Update app.py

app.py

@@ -1,12 +1,4 @@
 # -*- coding: utf-8 -*-
-"""Untitled8.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/17hKCetX4b9rOVZK9WPeKXBDYONdYYktD
-"""
-
 import streamlit as st
 import cv2
 import tempfile
@@ -18,14 +10,9 @@ from collections import defaultdict
 from ultralytics import YOLO
 
 # --- FONCTIONS UTILES ---
-
-def get_color_for_id(track_id):
-    np.random.seed(track_id)
-    return tuple(np.random.randint(0, 255, size=3).tolist())
-
 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
 
@@ -36,20 +23,42 @@ def draw_text_with_background(image, text, position, font=cv2.FONT_HERSHEY_SIMPL
     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 DE TRAITEMENT YOLO ---
+# --- CLASSE YOLO ---
 class YOLOVideoProcessor:
-    def __init__(self, model_path, video_path, output_path, tracker_method="bot"):
+    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.output_path = output_path 
 
-    def process_video(self):
+        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))
-        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        out = cv2.VideoWriter(self.output_path, fourcc, 30, (frame_width, 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()
@@ -59,30 +68,81 @@ class YOLOVideoProcessor:
             tracker = "botsort.yaml" if self.tracker_method.lower() == "bot" else "bytetrack.yaml"
             results = self.model.track(frame, persist=True, tracker=tracker, conf=0.25)
 
-            draw_text_with_background(frame, "Processing...", (7, 30))
+            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 YOLO")
+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"  # Mettre le bon chemin du modèle
-    output_path = "output_video.mp4"
-
-    if st.button("▶️ Lancer l'analyse"):
-        processor = YOLOVideoProcessor(model_path, tfile.name, output_path)
-        processor.process_video()
-        st.success("✅ Traitement terminé !")
-        st.video(output_path)
+    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**.")