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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 = "NFL Object Detection" | |
| models_ids = ['keremberke/yolov5n-nfl', 'keremberke/yolov5s-nfl', 'keremberke/yolov5m-nfl'] | |
| 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/nfl-object-detection'>huggingface.co/keremberke/nfl-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/57638_001089_Endzone_frame262_jpg.rf.4a34e04af4f7b46c8dd9454e34740317.jpg', 0.25, 'keremberke/yolov5m-nfl'], ['test_images/57660_001234_Endzone_frame0845_jpg.rf.745d52b49774ae36d821d752435c8481.jpg', 0.25, 'keremberke/yolov5m-nfl'], ['test_images/57848_002061_Sideline_frame0529_jpg.rf.3747d55691fd4bd0ca5f26e713531f6e.jpg', 0.25, 'keremberke/yolov5m-nfl'], ['test_images/57873_003005_Endzone_frame356_jpg.rf.5f45decabc82c2f9c102bfe4200ece25.jpg', 0.25, 'keremberke/yolov5m-nfl'], ['test_images/57928_002004_Sideline_frame0820_jpg.rf.31445da39fd67e4455b8107cbe7918f5.jpg', 0.25, 'keremberke/yolov5m-nfl'], ['test_images/58037_001432_Sideline_frame386_jpg.rf.0e72f6bd6a685a8149467eeb50184c56.jpg', 0.25, 'keremberke/yolov5m-nfl']] | |
| 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) | |