Add yolo training

modules/convert_labelme_nodata.sh

@@ -6,9 +6,18 @@ TARGET_DIR=$SCRIPT_DIR/../export_annotated
 
 # Process all JSON files in the target directory
 for file in "$TARGET_DIR"/*.json; do
-  # Use jq to set imageData to null and overwrite the original file
+  # Step 1: Set imageData to null
   jq '.imageData = null' "$file" > tmp && mv tmp "$file"
-  echo "Converted: $file"
+
+  # Step 2: Check if imagePath contains a subdirectory and modify it if needed
+  # jq '
+  #   if .imagePath | contains("/") then
+  #     .imagePath |= (split("/") | last)
+  #   else
+  #     .imagePath
+  #   end' "$file" > tmp && mv tmp "$file"
+
+  echo "Processed: $file"
 done
 
-echo "All files have been converted."
+echo "All files have been processed."

modules/labelme2yolo.sh

@@ -0,0 +1,43 @@
+#!/bin/bash
+
+# Get script dir
+SCRIPT_DIR=$(cd $(dirname $0); pwd)
+LABELME_DIR=$SCRIPT_DIR/../export_annotated
+YOLO_DIR=$SCRIPT_DIR/../export_yolo
+
+# Create necessary directories
+# mkdir -p $YOLO_DIR/train
+# mkdir -p $YOLO_DIR/val
+
+# Clean train and val directories by removing all files
+# rm -rf $YOLO_DIR/train/*
+# rm -rf $YOLO_DIR/val/*
+
+# Determine the number of files for validation (20%)
+LABELME_FILES=$(find "$LABELME_DIR" -name "*.json")
+
+# Copy labelme files and their corresponding images
+echo "$LABELME_FILES" | while read -r lableme_file; do
+  cp "$lableme_file" "$YOLO_DIR/"
+  
+  # Copy the corresponding image file (assuming it has the same base name)
+  image_file="${lableme_file%.json}.png"  # Change the extension to match your images
+  if [[ -f "$image_file" ]]; then
+    cp "$image_file" "$YOLO_DIR/"
+  fi
+done
+
+# Automatically generate LABEL_LIST by extracting labels from all JSON files
+LABEL_LIST=$(find "$LABELME_DIR" -name "*.json" -exec jq -r '.shapes[].label' {} + | sort | uniq | paste -sd "," -)
+
+echo "Detected labels: $LABEL_LIST"
+
+# Convert Labelme JSON to YOLO format for train and val
+cd $YOLO_DIR
+labelme2yolo --output_format bbox --json_dir $YOLO_DIR --val_size 0.1 --test_size 0.1 $LABEL_LIST
+
+# Remove temporary labelme files and their corresponding images
+find $YOLO_DIR -maxdepth 1 -name "*.json" -exec rm -f {} +
+find $YOLO_DIR -maxdepth 1 -name "*.png" -exec rm -f {} +
+
+echo "YOLO dataset conversion completed."

modules/yolov8_train.sh

@@ -0,0 +1,66 @@
+#!/bin/bash
+
+SCRIPT_DIR=$(cd $(dirname $0); pwd)
+# Parameters
+YOLO_DATASET_DIR=$SCRIPT_DIR/../export_yolo/YOLODataset
+MODEL="yolov8n.pt"  # YOLOv8 model type
+EPOCHS=1000
+OUTPUT_DIR=$SCRIPT_DIR/../export_yolo/train_output
+USE_GPU_TUNING=true
+RESUME_TRAINING=false
+
+mkdir -p $OUTPUT_DIR
+
+# Install ImageMagick if not already installed
+if ! command -v identify &> /dev/null
+then
+    echo "ImageMagick not found. Installing..."
+    sudo apt install -y imagemagick
+fi
+
+# Automatically detect image size from the training images
+IMG_SIZE=$(identify -format "%wx%h\n" $YOLO_DATASET_DIR/images/train/*.png | head -n 1 | cut -d 'x' -f 1)
+
+# If USE_GPU_TUNING is true, adjust parameters based on GPU memory
+if [ "$USE_GPU_TUNING" = true ]; then
+    # Get the GPU memory in MB
+    GPU_MEMORY=$(nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits | head -n 1)
+
+    # Set the batch size and learning rate based on GPU memory
+    if [ "$GPU_MEMORY" -ge 24576 ]; then
+        BATCH_SIZE=8   # For 24GB+ GPUs
+        BATCH_SIZE_PER_IMAGE=512
+    elif [ "$GPU_MEMORY" -ge 12288 ]; then
+        BATCH_SIZE=4   # For 12GB+ GPUs
+        BATCH_SIZE_PER_IMAGE=256
+    else
+        BATCH_SIZE=2   # For smaller GPUs
+        BATCH_SIZE_PER_IMAGE=128
+    fi
+
+    # Adjust learning rate based on batch size
+    BASE_LR=$(echo "0.00025 * $BATCH_SIZE / 2" | bc -l)
+    echo "GPU memory: $GPU_MEMORY MB"
+    echo "Batch size: $BATCH_SIZE, Batch size per image: $BATCH_SIZE_PER_IMAGE"
+    echo "Base learning rate: $BASE_LR"
+fi
+
+cd $OUTPUT_DIR
+# Run training with the YOLO model
+python3 - <<END
+from ultralytics import YOLO
+
+model = YOLO("$MODEL")
+results = model.train(
+    data="$YOLO_DATASET_DIR/dataset.yaml",
+    model="$MODEL",
+    epochs=$EPOCHS,
+    batch=$BATCH_SIZE,
+    imgsz=$IMG_SIZE,
+    project="$OUTPUT_DIR",
+    name="train_results",
+    lr0=$BASE_LR,
+    resume=$([ "$RESUME_TRAINING" = true ] && echo True || echo False)  # Convert bash boolean to Python boolean
+)
+print("YOLO training completed.")
+END

setup/install_labelme2yolo.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+python3 -m pip install -U labelme2yolo
+sudo apt install -y imagemagick