Create app.py

app.py

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+import torch
+import cv2
+import os
+import numpy as np
+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):
+    # Buka video input
+    cap = cv2.VideoCapture(video_path)
+
+    # Dapatkan spesifikasi video
+    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+
+    # Buat VideoWriter untuk menyimpan video output
+    output_dir = "/content/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
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Inferensi dengan YOLOv5
+        results = model(frame)
+        detections = results.pred[0]
+        names = model.names
+
+        # 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
+
+        # 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.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+
+        # Tulis frame yang telah dianotasi ke video output
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+    return output_path, person_count_total
+
+# Membuat antarmuka Gradio
+def gradio_interface(video):
+    output_path, person_count = count_people_in_video(video)
+    return output_path, f"Total people detected: {person_count}"
+
+# Antarmuka Gradio
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Video(source="upload", type="filepath"),
+    outputs=[gr.File(), gr.Text()],
+    live=True
+)
+
+# Menjalankan aplikasi
+iface.launch()