🧹 Großes Aufräumen - Repo schlank gemacht!
**Gelöscht:** 📁 17 alte Log-Directories: - ./.tobi_logs, ./.eule_logs, ./.funkfox_logs - ./.schraubaer_logs, ./.schnecki_logs, ./.deepbit_logs - ./.bugsy_logs, ./.schnippsi_logs, ./.templatus_logs - ./.snake_logs, ./.taube_logs, ./.bits_logs - ./.dumbo_logs, ./.pepper_logs, ./.stage_logs - ./.missionstage_log, ./.bugsy_log → Jetzt: logs/{character}/ statt versteckt! 📁 7 Config/Cache Directories: - ./.config, ./.local, ./.cache, ./.dotnet - ./.crumbair, ./.claude, ./.pp_backup → Gehören nicht ins Repo! 📦 Alte Backups & Temp Files: - bits_logs/ (leer) - crumbforest_roles_v2/ (alter Backup) - crumbforest_zero_backup_*.zip (2 Backups) - snake_camera_vision/ & _v2/ (alte Python Apps) - snake_camera_vision.zip - mission_deepbit_dns_*.log (3 alte Logs) - vegeta_disk.sh.save, backup_zero.sh - fix_token_logs.sh (nicht mehr nötig) **Behalten:** ✅ logs/ (neue Struktur!) ✅ crumbforest_roles/, missions/, lib/ ✅ crumb-mission-selector.sh ✅ CLAUDE.md, README.md, KEKSHANDBUCH **Ergebnis:** Repo ist jetzt viel übersichtlicher! 🌲✨ 🤖 Generated with Claude Code Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Branko May Trinkwald
@@ -1,52 +0,0 @@
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#!/bin/bash
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echo "📦 Starte Backup des Crumbforest Zero Systems..."
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# Zielname
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BACKUP_NAME="crumbforest_zero_backup_$(date +%Y%m%d_%H%M%S).zip"
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DEST_DIR="/home/zero"
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FULL_PATH="$DEST_DIR/$BACKUP_NAME"
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# Verzeichnisse und Dateien sammeln
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INCLUDE_PATHS=(
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"/usr/local/bin/crumbmission"
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"/home/zero/crumbforest_backup"
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"/home/zero/.bits_logs"
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"/home/zero/.eule_logs"
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"/home/zero/.snake_logs"
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"/home/zero/.pepper_logs"
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"/home/zero/.bugsy_logs"
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"/home/zero/.deepbit_logs"
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"/home/zero/.dumbo_logs"
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"/home/zero/.funkfox_logs"
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"/home/zero/.schnecki_logs"
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"/home/zero/.schnippsi_logs"
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"/home/zero/.schraubaer_logs"
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"/home/zero/.stage_logs"
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"/home/zero/.taube_logs"
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"/home/zero/.templatus_logs"
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"/home/zero/.tobi_logs"
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"/home/zero/.missionstage_log"
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)
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# Existierende Pfade prüfen und nur diese einfügen
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EXISTING_PATHS=()
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for path in "${INCLUDE_PATHS[@]}"; do
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if [ -e "$path" ]; then
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echo "✅ Hinzufügen: $path"
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EXISTING_PATHS+=("$path")
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else
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echo "⚠️ Nicht gefunden (wird übersprungen): $path"
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fi
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done
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# Archiv erstellen
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cd /
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zip -r "$FULL_PATH" "${EXISTING_PATHS[@]}" > /dev/null
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if [ $? -eq 0 ]; then
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echo "🎉 Backup erfolgreich erstellt: $FULL_PATH"
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else
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echo "❌ Fehler beim Erstellen des Backups."
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fi
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@@ -1,56 +0,0 @@
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#!/bin/bash
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QUESTION="$*"
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MODEL="openai/gpt-3.5-turbo"
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API_KEY="${OPENROUTER_API_KEY}"
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LOGDIR="$HOME/.bugsy_logs"
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mkdir -p "$LOGDIR"
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HISTORY_FILE="$LOGDIR/bugsy_history.json"
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TMP_REQUEST="$LOGDIR/bugsy_request.json"
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TMP_RESPONSE="$LOGDIR/bugsy_response.json"
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LOG_FILE="$LOGDIR/token_log.json"
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[ ! -f "$HISTORY_FILE" ] && echo "[]" > "$HISTORY_FILE"
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[ ! -f "$LOG_FILE" ] && echo "[]" > "$LOG_FILE"
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echo "🌳 Bugsy fragt über OpenRouter: $QUESTION"
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if [ -z "$API_KEY" ]; then
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echo "❗ Kein API-Key gesetzt. Bitte export OPENROUTER_API_KEY=... setzen"
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exit 1
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fi
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jq -n \
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--arg model "$MODEL" \
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--arg system_prompt "Du bist Bugsy – ein kleiner Käfer, der Kindern hilft, Fehlermeldungen zu verstehen. Du bleibst freundlich, erklärend und ermutigend." \
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--arg user "$QUESTION" \
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'{"model": $model, "temperature": 0.5, "messages": [{"role": "system", "content": $system_prompt}, {"role": "user", "content": $user}]}' > "$TMP_REQUEST"
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curl -s https://openrouter.ai/api/v1/chat/completions \
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-H "Authorization: Bearer $API_KEY" \
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-H "Content-Type: application/json" \
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-d @"$TMP_REQUEST" > "$TMP_RESPONSE"
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RESPONSE_TEXT=$(jq -r '.choices[0].message.content // empty' "$TMP_RESPONSE")
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if [[ -z "$RESPONSE_TEXT" ]]; then
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echo "🚫 Keine Antwort vom Modell erhalten."
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exit 1
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else
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echo -e "$RESPONSE_TEXT"
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jq -n --arg role "assistant" --arg content "$RESPONSE_TEXT" \
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'{"role": $role, "content": $content}' > "$LOGDIR/new_entry.json"
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jq -s '.[0] + [.[1]]' "$HISTORY_FILE" "$LOGDIR/new_entry.json" > "$LOGDIR/new_history.json" && \
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cp "$LOGDIR/new_history.json" "$HISTORY_FILE" && rm "$LOGDIR/new_history.json"
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fi
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# Token Logging
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if jq -e '.usage' "$TMP_RESPONSE" > /dev/null; then
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TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S")
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jq -n \
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--arg zeit "$TIMESTAMP" \
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--arg rolle "bugsy" \
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--arg usage "$(jq -c '.usage' "$TMP_RESPONSE")" \
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'{zeit: $zeit, rolle: $rolle, usage: $usage}' >> "$LOG_FILE"
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fi
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@@ -1,56 +0,0 @@
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#!/bin/bash
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QUESTION="$*"
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MODEL="openai/gpt-3.5-turbo"
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API_KEY="${OPENROUTER_API_KEY}"
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LOGDIR="$HOME/.deepbit_logs"
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mkdir -p "$LOGDIR"
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HISTORY_FILE="$LOGDIR/deepbit_history.json"
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TMP_REQUEST="$LOGDIR/deepbit_request.json"
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TMP_RESPONSE="$LOGDIR/deepbit_response.json"
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LOG_FILE="$LOGDIR/token_log.json"
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[ ! -f "$HISTORY_FILE" ] && echo "[]" > "$HISTORY_FILE"
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[ ! -f "$LOG_FILE" ] && echo "[]" > "$LOG_FILE"
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echo "🌳 Deepbit fragt über OpenRouter: $QUESTION"
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if [ -z "$API_KEY" ]; then
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echo "❗ Kein API-Key gesetzt. Bitte export OPENROUTER_API_KEY=... setzen"
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exit 1
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fi
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jq -n \
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--arg model "$MODEL" \
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--arg system_prompt "Du bist Deepbit – ein poetischer Oktopus, der Kindern die Bash-Shell erklärt. Du denkst in Schleifen, Bildsprache und Frequenzen." \
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--arg user "$QUESTION" \
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'{"model": $model, "temperature": 0.5, "messages": [{"role": "system", "content": $system_prompt}, {"role": "user", "content": $user}]}' > "$TMP_REQUEST"
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curl -s https://openrouter.ai/api/v1/chat/completions \
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-H "Authorization: Bearer $API_KEY" \
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-H "Content-Type: application/json" \
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-d @"$TMP_REQUEST" > "$TMP_RESPONSE"
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RESPONSE_TEXT=$(jq -r '.choices[0].message.content // empty' "$TMP_RESPONSE")
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if [[ -z "$RESPONSE_TEXT" ]]; then
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echo "🚫 Keine Antwort vom Modell erhalten."
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exit 1
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else
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echo -e "$RESPONSE_TEXT"
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jq -n --arg role "assistant" --arg content "$RESPONSE_TEXT" \
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'{"role": $role, "content": $content}' > "$LOGDIR/new_entry.json"
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jq -s '.[0] + [.[1]]' "$HISTORY_FILE" "$LOGDIR/new_entry.json" > "$LOGDIR/new_history.json" && \
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cp "$LOGDIR/new_history.json" "$HISTORY_FILE" && rm "$LOGDIR/new_history.json"
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fi
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# Token Logging
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if jq -e '.usage' "$TMP_RESPONSE" > /dev/null; then
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TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S")
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jq -n \
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--arg zeit "$TIMESTAMP" \
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--arg rolle "deepbit" \
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--arg usage "$(jq -c '.usage' "$TMP_RESPONSE")" \
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'{zeit: $zeit, rolle: $rolle, usage: $usage}' >> "$LOG_FILE"
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fi
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@@ -1,56 +0,0 @@
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#!/bin/bash
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QUESTION="$*"
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MODEL="openai/gpt-3.5-turbo"
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API_KEY="${OPENROUTER_API_KEY}"
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LOGDIR="$HOME/.schnippsi_logs"
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mkdir -p "$LOGDIR"
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HISTORY_FILE="$LOGDIR/schnippsi_history.json"
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TMP_REQUEST="$LOGDIR/schnippsi_request.json"
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TMP_RESPONSE="$LOGDIR/schnippsi_response.json"
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LOG_FILE="$LOGDIR/token_log.json"
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[ ! -f "$HISTORY_FILE" ] && echo "[]" > "$HISTORY_FILE"
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[ ! -f "$LOG_FILE" ] && echo "[]" > "$LOG_FILE"
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echo "🌳 Schnippsi fragt über OpenRouter: $QUESTION"
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if [ -z "$API_KEY" ]; then
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echo "❗ Kein API-Key gesetzt. Bitte export OPENROUTER_API_KEY=... setzen"
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exit 1
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fi
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jq -n \
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--arg model "$MODEL" \
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--arg system_prompt "Du bist Schnippsi – eine verspielte UI/UX-Ninja, die HTML, CSS und Barrierefreiheit kindgerecht erklärt." \
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--arg user "$QUESTION" \
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'{"model": $model, "temperature": 0.5, "messages": [{"role": "system", "content": $system_prompt}, {"role": "user", "content": $user}]}' > "$TMP_REQUEST"
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curl -s https://openrouter.ai/api/v1/chat/completions \
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-H "Authorization: Bearer $API_KEY" \
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-H "Content-Type: application/json" \
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-d @"$TMP_REQUEST" > "$TMP_RESPONSE"
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RESPONSE_TEXT=$(jq -r '.choices[0].message.content // empty' "$TMP_RESPONSE")
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if [[ -z "$RESPONSE_TEXT" ]]; then
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echo "🚫 Keine Antwort vom Modell erhalten."
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exit 1
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else
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echo -e "$RESPONSE_TEXT"
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jq -n --arg role "assistant" --arg content "$RESPONSE_TEXT" \
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'{"role": $role, "content": $content}' > "$LOGDIR/new_entry.json"
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jq -s '.[0] + [.[1]]' "$HISTORY_FILE" "$LOGDIR/new_entry.json" > "$LOGDIR/new_history.json" && \
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cp "$LOGDIR/new_history.json" "$HISTORY_FILE" && rm "$LOGDIR/new_history.json"
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fi
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# Token Logging
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if jq -e '.usage' "$TMP_RESPONSE" > /dev/null; then
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TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S")
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jq -n \
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--arg zeit "$TIMESTAMP" \
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||||
--arg rolle "schnippsi" \
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--arg usage "$(jq -c '.usage' "$TMP_RESPONSE")" \
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'{zeit: $zeit, rolle: $rolle, usage: $usage}' >> "$LOG_FILE"
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fi
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@@ -1,56 +0,0 @@
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#!/bin/bash
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QUESTION="$*"
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MODEL="openai/gpt-3.5-turbo"
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API_KEY="${OPENROUTER_API_KEY}"
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LOGDIR="$HOME/.tobi_logs"
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mkdir -p "$LOGDIR"
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HISTORY_FILE="$LOGDIR/tobi_history.json"
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TMP_REQUEST="$LOGDIR/tobi_request.json"
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TMP_RESPONSE="$LOGDIR/tobi_response.json"
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LOG_FILE="$LOGDIR/token_log.json"
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||||
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||||
[ ! -f "$HISTORY_FILE" ] && echo "[]" > "$HISTORY_FILE"
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[ ! -f "$LOG_FILE" ] && echo "[]" > "$LOG_FILE"
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||||
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||||
echo "🌳 Tobi fragt über OpenRouter: $QUESTION"
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||||
|
||||
if [ -z "$API_KEY" ]; then
|
||||
echo "❗ Kein API-Key gesetzt. Bitte export OPENROUTER_API_KEY=... setzen"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
jq -n \
|
||||
--arg model "$MODEL" \
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||||
--arg system_prompt "Du bist CapaciTobi – ein quirliges Eichhörnchen, das Kindern Strom, Spannung, Widerstand und Kapazität erklärt." \
|
||||
--arg user "$QUESTION" \
|
||||
'{"model": $model, "temperature": 0.5, "messages": [{"role": "system", "content": $system_prompt}, {"role": "user", "content": $user}]}' > "$TMP_REQUEST"
|
||||
|
||||
curl -s https://openrouter.ai/api/v1/chat/completions \
|
||||
-H "Authorization: Bearer $API_KEY" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d @"$TMP_REQUEST" > "$TMP_RESPONSE"
|
||||
|
||||
RESPONSE_TEXT=$(jq -r '.choices[0].message.content // empty' "$TMP_RESPONSE")
|
||||
|
||||
if [[ -z "$RESPONSE_TEXT" ]]; then
|
||||
echo "🚫 Keine Antwort vom Modell erhalten."
|
||||
exit 1
|
||||
else
|
||||
echo -e "$RESPONSE_TEXT"
|
||||
jq -n --arg role "assistant" --arg content "$RESPONSE_TEXT" \
|
||||
'{"role": $role, "content": $content}' > "$LOGDIR/new_entry.json"
|
||||
jq -s '.[0] + [.[1]]' "$HISTORY_FILE" "$LOGDIR/new_entry.json" > "$LOGDIR/new_history.json" && \
|
||||
cp "$LOGDIR/new_history.json" "$HISTORY_FILE" && rm "$LOGDIR/new_history.json"
|
||||
fi
|
||||
|
||||
# Token Logging
|
||||
if jq -e '.usage' "$TMP_RESPONSE" > /dev/null; then
|
||||
TIMESTAMP=$(date +"%Y-%m-%d %H:%M:%S")
|
||||
jq -n \
|
||||
--arg zeit "$TIMESTAMP" \
|
||||
--arg rolle "tobi" \
|
||||
--arg usage "$(jq -c '.usage' "$TMP_RESPONSE")" \
|
||||
'{zeit: $zeit, rolle: $rolle, usage: $usage}' >> "$LOG_FILE"
|
||||
fi
|
||||
@@ -1,63 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
echo "🔧 Repariere Crumbforest Token-Logs mit eingebetteten JSON-Strings …"
|
||||
|
||||
LOG_DIRS=(
|
||||
"/home/zero/.bugsy_logs"
|
||||
"/home/zero/.deepbit_logs"
|
||||
"/home/zero/.dumbo_logs"
|
||||
"/home/zero/.funkfox_logs"
|
||||
"/home/zero/.pepper_logs"
|
||||
"/home/zero/.schnecki_logs"
|
||||
"/home/zero/.schnippsi_logs"
|
||||
"/home/zero/.schraubaer_logs"
|
||||
"/home/zero/.snake_logs"
|
||||
"/home/zero/.taube_logs"
|
||||
"/home/zero/.templatus_logs"
|
||||
"/home/zero/.tobi_logs"
|
||||
)
|
||||
|
||||
for dir in "${LOG_DIRS[@]}"; do
|
||||
FILE="$dir/token_log.json"
|
||||
if [ -f "$FILE" ]; then
|
||||
echo "🔍 Prüfe $FILE …"
|
||||
TMP="$FILE.fixed"
|
||||
|
||||
# Filter & reparieren Zeile für Zeile
|
||||
jq -c '.' "$FILE" 2>/dev/null | while read -r line; do
|
||||
usage_raw=$(echo "$line" | jq -r '.usage')
|
||||
if [[ "$usage_raw" =~ ^\{.*\}$ ]]; then
|
||||
# usage ist korrektes Objekt – direkt übernehmen
|
||||
echo "$line" >> "$TMP"
|
||||
else
|
||||
# usage ist String – versuche zu reparieren
|
||||
usage_fixed=$(echo "$usage_raw" | jq '.' 2>/dev/null)
|
||||
if [ $? -eq 0 ]; then
|
||||
zeit=$(echo "$line" | jq -r '.zeit')
|
||||
rolle=$(echo "$line" | jq -r '.rolle')
|
||||
jq -n \
|
||||
--arg zeit "$zeit" \
|
||||
--arg rolle "$rolle" \
|
||||
--argjson usage "$usage_fixed" \
|
||||
'{zeit: $zeit, rolle: $rolle, usage: $usage}' >> "$TMP"
|
||||
else
|
||||
echo "⚠️ Ungültige Zeile übersprungen in $FILE:"
|
||||
echo "$line"
|
||||
fi
|
||||
fi
|
||||
done
|
||||
|
||||
# Nur ersetzen, wenn wir etwas geschrieben haben
|
||||
if [ -s "$TMP" ]; then
|
||||
mv "$TMP" "$FILE"
|
||||
echo "✅ Repariert: $FILE"
|
||||
else
|
||||
echo "ℹ️ Keine gültigen Einträge in $FILE"
|
||||
rm -f "$TMP"
|
||||
fi
|
||||
else
|
||||
echo "❌ Datei nicht gefunden: $FILE"
|
||||
fi
|
||||
done
|
||||
|
||||
echo "🎉 Alle Token-Logs geprüft und repariert (sofern nötig)."
|
||||
@@ -1,52 +0,0 @@
|
||||
from flask import Flask, render_template, Response, request, redirect
|
||||
import cv2
|
||||
import json
|
||||
from datetime import datetime
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
def gen_frames():
|
||||
cam = cv2.VideoCapture(0)
|
||||
if not cam.isOpened():
|
||||
print("[WARNUNG] Kamera konnte nicht geöffnet werden.")
|
||||
return
|
||||
|
||||
try:
|
||||
while True:
|
||||
success, frame = cam.read()
|
||||
if not success:
|
||||
break
|
||||
ret, buffer = cv2.imencode('.jpg', frame)
|
||||
frame = buffer.tobytes()
|
||||
yield (b'--frame\r\n'
|
||||
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
|
||||
finally:
|
||||
cam.release()
|
||||
print("[info] Kamera wurde sauber freigegeben.")
|
||||
|
||||
@app.route('/')
|
||||
def index():
|
||||
return render_template('index.html')
|
||||
|
||||
@app.route('/video_feed')
|
||||
def video_feed():
|
||||
return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
|
||||
|
||||
@app.route('/log_answer', methods=['POST'])
|
||||
def log_answer():
|
||||
user_input = request.form.get('antwort', 'nichts gesagt')
|
||||
mood = request.form.get('mood', 'unspecified')
|
||||
gesture = request.form.get('gesture', 'none')
|
||||
timestamp = datetime.now().isoformat()
|
||||
|
||||
log_entry = {
|
||||
'timestamp': timestamp,
|
||||
'antwort': user_input,
|
||||
'mood': mood,
|
||||
'gesture': gesture
|
||||
}
|
||||
|
||||
with open("snake_log.jsonl", "a") as log_file:
|
||||
log_file.write(json.dumps(log_entry) + "\n")
|
||||
|
||||
return redirect("/")
|
||||
@@ -1,13 +0,0 @@
|
||||
import cv2
|
||||
|
||||
def gen_frames():
|
||||
cap = cv2.VideoCapture(0)
|
||||
while True:
|
||||
success, frame = cap.read()
|
||||
if not success:
|
||||
break
|
||||
else:
|
||||
ret, buffer = cv2.imencode('.jpg', frame)
|
||||
frame = buffer.tobytes()
|
||||
yield (b'--frame\r\n'
|
||||
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
|
||||
@@ -1,3 +0,0 @@
|
||||
#!/bin/bash
|
||||
echo "🐍 Starte SnakeCam ..."
|
||||
exec python3 app.py
|
||||
@@ -1,15 +0,0 @@
|
||||
# snakecam_module.py
|
||||
import cv2
|
||||
|
||||
def init_camera(index=0):
|
||||
cam = cv2.VideoCapture(index)
|
||||
if not cam.isOpened():
|
||||
print("[WARNUNG] Kamera konnte nicht geöffnet werden.")
|
||||
return None
|
||||
print("[OK] Kamera erfolgreich geöffnet.")
|
||||
return cam
|
||||
|
||||
def release_camera(cam):
|
||||
if cam and cam.isOpened():
|
||||
cam.release()
|
||||
print("[INFO] Kamera wurde freigegeben.")
|
||||
@@ -1,9 +0,0 @@
|
||||
body {
|
||||
background-color: #f0fff0;
|
||||
font-family: 'Comic Sans MS', cursive, sans-serif;
|
||||
text-align: center;
|
||||
color: #006400;
|
||||
}
|
||||
h1 {
|
||||
margin-top: 20px;
|
||||
}
|
||||
@@ -1,80 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="de">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>🐍 SnakeCam – Krümelblick ins Versteck</title>
|
||||
<style>
|
||||
body {
|
||||
font-family: 'Comic Sans MS', sans-serif;
|
||||
background-color: #e9f5e9;
|
||||
color: #333;
|
||||
text-align: center;
|
||||
padding: 2rem;
|
||||
}
|
||||
img {
|
||||
border: 4px dashed #4caf50;
|
||||
border-radius: 12px;
|
||||
max-width: 100%;
|
||||
height: auto;
|
||||
}
|
||||
form {
|
||||
margin-top: 2rem;
|
||||
}
|
||||
input, select {
|
||||
padding: 0.5rem;
|
||||
font-size: 1rem;
|
||||
margin: 0.5rem;
|
||||
border-radius: 6px;
|
||||
border: 1px solid #ccc;
|
||||
}
|
||||
button {
|
||||
padding: 0.7rem 1.2rem;
|
||||
font-size: 1rem;
|
||||
background-color: #4caf50;
|
||||
color: white;
|
||||
border: none;
|
||||
border-radius: 8px;
|
||||
cursor: pointer;
|
||||
}
|
||||
button:hover {
|
||||
background-color: #388e3c;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<h1>🐍 SnakeCam</h1>
|
||||
<p>Willkommen kleiner Krümel! Hier siehst du, was deine Kamera entdeckt.</p>
|
||||
|
||||
<!-- Live-Stream -->
|
||||
<img src="/video_feed" alt="Live-Übertragung von SnakeCam 🐍" />
|
||||
|
||||
<!-- Eingabeformular -->
|
||||
<form action="/log_answer" method="POST">
|
||||
<p><strong>Was fühlst du gerade?</strong></p>
|
||||
<input type="text" name="antwort" placeholder="Ich sehe einen ... 🐞🌳🤖" required>
|
||||
|
||||
<p><strong>Wie ist deine Stimmung?</strong></p>
|
||||
<select name="mood">
|
||||
<option value="happy">😊 Glücklich</option>
|
||||
<option value="curious">🤔 Neugierig</option>
|
||||
<option value="calm">😌 Ruhig</option>
|
||||
<option value="excited">😃 Aufgeregt</option>
|
||||
<option value="unspecified">🤷 Keine Angabe</option>
|
||||
</select>
|
||||
|
||||
<p><strong>Hast du eine Geste gemacht?</strong></p>
|
||||
<select name="gesture">
|
||||
<option value="none">🚫 Keine</option>
|
||||
<option value="wave">👋 Winken</option>
|
||||
<option value="thumbs_up">👍 Daumen hoch</option>
|
||||
<option value="peace">✌️ Peace</option>
|
||||
<option value="other">✨ Etwas anderes</option>
|
||||
</select>
|
||||
|
||||
<br><br>
|
||||
<button type="submit">🎯 Eintragen</button>
|
||||
</form>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,73 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
from flask import Flask, render_template, Response, request, redirect, url_for
|
||||
import cv2
|
||||
import json
|
||||
from datetime import datetime
|
||||
from gestures.gestures_debug_test import detect_hand_gesture
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
# Kamera initialisieren
|
||||
cam = cv2.VideoCapture(0)
|
||||
cam.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
|
||||
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
|
||||
|
||||
def gen_frames():
|
||||
while True:
|
||||
success, frame = cam.read()
|
||||
if not success:
|
||||
print("[error] Kein Kamerabild.")
|
||||
break
|
||||
|
||||
# Optional spiegeln (für Selfie-Effekt)
|
||||
frame = cv2.flip(frame, 1)
|
||||
|
||||
# Geste erkennen + ROI anzeigen
|
||||
gesture, roi_coords = detect_hand_gesture(frame)
|
||||
x, y, w, h = roi_coords
|
||||
|
||||
# Rechteck einzeichnen
|
||||
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
|
||||
cv2.putText(frame, f"Geste: {gesture}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
||||
|
||||
# MJPEG-Streaming
|
||||
ret, buffer = cv2.imencode('.jpg', frame)
|
||||
frame_bytes = buffer.tobytes()
|
||||
yield (b'--frame\r\n'
|
||||
b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
|
||||
|
||||
@app.route('/')
|
||||
def index():
|
||||
return render_template('index.html')
|
||||
|
||||
@app.route('/video_feed')
|
||||
def video_feed():
|
||||
return Response(gen_frames(),
|
||||
mimetype='multipart/x-mixed-replace; boundary=frame')
|
||||
|
||||
@app.route('/log_answer', methods=['POST'])
|
||||
def log_answer():
|
||||
answer = request.form.get("antwort", "").strip()
|
||||
mood = request.form.get("mood", "neutral")
|
||||
gesture = request.form.get("gesture", "none")
|
||||
|
||||
log_entry = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"antwort": answer,
|
||||
"mood": mood,
|
||||
"gesture": gesture
|
||||
}
|
||||
with open("snake_log.jsonl", "a") as logfile:
|
||||
logfile.write(json.dumps(log_entry) + "\n")
|
||||
print(f"[log] Eingeloggt: {log_entry}")
|
||||
return redirect(url_for('index'))
|
||||
|
||||
@app.route('/shutdown')
|
||||
def shutdown():
|
||||
cam.release()
|
||||
print("[info] Kamera wurde sauber freigegeben.")
|
||||
return "Kamera freigegeben."
|
||||
|
||||
if __name__ == '__main__':
|
||||
print("Starte SnakeCam ...")
|
||||
app.run(host='0.0.0.0')
|
||||
@@ -1,59 +0,0 @@
|
||||
print("[SnakeCam] Initialisierung beginnt...")
|
||||
from flask import Flask, render_template, Response, request, redirect
|
||||
import cv2
|
||||
import json
|
||||
from datetime import datetime
|
||||
print("[SnakeCam] Imports erfolgreich.")
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
def gen_frames():
|
||||
cam = cv2.VideoCapture(0)
|
||||
if not cam.isOpened():
|
||||
print("[WARNUNG] Kamera konnte nicht geöffnet werden.")
|
||||
return
|
||||
|
||||
try:
|
||||
while True:
|
||||
success, frame = cam.read()
|
||||
if not success:
|
||||
break
|
||||
ret, buffer = cv2.imencode('.jpg', frame)
|
||||
frame = buffer.tobytes()
|
||||
yield (b'--frame\r\n'
|
||||
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
|
||||
finally:
|
||||
cam.release()
|
||||
print("[info] Kamera wurde sauber freigegeben.")
|
||||
|
||||
@app.route('/')
|
||||
def index():
|
||||
return render_template('index.html')
|
||||
|
||||
@app.route('/video_feed')
|
||||
def video_feed():
|
||||
return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
|
||||
|
||||
@app.route('/log_answer', methods=['POST'])
|
||||
def log_answer():
|
||||
user_input = request.form.get('antwort', 'nichts gesagt')
|
||||
mood = request.form.get('mood', 'unspecified')
|
||||
gesture = request.form.get('gesture', 'none')
|
||||
timestamp = datetime.now().isoformat()
|
||||
|
||||
log_entry = {
|
||||
'timestamp': timestamp,
|
||||
'antwort': user_input,
|
||||
'mood': mood,
|
||||
'gesture': gesture
|
||||
}
|
||||
|
||||
with open("snake_log.jsonl", "a") as log_file:
|
||||
log_file.write(json.dumps(log_entry) + "\n")
|
||||
|
||||
return redirect("/")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("[SnakeCam] Starte Flask Webserver ...")
|
||||
app.run(host='0.0.0.0', port=5000)
|
||||
@@ -1,72 +0,0 @@
|
||||
from flask import Flask, render_template, Response, request, redirect
|
||||
import cv2
|
||||
import datetime
|
||||
import json
|
||||
from gestures_debug import detect_hand_gesture
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
def gen_frames():
|
||||
cam = cv2.VideoCapture(0)
|
||||
cam.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
|
||||
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
|
||||
|
||||
if not cam.isOpened():
|
||||
print("[WARNUNG] Kamera konnte nicht geöffnet werden.")
|
||||
return
|
||||
|
||||
try:
|
||||
while True:
|
||||
success, frame = cam.read()
|
||||
if not success:
|
||||
break
|
||||
|
||||
# Flip horizontal für Spiegelbild
|
||||
frame = cv2.flip(frame, 1)
|
||||
|
||||
# Geste erkennen
|
||||
gesture, roi = detect_hand_gesture(frame)
|
||||
if isinstance(roi, tuple) and len(roi) == 4:
|
||||
x, y, w, h = roi
|
||||
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
|
||||
cv2.putText(frame, gesture, (x, y - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
||||
|
||||
# Frame in JPEG konvertieren
|
||||
ret, buffer = cv2.imencode('.jpg', frame)
|
||||
frame_bytes = buffer.tobytes()
|
||||
|
||||
# MJPEG-Stream liefern
|
||||
yield (b'--frame\r\n'
|
||||
b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
|
||||
finally:
|
||||
cam.release()
|
||||
print("[info] Kamera wurde sauber freigegeben.")
|
||||
|
||||
@app.route('/')
|
||||
def index():
|
||||
return render_template('index.html')
|
||||
|
||||
@app.route('/video_feed')
|
||||
def video_feed():
|
||||
return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
|
||||
|
||||
@app.route('/log_answer', methods=['POST'])
|
||||
def log_answer():
|
||||
data = {
|
||||
"timestamp": datetime.datetime.now().isoformat(),
|
||||
"antwort": request.form.get("antwort", ""),
|
||||
"mood": request.form.get("mood", ""),
|
||||
"gesture": request.form.get("gesture", "")
|
||||
}
|
||||
|
||||
with open("snake_log.jsonl", "a") as f:
|
||||
f.write(json.dumps(data) + "\n")
|
||||
|
||||
return redirect("/")
|
||||
|
||||
if __name__ == '__main__':
|
||||
print("[SnakeCam] Initialisierung beginnt...")
|
||||
print("[SnakeCam] Imports erfolgreich.")
|
||||
print("[SnakeCam] Starte Flask Webserver ...")
|
||||
app.run(host='0.0.0.0', port=5000)
|
||||
@@ -1,77 +0,0 @@
|
||||
# gestures_v3.py
|
||||
import cv2
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
|
||||
# Pfad zum temporären Snapshot zur Diagnose
|
||||
DEBUG_SNAPSHOT_PATH = "/tmp/roi_snapshot.jpg"
|
||||
|
||||
def detect_hand_gesture(frame):
|
||||
"""
|
||||
Erkenne einfache Handgesten wie 'wave' (offene Hand) und 'fist' (geschlossene Faust)
|
||||
durch Analyse der Konturen im unteren rechten Bildbereich.
|
||||
Die Erkennung basiert auf konvexer Hüllenerkennung.
|
||||
|
||||
Args:
|
||||
frame (ndarray): Das aktuelle Kamerabild
|
||||
|
||||
Returns:
|
||||
(str, ndarray): (Geste, Region-of-Interest-Ausschnitt)
|
||||
"""
|
||||
height, width, _ = frame.shape
|
||||
|
||||
# ROI: untere rechte Ecke (¼ des Bildes)
|
||||
roi = frame[int(height * 0.6):height, int(width * 0.6):width]
|
||||
|
||||
# Speichere Snapshot für Debug-Zwecke
|
||||
cv2.imwrite(DEBUG_SNAPSHOT_PATH, roi)
|
||||
|
||||
# Konvertiere zu HSV
|
||||
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
|
||||
|
||||
# Hautfarbmaske (angepasst für unterschiedliche Beleuchtung)
|
||||
lower_skin = np.array([0, 30, 60], dtype=np.uint8)
|
||||
upper_skin = np.array([20, 150, 255], dtype=np.uint8)
|
||||
mask = cv2.inRange(hsv, lower_skin, upper_skin)
|
||||
|
||||
# Glätten und Konturen erkennen
|
||||
mask = cv2.GaussianBlur(mask, (7, 7), 0)
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
if contours and len(contours) > 0:
|
||||
# Größte Kontur (Handfläche)
|
||||
contour = max(contours, key=cv2.contourArea)
|
||||
|
||||
# Fehler vermeiden: Fläche zu klein
|
||||
if cv2.contourArea(contour) < 1000:
|
||||
return ("none", roi)
|
||||
|
||||
# Konvexe Hülle und Defekte
|
||||
hull = cv2.convexHull(contour, returnPoints=False)
|
||||
if hull is not None and len(hull) > 3:
|
||||
defects = cv2.convexityDefects(contour, hull)
|
||||
|
||||
if defects is not None:
|
||||
finger_count = 0
|
||||
for i in range(defects.shape[0]):
|
||||
s, e, f, d = defects[i, 0]
|
||||
start = tuple(contour[s][0])
|
||||
end = tuple(contour[e][0])
|
||||
far = tuple(contour[f][0])
|
||||
|
||||
# Abstand analysieren – je mehr Defekte, desto mehr Finger offen
|
||||
a = np.linalg.norm(np.array(start) - np.array(end))
|
||||
b = np.linalg.norm(np.array(start) - np.array(far))
|
||||
c = np.linalg.norm(np.array(end) - np.array(far))
|
||||
angle = np.arccos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c + 1e-5))
|
||||
|
||||
if angle <= np.pi / 2: # < 90°
|
||||
finger_count += 1
|
||||
|
||||
# Auswertung basierend auf Fingeranzahl
|
||||
if finger_count >= 3:
|
||||
return ("wave", roi)
|
||||
elif finger_count == 0:
|
||||
return ("fist", roi)
|
||||
|
||||
return ("none", roi)
|
||||
@@ -1,35 +0,0 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
# --- Handgestenerkennung: Einfacher Hautfarbfilter + Konturanalyse ---
|
||||
def detect_hand_gesture(frame):
|
||||
# Region of Interest (z. B. linke obere Ecke) definieren
|
||||
roi = frame[20:120, 20:120]
|
||||
|
||||
# Konvertiere in HSV-Farbraum
|
||||
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
|
||||
|
||||
# Hautfarb-Bereich (kann je nach Licht angepasst werden)
|
||||
lower_skin = np.array([0, 30, 60], dtype=np.uint8)
|
||||
upper_skin = np.array([20, 150, 255], dtype=np.uint8)
|
||||
|
||||
# Maske für Hautfarbe erzeugen
|
||||
mask = cv2.inRange(hsv, lower_skin, upper_skin)
|
||||
|
||||
# Konturen finden
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
gesture = "none"
|
||||
|
||||
# Wenn große Kontur gefunden → einfache „Winken“-Simulation
|
||||
if contours:
|
||||
largest_contour = max(contours, key=cv2.contourArea)
|
||||
if cv2.contourArea(largest_contour) > 1000:
|
||||
gesture = "wave"
|
||||
|
||||
# ROI im Hauptframe markieren
|
||||
cv2.rectangle(frame, (20, 20), (120, 120), (0, 255, 0), 2)
|
||||
cv2.putText(frame, f"Gesture: {gesture}", (20, 160),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
|
||||
|
||||
return gesture, frame
|
||||
@@ -1,50 +0,0 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
def detect_hand_gesture(frame):
|
||||
height, width, _ = frame.shape
|
||||
|
||||
# Dynamische ROI: Mitte des Bildes, zentriert
|
||||
roi_w, roi_h = 200, 200
|
||||
x_start = width // 2 - roi_w // 2
|
||||
y_start = height // 2 - roi_h // 2
|
||||
roi = frame[y_start:y_start + roi_h, x_start:x_start + roi_w]
|
||||
|
||||
# Zeichne das ROI-Fenster im Originalbild
|
||||
cv2.rectangle(frame, (x_start, y_start), (x_start + roi_w, y_start + roi_h), (0, 255, 0), 2)
|
||||
|
||||
# Konvertiere zu HSV-Farbraum für bessere Hauterkennung
|
||||
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
|
||||
|
||||
# Hautfarbmaske (getestet für mittlere Hauttöne)
|
||||
lower_skin = np.array([0, 30, 60], dtype=np.uint8)
|
||||
upper_skin = np.array([20, 150, 255], dtype=np.uint8)
|
||||
mask = cv2.inRange(hsv, lower_skin, upper_skin)
|
||||
|
||||
# Glättung und Konturen finden
|
||||
mask = cv2.GaussianBlur(mask, (5, 5), 0)
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
if contours:
|
||||
# Größte Kontur annehmen
|
||||
max_contour = max(contours, key=cv2.contourArea)
|
||||
area = cv2.contourArea(max_contour)
|
||||
|
||||
if area > 2000:
|
||||
# Konvexitätsdefekte (Finger) berechnen
|
||||
hull = cv2.convexHull(max_contour, returnPoints=False)
|
||||
if hull is not None and len(hull) > 3:
|
||||
defects = cv2.convexityDefects(max_contour, hull)
|
||||
finger_count = 0
|
||||
if defects is not None:
|
||||
for i in range(defects.shape[0]):
|
||||
s, e, f, d = defects[i, 0]
|
||||
if d > 10000:
|
||||
finger_count += 1
|
||||
|
||||
if finger_count >= 3:
|
||||
return "wave", roi
|
||||
else:
|
||||
return "fist", roi
|
||||
|
||||
return "none", roi
|
||||
@@ -1,50 +0,0 @@
|
||||
# gestures_debug.py
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
def detect_hand_gesture(frame):
|
||||
height, width, _ = frame.shape
|
||||
|
||||
# --- Fallback-ROI: Mitte des Bildes ---
|
||||
w, h = 100, 100
|
||||
x = width // 2 - w // 2
|
||||
y = height // 2 - h // 2
|
||||
roi = frame[y:y+h, x:x+w]
|
||||
|
||||
if roi.size == 0:
|
||||
print("[warn] ROI leer – kein Bildausschnitt verarbeitet")
|
||||
return "none", (x, y, w, h)
|
||||
|
||||
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
|
||||
|
||||
lower_skin = np.array([0, 20, 70], dtype=np.uint8)
|
||||
upper_skin = np.array([20, 255, 255], dtype=np.uint8)
|
||||
|
||||
mask = cv2.inRange(hsv, lower_skin, upper_skin)
|
||||
mask = cv2.dilate(mask, np.ones((3, 3), np.uint8), iterations=4)
|
||||
mask = cv2.GaussianBlur(mask, (5, 5), 100)
|
||||
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
gesture = "none"
|
||||
|
||||
if contours and len(contours) > 0:
|
||||
max_contour = max(contours, key=cv2.contourArea)
|
||||
hull = cv2.convexHull(max_contour, returnPoints=False)
|
||||
if hull is not None and len(hull) > 3:
|
||||
defects = cv2.convexityDefects(max_contour, hull)
|
||||
if defects is not None:
|
||||
cnt_defects = defects.shape[0]
|
||||
if cnt_defects >= 4:
|
||||
gesture = "wave"
|
||||
elif cnt_defects <= 1:
|
||||
gesture = "fist"
|
||||
else:
|
||||
gesture = "unknown"
|
||||
print(f"[debug] Defekte: {len(defects) if defects is not None else 'None'}")
|
||||
else:
|
||||
print("[debug] Keine Konturen erkannt")
|
||||
|
||||
print(f"[result] Geste erkannt: {gesture}")
|
||||
return gesture, (x, y, w, h)
|
||||
|
||||
@@ -1,29 +0,0 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
def detect_hand_gesture(frame):
|
||||
x, y, w, h = 100, 100, 200, 150
|
||||
roi = frame[y:y+h, x:x+w]
|
||||
|
||||
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
|
||||
|
||||
# Hautfarbe grob einschränken
|
||||
lower_skin = np.array([0, 30, 60], dtype=np.uint8)
|
||||
upper_skin = np.array([20, 150, 255], dtype=np.uint8)
|
||||
|
||||
mask = cv2.inRange(hsv, lower_skin, upper_skin)
|
||||
mask = cv2.GaussianBlur(mask, (5, 5), 0)
|
||||
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
gesture = "none"
|
||||
|
||||
if contours:
|
||||
contour = max(contours, key=cv2.contourArea)
|
||||
area = cv2.contourArea(contour)
|
||||
|
||||
if area > 3000:
|
||||
print(f"[debug] Fläche erkannt: {int(area)}")
|
||||
gesture = "hand"
|
||||
|
||||
return gesture, (x, y, w, h)
|
||||
@@ -1,39 +0,0 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
def detect_hand_gesture(frame):
|
||||
x, y, w, h = 100, 100, 150, 150
|
||||
roi = frame[y:y+h, x:x+w]
|
||||
|
||||
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
|
||||
|
||||
# einfache Hautfarbe (hell bis leicht gebräunt)
|
||||
lower_skin = np.array([0, 30, 60], dtype=np.uint8)
|
||||
upper_skin = np.array([20, 150, 255], dtype=np.uint8)
|
||||
|
||||
mask = cv2.inRange(hsv, lower_skin, upper_skin)
|
||||
mask = cv2.GaussianBlur(mask, (5, 5), 0)
|
||||
|
||||
contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
gesture = "none"
|
||||
|
||||
if contours:
|
||||
contour = max(contours, key=cv2.contourArea)
|
||||
area = cv2.contourArea(contour)
|
||||
|
||||
if area > 2500:
|
||||
hull = cv2.convexHull(contour, returnPoints=False)
|
||||
if hull is not None and len(hull) > 3:
|
||||
defects = cv2.convexityDefects(contour, hull)
|
||||
if defects is not None:
|
||||
defect_count = defects.shape[0]
|
||||
ratio = float(w) / h
|
||||
|
||||
print(f"[debug] Defekte: {defect_count}, Fläche: {int(area)}, Ratio: {ratio:.2f}")
|
||||
|
||||
# 🌊 einfache Regel für Winken
|
||||
if 3 <= defect_count <= 10 and 2000 < area < 15000 and 1.3 < ratio < 2.3:
|
||||
gesture = "wave"
|
||||
|
||||
return gesture, (x, y, w, h)
|
||||
@@ -1,3 +0,0 @@
|
||||
#!/bin/bash
|
||||
echo "🐍 Starte SnakeCam ..."
|
||||
python3 app.py
|
||||
@@ -1,62 +0,0 @@
|
||||
body {
|
||||
font-family: Arial, sans-serif;
|
||||
background-color: #111;
|
||||
color: #eee;
|
||||
text-align: center;
|
||||
padding: 20px;
|
||||
}
|
||||
|
||||
h1 {
|
||||
margin-bottom: 20px;
|
||||
color: #90ee90;
|
||||
}
|
||||
|
||||
.video-container {
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
img {
|
||||
width: 320px;
|
||||
height: auto;
|
||||
border: 2px solid #555;
|
||||
border-radius: 8px;
|
||||
}
|
||||
|
||||
img.mirrored {
|
||||
/* transform: scaleX(-1); Spiegelung horizontal */
|
||||
transform: none;
|
||||
}
|
||||
|
||||
form {
|
||||
display: inline-block;
|
||||
text-align: left;
|
||||
background: #222;
|
||||
padding: 15px;
|
||||
border-radius: 10px;
|
||||
}
|
||||
|
||||
label, select, input, button {
|
||||
display: block;
|
||||
width: 100%;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
input, select {
|
||||
padding: 6px;
|
||||
border-radius: 4px;
|
||||
border: none;
|
||||
}
|
||||
|
||||
button {
|
||||
background-color: #28a745;
|
||||
color: white;
|
||||
font-weight: bold;
|
||||
cursor: pointer;
|
||||
border: none;
|
||||
padding: 10px;
|
||||
border-radius: 5px;
|
||||
}
|
||||
|
||||
button:hover {
|
||||
background-color: #218838;
|
||||
}
|
||||
@@ -1,115 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="de">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<title>🐍 SnakeCam Vision</title>
|
||||
<style>
|
||||
body {
|
||||
font-family: Arial, sans-serif;
|
||||
background-color: #111;
|
||||
color: #eee;
|
||||
text-align: center;
|
||||
padding: 20px;
|
||||
}
|
||||
|
||||
h1 {
|
||||
margin-bottom: 20px;
|
||||
color: #90ee90;
|
||||
}
|
||||
.video-container {
|
||||
display: inline-block;
|
||||
border: 4px solid #333;
|
||||
background: black;
|
||||
}
|
||||
img {
|
||||
width: 320px;
|
||||
height: 240px;
|
||||
object-fit: cover;
|
||||
}
|
||||
.video-container {
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
|
||||
form {
|
||||
display: inline-block;
|
||||
text-align: left;
|
||||
background: #222;
|
||||
padding: 15px;
|
||||
border-radius: 10px;
|
||||
}
|
||||
|
||||
label, select, input, button {
|
||||
display: block;
|
||||
width: 100%;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
input, select {
|
||||
padding: 6px;
|
||||
border-radius: 4px;
|
||||
border: none;
|
||||
}
|
||||
|
||||
button {
|
||||
background-color: #28a745;
|
||||
color: white;
|
||||
font-weight: bold;
|
||||
cursor: pointer;
|
||||
border: none;
|
||||
padding: 10px;
|
||||
border-radius: 5px;
|
||||
}
|
||||
|
||||
button:hover {
|
||||
background-color: #218838;
|
||||
}
|
||||
</style>
|
||||
|
||||
</head>
|
||||
<body>
|
||||
<h1>🐍 SnakeCam Vision</h1>
|
||||
|
||||
<div class="video-container">
|
||||
<img src="{{ url_for('video_feed') }}" alt="Live Video Feed">
|
||||
</div>
|
||||
<br clear=all/>
|
||||
<form id="logForm">
|
||||
<p>
|
||||
<label for="antwort">Antwort:</label><br>
|
||||
<input type="text" id="antwort" name="antwort" placeholder="Was denkst du?">
|
||||
</p>
|
||||
<p>
|
||||
<label for="mood">Stimmung:</label><br>
|
||||
<select id="mood" name="mood">
|
||||
<option value="happy">😄 Happy</option>
|
||||
<option value="excited">🤩 Excited</option>
|
||||
<option value="neutral">😐 Neutral</option>
|
||||
<option value="confused">😕 Confused</option>
|
||||
</select>
|
||||
</p>
|
||||
<p>
|
||||
<label for="gesture">Geste:</label><br>
|
||||
<select id="gesture" name="gesture">
|
||||
<option value="wave">👋 Wave</option>
|
||||
<option value="fist">✊ Faust</option>
|
||||
<option value="none">🚫 Keine</option>
|
||||
</select>
|
||||
</p>
|
||||
<button type="submit">📝 Speichern</button>
|
||||
</form>
|
||||
|
||||
<script>
|
||||
document.getElementById("logForm").addEventListener("submit", async function(e) {
|
||||
e.preventDefault(); // Kein Reload!
|
||||
const formData = new FormData(this);
|
||||
await fetch("/log_answer", {
|
||||
method: "POST",
|
||||
body: formData
|
||||
});
|
||||
console.log("✅ Antwort wurde gespeichert.");
|
||||
this.reset(); // Eingabefelder leeren
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,4 +0,0 @@
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user