Delete app.py

app.py

@@ -1,148 +0,0 @@
-# -*- coding: utf-8 -*-
-import streamlit as st
-import cv2
-import tempfile
-import os
-import time
-import numpy as np
-import pandas as pd
-from collections import defaultdict
-from ultralytics import YOLO
-
-# --- FONCTIONS UTILES ---
-def draw_text_with_background(image, text, position, font=cv2.FONT_HERSHEY_SIMPLEX,
-                              font_scale=1, font_thickness=2, text_color=(255, 255, 255), bg_color=(0, 0, 0), padding=5):
-    """Ajoute du texte avec un fond sur une image OpenCV."""
-    text_size = cv2.getTextSize(text, font, font_scale, font_thickness)[0]
-    text_width, text_height = text_size
-
-    x, y = position
-    top_left = (x, y - text_height - padding)
-    bottom_right = (x + text_width + padding * 2, y + padding)
-
-    cv2.rectangle(image, top_left, bottom_right, bg_color, -1)
-    cv2.putText(image, text, (x + padding, y), font, font_scale, text_color, font_thickness, cv2.LINE_AA)
-
-# --- CLASSE YOLO ---
-class YOLOVideoProcessor:
-    def __init__(self, model_path, video_path, output_path, poly1, poly2, tracker_method="bot"):
-        self.model = YOLO(model_path, task="detect")
-        self.tracker_method = tracker_method
-        self.video_path = video_path
-        self.output_path = output_path 
-
-        self.unique_region1_ids = set()
-        self.unique_region2_ids = set()
-        self.poly1 = poly1
-        self.poly2 = poly2
-
-    def is_in_region(self, center, poly):
-        poly_np = np.array(poly, dtype=np.int32)
-        return cv2.pointPolygonTest(poly_np, center, False) >= 0
-
-    def process_video(self, progress_bar):
-        cap = cv2.VideoCapture(self.video_path)
-        if not cap.isOpened():
-            st.error("⚠️ Erreur : Impossible d'ouvrir la vidéo.")
-            return
-
-        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-        fps = int(cap.get(cv2.CAP_PROP_FPS))
-
-        if fps == 0:
-            fps = 30  # Valeur par défaut si FPS est invalide
-
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v') 
-        out = cv2.VideoWriter(self.output_path, fourcc, fps, (frame_width, frame_height))  
-
-        frame_count = 0
-        processed_frames = 0
-        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-        while cap.isOpened():
-            success, frame = cap.read()
-            if not success:
-                break
-
-            tracker = "botsort.yaml" if self.tracker_method.lower() == "bot" else "bytetrack.yaml"
-            results = self.model.track(frame, persist=True, tracker=tracker, conf=0.25)
-
-            track_ids = []
-            if len(results[0].boxes) > 0:
-                try:
-                    track_ids = results[0].boxes.id.int().cpu().tolist()
-                except AttributeError:
-                    track_ids = [i for i in range(len(results[0].boxes.xywh.cpu().numpy()))]
-
-            # Dessiner les polygones
-            cv2.polylines(frame, [np.array(self.poly1, np.int32)], isClosed=True, color=(0, 255, 0), thickness=2)
-            cv2.polylines(frame, [np.array(self.poly2, np.int32)], isClosed=True, color=(255, 0, 0), thickness=2)
-
-            for box, track_id in zip(results[0].boxes.xywh.cpu().numpy(), track_ids):
-                x, y, w, h = box
-                center_point = (int(x), int(y))
-
-                if self.is_in_region(center_point, self.poly1):
-                    self.unique_region1_ids.add(track_id)
-                if self.is_in_region(center_point, self.poly2):
-                    self.unique_region2_ids.add(track_id)
-
-            # Affichage du comptage des véhicules
-            draw_text_with_background(frame, f'Total Sens 1: {len(self.unique_region1_ids)}', (10, frame_height - 50))
-            draw_text_with_background(frame, f'Total Sens 2: {len(self.unique_region2_ids)}', (880, frame_height - 50))
-
-            out.write(frame)
-            processed_frames += 1
-
-            # Mise à jour de la barre de progression
-            progress_bar.progress(min(int((processed_frames / total_frames) * 100), 100))
-
-        cap.release()
-        out.release()
-        cv2.destroyAllWindows()
-
-        if processed_frames == 0:
-            st.error("⚠️ Aucune image n'a été écrite dans la vidéo de sortie !")
-
-# --- INTERFACE STREAMLIT ---
-st.title("🚗 Détection de Véhicules avec Polygones")
-
-uploaded_file = st.file_uploader("📂 Upload une vidéo", type=["mp4", "avi", "mov"])
-
-# Entrée utilisateur pour les polygones
-st.sidebar.header("🔹 Saisie des polygones")
-
-st.sidebar.subheader("📍 Polygone 1 (vert)")
-poly1_input = st.sidebar.text_area("Entrez 4 points (x,y) séparés par des espaces", "465,350 609,350 520,630 3,630")
-
-st.sidebar.subheader("📍 Polygone 2 (rouge)")
-poly2_input = st.sidebar.text_area("Entrez 4 points (x,y) séparés par des espaces", "678,350 815,350 1203,630 743,630")
-
-def parse_polygon(input_text):
-    try:
-        return [tuple(map(int, point.split(','))) for point in input_text.split()]
-    except:
-        return []
-
-poly1 = parse_polygon(poly1_input)
-poly2 = parse_polygon(poly2_input)
-
-if uploaded_file is not None:
-    tfile = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
-    tfile.write(uploaded_file.read())
-
-    st.video(tfile.name)
-
-    model_path = "best.pt"  # Assurez-vous que le modèle est bien disponible
-    output_path = os.path.join(tempfile.gettempdir(), "output_video.mp4")
-
-    if st.button("▶️ Lancer la détection"):
-        if len(poly1) == 4 and len(poly2) == 4:
-            progress_bar = st.progress(0)
-            processor = YOLOVideoProcessor(model_path, tfile.name, output_path, poly1, poly2)
-            processor.process_video(progress_bar)
-            st.success("✅ Traitement terminé !")
-            st.video(output_path)
-        else:
-            st.error("❌ Les coordonnées des polygones doivent contenir **exactement 4 points**.")