|
import gradio as gr |
|
from ultralytics import YOLO |
|
from PIL import Image |
|
import os |
|
import cv2 |
|
import torch |
|
|
|
|
|
def load_model(model_input): |
|
model = YOLO(model_input) |
|
if torch.cuda.is_available(): |
|
model.to('cuda') |
|
print("Using GPU for inference") |
|
else: |
|
print("Using CPU for inference") |
|
|
|
return model |
|
|
|
def detect_objects_in_files(model_input, files): |
|
""" |
|
Processes uploaded images for object detection. |
|
""" |
|
if not files: |
|
return "No files uploaded.", [] |
|
|
|
model = load_model(model_input) |
|
|
|
results_images = [] |
|
for file in files: |
|
try: |
|
image = Image.open(file).convert("RGB") |
|
results = model(image) |
|
result_img_bgr = results[0].plot() |
|
result_img_rgb = cv2.cvtColor(result_img_bgr, cv2.COLOR_BGR2RGB) |
|
results_images.append(result_img_rgb) |
|
|
|
|
|
|
|
|
|
except Exception as e: |
|
return f"Error processing file: {file}. Exception: {str(e)}", [] |
|
|
|
del model |
|
torch.cuda.empty_cache() |
|
|
|
return "Processing completed.", results_images |
|
|
|
interface = gr.Interface( |
|
fn=detect_objects_in_files, |
|
inputs=[ |
|
gr.File(label="Upload Model file"), |
|
gr.Files(file_types=["image"], label="Select Images"), |
|
], |
|
outputs=[ |
|
gr.Textbox(label="Status"), |
|
gr.Gallery(label="Results") |
|
], |
|
title="Object Detection on Images", |
|
description="Upload images to perform object detection. The model will process each image and display the results." |
|
) |
|
|
|
if __name__ == "__main__": |
|
interface.launch(server_name="0.0.0.0", server_port=8800) |
|
|
