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- import os
- import json
- import streamlit as st
- import pandas as pd
- from PIL import Image
- from pathlib import Path
- import time
- import folium
- from streamlit_folium import st_folium
- import math
- import base64
- from io import BytesIO
-
- # --- CACHE IMMAGINE ---
- @st.cache_data
- def get_image_base64(img_path):
- img = Image.open(img_path).convert("RGBA")
- w, h = img.size
- buffered = BytesIO()
- img.save(buffered, format="PNG")
- img_str = base64.b64encode(buffered.getvalue()).decode("ascii")
- return f"data:image/png;base64,{img_str}", w, h
-
- # --- FUNZIONE SALVATAGGIO FISICO ---
- def force_save_json(path, data):
- try:
- with open(path, "w", encoding='utf-8') as f:
- json.dump(data, f, indent=4)
- return True
- except Exception as e:
- st.error(f"Errore scrittura disco: {e}")
- return False
-
- def show_mapper(cfg):
- MAPS_DIR = Path(cfg['maps']['map_dir'])
- SAMPLES_DIR = Path("/data/train/samples")
- JOBS_BASE = Path(cfg['collect_train']['jobs_dir'])
- PENDING_DIR = JOBS_BASE / "pending"
- ERROR_DIR = JOBS_BASE / "error"
- BEACONS_FILE = "/data/config/beacons.csv"
- [p.mkdir(parents=True, exist_ok=True) for p in [MAPS_DIR, SAMPLES_DIR, PENDING_DIR, ERROR_DIR]]
-
- # --- 1. GESTIONE UPLOAD ---
- # La logica ora gestisce stringhe per floor_id permettendo il carattere "-"
- maps = sorted([f.replace(cfg['maps']['floor_prefix'], "").split('.')[0]
- for f in os.listdir(MAPS_DIR) if f.startswith(cfg['maps']['floor_prefix'])])
-
- with st.expander("📂 Carica Nuova Planimetria", expanded=not maps):
- c_up1, c_up2 = st.columns([1, 2])
- new_f_id = c_up1.text_input("ID Piano (es. 0, -1):", key="new_f_id_v21")
- up_file = c_up2.file_uploader("Immagine (PNG/JPG):", type=['png', 'jpg', 'jpeg'], key="up_v21")
- if st.button("🚀 SALVA PIANO", use_container_width=True):
- if new_f_id and up_file:
- ext = Path(up_file.name).suffix
- # Il nome file includerà il "-" se presente in new_f_id
- img_save_path = MAPS_DIR / f"{cfg['maps']['floor_prefix']}{new_f_id}{ext}"
- with open(img_save_path, "wb") as f:
- f.write(up_file.getbuffer())
-
- m_path = MAPS_DIR / f"{cfg['maps']['meta_prefix']}{new_f_id}.json"
- force_save_json(m_path, {"pixel_ratio": 1.0, "calibrated": False, "origin": [0, 0], "grid_size": 50})
- st.session_state.current_floor = new_f_id
- st.rerun()
-
- if not maps: return
-
- # --- 2. RIGA STATO ---
- c_p, c_s, c_o = st.columns([2.5, 2, 2])
- with c_p:
- s1, s2 = st.columns([1, 1.2])
- s1.markdown("<p style='padding-top:35px; font-weight:bold;'>Piano Attivo:</p>", unsafe_allow_html=True)
- def_idx = maps.index(st.session_state.current_floor) if st.session_state.get("current_floor") in maps else 0
- floor_id = s2.selectbox("", maps, index=def_idx, key="f_v21", label_visibility="collapsed")
- if st.session_state.get("prev_floor") != floor_id:
- st.session_state.cal_points = []
- st.session_state.temp_lat, st.session_state.temp_lng = None, None
- st.session_state.prev_floor = floor_id
-
- meta_path = MAPS_DIR / f"{cfg['maps']['meta_prefix']}{floor_id}.json"
- if meta_path.exists():
- with open(meta_path, "r") as f: meta = json.load(f)
- else:
- meta = {"pixel_ratio": 1.0, "calibrated": False, "origin": [0, 0]}
-
- with c_s: st.info(f"📏 Scala: {'✅' if meta['calibrated'] else '❌'}\n({meta['pixel_ratio']:.4f} px/cm)")
- with c_o: st.info(f"🎯 Origine: {'✅' if meta['origin'] != [0,0] else '❌'}\n(X:{meta['origin'][0]}, Y:{meta['origin'][1]})")
-
- if not meta["calibrated"] or meta["origin"] == [0, 0]:
- st.warning(f"💡 **Piano {floor_id} da configurare**: Esegui **Calibra** e imposta l'**Origine**.")
-
- # --- 3. IMPOSTAZIONI GLOBALI (Default disabilitati) ---
- st.markdown("---")
- g1, g2, g3, g4 = st.columns([1.2, 1.5, 1.2, 2])
- with g1:
- # Griglia disattivata di default per evitare interferenze iniziali
- show_grid = st.toggle("Griglia", value=False, key="grid_v21")
- with g2:
- grid_cm = st.select_slider("Passo (cm):", options=[25, 50, 100, 200], value=50, key="step_v21")
- with g3:
- # Stay Grid disattivato di default
- snap_on = st.toggle("Stay Grid", value=False, key="snap_v21")
- with g4:
- m_size = st.slider("Dimensione Marker:", 5, 20, 8, key="msize_v21")
- st.markdown("---")
-
- # --- 4. TOOLSET ---
- t1, t2, t3 = st.columns(3)
- if t1.button("📏 CALIBRA", use_container_width=True): st.session_state.map_tool = "Calibra"; st.session_state.cal_points = []
- if t2.button("🎯 SET ORIGINE", use_container_width=True): st.session_state.map_tool = "Origine"
- if t3.button("📡 RILEVA", use_container_width=True): st.session_state.map_tool = "Rileva"
- tool = st.session_state.get('map_tool', 'Rileva')
-
- # --- 5. MAPPA ---
- col_map, col_ui = st.columns([3, 1])
- with col_map:
- img_p = next((MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}{e}" for e in ['.png','.jpg','.jpeg'] if (MAPS_DIR / f"{cfg['maps']['floor_prefix']}{floor_id}{e}").exists()))
- img_data, w, h = get_image_base64(img_p)
- bounds = [[0, 0], [h, w]]
- m = folium.Map(location=[h/2, w/2], crs="Simple", tiles=None, attribution_control=False)
- m.fit_bounds(bounds)
- m.options.update({"minZoom": -6, "maxZoom": 6, "zoomSnap": 0.25, "maxBounds": bounds, "maxBoundsViscosity": 1.0})
- folium.raster_layers.ImageOverlay(image=img_data, bounds=bounds, interactive=True).add_to(m)
-
- # ORIGINE (Sempre visibile se impostata)
- if meta["origin"] != [0, 0]:
- ox, oy = meta["origin"]
- folium.CircleMarker(location=[oy, ox], radius=6, color="black", fill=True, zindex=1000, tooltip="Origine (0,0)").add_to(m)
-
- # GRIGLIA
- if show_grid and meta["calibrated"] and meta["origin"] != [0, 0]:
- px_step = grid_cm * meta["pixel_ratio"]
- ox, oy = meta["origin"]
- for x in sorted(list(range(int(ox), w, int(px_step))) + list(range(int(ox), 0, -int(px_step)))):
- folium.PolyLine([[0, x], [h, x]], color="blue", weight=1, opacity=0.1).add_to(m)
- for y in sorted(list(range(int(oy), h, int(px_step))) + list(range(int(oy), 0, -int(px_step)))):
- folium.PolyLine([[y, 0], [y, w]], color="blue", weight=1, opacity=0.1).add_to(m)
-
- # Disegno Punti Storici
- def draw_points(directory, color, shape="circle"):
- if not meta["calibrated"] or meta["origin"] == [0,0]: return
- for f in Path(directory).glob("*.csv"):
- try:
- df = pd.read_csv(f, sep=";")
- df.columns = [c.lower() for c in df.columns]
- # Confronto tra stringhe per gestire piani negativi e ID complessi
- if str(df.iloc[0].get('z', df.iloc[0].get('floor'))) == str(floor_id):
- px_x = (df.iloc[0]['x'] * meta["pixel_ratio"]) + meta["origin"][0]
- px_y = meta["origin"][1] - (df.iloc[0]['y'] * meta["pixel_ratio"])
- if shape == "circle": folium.CircleMarker(location=[px_y, px_x], radius=m_size, color=color, fill=True, fill_opacity=0.8).add_to(m)
- elif shape == "square": folium.RegularPolygonMarker(location=[px_y, px_x], number_of_sides=4, radius=m_size, color="black", weight=2, fill=True, fill_color=color).add_to(m)
- elif shape == "diamond": folium.RegularPolygonMarker(location=[px_y, px_x], number_of_sides=4, rotation=45, radius=m_size, color="black", weight=1, fill=True, fill_color=color).add_to(m)
- except: continue
-
- draw_points(SAMPLES_DIR, "green", "circle")
- draw_points(PENDING_DIR, "yellow", "diamond")
- draw_points(ERROR_DIR, "red", "square")
-
- # Feedback Visivo Click
- if tool == "Calibra" and st.session_state.get("cal_points"):
- for p in st.session_state.cal_points: folium.CircleMarker(location=p, radius=6, color="red", fill=True).add_to(m)
- if len(st.session_state.cal_points) == 2: folium.PolyLine(st.session_state.cal_points, color="red", weight=3).add_to(m)
- elif st.session_state.get("temp_lat") is not None:
- folium.CircleMarker(location=[st.session_state.temp_lat, st.session_state.temp_lng], radius=m_size+2, color="blue", fill=True, zindex=500).add_to(m)
-
- out = st_folium(m, height=600, width=None, key=f"map_v21_{floor_id}_{tool}")
- click = out.get("last_clicked")
- if click:
- clat, clng = click["lat"], click["lng"]
- if tool == "Calibra":
- if len(st.session_state.get("cal_points", [])) < 2:
- st.session_state.cal_points.append([clat, clng]); st.rerun()
- else:
- if tool == "Rileva" and snap_on and meta["calibrated"]:
- px_step = grid_cm * meta["pixel_ratio"]
- st.session_state.temp_lng = meta["origin"][0] + round((clng - meta["origin"][0]) / px_step) * px_step
- st.session_state.temp_lat = meta["origin"][1] + round((clat - meta["origin"][1]) / px_step) * px_step
- else:
- st.session_state.temp_lat, st.session_state.temp_lng = clat, clng
- st.rerun()
-
- # --- 6. LOGICA UI ---
- with col_ui:
- st.write(f"### Tool: **{tool}**")
- err_files = list(ERROR_DIR.glob("*.csv"))
- if err_files and st.button(f"🗑️ PULISCI ERRORI", use_container_width=True):
- for f in err_files: os.remove(f); st.rerun()
-
- if tool == "Calibra":
- pts = st.session_state.get("cal_points", [])
- if len(pts) == 2:
- dist_cm = st.number_input("Distanza reale (cm):", value=100.0)
- if st.button("📏 SALVA SCALA", use_container_width=True, type="primary"):
- px_d = math.sqrt((pts[1][1]-pts[0][1])**2 + (pts[1][0]-pts[0][0])**2)
- meta.update({"pixel_ratio": px_d / dist_cm, "calibrated": True})
- if force_save_json(meta_path, meta): st.session_state.cal_points = []; st.rerun()
-
- elif st.session_state.get("temp_lat") is not None:
- px_x, px_y = st.session_state.temp_lng, st.session_state.temp_lat
- if tool == "Origine":
- st.metric("X (px)", int(px_x)); st.metric("Y (px)", int(px_y))
- if st.button("💾 SALVA ORIGINE", use_container_width=True, type="primary"):
- meta["origin"] = [int(px_x), int(px_y)]
- if force_save_json(meta_path, meta): st.rerun()
- elif tool == "Rileva" and meta["calibrated"]:
- rx = (px_x - meta["origin"][0]) / meta["pixel_ratio"]
- ry = (meta["origin"][1] - px_y) / meta["pixel_ratio"]
- sx, sy = int(round(rx)), int(round(ry))
- st.metric("X (cm)", sx); st.metric("Y (cm)", sy)
- if os.path.exists(BEACONS_FILE):
- b_df = pd.read_csv(BEACONS_FILE, sep=";")
- sel_b = st.selectbox("Beacon:", b_df.apply(lambda x: f"{x['BeaconName']} | {x['MAC']}", axis=1))
- if st.button("🚀 REGISTRA", use_container_width=True, type="primary"):
- b_name, b_mac = sel_b.split(" | ")
- id_str = f"{b_name}_{floor_id}_{sx}_{sy}"
- # Salvataggio Z come stringa per mantenere il "-"
- data = {"Position": id_str, "Floor": floor_id, "RoomName": b_name, "X": sx, "Y": sy, "Z": floor_id, "BeaconName": b_name, "MAC": b_mac}
- pd.DataFrame([data]).to_csv(PENDING_DIR / f"{id_str}.csv", sep=";", index=False); st.rerun()
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