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This commit is contained in:
228
batch-clean.sh
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228
batch-clean.sh
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#!/usr/bin/env bash
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# ============================================================
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# batch-clean.sh
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# Batch manga text removal (inpainting) using bubbles.json
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#
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# Usage:
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# ./batch-clean.sh <folder>
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# ./batch-clean.sh <folder> --start 3 --end 7
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#
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# Output per page lands in:
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# <folder>/translated/<page_stem>/
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# └── <page_stem>_cleaned.png
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# ============================================================
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set -uo pipefail
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# ─────────────────────────────────────────────────────────────
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# CONFIGURATION
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# ─────────────────────────────────────────────────────────────
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START_PAGE=1
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END_PAGE=999999
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PYTHON_BIN="python"
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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CLEANER="${SCRIPT_DIR}/clean_bubbles.py"
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# ─────────────────────────────────────────────────────────────
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# COLOURS
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# ─────────────────────────────────────────────────────────────
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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CYAN='\033[0;36m'
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BOLD='\033[1m'
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RESET='\033[0m'
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# ─────────────────────────────────────────────────────────────
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# HELPERS
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# ─────────────────────────────────────────────────────────────
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usage() {
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echo ""
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echo -e "${BOLD}Usage:${RESET}"
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echo " $0 <folder> [options]"
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echo ""
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echo -e "${BOLD}Options:${RESET}"
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echo " --start First page number (default: 1)"
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echo " --end Last page number (default: all)"
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echo " --python Python binary (default: python)"
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echo " --help, -h Show this help"
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echo ""
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echo -e "${BOLD}Examples:${RESET}"
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echo " $0 pages-for-tests"
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echo " $0 pages-for-tests --start 3 --end 7"
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echo ""
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}
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log_info() { echo -e "${CYAN}ℹ️ $*${RESET}"; }
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log_ok() { echo -e "${GREEN}✅ $*${RESET}"; }
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log_warn() { echo -e "${YELLOW}⚠️ $*${RESET}"; }
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log_error() { echo -e "${RED}❌ $*${RESET}"; }
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log_section() {
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echo -e "\n${BOLD}${CYAN}══════════════════════════════════════════${RESET}"
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echo -e "${BOLD}${CYAN} 🧹 $*${RESET}"
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echo -e "${BOLD}${CYAN}══════════════════════════════════════════${RESET}"
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}
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# ─────────────────────────────────────────────────────────────
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# ARGUMENT PARSING
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# ─────────────────────────────────────────────────────────────
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if [[ $# -eq 0 ]]; then
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log_error "No folder specified."
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usage
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exit 1
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fi
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FOLDER="$1"
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shift
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while [[ $# -gt 0 ]]; do
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case "$1" in
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--start) START_PAGE="$2"; shift 2 ;;
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--end) END_PAGE="$2"; shift 2 ;;
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--python) PYTHON_BIN="$2"; shift 2 ;;
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--help|-h) usage; exit 0 ;;
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*)
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log_error "Unknown option: $1"
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usage
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exit 1
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;;
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esac
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done
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# ─────────────────────────────────────────────────────────────
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# VALIDATION
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# ─────────────────────────────────────────────────────────────
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if [[ ! -d "$FOLDER" ]]; then
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log_error "Folder not found: $FOLDER"
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exit 1
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fi
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if [[ ! -f "$CLEANER" ]]; then
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log_error "clean_bubbles.py not found at: $CLEANER"
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exit 1
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fi
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if ! command -v "$PYTHON_BIN" &>/dev/null; then
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log_error "Python binary not found: $PYTHON_BIN"
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log_error "Try --python python3"
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exit 1
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fi
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# ─────────────────────────────────────────────────────────────
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# DISCOVER IMAGES
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# ─────────────────────────────────────────────────────────────
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ALL_IMAGES=()
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while IFS= read -r -d '' img; do
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ALL_IMAGES+=("$img")
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done < <(
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find "$FOLDER" -maxdepth 1 -type f \
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\( -iname "*.jpg" -o -iname "*.jpeg" \
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-o -iname "*.png" -o -iname "*.webp" \) \
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-print0 | sort -z
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)
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TOTAL=${#ALL_IMAGES[@]}
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if [[ $TOTAL -eq 0 ]]; then
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log_error "No image files found in: $FOLDER"
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exit 1
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fi
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# ─────────────────────────────────────────────────────────────
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# SLICE TO REQUESTED PAGE RANGE
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# ─────────────────────────────────────────────────────────────
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PAGES=()
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for i in "${!ALL_IMAGES[@]}"; do
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PAGE_NUM=$(( i + 1 ))
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if [[ $PAGE_NUM -ge $START_PAGE && $PAGE_NUM -le $END_PAGE ]]; then
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PAGES+=("${ALL_IMAGES[$i]}")
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fi
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done
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if [[ ${#PAGES[@]} -eq 0 ]]; then
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log_error "No pages in range [${START_PAGE}, ${END_PAGE}] (total: ${TOTAL})"
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exit 1
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fi
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# ─────────────────────────────────────────────────────────────
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# SUMMARY HEADER
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# ─────────────────────────────────────────────────────────────
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log_section "BATCH MANGA CLEANER"
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log_info "📂 Folder : $(realpath "$FOLDER")"
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log_info "📄 Pages : ${#PAGES[@]} of ${TOTAL} total"
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log_info "🔢 Range : ${START_PAGE} → ${END_PAGE}"
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echo ""
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# ─────────────────────────────────────────────────────────────
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# PROCESS EACH PAGE
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# ─────────────────────────────────────────────────────────────
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PASS=0
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FAIL=0
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FAIL_LIST=()
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for i in "${!PAGES[@]}"; do
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IMAGE="${PAGES[$i]}"
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PAGE_NUM=$(( START_PAGE + i ))
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STEM="$(basename "${IMAGE%.*}")"
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WORKDIR="${FOLDER}/translated/${STEM}"
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echo ""
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echo -e "${BOLD}──────────────────────────────────────────${RESET}"
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echo -e "${BOLD} 🖼️ [${PAGE_NUM}/${TOTAL}] ${STEM}${RESET}"
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echo -e "${BOLD}──────────────────────────────────────────${RESET}"
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OUTPUT_JSON="${WORKDIR}/bubbles.json"
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OUTPUT_CLEANED="${WORKDIR}/${STEM}_cleaned.png"
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if [[ ! -f "$OUTPUT_JSON" ]]; then
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log_warn "Skipping: bubbles.json not found in ${WORKDIR}"
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FAIL=$(( FAIL + 1 ))
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FAIL_LIST+=("${STEM} (No JSON)")
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continue
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fi
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log_info "🗂️ Image : $(basename "$IMAGE")"
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log_info "🧹 Cleaning text..."
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# ── Run the cleaner ───────────────────────────────────────
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if "$PYTHON_BIN" "$CLEANER" \
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-i "$IMAGE" \
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-j "$OUTPUT_JSON" \
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-o "$OUTPUT_CLEANED"; then
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if [[ -f "$OUTPUT_CLEANED" ]]; then
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log_ok "Cleaned image saved → ${STEM}_cleaned.png"
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PASS=$(( PASS + 1 ))
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else
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log_error "Script ran but output image is missing."
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FAIL=$(( FAIL + 1 ))
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FAIL_LIST+=("${STEM} (Missing Output)")
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fi
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else
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log_error "Page ${PAGE_NUM} FAILED — check output above."
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FAIL=$(( FAIL + 1 ))
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FAIL_LIST+=("${STEM} (Script Error)")
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fi
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done
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# ─────────────────────────────────────────────────────────────
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# FINAL SUMMARY
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# ─────────────────────────────────────────────────────────────
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log_section "BATCH CLEANING COMPLETE"
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echo -e " ✅ ${GREEN}Passed : ${PASS}${RESET}"
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echo -e " ❌ ${RED}Failed : ${FAIL}${RESET}"
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if [[ ${#FAIL_LIST[@]} -gt 0 ]]; then
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echo ""
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log_warn "Failed pages:"
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for NAME in "${FAIL_LIST[@]}"; do
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echo -e " ❌ ${RED}${NAME}${RESET}"
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done
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fi
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echo ""
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log_info "📦 Output folder: $(realpath "${FOLDER}/translated")"
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echo ""
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[[ $FAIL -eq 0 ]] && exit 0 || exit 1
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109
clean_bubbles.py
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109
clean_bubbles.py
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import cv2
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import numpy as np
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import json
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import os
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import argparse
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def clean_text_from_box(image_bgr, box, bg_type="complex"):
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"""
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Erases the text inside a specific bounding box.
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Uses a solid white fill for 'white' backgrounds, and inpainting for 'complex' ones.
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"""
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x, y = int(box["x"]), int(box["y"])
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w, h = int(box["w"]), int(box["h"])
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# Image dimensions for boundary checking
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img_h, img_w = image_bgr.shape[:2]
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# Ensure coordinates don't go outside the image boundaries
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x = max(0, x)
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y = max(0, y)
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w = min(w, img_w - x)
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h = min(h, img_h - y)
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# 1. Fast Path: Plain white background
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if bg_type == "white":
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cv2.rectangle(image_bgr, (x, y), (x+w, y+h), (255, 255, 255), -1)
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return image_bgr
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# 2. Complex Path: Inpainting for screentones/artwork
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roi = image_bgr[y:y+h, x:x+w]
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if roi.size == 0:
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return image_bgr
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# Create a mask for the dark text
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gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
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# Threshold: Pixels darker than 120 become white (the mask), others become black
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_, mask = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY_INV)
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# Dilate the mask slightly to ensure the edges of the letters are fully covered
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kernel = np.ones((3, 3), np.uint8)
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mask = cv2.dilate(mask, kernel, iterations=1)
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# Inpaint the background to erase the text
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inpainted_roi = cv2.inpaint(roi, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)
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# Paste the cleaned region back into the main image
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image_bgr[y:y+h, x:x+w] = inpainted_roi
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return image_bgr
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def main():
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parser = argparse.ArgumentParser(description="Clean manga text using bubbles.json")
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parser.add_argument("-i", "--image", required=True, help="Path to the original manga image")
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parser.add_argument("-j", "--json", required=True, help="Path to the bubbles.json file")
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parser.add_argument("-o", "--output", help="Path to save the cleaned image (optional)")
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args = parser.parse_args()
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if not os.path.exists(args.image):
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print(f"❌ Error: Image file not found at {args.image}")
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return
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if not os.path.exists(args.json):
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print(f"❌ Error: JSON file not found at {args.json}")
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return
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# Load the image
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print(f"📂 Loading image: {args.image}")
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image = cv2.imread(args.image)
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# Load the JSON data
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print(f"📂 Loading JSON: {args.json}")
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with open(args.json, "r", encoding="utf-8") as f:
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bubbles_data = json.load(f)
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# Process each box
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print("🧹 Cleaning text from bounding boxes...")
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white_count = 0
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complex_count = 0
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for box_id, data in bubbles_data.items():
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if "box" in data:
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# Default to "complex" if the flag is missing for backward compatibility
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bg_type = data.get("background_type", "complex")
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if bg_type == "white":
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white_count += 1
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else:
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complex_count += 1
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image = clean_text_from_box(image, data["box"], bg_type)
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print(f" ✓ Cleaned {white_count} white boxes (fast fill)")
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print(f" ✓ Cleaned {complex_count} complex boxes (inpainting)")
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# Determine output path
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if args.output:
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out_path = args.output
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else:
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base_name, ext = os.path.splitext(args.image)
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out_path = f"{base_name}_cleaned{ext}"
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# Save the result
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cv2.imwrite(out_path, image)
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print(f"✅ Cleaned image saved successfully to: {out_path}")
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if __name__ == "__main__":
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main()
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@@ -1,11 +1,12 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import cv2
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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import textwrap
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import os
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import re
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import json
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import cv2
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import numpy as np
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import warnings
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from typing import List, Tuple, Dict, Any, Optional
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@@ -519,6 +520,36 @@ def build_region_flags(raw_text, corrected_text, region_type, conf):
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# ============================================================
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# HELPERS
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# ============================================================
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def inpaint_text_only(image_bgr, box_xywh):
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"""
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Erases the original text inside the bounding box using inpainting,
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leaving the background intact without drawing new text.
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"""
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x, y = int(box_xywh["x"]), int(box_xywh["y"])
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w, h = int(box_xywh["w"]), int(box_xywh["h"])
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# 1. Extract the Region of Interest (ROI)
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roi = image_bgr[y:y+h, x:x+w]
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if roi.size == 0:
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return image_bgr
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# 2. Create a mask for the dark text
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gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
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# Threshold: Pixels darker than 100 become white (the mask), others become black
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_, mask = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY_INV)
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# Dilate the mask slightly to ensure the edges of the letters are fully covered
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kernel = np.ones((3,3), np.uint8)
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mask = cv2.dilate(mask, kernel, iterations=1)
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# 3. Inpaint the background to erase the text
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inpainted_roi = cv2.inpaint(roi, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)
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# Put the erased background back into the main image
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image_bgr[y:y+h, x:x+w] = inpainted_roi
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return image_bgr
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def split_boxes_by_horizontal_gap(out_boxes, out_indices, out_quads, out_lines, ocr, gap_multiplier=1.5):
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"""
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Splits a single bounding box into multiple boxes if there is a large horizontal
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@@ -2749,6 +2780,14 @@ def process_manga_page(image_path: str,
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# Bubble groups (lines as rendered in the bubble)
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bubble_groups = build_text_from_layout(indices, ocr)
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# ── Step 12.5: Detect Background Complexity ───────────
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# Analyze the pixels to see if it's a plain white bubble or complex artwork
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feats = contour_features_for_box(image_bgr, adjusted_box_xyxy)
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if feats["whiteness_ratio"] > 0.75 and feats["edge_density"] < 0.10:
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bg_type = "white"
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else:
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bg_type = "complex"
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# ── Step 13: Translate ────────────────────────────────
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translated = ""
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translation_input = corrected_text
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@@ -2779,6 +2818,7 @@ def process_manga_page(image_path: str,
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results[str(bid)] = {
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"order": order_idx,
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"region_type": region_type,
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"background_type": bg_type, # <--- NEW FLAG ADDED HERE
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"confidence": round(conf, 4),
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"ocr_source": ocr_source,
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"raw_ocr": raw_text,
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@@ -2787,7 +2827,7 @@ def process_manga_page(image_path: str,
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"translated": translated,
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"flags": flags,
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"bubble_groups": bubble_groups,
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"box": xyxy_to_xywh(adjusted_box_xyxy), # <--- Uses the adjusted box
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"box": xyxy_to_xywh(adjusted_box_xyxy),
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"lines": bubble_groups,
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}
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@@ -2801,6 +2841,7 @@ def process_manga_page(image_path: str,
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_write_txt_output(results, output_txt)
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return results
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# ============================================================
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# OUTPUT WRITERS
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# ============================================================
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Reference in New Issue
Block a user