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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 = "Forklift Object Detection" | |
| models_ids = ['keremberke/yolov5n-forklift', 'keremberke/yolov5s-forklift', 'keremberke/yolov5m-forklift'] | |
| article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>model</a> | <a href='https://huggingface.co/keremberke/forklift-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 = [ | |
| ['test_images/0EZMOXIBCFJ2_jpg.rf.e02f09f440a32adc6b8cd173d64048dd.jpg', 0.25, 'keremberke/yolov5m-forklift'], | |
| ['test_images/32LSCZQDHZO7_jpg.rf.8fddaa4b5ed4db87d19a32d4554b9c23.jpg', 0.25, 'keremberke/yolov5m-forklift'], | |
| ['test_images/998PSJQQZBXV_jpg.rf.b1302d4d0209aed943753848438f8aa6.jpg', 0.25, 'keremberke/yolov5m-forklift'], | |
| ['test_images/KUN8WG9S97QO_jpg.rf.7ec95d32b4559f0c5d4e4e1a5f0623d7.jpg', 0.25, 'keremberke/yolov5m-forklift'], | |
| ['test_images/LTDX8N8ZKBT2_jpg.rf.6e09889a432d15c19fa0fbdbb62d347f.jpg', 0.25, 'keremberke/yolov5m-forklift'], | |
| ['test_images/V75EBJ0AG2HV_jpg.rf.88822c95c57d6bfb33092eb5ec0a020c.jpg', 0.25, 'keremberke/yolov5m-forklift'], | |
| ['test_images/XVMZ924NFFU3_jpg.rf.5c1f76b37c69b3db293151909c912e42.jpg', 0.25, 'keremberke/yolov5m-forklift'], | |
| ] | |
| 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) | |