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| import json | |
| import gradio as gr | |
| import yolov5 | |
| from PIL import Image | |
| from huggingface_hub import hf_hub_download | |
| app_title = "Valorant Object Detection" | |
| models_ids = ['keremberke/yolov5n-valorant', 'keremberke/yolov5s-valorant', 'keremberke/yolov5m-valorant'] | |
| article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>huggingface.co/{models_ids[-1]}</a> | <a href='https://huggingface.co/keremberke/valorant-object-detection'>huggingface.co/keremberke/valorant-object-detection</a> | <a href='https://github.com/keremberke/awesome-yolov5-models'>awesome-yolov5-models</a> </p>" | |
| current_model_id = models_ids[-1] | |
| model = yolov5.load(current_model_id) | |
| examples = [['test_images/ss.png', 0.25, 'keremberke/yolov5m-valorant'], ['test_images/ss2.png', 0.25, 'keremberke/yolov5m-valorant'], ['test_images/ss3.png', 0.25, 'keremberke/yolov5m-valorant'], ['test_images/ss4.jpg', 0.25, 'keremberke/yolov5m-valorant'], ['test_images/ss5.jpg', 0.25, 'keremberke/yolov5m-valorant'], ['test_images/ss6.jpg', 0.25, 'keremberke/yolov5m-valorant']] | |
| def predict(image, threshold=0.25, model_id=None): | |
| # update model if required | |
| global current_model_id | |
| global model | |
| if model_id != current_model_id: | |
| model = yolov5.load(model_id) | |
| current_model_id = model_id | |
| # get model input size | |
| config_path = hf_hub_download(repo_id=model_id, filename="config.json") | |
| with open(config_path, "r") as f: | |
| config = json.load(f) | |
| input_size = config["input_size"] | |
| # perform inference | |
| model.conf = threshold | |
| results = model(image, size=input_size) | |
| numpy_image = results.render()[0] | |
| output_image = Image.fromarray(numpy_image) | |
| return output_image | |
| gr.Interface( | |
| title=app_title, | |
| description="Created by 'keremberke'", | |
| article=article, | |
| fn=predict, | |
| inputs=[ | |
| gr.Image(type="pil"), | |
| gr.Slider(maximum=1, step=0.01, value=0.25), | |
| gr.Dropdown(models_ids, value=models_ids[-1]), | |
| ], | |
| outputs=gr.Image(type="pil"), | |
| examples=examples, | |
| cache_examples=True if examples else False, | |
| ).launch(enable_queue=True) | |