Create app.py

app.py

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+import cv2
+import gradio as gr
+import numpy as np
+import tempfile
+import os
+from ultralytics import YOLO
+from deep_sort_realtime.deepsort_tracker import DeepSort
+from collections import defaultdict
+
+# Dictionnaire pour compter les objets détectés
+class_counts = defaultdict(set)
+
+# Charger modèle YOLOv8
+model = YOLO("best.pt")  # Assure-toi que ce fichier est bien dans le même dossier
+
+# Initialiser DeepSORT
+tracker = DeepSort(max_age=30)
+
+# 📸 Détection image
+def detect_on_image(image):
+    results = model(image)[0]
+    for box in results.boxes:
+        cls_id = int(box.cls[0])
+        conf = float(box.conf[0])
+        x1, y1, x2, y2 = map(int, box.xyxy[0])
+        if conf > 0.4:
+            label = f"{model.names[cls_id]} {conf:.2f}"
+            cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
+            cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
+    return image
+
+# 🎥 Détection vidéo
+def detect_and_track_video(video_path):
+    if not os.path.exists(video_path):
+        return None
+
+    cap = cv2.VideoCapture(video_path)
+    width = int(cap.get(3))
+    height = int(cap.get(4))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    temp_output = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
+    out = cv2.VideoWriter(temp_output.name, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
+    class_counts.clear()
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        results = model(frame)[0]
+        detections = []
+
+        for box in results.boxes:
+            cls_id = int(box.cls[0])
+            conf = float(box.conf[0])
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            if conf > 0.4:
+                detections.append(([x1, y1, x2 - x1, y2 - y1], conf, model.names[cls_id]))
+
+        tracks = tracker.update_tracks(detections, frame=frame)
+
+        for track in tracks:
+            if not track.is_confirmed():
+                continue
+            track_id = track.track_id
+            l, t, r, b = map(int, track.to_ltrb())
+            label = track.get_det_class()
+            cv2.rectangle(frame, (l, t), (r, b), (0, 255, 0), 2)
+            cv2.putText(frame, f'{label} ID {track_id}', (l, t - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+            class_counts[label].add(track_id)
+
+        out.write(frame)
+
+    cap.release()
+    out.release()
+
+    return temp_output.name
+
+# Interfaces Gradio
+image_interface = gr.Interface(
+    fn=detect_on_image,
+    inputs=gr.Image(type="numpy", label="Image de surveillance"),
+    outputs=gr.Image(type="numpy", label="Image annotée"),
+    title="📸 Détection sur Image",
+    description="Détection de bagages et objets avec YOLOv8."
+)
+
+video_interface = gr.Interface(
+    fn=detect_and_track_video,
+    inputs=gr.Video(label="Vidéo de surveillance"),
+    outputs=gr.Video(label="Vidéo annotée avec suivi"),
+    title="🎥 Suivi sur Vidéo",
+    description="Suivi multi-objets avec DeepSORT + YOLOv8."
+)
+
+# Interface finale
+gr.TabbedInterface(
+    [image_interface, video_interface],
+    tab_names=["📷 Image", "🎥 Vidéo"]
+).launch()