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import gradio as gr
from dotenv import load_dotenv
from roboflow import Roboflow
import tempfile
import os
import requests
from PIL import Image
# Muat variabel lingkungan dari file .env
load_dotenv()
api_key = os.getenv("ROBOFLOW_API_KEY")
workspace = os.getenv("ROBOFLOW_WORKSPACE")
project_name = os.getenv("ROBOFLOW_PROJECT")
model_version = int(os.getenv("ROBOFLOW_MODEL_VERSION"))
# Inisialisasi Roboflow menggunakan data yang diambil dari secrets
rf = Roboflow(api_key=api_key)
project = rf.workspace(workspace).project(project_name)
model = project.version(model_version).model
# Fungsi untuk memotong gambar menjadi potongan-potongan kecil
def slice_image(image, slice_size=512, overlap=0):
width, height = image.size
slices = []
step = slice_size - overlap
for top in range(0, height, step):
for left in range(0, width, step):
bottom = min(top + slice_size, height)
right = min(left + slice_size, width)
slices.append((left, top, right, bottom))
return slices
# Fungsi untuk menangani input dan output gambar
def detect_objects(image):
slice_size = 512
overlap = 50
# Potong gambar menjadi bagian kecil
slices = slice_image(image, slice_size, overlap)
results = []
class_count = {}
total_count = 0
for i, (left, top, right, bottom) in enumerate(slices):
sliced_image = image.crop((left, top, right, bottom))
# Simpan gambar slice sementara
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
sliced_image.save(temp_file, format="JPEG")
temp_file_path = temp_file.name
try:
# Lakukan prediksi pada setiap slice
predictions = model.predict(temp_file_path, confidence=60, overlap=80).json()
for prediction in predictions['predictions']:
prediction["left"] += left
prediction["top"] += top
prediction["right"] += left
prediction["bottom"] += top
results.append(prediction)
# Perbarui jumlah objek per kelas
class_name = prediction['class']
class_count[class_name] = class_count.get(class_name, 0) + 1
total_count += 1
except requests.exceptions.HTTPError as http_err:
return f"HTTP error occurred: {http_err}", None
except Exception as err:
return f"An error occurred: {err}", None
finally:
os.remove(temp_file_path)
# Gabungkan hasil deteksi
result_text = "Product Nestle\n\n"
for class_name, count in class_count.items():
result_text += f"{class_name}: {count}\n"
result_text += f"\nTotal Product Nestle: {total_count}"
# Kembalikan hasil
return image, result_text
# Membuat antarmuka Gradio dengan tata letak fleksibel
with gr.Blocks() as iface:
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil", label="Input Image")
with gr.Column():
output_image = gr.Image(label="Detect Object")
with gr.Column():
output_text = gr.Textbox(label="Counting Object")
# Tombol untuk memproses input
detect_button = gr.Button("Detect")
# Hubungkan tombol dengan fungsi deteksi
detect_button.click(
fn=detect_objects,
inputs=input_image,
outputs=[output_image, output_text]
)
# Menjalankan antarmuka
iface.launch()
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