Update app.py (#3)

- Update app.py (9448e60018852a3f118c012304509e40cae0259e)


Co-authored-by: Takamine Takagi <[email protected]>

app.py

@@ -149,45 +149,17 @@ def process_video_and_analyze(video_path, selected_model_name, progress=gr.Progr
 
             # Prediksi
             results = model.predict(frame_resized)
-            # Gunakan rendering bawaan untuk YOLOv5m, render_result untuk YOLOv8m
-            if selected_model_name == "YOLOv5m (Generalis Konstruksi)":
-                try:
-                    annotated_frame = results[0].plot()  # Rendering bawaan YOLO
-                    if annotated_frame is None:
-                        logger.warning("Rendering bawaan YOLOv5m gagal, menggunakan frame asli.")
-                        annotated_frame = frame_resized
-                except Exception as e:
-                    logger.warning(f"Gagal merender frame dengan plot: {e}. Menggunakan frame asli.")
-                    annotated_frame = frame_resized
-            else:
-                try:
-                    annotated_frame = render_result(model=model, image=frame_resized, result=results[0])
-                    annotated_frame = np.array(annotated_frame)  # Konversi PIL ke NumPy
-                    annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)  # Konversi RGB ke BGR
-                except Exception as e:
-                    logger.warning(f"Gagal merender frame dengan render_result: {e}. Menggunakan frame asli.")
-                    annotated_frame = frame_resized
-
-            # Hitung deteksi dengan pengecekan tipe data
-            try:
-                detection_count += len(results[0].boxes)
-                logger.debug(f"Model names: {model.names}")
-                logger.debug(f"Boxes: {results[0].boxes}")
-                for box in results[0].boxes:
-                    try:
-                        class_id = int(box.cls.item()) if hasattr(box.cls, 'item') else int(box.cls)
-                        if class_id < len(model.names):
-                            class_name = str(model.names[class_id]).lower()
-                            if class_name in ['hardhat', 'helmet']:  # Sesuaikan dengan kelas YOLOv5m
-                                helm_detected_count += 1
-                        else:
-                            logger.warning(f"Class ID {class_id} tidak valid untuk model {selected_model_name}")
-                    except Exception as e:
-                        logger.warning(f"Error saat memproses box.cls: {e}")
-                        continue
-            except Exception as e:
-                logger.error(f"Error saat menghitung deteksi: {e}")
-                continue
+            annotated_frame = render_result(model=model, image=frame_resized, result=results[0])
+            annotated_frame = np.array(annotated_frame)  # Konversi PIL ke NumPy
+            annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)  # Konversi RGB ke BGR
+
+            # Hitung deteksi
+            detection_count += len(results[0].boxes)
+            for box in results[0].boxes:
+                class_id = int(box.cls)
+                class_name = model.names[class_id].lower()
+                if class_name in ['hardhat', 'helmet']:
+                    helm_detected_count += 1
 
             out.write(annotated_frame)
 
@@ -213,7 +185,7 @@ def process_video_and_analyze(video_path, selected_model_name, progress=gr.Progr
         - **Jumlah Deteksi Helm:** {helm_detected_count} objek
         **Catatan:**
         - **Model Spesialis (YOLOv8m):** Fokus pada helm, akurasi tinggi untuk 'Hardhat'/'Helmet'.
-        - **Model Generalis (YOLOv5m):** Deteksi berbagai objek, termasuk 'Hardhat' (kelas 4).
+        - **Model Generalis (YOLOv5m):** Deteksi berbagai objek, akurasi helm mungkin lebih rendah.
         """
 
         return temp_output_path, f"Status: Video berhasil diproses! ({processing_time:.2f} detik)", analysis_text