Update app.py

app.py

@@ -1,111 +1,111 @@
-import streamlit as st
-import cv2
-import tempfile
-import os
-import time
-import numpy as np
-from ultralytics import YOLO
-import threading
-from PIL import Image
-import torch
-import queue
-from streamlit.runtime.scriptrunner import add_script_run_ctx
-
-# --- 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 OPTIMISÉE ---
-class YOLOVideoProcessor:
-    def __init__(self, model_path, tracker_method="bot"):
-        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        self.frame_skip = 2
-        self.downsample_factor = 0.5
-        self.img_size = 640
-        self.conf_threshold = 0.35
-        self.model = YOLO(model_path, task="detect").to(self.device)
-        self.tracker_method = tracker_method
-        self.stop_processing = False
-
-    def process_webcam(self, camera_id=0, display_placeholder=None):
-        cap = cv2.VideoCapture(camera_id)
-        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
-        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
-        if not cap.isOpened():
-            st.error("⚠️ Erreur : Impossible d'ouvrir la webcam.")
-            return
-        self.stop_processing = False
-        
-        while not self.stop_processing:
-            success, frame = cap.read()
-            if not success:
-                break
-            display_placeholder.image(frame, channels="BGR", use_column_width=True)
-        cap.release()
-        st.success("✅ Flux vidéo arrêté.")
-
-# --- INTERFACE STREAMLIT ---
-def main():
-    st.set_page_config(page_title="Détecteur de Véhicules", page_icon="🚗", layout="wide")
-    st.title("🚗 Détection et comptage de Véhicules sur l'Autoroute de l'Avenir")
-    
-    model_path = "best.pt"
-    if not os.path.exists(model_path):
-        st.error("❌ Modèle YOLO introuvable!")
-        return
-    
-    tab1, tab2 = st.tabs(["🎥 Détection en Temps Réel", "📹 Analyse de Vidéo"])
-    
-    with st.sidebar:
-        st.header("🔹 Paramètres")
-        tracker_method = st.selectbox("Méthode de tracking", ["bot", "byte"], index=0)
-        frame_skip = st.slider("Frames Skip", 1, 5, 2)
-        downsample = st.slider("Facteur d'échelle", 0.3, 1.0, 0.5, 0.1)
-        conf_threshold = st.slider("Seuil de confiance", 0.1, 0.9, 0.35, 0.05)
-    
-    with tab1:
-        st.header("Détection en Temps Réel avec Webcam")
-        camera_id = 0
-        video_placeholder = st.empty()
-        
-        start_detection = st.button("▶️ Lancer la détection en temps réel")
-        stop_detection = st.button("⏹️ Arrêter la détection")
-        
-        if start_detection:
-            processor = YOLOVideoProcessor(model_path, tracker_method)
-            processor.frame_skip = frame_skip
-            processor.downsample_factor = downsample
-            processor.conf_threshold = conf_threshold
-            processing_thread = threading.Thread(
-                target=processor.process_webcam, args=(camera_id, video_placeholder))
-            processing_thread.daemon = True
-            add_script_run_ctx(processing_thread)
-            processing_thread.start()
-            st.success("✅ Détection en cours...")
-            
-        if stop_detection:
-            st.success("✅ Détection arrêtée.")
-    
-    with tab2:
-        st.header("Analyse de Vidéo")
-        uploaded_file = st.file_uploader("📂 Upload une vidéo", type=["mp4", "avi", "mov"])
-        if uploaded_file is not None:
-            temp_dir = tempfile.mkdtemp()
-            input_video_path = os.path.join(temp_dir, "input_video.mp4")
-            with open(input_video_path, "wb") as f:
-                f.write(uploaded_file.getbuffer())
-            st.video(input_video_path)
-            st.success("Vidéo chargée avec succès.")
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import cv2
+import tempfile
+import os
+import time
+import numpy as np
+from ultralytics import YOLO
+import threading
+from PIL import Image
+import torch
+import queue
+from streamlit.runtime.scriptrunner import add_script_run_ctx
+
+# --- 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 OPTIMISÉE ---
+class YOLOVideoProcessor:
+    def __init__(self, model_path, tracker_method="bot"):
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.frame_skip = 2
+        self.downsample_factor = 0.5
+        self.img_size = 640
+        self.conf_threshold = 0.35
+        self.model = YOLO(model_path, task="detect").to(self.device)
+        self.tracker_method = tracker_method
+        self.stop_processing = False
+
+    def process_webcam(self, camera_id=0, display_placeholder=None):
+        cap = cv2.VideoCapture(camera_id)
+        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
+        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
+        if not cap.isOpened():
+            st.error("⚠️ Erreur : Impossible d'ouvrir la webcam.")
+            return
+        self.stop_processing = False
+        
+        while not self.stop_processing:
+            success, frame = cap.read()
+            if not success:
+                break
+            display_placeholder.image(frame, channels="BGR", use_column_width=True)
+        cap.release()
+        st.success("✅ Flux vidéo arrêté.")
+
+# --- INTERFACE STREAMLIT ---
+def main():
+    st.set_page_config(page_title="Détecteur de Véhicules", page_icon="🚗", layout="wide")
+    st.title("🚗 Détection et comptage de Véhicules sur l'Autoroute de l'Avenir")
+    
+    model_path = hf_hub_download(repo_id="ModuMLTECH/Trafic_congestion", filename="best.pt")
+    if not os.path.exists(model_path):
+        st.error("❌ Modèle YOLO introuvable!")
+        return
+    
+    tab1, tab2 = st.tabs(["🎥 Détection en Temps Réel", "📹 Analyse de Vidéo"])
+    
+    with st.sidebar:
+        st.header("🔹 Paramètres")
+        tracker_method = st.selectbox("Méthode de tracking", ["bot", "byte"], index=0)
+        frame_skip = st.slider("Frames Skip", 1, 5, 2)
+        downsample = st.slider("Facteur d'échelle", 0.3, 1.0, 0.5, 0.1)
+        conf_threshold = st.slider("Seuil de confiance", 0.1, 0.9, 0.35, 0.05)
+    
+    with tab1:
+        st.header("Détection en Temps Réel avec Webcam")
+        camera_id = 0
+        video_placeholder = st.empty()
+        
+        start_detection = st.button("▶️ Lancer la détection en temps réel")
+        stop_detection = st.button("⏹️ Arrêter la détection")
+        
+        if start_detection:
+            processor = YOLOVideoProcessor(model_path, tracker_method)
+            processor.frame_skip = frame_skip
+            processor.downsample_factor = downsample
+            processor.conf_threshold = conf_threshold
+            processing_thread = threading.Thread(
+                target=processor.process_webcam, args=(camera_id, video_placeholder))
+            processing_thread.daemon = True
+            add_script_run_ctx(processing_thread)
+            processing_thread.start()
+            st.success("✅ Détection en cours...")
+            
+        if stop_detection:
+            st.success("✅ Détection arrêtée.")
+    
+    with tab2:
+        st.header("Analyse de Vidéo")
+        uploaded_file = st.file_uploader("📂 Upload une vidéo", type=["mp4", "avi", "mov"])
+        if uploaded_file is not None:
+            temp_dir = tempfile.mkdtemp()
+            input_video_path = os.path.join(temp_dir, "input_video.mp4")
+            with open(input_video_path, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+            st.video(input_video_path)
+            st.success("Vidéo chargée avec succès.")
+
+if __name__ == "__main__":
+    main()