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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 = "License Plate Object Detection" | |
| models_ids = ['keremberke/yolov5n-license-plate', 'keremberke/yolov5s-license-plate', 'keremberke/yolov5m-license-plate'] | |
| article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>model</a> | <a href='https://huggingface.co/keremberke/license-plate-object-detection'>dataset</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 = [['CarLongPlate686_jpg.rf.97172961f3f90ae6e4b0ef1edfa24b98.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['CarLongPlate834_jpg.rf.c6da1db4c7c6ce9d9d864a90bb46ff1d.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['CarLongPlateGen3663_jpg.rf.26f54b241dbee94a3faabc9a08fd638a.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['CarLongPlateGen570_jpg.rf.305252bdd2798c370af7f1d702c0dd97.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['xemay1024_jpg.rf.1d25cb47787faa4e72967cf4c356af2a.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['xemay1349_jpg.rf.759edbd383937d1fdc243203450a1823.jpg', 0.25, 'keremberke/yolov5m-license-plate']] | |
| 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) | |
| predictions = results.pred[0] | |
| print(predictions[:, :4]) | |
| #x1 = predictions[0] | |
| #y1 = predictions[1] | |
| #x2 = predictions[2] | |
| #y2 = predictions[3] | |
| #print(predictions) | |
| #print("Das Kennzeichen befindet sich im Rechteck zwischen den folgenden Punkten:") | |
| #print("X1: " + x1 + "Y1: " + y1) | |
| #print("X2: " + x2 + "Y2: " + y2) | |
| 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) | |