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import gradio as gr
from roboflow import Roboflow
import tempfile
import os
from sahi.slicing import slice_image
from sahi.postprocess import postprocess_predictions
# Inisialisasi Roboflow (for model path)
rf = Roboflow(api_key="Otg64Ra6wNOgDyjuhMYU")
project = rf.workspace("alat-pelindung-diri").project("nescafe-4base")
model = project.version(16).model
# Fungsi untuk deteksi objek menggunakan SAHI dan Roboflow Model
def detect_objects(image):
# Menyimpan gambar sementara
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
image.save(temp_file, format="JPEG")
temp_file_path = temp_file.name
# Slice gambar menjadi potongan-potongan kecil
slice_image_result = slice_image(
image=temp_file_path,
output_file_name="sliced_image",
output_dir="/tmp/sliced/",
slice_height=256,
slice_width=256,
overlap_height_ratio=0.1,
overlap_width_ratio=0.1
)
# Mendapatkan path-potongan gambar
sliced_image_paths = slice_image_result['sliced_image_paths']
# Menyimpan semua prediksi untuk setiap potongan gambar
all_predictions = []
# Prediksi pada setiap potongan gambar
for sliced_image_path in sliced_image_paths:
predictions = model.predict(image_path=sliced_image_path).json()
all_predictions.extend(predictions['predictions'])
# Postprocess dan gabungkan hasil prediksi
postprocessed_predictions = postprocess_predictions(
predictions=all_predictions,
postprocess_type='NMS',
iou_threshold=0.5
)
# Annotate gambar dengan hasil prediksi
annotated_image = model.annotate_image_with_predictions(temp_file_path, postprocessed_predictions)
# Simpan gambar hasil annotasi
output_image_path = "/tmp/prediction.jpg"
annotated_image.save(output_image_path)
# Menghitung jumlah objek per kelas
class_count = {}
for detection in postprocessed_predictions:
class_name = detection['class']
if class_name in class_count:
class_count[class_name] += 1
else:
class_count[class_name] = 1
# Hasil perhitungan objek
result_text = "Jumlah objek per kelas:\n"
for class_name, count in class_count.items():
result_text += f"{class_name}: {count} objek\n"
# Hapus file sementara
os.remove(temp_file_path)
return output_image_path, result_text
# Membuat antarmuka Gradio
iface = gr.Interface(
fn=detect_objects, # Fungsi yang dipanggil saat gambar diupload
inputs=gr.Image(type="pil"), # Input berupa gambar
outputs=[gr.Image(), gr.Textbox()], # Output gambar dan teks
live=True # Menampilkan hasil secara langsung
)
# Menjalankan antarmuka
iface.launch()
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