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import streamlit as st
import cv2
from PIL import Image
import numpy as np
from ultralytics import YOLO
# Load YOLO models
model_sawit = YOLO("best_yolov8.pt") # Ganti dengan path model YOLOv8 untuk pohon sawit
model_apel = YOLO("best_apple.pt") # Ganti dengan path model YOLOv8 untuk apel
# Sidebar menu
menu = st.sidebar.selectbox("Pilih Aplikasi", ["Deteksi Pohon Sawit", "Deteksi Warna Apel"])
if menu == "Deteksi Pohon Sawit":
st.title("Deteksi Pohon Sawit dengan YOLOv8")
confidence_threshold = st.slider("Confidence Threshold", min_value=0.0, max_value=1.0, value=0.5, step=0.05)
uploaded_file = st.file_uploader("Upload gambar", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Baca gambar
image = Image.open(uploaded_file)
img_array = np.array(image)
# Jalankan deteksi dengan pengaturan confidence threshold
results = model_sawit(img_array, conf=confidence_threshold)
# Tambahkan bounding box dan nomor urut dengan background warna
for i, box in enumerate(results[0].boxes):
x1, y1, x2, y2 = map(int, box.xyxy[0])
# Gambar bounding box
cv2.rectangle(img_array, (x1, y1), (x2, y2), (0, 0, 255), 2) # Gunakan warna merah
# Tambahkan nomor urut dengan background
text = f"{i+1}"
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.9
thickness = 2
# Ukuran teks dan lokasi
(text_width, text_height), baseline = cv2.getTextSize(text, font, font_scale, thickness)
text_x, text_y = x1, y1 - 10 # Posisi teks di atas kotak
rect_x1, rect_y1 = text_x, text_y - text_height - 5
rect_x2, rect_y2 = text_x + text_width + 10, text_y + baseline - 5
# Gambar background untuk teks
cv2.rectangle(img_array, (rect_x1, rect_y1), (rect_x2, rect_y2), (0, 0, 255), -1) # Background merah
cv2.putText(img_array, text, (text_x, text_y), font, font_scale, (255, 255, 255), thickness) # Teks putih
# Tampilkan hasil
st.image(img_array, caption=f"Total objek terdeteksi: {len(results[0].boxes)}", use_column_width=True)
elif menu == "Deteksi Warna Apel":
st.title("Deteksi Warna Apel dengan YOLOv8")
st.subheader("Unggah gambar apel untuk mendeteksi dan menampilkan hasil crop sesuai warna")
# Upload gambar
uploaded_file = st.file_uploader("Unggah gambar", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Baca gambar dari upload
file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
# Deteksi objek menggunakan YOLOv8
results = model_apel(image)[0] # Ambil hasil prediksi pertama
# Tampilkan gambar asli
st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Gambar Asli", use_column_width=True)
st.write("### Hasil Crop:")
for i, box in enumerate(results.boxes):
cls = int(box.cls) # Indeks kelas
confidence = box.conf.item() # Tingkat kepercayaan
class_name = model_apel.names[cls] # Nama kelas (e.g., yellow, green, red)
# Bounding box koordinat
x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
cropped_image = image[y1:y2, x1:x2]
# Konversi ke RGB untuk ditampilkan di Streamlit
cropped_image_rgb = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)
# Tampilkan hasil crop
st.image(cropped_image_rgb, caption=f"{class_name} ({confidence:.2f})", use_column_width=False)
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