Update app.py

app.py

@@ -1,89 +1,89 @@
-import gradio as gr
-import cv2
-from ultralytics import YOLO
-import tempfile
-import os
-
-# Charger le modèle
-model = YOLO("yolo12m.pt")  # ou yolov8n.pt
-
-# Classes à détecter
-target_classes = ["backpack", "suitcase", "handbag"]
-class_name_to_id = {name: idx for idx, name in model.names.items()}
-target_ids = [class_name_to_id[c] for c in target_classes]
-
-def process_video(video_path):
-    cap = cv2.VideoCapture(video_path)
-    ret, frame = cap.read()
-    if not ret:
-        return "Erreur lecture vidéo"
-
-    H, W = frame.shape[:2]
-    line_y = int(H * 0.6)
-    tolerance = 25
-
-    counted_ids = set()
-    class_counts = {c: 0 for c in target_classes}
-    total_count = 0
-
-    # Output temporaire
-    temp_out = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-    out = cv2.VideoWriter(temp_out.name, fourcc, 20, (W, H))
-
-    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
-
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        results = model.track(frame, persist=True, classes=target_ids, verbose=False)
-
-        if results[0].boxes.id is not None:
-            ids = results[0].boxes.id.int().cpu().tolist()
-            clss = results[0].boxes.cls.int().cpu().tolist()
-            boxes = results[0].boxes.xyxy.cpu().tolist()
-
-            for obj_id, cls, box in zip(ids, clss, boxes):
-                x1, y1, x2, y2 = map(int, box)
-                center_x = (x1 + x2) // 2
-                center_y = (y1 + y2) // 2
-                class_name = model.names[cls]
-
-                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
-                cv2.putText(frame, f"{class_name} ID:{obj_id}", (x1, y1 - 10),
-                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
-
-                if obj_id not in counted_ids and (line_y - tolerance < center_y < line_y + tolerance):
-                    counted_ids.add(obj_id)
-                    total_count += 1
-                    class_counts[class_name] += 1
-
-        # Affichage des résultats
-        y_offset = 30
-        for name in target_classes:
-            cv2.putText(frame, f"{name}: {class_counts[name]}", (20, y_offset),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
-            y_offset += 30
-        cv2.putText(frame, f"Total: {total_count}", (20, y_offset + 10),
-                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
-        cv2.line(frame, (0, line_y), (W, line_y), (0, 0, 255), 2)
-
-        out.write(frame)
-
-    cap.release()
-    out.release()
-    return temp_out.name
-
-# Interface Gradio
-demo = gr.Interface(
-    fn=process_video,
-    inputs=gr.Video(label="Importer une vidéo de bagages"),
-    outputs=gr.Video(label="Vidéo annotée avec comptage"),
-    title="🎒 Compteur intelligent de bagages",
-    description="Détecte et compte les sacs, valises, et sacs à dos avec YOLO."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import cv2
+from ultralytics import YOLO
+import tempfile
+import os
+
+# Charger le modèle
+model = YOLO("./yolo12m.pt")  # ou yolov8n.pt
+
+# Classes à détecter
+target_classes = ["backpack", "suitcase", "handbag"]
+class_name_to_id = {name: idx for idx, name in model.names.items()}
+target_ids = [class_name_to_id[c] for c in target_classes]
+
+def process_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    ret, frame = cap.read()
+    if not ret:
+        return "Erreur lecture vidéo"
+
+    H, W = frame.shape[:2]
+    line_y = int(H * 0.6)
+    tolerance = 25
+
+    counted_ids = set()
+    class_counts = {c: 0 for c in target_classes}
+    total_count = 0
+
+    # Output temporaire
+    temp_out = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(temp_out.name, fourcc, 20, (W, H))
+
+    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        results = model.track(frame, persist=True, classes=target_ids, verbose=False)
+
+        if results[0].boxes.id is not None:
+            ids = results[0].boxes.id.int().cpu().tolist()
+            clss = results[0].boxes.cls.int().cpu().tolist()
+            boxes = results[0].boxes.xyxy.cpu().tolist()
+
+            for obj_id, cls, box in zip(ids, clss, boxes):
+                x1, y1, x2, y2 = map(int, box)
+                center_x = (x1 + x2) // 2
+                center_y = (y1 + y2) // 2
+                class_name = model.names[cls]
+
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                cv2.putText(frame, f"{class_name} ID:{obj_id}", (x1, y1 - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+
+                if obj_id not in counted_ids and (line_y - tolerance < center_y < line_y + tolerance):
+                    counted_ids.add(obj_id)
+                    total_count += 1
+                    class_counts[class_name] += 1
+
+        # Affichage des résultats
+        y_offset = 30
+        for name in target_classes:
+            cv2.putText(frame, f"{name}: {class_counts[name]}", (20, y_offset),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
+            y_offset += 30
+        cv2.putText(frame, f"Total: {total_count}", (20, y_offset + 10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
+        cv2.line(frame, (0, line_y), (W, line_y), (0, 0, 255), 2)
+
+        out.write(frame)
+
+    cap.release()
+    out.release()
+    return temp_out.name
+
+# Interface Gradio
+demo = gr.Interface(
+    fn=process_video,
+    inputs=gr.Video(label="Importer une vidéo de bagages"),
+    outputs=gr.Video(label="Vidéo annotée avec comptage"),
+    title="🎒 Compteur intelligent de bagages",
+    description="Détecte et compte les sacs, valises, et sacs à dos avec YOLO."
+)
+
+if __name__ == "__main__":
+    demo.launch()