir_info(self) -> str:
|
||
"""Get info about current images directory."""
|
||
return f"📁 {self.images_dir} ({len(self.image_paths)} images)"
|
||
|
||
def get_current_image(self) -> tuple[Image.Image, str]:
|
||
"""Get current image and filename."""
|
||
if not self.image_paths:
|
||
return None, ""
|
||
path = self.image_paths[self.current_idx]
|
||
img = Image.open(path).convert("RGB")
|
||
return img, path.name
|
||
|
||
def draw_boxes_on_image(self, img: Image.Image, boxes: list[dict]) -> Image.Image:
|
||
"""Draw bounding boxes on image."""
|
||
img_draw = img.copy()
|
||
draw = ImageDraw.Draw(img_draw)
|
||
|
||
for box in boxes:
|
||
x1, y1, x2, y2 = box["bbox"]
|
||
label = box.get("label", "knot")
|
||
conf = box.get("confidence", 1.0)
|
||
|
||
# Draw box
|
||
draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
|
||
|
||
# Draw label
|
||
text = f"{label} {conf:.2f}" if conf < 1.0 else label
|
||
draw.text((x1, y1 - 20), text, fill="red")
|
||
|
||
return img_draw
|
||
|
||
def auto_label_current(self, threshold: float = 0.5) -> tuple[Image.Image, str, str]:
|
||
"""Auto-label current image using loaded model."""
|
||
if not self.model:
|
||
return None, "", "❌ No model loaded"
|
||
|
||
img, filename = self.get_current_image()
|
||
if not img:
|
||
return None, "", "No images"
|
||
|
||
try:
|
||
# Run inference based on model type
|
||
if self.current_model_type == "rf-detr":
|
||
# RF-DETR custom prediction
|
||
detections = self.model.predict(img, threshold=threshold)
|
||
boxes = []
|
||
for i in range(len(detections)):
|
||
xyxy = detections.xyxy[i]
|
||
conf = float(detections.confidence[i]) if detections.confidence is not None else 1.0
|
||
x1, y1, x2, y2 = xyxy
|
||
boxes.append({
|
||
"bbox": [float(x1), float(y1), float(x2), float(y2)],
|
||
"label": "knot",
|
||
"confidence": conf,
|
||
"source": "auto"
|
||
})
|
||
else:
|
||
# Ultralytics models (RT-DETR, YOLOv6, YOLOX)
|
||
results = self.model.predict(
|
||
source=img,
|
||
conf=threshold,
|
||
save=False,
|
||
verbose=False
|
||
)
|
||
|
||
boxes = []
|
||
for result in results:
|
||
for box in result.boxes:
|
||
x1, y1, x2, y2 = box.xyxy[0].tolist()
|
||
conf = float(box.conf[0])
|
||
boxes.append({
|
||
"bbox": [x1, y1, x2, y2],
|
||
"label": "knot",
|
||
"confidence": conf,
|
||
"source": "auto"
|
||
})
|
||
|
||
# Add to existing annotations
|
||
if filename not in self.annotations:
|
||
self.annotations[filename] = []
|
||
self.annotations[filename].extend(boxes)
|
||
self._save_annotations()
|
||
|
||
# Redraw
|
||
img_with_boxes = self.draw_boxes_on_image(img, self.annotations[filename])
|
||
boxes_text = self._format_boxes_text(self.annotations[filename])
|
||
info = f"🤖 Auto-labeled: {len(boxes)} detections | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||
|
||
return img_with_boxes, boxes_text, info
|
||
|
||
except Exception as e:
|
||
return img, self._format_boxes_text(self.annotations.get(filename, [])), f"❌ Auto-label failed: {e}"
|
||
|
||
def _format_boxes_text(self, boxes: list[dict]) -> str:
|
||
"""Format boxes for display."""
|
||
if not boxes:
|
||
return "No annotations"
|
||
|
||
lines = []
|
||
for i, box in enumerate(boxes):
|
||
x1, y1, x2, y2 = box["bbox"]
|
||
conf = box.get("confidence", 1.0)
|
||
source = box.get("source", "manual")
|
||
lines.append(f"{i}: [{x1:.0f}, {y1:.0f}, {x2:.0f}, {y2:.0f}] conf={conf:.2f} ({source})")
|
||
|
||
return "\n".join(lines)
|
||
|
||
def load_image(self, direction: str = "current") -> tuple[Image.Image, str, str]:
|
||
"""Load image (current/next/prev)."""
|
||
if direction == "next":
|
||
self.current_idx = min(self.current_idx + 1, len(self.image_paths) - 1)
|
||
elif direction == "prev":
|
||
self.current_idx = max(self.current_idx - 1, 0)
|
||
|
||
img, filename = self.get_current_image()
|
||
if not img:
|
||
return None, "", "No images"
|
||
|
||
# Load existing annotations
|
||
boxes = self.annotations.get(filename, [])
|
||
img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img
|
||
boxes_text = self._format_boxes_text(boxes)
|
||
info = f"Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||
|
||
return img_with_boxes, boxes_text, info
|
||
|
||
def add_box_manual(self, x1: int, y1: int, x2: int, y2: int) -> tuple[Image.Image, str, str]:
|
||
"""Manually add a bounding box."""
|
||
img, filename = self.get_current_image()
|
||
if not img:
|
||
return None, "", "No images"
|
||
|
||
# Add box
|
||
box = {
|
||
"bbox": [float(x1), float(y1), float(x2), float(y2)],
|
||
"label": "knot",
|
||
"confidence": 1.0,
|
||
"source": "manual"
|
||
}
|
||
|
||
if filename not in self.annotations:
|
||
self.annotations[filename] = []
|
||
self.annotations[filename].append(box)
|
||
self._save_annotations()
|
||
|
||
# Redraw
|
||
boxes = self.annotations[filename]
|
||
img_with_boxes = self.draw_boxes_on_image(img, boxes)
|
||
boxes_text = self._format_boxes_text(boxes)
|
||
info = f"✓ Added box: {len(boxes)} total | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||
|
||
return img_with_boxes, boxes_text, info
|
||
|
||
def delete_last_box(self) -> tuple[Image.Image, str, str]:
|
||
"""Delete the last box from current image."""
|
||
img, filename = self.get_current_image()
|
||
if not img:
|
||
return None, "", "No images"
|
||
|
||
if filename in self.annotations and self.annotations[filename]:
|
||
self.annotations[filename].pop()
|
||
self._save_annotations()
|
||
|
||
# Redraw
|
||
boxes = self.annotations.get(filename, [])
|
||
img_with_boxes = self.draw_boxes_on_image(img, boxes) if boxes else img
|
||
boxes_text = self._format_boxes_text(boxes)
|
||
info = f"✓ Deleted last box: {len(boxes)} remaining | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||
|
||
return img_with_boxes, boxes_text, info
|
||
|
||
def clear_boxes(self) -> tuple[Image.Image, str, str]:
|
||
"""Clear all boxes from current image."""
|
||
img, filename = self.get_current_image()
|
||
if not img:
|
||
return None, "", "No images"
|
||
|
||
self.annotations[filename] = []
|
||
self._save_annotations()
|
||
|
||
boxes_text = "No annotations"
|
||
info = f"✓ Cleared all boxes | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||
|
||
return img, boxes_text, info
|
||
|
||
def auto_label_current(self, threshold: float = 0.5) -> tuple[Image.Image, str, str]:
|
||
"""Auto-label current image using loaded model."""
|
||
if not self.model:
|
||
return None, "", "❌ No model loaded"
|
||
|
||
img, filename = self.get_current_image()
|
||
if not img:
|
||
return None, "", "No images"
|
||
|
||
try:
|
||
# Run inference based on model type
|
||
if self.current_model_type == "rf-detr":
|
||
# RF-DETR custom prediction
|
||
detections = self.model.predict(img, threshold=threshold)
|
||
boxes = []
|
||
for i in range(len(detections)):
|
||
xyxy = detections.xyxy[i]
|
||
conf = float(detections.confidence[i]) if detections.confidence is not None else 1.0
|
||
x1, y1, x2, y2 = xyxy
|
||
boxes.append({
|
||
"bbox": [float(x1), float(y1), float(x2), float(y2)],
|
||
"label": "knot",
|
||
"confidence": conf,
|
||
"source": "auto"
|
||
})
|
||
else:
|
||
# Ultralytics models (RT-DETR, YOLOv6, YOLOX)
|
||
results = self.model.predict(
|
||
source=img,
|
||
conf=threshold,
|
||
save=False,
|
||
verbose=False
|
||
)
|
||
|
||
boxes = []
|
||
for result in results:
|
||
for box in result.boxes:
|
||
x1, y1, x2, y2 = box.xyxy[0].tolist()
|
||
conf = float(box.conf[0])
|
||
boxes.append({
|
||
"bbox": [x1, y1, x2, y2],
|
||
"label": "knot",
|
||
"confidence": conf,
|
||
"source": "auto"
|
||
})
|
||
|
||
# Add to existing annotations
|
||
if filename not in self.annotations:
|
||
self.annotations[filename] = []
|
||
self.annotations[filename].extend(boxes)
|
||
self._save_annotations()
|
||
|
||
# Redraw
|
||
img_with_boxes = self.draw_boxes_on_image(img, self.annotations[filename])
|
||
boxes_text = self._format_boxes_text(self.annotations[filename])
|
||
info = f"🤖 Auto-labeled: {len(boxes)} detections | Image {self.current_idx + 1}/{len(self.image_paths)}: {filename}"
|
||
|
||
return img_with_boxes, boxes_text, info
|
||
|
||
except Exception as e:
|
||
return img, self._format_boxes_text(self.annotations.get(filename, [])), f"❌ Auto-label failed: {e}"
|
||
|
||
def _save_annotations(self):
|
||
"""Save annotations to JSON file."""
|
||
with self.ann_file.open("w") as f:
|
||
json.dump(self.annotations, f, indent=2)
|
||
|
||
def export_to_coco(self, output_path: Path):
|
||
"""Export annotations to COCO format."""
|
||
coco_data = {
|
||
"images": [],
|
||
"annotations": [],
|
||
"categories": [{"id": 0, "name": "knot", "supercategory": "defect"}]
|
||
}
|
||
|
||
ann_id = 0
|
||
for img_id, img_path in enumerate(self.image_paths):
|
||
filename = img_path.name
|
||
img = Image.open(img_path)
|
||
width, height = img.size
|
||
|
||
coco_data["images"].append({
|
||
"id": img_id,
|
||
"file_name": filename,
|
||
"width": width,
|
||
"height": height
|
||
})
|
||
|
||
# Add annotations
|
||
boxes = self.annotations.get(filename, [])
|
||
for box in boxes:
|
||
x1, y1, x2, y2 = box["bbox"]
|
||
w = x2 - x1
|
||
h = y2 - y1
|
||
|
||
coco_data["annotations"].append({
|
||
"id": ann_id,
|
||
"image_id": img_id,
|
||
"category_id": 0,
|
||
"bbox": [x1, y1, w, h],
|
||
"area": w * h,
|
||
"iscrowd": 0,
|
||
"score": box.get("confidence", 1.0)
|
||
})
|
||
ann_id += 1
|
||
|
||
with output_path.open("w") as f:
|
||
json.dump(coco_data, f, indent=2)
|
||
|
||
return f"✓ Exported {len(coco_data['annotations'])} annotations to {output_path}"
|
||
|
||
def prepare_training_dataset(self, output_dir: Path, train_split: float = 0.8, valid_split: float = 0.1):
|
||
"""Prepare dataset in RF-DETR format (train/valid/test splits)."""
|
||
output_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
# Create splits
|
||
import random
|
||
annotated_images = [img for img in self.image_paths if img.name in self.annotations and self.annotations[img.name]]
|
||
|
||
if len(annotated_images) < 10:
|
||
return f"⚠️ Need at least 10 annotated images, have {len(annotated_images)}"
|
||
|
||
random.shuffle(annotated_images)
|
||
n = len(annotated_images)
|
||
train_n = int(n * train_split)
|
||
valid_n = int(n * valid_split)
|
||
|
||
splits = {
|
||
"train": annotated_images[:train_n],
|
||
"valid": annotated_images[train_n:train_n + valid_n],
|
||
"test": annotated_images[train_n + valid_n:]
|
||
}
|
||
|
||
# Create directories and copy images
|
||
import shutil
|
||
for split_name, split_images in splits.items():
|
||
split_dir = output_dir / split_name
|
||
split_dir.mkdir(exist_ok=True)
|
||
|
||
# Prepare COCO JSON for this split
|
||
coco_data = {
|
||
"images": [],
|
||
"annotations": [],
|
||
"categories": [{"id": 0, "name": "knot", "supercategory": "defect"}]
|
||
}
|
||
|
||
ann_id = 0
|
||
for img_id, img_path in enumerate(split_images):
|
||
# Copy image
|
||
dest = split_dir / img_path.name
|
||
shutil.copy2(img_path, dest)
|
||
|
||
# Add to COCO
|
||
img = Image.open(img_path)
|
||
width, height = img.size
|
||
|
||
coco_data["images"].append({
|
||
"id": img_id,
|
||
"file_name": img_path.name,
|
||
"width": width,
|
||
"height": height
|
||
})
|
||
|
||
# Add annotations
|
||
boxes = self.annotations.get(img_path.name, [])
|
||
for box in boxes:
|
||
x1, y1, x2, y2 = box["bbox"]
|
||
w = x2 - x1
|
||
h = y2 - y1
|
||
|
||
coco_data["annotations"].append({
|
||
"id": ann_id,
|
||
"image_id": img_id,
|
||
"category_id": 0,
|
||
"bbox": [x1, y1, w, h],
|
||
"area": w * h,
|
||
"iscrowd": 0
|
||
})
|
||
ann_id += 1
|
||
|
||
# Save COCO JSON
|
||
with (split_dir / "_annotations.coco.json").open("w") as f:
|
||
json.dump(coco_data, f, indent=2)
|
||
|
||
return f"✓ Dataset prepared: {len(splits['train'])} train, {len(splits['valid'])} valid, {len(splits['test'])} test"
|
||
|
||
def start_training(self, framework: str, dataset_dir: str, output_dir: str, model_size: str,
|
||
epochs: int, batch_size: int, lr: float, progress=gr.Progress()):
|
||
"""Start training in background."""
|
||
dataset_path = Path(dataset_dir)
|
||
output_path = Path(output_dir)
|
||
|
||
if not dataset_path.exists():
|
||
return "❌ Dataset directory not found"
|
||
|
||
if self.training_process and self.training_process.poll() is None:
|
||
return "⚠️ Training already in progress"
|
||
|
||
output_path.mkdir(parents=True, exist_ok=True)
|
||
|
||
# Build training command based on framework
|
||
venv_python = Path(__file__).parent / ".venv/bin/python"
|
||
|
||
if framework == "RF-DETR":
|
||
train_script = Path(__file__).parent / "train_rfdetr.py"
|
||
# Map sizes: nano->nano, small->small, medium->medium, base->base
|
||
size_map = {"nano": "nano", "small": "small", "medium": "medium", "base": "base"}
|
||
model_arg = size_map.get(model_size, "medium")
|
||
|
||
cmd = [
|
||
str(venv_python),
|
||
str(train_script),
|
||
"--dataset-dir", str(dataset_path),
|
||
"--output-dir", str(output_path),
|
||
"--model", model_arg,
|
||
"--epochs", str(epochs),
|
||
"--batch-size", str(batch_size),
|
||
"--grad-accum-steps", "2", # Default grad accum
|
||
"--lr", str(lr)
|
||
]
|
||
elif framework == "RT-DETR":
|
||
train_script = Path(__file__).parent / "train_rtdetr.py"
|
||
# Map sizes: nano->r18, small->r34, medium->r50, base->l
|
||
size_map = {"nano": "rtdetr-r18", "small": "rtdetr-r34", "medium": "rtdetr-r50", "base": "rtdetr-l"}
|
||
model_arg = size_map.get(model_size, "rtdetr-r18")
|
||
|
||
cmd = [
|
||
str(venv_python),
|
||
str(train_script),
|
||
"--dataset-dir", str(dataset_path),
|
||
"--output-dir", str(output_path),
|
||
"--model", model_arg,
|
||
"--epochs", str(epochs),
|
||
"--batch-size", str(batch_size),
|
||
"--lr", str(lr)
|
||
]
|
||
elif framework == "YOLOv6":
|
||
train_script = Path(__file__).parent / "train_yolov6.py"
|
||
# Map sizes: nano->n, small->s, medium->m, base->l
|
||
size_map = {"nano": "yolov6n", "small": "yolov6s", "medium": "yolov6m", "base": "yolov6l"}
|
||
model_arg = size_map.get(model_size, "yolov6n")
|
||
|
||
cmd = [
|
||
str(venv_python),
|
||
str(train_script),
|
||
"--dataset-dir", str(dataset_path),
|
||
"--output-dir", str(output_path),
|
||
"--model", model_arg,
|
||
"--epochs", str(epochs),
|
||
"--batch-size", str(batch_size),
|
||
"--lr", str(lr)
|
||
]
|
||
elif framework == "YOLOX":
|
||
train_script = Path(__file__).parent / "train_yolox.py"
|
||
# Map sizes: nano->nano, small->s, medium->m, base->l
|
||
size_map = {"nano": "yolox-nano", "small": "yolox-s", "medium": "yolox-m", "base": "yolox-l"}
|
||
model_arg = size_map.get(model_size, "yolox-nano")
|
||
|
||
cmd = [
|
||
str(venv_python),
|
||
str(train_script),
|
||
"--dataset-dir", str(dataset_path),
|
||
"--output-dir", str(output_path),
|
||
"--model", model_arg,
|
||
"--epochs", str(epochs),
|
||
"--batch-size", str(batch_size),
|
||
"--lr", str(lr)
|
||
]
|
||
else:
|
||
return f"❌ Unknown framework: {framework}"
|
||
|
||
# Start training process
|
||
log_file = output_path / "training.log"
|
||
self.training_status = f"🚀 Starting {framework} training..."
|
||
|
||
def run_training():
|
||
try:
|
||
with log_file.open("w") as f:
|
||
self.training_process = subprocess.Popen(
|
||
cmd,
|
||
stdout=f,
|
||
stderr=subprocess.STDOUT,
|
||
text=True
|
||
)
|
||
self.training_status = f"⏳ Training in progress (PID: {self.training_process.pid})"
|
||
self.training_process.wait()
|
||
|
||
if self.training_process.returncode == 0:
|
||
self.training_status = "✅ Training completed successfully!"
|
||
# Reload model with new weights
|
||
if framework == "RF-DETR":
|
||
# RF-DETR uses checkpoint_best_total.pth
|
||
best_weights = output_path / "checkpoint_best_total.pth"
|
||
model_type = "rf-detr"
|
||
elif framework == "RT-DETR":
|
||
# RT-DETR uses best.pt in weights/ subdirectory (Ultralytics)
|
||
best_weights = output_path / "weights" / "best.pt"
|
||
model_type = "rt-detr"
|
||
elif framework == "YOLOv6":
|
||
best_weights = output_path / "weights" / "best.pt"
|
||
model_type = "yolov6"
|
||
elif framework == "YOLOX":
|
||
best_weights = output_path / "weights" / "best.pt"
|
||
model_type = "yolox"
|
||
|
||
if best_weights.exists():
|
||
self._load_model(best_weights, model_type)
|
||
else:
|
||
self.training_status = f"❌ Training failed (exit code {self.training_process.returncode})"
|
||
except Exception as e:
|
||
self.training_status = f"❌ Error: {e}"
|
||
|
||
self.training_thread = threading.Thread(target=run_training, daemon=True)
|
||
self.training_thread.start()
|
||
|
||
return f"✓ Training started! Check {log_file} for progress"
|
||
|
||
def get_training_status(self):
|
||
"""Get current training status."""
|
||
return self.training_status
|
||
|
||
def stop_training(self):
|
||
"""Stop the training process."""
|
||
if self.training_process and self.training_process.poll() is None:
|
||
self.training_process.terminate()
|
||
self.training_status = "⏹️ Training stopped by user"
|
||
return "✓ Training process terminated"
|
||
return "⚠️ No training in progress"
|
||
|
||
def export_for_oak_d(self, model_path: str, output_dir: str = "oak_d_export", img_size: int = 640):
|
||
"""Export trained model for OAK-D camera deployment."""
|
||
try:
|
||
weights_path = Path(model_path)
|
||
output_path = Path(output_dir)
|
||
|
||
if not weights_path.exists():
|
||
return "❌ Model weights not found"
|
||
|
||
output_path.mkdir(parents=True, exist_ok=True)
|
||
|
||
# Determine model type
|
||
model_type = self._guess_model_type_from_path(weights_path)
|
||
|
||
print(f"Exporting {model_type} model for OAK-D...")
|
||
|
||
if model_type == "rf-detr":
|
||
# RF-DETR export - use existing export_onnx.py logic
|
||
from rfdetr import RFDETRBase
|
||
|
||
model = RFDETRBase(pretrain_weights=str(weights_path))
|
||
model.export() # Creates output/model.onnx
|
||
|
||
# Move to output directory
|
||
onnx_source = Path("output/model.onnx")
|
||
if onnx_source.exists():
|
||
onnx_dest = output_path / "rf_detr_model.onnx"
|
||
onnx_source.rename(onnx_dest)
|
||
|
||
return f"✓ RF-DETR exported for OAK-D!\n📁 Output: {output_path}\n🔗 Next: Convert ONNX to blob using blobconverter.luxonis.com"
|
||
else:
|
||
return "❌ ONNX export failed"
|
||
|
||
else:
|
||
# Ultralytics models (RT-DETR, YOLOv6, YOLOX)
|
||
if model_type == "rt-detr":
|
||
from ultralytics import RTDETR
|
||
model = RTDETR(str(weights_path))
|
||
else:
|
||
from ultralytics import YOLO
|
||
model = YOLO(str(weights_path))
|
||
|
||
# Export to ONNX
|
||
onnx_path = model.export(
|
||
format="onnx",
|
||
imgsz=img_size,
|
||
simplify=True,
|
||
opset=11, # OAK-compatible opset
|
||
)
|
||
|
||
# Move ONNX to output directory
|
||
if Path(onnx_path).exists():
|
||
final_onnx = output_path / f"{model_type}_model.onnx"
|
||
Path(onnx_path).rename(final_onnx)
|
||
onnx_path = final_onnx
|
||
|
||
# Try to export to OpenVINO if available
|
||
try:
|
||
openvino_path = model.export(
|
||
format="openvino",
|
||
imgsz=img_size,
|
||
half=False, # Use FP32 for better compatibility
|
||
)
|
||
|
||
# Move OpenVINO files to output directory
|
||
if Path(openvino_path).exists():
|
||
import shutil
|
||
openvino_dir = Path(openvino_path)
|
||
for file in openvino_dir.glob("*"):
|
||
if file.is_file():
|
||
shutil.move(str(file), str(output_path / file.name))
|
||
openvino_dir.rmdir() # Remove empty dir
|
||
|
||
return f"✓ {model_type.upper()} exported for OAK-D!\n📁 Output: {output_path}\n🔗 Next: Convert .xml/.bin to blob using blobconverter.luxonis.com"
|
||
|
||
except Exception as e:
|
||
# OpenVINO not available, just return ONNX
|
||
return f"✓ {model_type.upper()} exported to ONNX!\n📁 Output: {output_path}\n🔗 Next: Convert ONNX to blob using blobconverter.luxonis.com\n⚠️ OpenVINO not available: {str(e)}"
|
||
|
||
except Exception as e:
|
||
return f"❌ Export failed: {str(e)}"
|
||
|
||
|
||
def create_ui(app: AnnotationApp) -> gr.Blocks:
|
||
"""Create Gradio UI."""
|
||
|
||
with gr.Blocks(title="Knot Annotation Tool") as demo:
|
||
gr.Markdown("""
|
||
# 🪵 Wood Knot Annotation Tool
|
||
**Label → Train → Auto-Label → Repeat**
|
||
|
||
- Manually annotate images or use **Auto-Label** with your trained model
|
||
- Export and prepare dataset for training
|
||
- Train **RF-DETR, RT-DETR, YOLOv6, or YOLOX** (all free for commercial use!)
|
||
- Optimized for OAK-D camera deployment
|
||
- Use trained model to auto-label more images
|
||
""")
|
||
|
||
# Settings section at the top
|
||
with gr.Accordion("⚙️ Settings", open=False):
|
||
with gr.Row():
|
||
with gr.Column():
|
||
images_dir_input = gr.Textbox(
|
||
label="Images Directory",
|
||
value=str(app.images_dir),
|
||
placeholder="/path/to/images"
|
||
)
|
||
load_images_btn = gr.Button("📁 Load Images Directory")
|
||
dir_info = gr.Textbox(label="Current Directory", value=app.get_current_dir_info(), interactive=False)
|
||
|
||
with gr.Column():
|
||
# Quick Model Selector
|
||
model_selector = gr.Dropdown(
|
||
choices=app.get_available_models_list(),
|
||
value=None,
|
||
label="🚀 Quick Model Switcher",
|
||
info="Select from available trained models (refresh with scan)",
|
||
allow_custom_value=True
|
||
)
|
||
quick_load_btn = gr.Button("⚡ Load Selected Model", variant="primary")
|
||
|
||
# Manual Model Loading
|
||
model_weights_input = gr.Textbox(
|
||
label="Model Weights Path",
|
||
value=str(app.current_model_path) if app.current_model_path else "",
|
||
placeholder="runs/training/checkpoint_best_total.pth"
|
||
)
|
||
model_type_dropdown = gr.Dropdown(
|
||
choices=["Auto-detect", "rf-detr", "rt-detr", "yolov6", "yolox"],
|
||
value="Auto-detect",
|
||
label="Model Type",
|
||
info="Auto-detect will try to determine from file path"
|
||
)
|
||
with gr.Row():
|
||
load_model_btn = gr.Button("🤖 Load Model Weights")
|
||
scan_models_btn = gr.Button("🔍 Scan for Models")
|
||
model_info = gr.Textbox(label="Current Model", value=app.get_current_model_info(), interactive=False)
|
||
|
||
with gr.Row():
|
||
with gr.Column(scale=3):
|
||
image_display = gr.Image(label="Current Image", type="pil")
|
||
|
||
with gr.Row():
|
||
prev_btn = gr.Button("⬅️ Previous")
|
||
next_btn = gr.Button("Next ➡️")
|
||
auto_label_btn = gr.Button("🤖 Auto-Label", variant="primary")
|
||
|
||
with gr.Row():
|
||
threshold_slider = gr.Slider(0.1, 0.9, DEFAULT_DETECTION_THRESHOLD, label="Detection Threshold")
|
||
|
||
with gr.Column(scale=1):
|
||
info_text = gr.Textbox(label="Status", lines=2)
|
||
boxes_text = gr.Textbox(label="Annotations", lines=10)
|
||
|
||
gr.Markdown("### Manual Annotation")
|
||
with gr.Row():
|
||
x1_input = gr.Number(label="x1", value=100)
|
||
y1_input = gr.Number(label="y1", value=100)
|
||
with gr.Row():
|
||
x2_input = gr.Number(label="x2", value=200)
|
||
y2_input = gr.Number(label="y2", value=200)
|
||
|
||
add_box_btn = gr.Button("➕ Add Box")
|
||
delete_btn = gr.Button("🗑️ Delete Last")
|
||
clear_btn = gr.Button("❌ Clear All")
|
||
|
||
gr.Markdown("### Export & Training")
|
||
export_path = gr.Textbox(
|
||
label="Export Path",
|
||
value="annotations_coco.json"
|
||
)
|
||
export_btn = gr.Button("💾 Export COCO")
|
||
export_result = gr.Textbox(label="Export Result", lines=1)
|
||
|
||
# Training tab
|
||
with gr.Tab("🎯 Training"):
|
||
gr.Markdown("""
|
||
### Train Object Detection Model
|
||
|
||
**Choose your framework:**
|
||
- **RF-DETR** (MIT): Custom transformer, high accuracy
|
||
- **RT-DETR** (Apache 2.0): Ultralytics transformer, great accuracy
|
||
- **YOLOv6** (MIT): Fast, proven on OAK cameras
|
||
- **YOLOX** (MIT): Similar to YOLOv6, slight differences
|
||
|
||
**All MIT/Apache 2.0 licensed - free for commercial use!** ✅
|
||
|
||
**Steps:**
|
||
1. Annotate at least 50-100 images in the Annotation tab
|
||
2. Click "Prepare Dataset" to create train/valid/test splits
|
||
3. Select your framework and configure training parameters
|
||
4. Click "Start Training" (runs in background)
|
||
5. After training, export for OAK-D deployment
|
||
""")
|
||
|
||
with gr.Row():
|
||
with gr.Column():
|
||
dataset_prep_dir = gr.Textbox(
|
||
label="Dataset Output Directory",
|
||
value="dataset_prepared"
|
||
)
|
||
train_split = gr.Slider(0.5, 0.9, 0.8, label="Train Split Ratio")
|
||
valid_split = gr.Slider(0.05, 0.3, 0.1, label="Valid Split Ratio")
|
||
prep_btn = gr.Button("📦 Prepare Dataset", variant="secondary")
|
||
prep_result = gr.Textbox(label="Preparation Result", lines=2)
|
||
|
||
with gr.Column():
|
||
gr.Markdown("### Training Configuration")
|
||
model_framework = gr.Dropdown(
|
||
choices=["RF-DETR", "RT-DETR", "YOLOv6", "YOLOX"],
|
||
value="RT-DETR",
|
||
label="Model Framework",
|
||
info="All MIT/Apache 2.0 licensed - free for commercial use. Optimized for OAK cameras."
|
||
)
|
||
train_dataset_dir = gr.Textbox(
|
||
label="Dataset Directory",
|
||
value="dataset_prepared"
|
||
)
|
||
train_output_dir = gr.Textbox(
|
||
label="Output Directory",
|
||
value="runs/gui_training"
|
||
)
|
||
model_size = gr.Dropdown(
|
||
choices=["nano", "small", "medium", "base"],
|
||
value=DEFAULT_MODEL_SIZE,
|
||
label="Model Size"
|
||
)
|
||
epochs = gr.Slider(5, 100, DEFAULT_TRAIN_EPOCHS, step=5, label="Epochs")
|
||
batch_size = gr.Slider(1, 16, DEFAULT_BATCH_SIZE, step=1, label="Batch Size")
|
||
learning_rate = gr.Number(value=DEFAULT_LEARNING_RATE, label="Learning Rate")
|
||
|
||
with gr.Row():
|
||
start_train_btn = gr.Button("🚀 Start Training", variant="primary")
|
||
stop_train_btn = gr.Button("⏹️ Stop Training", variant="stop")
|
||
refresh_status_btn = gr.Button("🔄 Refresh Status")
|
||
|
||
training_status = gr.Textbox(
|
||
label="Training Status",
|
||
value="Not training",
|
||
lines=3
|
||
)
|
||
|
||
gr.Markdown("""
|
||
**Note**: Training runs in the background. You can continue annotating while training.
|
||
Check the training log file for detailed progress.
|
||
""")
|
||
|
||
# OAK-D Deployment tab
|
||
with gr.Tab("🚀 OAK-D Deployment"):
|
||
gr.Markdown("""
|
||
### Deploy Trained Model to OAK-D Camera
|
||
|
||
Convert your trained model to work with the **OAK-D 4 Pro** camera for real-time edge inference.
|
||
|
||
**Supported Models**: RF-DETR, RT-DETR, YOLOv6, YOLOX
|
||
|
||
**Process**:
|
||
1. Select a trained model from your runs/ directory
|
||
2. Export to ONNX and OpenVINO formats
|
||
3. Convert OpenVINO model to blob for OAK-D
|
||
4. Deploy blob to your OAK-D camera
|
||
""")
|
||
|
||
with gr.Row():
|
||
with gr.Column():
|
||
oak_model_selector = gr.Dropdown(
|
||
choices=app.get_available_models_list(),
|
||
value=None,
|
||
label="Select Trained Model",
|
||
info="Choose a model from your training runs",
|
||
allow_custom_value=True
|
||
)
|
||
oak_output_dir = gr.Textbox(
|
||
label="Output Directory",
|
||
value="oak_d_deployment",
|
||
placeholder="oak_d_deployment"
|
||
)
|
||
oak_img_size = gr.Dropdown(
|
||
choices=[320, 416, 512, 640, 800, 1024],
|
||
value=640,
|
||
label="Image Size",
|
||
info="Input size for the model (should match training)"
|
||
)
|
||
|
||
with gr.Row():
|
||
oak_scan_btn = gr.Button("🔍 Scan for Models")
|
||
oak_export_btn = gr.Button("🚀 Export for OAK-D", variant="primary")
|
||
|
||
oak_status = gr.Textbox(
|
||
label="Export Status",
|
||
value="Ready to export",
|
||
lines=4
|
||
)
|
||
|
||
with gr.Column():
|
||
gr.Markdown("""
|
||
### 📋 Deployment Instructions
|
||
|
||
**After Export:**
|
||
1. **Test OpenVINO Model** (optional):
|
||
```bash
|
||
python -c "from openvino.runtime import Core; core = Core(); model = core.read_model('model.xml'); print('✓ Model loaded')"
|
||
```
|
||
|
||
2. **Convert to Blob**:
|
||
- Go to: https://blobconverter.luxonis.com/
|
||
- Upload your `.xml` and `.bin` files
|
||
- Select OAK-D device
|
||
- Download the `.blob` file
|
||
|
||
3. **Deploy to OAK-D**:
|
||
- Use DepthAI Python API
|
||
- Or use OAK-D examples with your blob
|
||
|
||
### 💡 Tips
|
||
- Use **FP32** for best accuracy (default)
|
||
- **Nano models** work best on edge devices
|
||
- Test inference speed vs accuracy trade-off
|
||
""")
|
||
|
||
# Event handlers
|
||
def on_load():
|
||
return app.load_image("current")
|
||
|
||
# Settings handlers
|
||
load_images_btn.click(
|
||
app.load_new_images_dir,
|
||
inputs=[images_dir_input],
|
||
outputs=[image_display, boxes_text, info_text]
|
||
).then(
|
||
lambda: (app.get_current_dir_info(), app.get_current_model_info()),
|
||
outputs=[dir_info, model_info]
|
||
)
|
||
|
||
load_model_btn.click(
|
||
app.load_new_model,
|
||
inputs=[model_weights_input, model_type_dropdown],
|
||
outputs=[model_info]
|
||
).then(
|
||
app.get_available_models_list,
|
||
outputs=[model_selector]
|
||
)
|
||
|
||
scan_models_btn.click(
|
||
app.scan_for_models,
|
||
outputs=[model_info]
|
||
).then(
|
||
app.get_available_models_list,
|
||
outputs=[model_selector]
|
||
)
|
||
|
||
quick_load_btn.click(
|
||
app.load_model_by_index,
|
||
inputs=[model_selector],
|
||
outputs=[model_info]
|
||
).then(
|
||
app.get_available_models_list,
|
||
outputs=[model_selector]
|
||
)
|
||
|
||
prev_btn.click(
|
||
lambda: app.load_image("prev"),
|
||
outputs=[image_display, boxes_text, info_text]
|
||
)
|
||
|
||
next_btn.click(
|
||
lambda: app.load_image("next"),
|
||
outputs=[image_display, boxes_text, info_text]
|
||
)
|
||
|
||
auto_label_btn.click(
|
||
lambda t: app.auto_label_current(t),
|
||
inputs=[threshold_slider],
|
||
outputs=[image_display, boxes_text, info_text]
|
||
)
|
||
|
||
add_box_btn.click(
|
||
app.add_box_manual,
|
||
inputs=[x1_input, y1_input, x2_input, y2_input],
|
||
outputs=[image_display, boxes_text, info_text]
|
||
)
|
||
|
||
delete_btn.click(
|
||
app.delete_last_box,
|
||
outputs=[image_display, boxes_text, info_text]
|
||
)
|
||
|
||
clear_btn.click(
|
||
app.clear_boxes,
|
||
outputs=[image_display, boxes_text, info_text]
|
||
)
|
||
|
||
export_btn.click(
|
||
lambda path: app.export_to_coco(Path(path)),
|
||
inputs=[export_path],
|
||
outputs=[export_result]
|
||
)
|
||
|
||
# Training handlers
|
||
prep_btn.click(
|
||
lambda out, train, valid: app.prepare_training_dataset(Path(out), train, valid),
|
||
inputs=[dataset_prep_dir, train_split, valid_split],
|
||
outputs=[prep_result]
|
||
)
|
||
|
||
start_train_btn.click(
|
||
app.start_training,
|
||
inputs=[model_framework, train_dataset_dir, train_output_dir, model_size, epochs, batch_size, learning_rate],
|
||
outputs=[training_status]
|
||
)
|
||
|
||
stop_train_btn.click(
|
||
app.stop_training,
|
||
outputs=[training_status]
|
||
)
|
||
|
||
refresh_status_btn.click(
|
||
app.get_training_status,
|
||
outputs=[training_status]
|
||
)
|
||
|
||
# OAK-D Deployment handlers
|
||
oak_scan_btn.click(
|
||
app.scan_for_models,
|
||
outputs=[oak_status]
|
||
).then(
|
||
app.get_available_models_list,
|
||
outputs=[oak_model_selector]
|
||
)
|
||
|
||
oak_export_btn.click(
|
||
app.export_for_oak_d,
|
||
inputs=[oak_model_selector, oak_output_dir, oak_img_size],
|
||
outputs=[oak_status]
|
||
)
|
||
|
||
# Load first image on start
|
||
demo.load(on_load, outputs=[image_display, boxes_text, info_text])
|
||
|
||
return demo
|
||
|
||
|
||
def main():
|
||
parser = argparse.ArgumentParser(description="Simple annotation GUI with auto-labeling")
|
||
parser.add_argument(
|
||
"--images-dir",
|
||
type=Path,
|
||
default=Path(DEFAULT_IMAGES_DIR) if DEFAULT_IMAGES_DIR else None,
|
||
help="Default directory with images (can be changed in GUI)"
|
||
)
|
||
parser.add_argument(
|
||
"--model-weights",
|
||
type=Path,
|
||
default=Path(DEFAULT_MODEL_WEIGHTS) if DEFAULT_MODEL_WEIGHTS else None,
|
||
help="Default trained model for auto-labeling (can be changed in GUI)"
|
||
)
|
||
args = parser.parse_args()
|
||
|
||
# Validate paths if provided
|
||
if args.images_dir and not args.images_dir.exists():
|
||
print(f"⚠️ Warning: Images directory not found: {args.images_dir}")
|
||
print("You can load a different directory from the GUI Settings")
|
||
args.images_dir = None
|
||
|
||
if args.model_weights and not args.model_weights.exists():
|
||
print(f"⚠️ Warning: Model weights not found: {args.model_weights}")
|
||
print("You can load different weights from the GUI Settings")
|
||
args.model_weights = None
|
||
|
||
# Create app
|
||
app = AnnotationApp(args.images_dir, args.model_weights)
|
||
|
||
# Scan for available models on startup
|
||
app.scan_for_models(return_info=False)
|
||
|
||
# Create and launch UI
|
||
demo = create_ui(app)
|
||
|
||
print(f"\n{'='*60}")
|
||
print(f"🚀 Starting annotation tool...")
|
||
if args.images_dir:
|
||
print(f"📁 Default images: {args.images_dir} ({len(app.image_paths)} images)")
|
||
else:
|
||
print(f"📁 No default images - load directory from Settings")
|
||
if app.model:
|
||
print(f"🤖 Model: Loaded from {args.model_weights}")
|
||
else:
|
||
print(f"⚠️ No model loaded - load from Settings or train one")
|
||
print(f"💡 You can change images directory and model weights from the Settings panel")
|
||
print(f"{'='*60}\n")
|
||
|
||
demo.launch(
|
||
server_name="0.0.0.0",
|
||
server_port=7860,
|
||
share=False
|
||
)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|