Update app.py

app.py

@@ -7,8 +7,13 @@ import gradio as gr
 # Muat model pre-trained YOLOv5
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
 
-# Fungsi untuk deteksi dan menghitung objek manusia
-def count_people_in_video(video_path):
+# Fungsi untuk memproses video dan menghitung jumlah manusia
+def process_video(video_path):
+    # Direktori output
+    output_dir = "output_videos"
+    os.makedirs(output_dir, exist_ok=True)
+    output_path = os.path.join(output_dir, "person_counter_output.mp4")
+
     # Buka video input
     cap = cv2.VideoCapture(video_path)
 
@@ -18,14 +23,11 @@ def count_people_in_video(video_path):
     fps = int(cap.get(cv2.CAP_PROP_FPS))
 
     # Buat VideoWriter untuk menyimpan video output
-    output_dir = "output_videos"
-    os.makedirs(output_dir, exist_ok=True)
-    output_path = os.path.join(output_dir, "person_counter_output.mp4")
     fourcc = cv2.VideoWriter_fourcc(*"mp4v")
     out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
 
-    # Proses video
-    person_count_total = 0
+    total_person_count = 0
+
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
@@ -39,34 +41,39 @@ def count_people_in_video(video_path):
         # Filter hanya label 'person'
         person_detections = [d for d in detections if names[int(d[-1])] == "person"]
         person_count = len(person_detections)
-        person_count_total += person_count
+        total_person_count += person_count
 
         # Render frame dan buat salinan eksplisit
         annotated_frame = results.render()[0]
         annotated_frame = np.copy(annotated_frame)
 
         # Tambahkan teks ke frame
-        cv2.putText(annotated_frame, f"Person Count: {person_count_total}", (10, 30),
+        cv2.putText(annotated_frame, f"Person Count: {person_count}", (10, 30),
                     cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 
         # Tulis frame yang telah dianotasi ke video output
         out.write(annotated_frame)
 
+    # Tutup video input dan output
     cap.release()
     out.release()
-    return output_path, person_count_total
 
-# Membuat antarmuka Gradio
+    return output_path, total_person_count
+
+# Fungsi Gradio untuk antarmuka
 def gradio_interface(video_file):
-    output_path, person_count = count_people_in_video(video_file)
-    return output_path, f"Total people detected: {person_count}"
+    output_path, total_person_count = process_video(video_file)
+    return output_path, f"Total people detected: {total_person_count}"
 
 # Antarmuka Gradio
 iface = gr.Interface(
     fn=gradio_interface,
-    inputs=gr.File(type="filepath"),
-    outputs=[gr.File(), gr.Text()],
+    inputs=gr.File(type="filepath"),  # Input berupa file video
+    outputs=[gr.File(label="Processed Video"), gr.Text(label="Total People Count")],
+    title="Person Counter using YOLOv5",
+    description="Upload a video file to detect and count the number of people using YOLOv5."
 )
 
 # Menjalankan aplikasi
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()