Create app.py

app.py

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+import streamlit as st
+import torch
+import cv2
+import tempfile
+import os
+from PIL import Image
+import numpy as np
+
+# Muat model YOLOv5 yang sudah dilatih
+@st.cache_resource
+def load_model():
+    model = torch.hub.load('ultralytics/yolov11', 'custom', path='best.pt', force_reload=True)
+    return model
+
+model = load_model()
+
+# Fungsi untuk deteksi pada gambar
+def detect_image(image):
+    results = model(image)
+    results.render()  # Tambahkan bounding box ke gambar
+    annotated_image = results.imgs[0]  # Ambil gambar yang sudah dianotasi
+    return annotated_image
+
+# Fungsi untuk deteksi pada video
+def detect_video(video_path, output_path):
+    cap = cv2.VideoCapture(video_path)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        results = model(frame)
+        results.render()
+        annotated_frame = np.squeeze(results.render())
+        out.write(annotated_frame)
+
+    cap.release()
+    out.release()
+
+# Streamlit Interface
+st.title("YOLO Object Detection")
+
+# Opsi untuk memilih gambar atau video
+option = st.radio("Pilih jenis input:", ("Gambar", "Video"))
+
+if option == "Gambar":
+    uploaded_image = st.file_uploader("Unggah gambar", type=["jpg", "jpeg", "png"])
+    if uploaded_image is not None:
+        image = Image.open(uploaded_image)
+        st.image(image, caption="Gambar asli", use_column_width=True)
+        
+        # Deteksi objek
+        annotated_image = detect_image(np.array(image))
+        st.image(annotated_image, caption="Hasil deteksi", use_column_width=True)
+
+elif option == "Video":
+    uploaded_video = st.file_uploader("Unggah video", type=["mp4", "avi", "mov"])
+    if uploaded_video is not None:
+        # Simpan video sementara
+        temp_video_path = tempfile.NamedTemporaryFile(delete=False).name
+        with open(temp_video_path, "wb") as f:
+            f.write(uploaded_video.read())
+        
+        # Proses video
+        output_video_path = "output_video.mp4"
+        st.text("Sedang memproses video...")
+        detect_video(temp_video_path, output_video_path)
+        st.text("Proses selesai!")
+
+        # Tampilkan hasil video
+        st.video(output_video_path)