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app.py

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+# -*- 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
+import os
+import time
+import numpy as np
+import pandas as pd
+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):
+
+    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 DE TRAITEMENT YOLO ---
+class YOLOVideoProcessor:
+    def __init__(self, model_path, video_path, output_path, 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
+
+    def process_video(self):
+        cap = cv2.VideoCapture(self.video_path)
+        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))
+
+        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)
+
+            draw_text_with_background(frame, "Processing...", (7, 30))
+
+            out.write(frame)
+
+        cap.release()
+        out.release()
+        cv2.destroyAllWindows()
+
+# --- INTERFACE STREAMLIT ---
+st.title("🚗 Détection de Véhicules avec YOLO")
+
+uploaded_file = st.file_uploader("📂 Upload une vidéo", type=["mp4", "avi", "mov"])
+
+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)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9005aa4c734a5102d3d69e84ee77f4f992be1b025b640357436674d2748b59a2
+size 6239907

requirements.txt

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+streamlit
+ultralytics
+opencv-python
+numpy
+pandas