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| import gradio as gr | |
| import torch | |
| import yolov5 | |
| # Images | |
| torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg') | |
| torch.hub.download_url_to_file('https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg') | |
| def yolov5_inference( | |
| image: gr.inputs.Image = None, | |
| model_path: gr.inputs.Dropdown = None, | |
| image_size: gr.inputs.Slider = 640, | |
| conf_threshold: gr.inputs.Slider = 0.25, | |
| iou_threshold: gr.inputs.Slider = 0.45, | |
| ): | |
| """ | |
| YOLOv5 inference function | |
| Args: | |
| image: Input image | |
| model_path: Path to the model | |
| image_size: Image size | |
| conf_threshold: Confidence threshold | |
| iou_threshold: IOU threshold | |
| Returns: | |
| Rendered image | |
| """ | |
| model = yolov5.load(model_path, device="cpu") | |
| model.conf = conf_threshold | |
| model.iou = iou_threshold | |
| results = model([image], size=image_size) | |
| return results.render()[0] | |
| inputs = [ | |
| gr.inputs.Image(type="pil", label="Input Image"), | |
| gr.inputs.Dropdown(["yolov5s.pt", "alshimaa/yolo5_epoch100"], label="Model"), | |
| gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"), | |
| gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"), | |
| gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"), | |
| ] | |
| outputs = gr.outputs.Image(type="filepath", label="Output Image") | |
| title = "YOLOv5" | |
| description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model." | |
| examples = [['zidane.jpg', 'yolov5s.pt', 640, 0.25, 0.45], ['image3.jpg', 'yolov5s.pt', 640, 0.25, 0.45]] | |
| demo_app = gr.Interface( | |
| fn=yolov5_inference, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| examples=examples, | |
| cache_examples=True, | |
| live=True, | |
| theme='huggingface', | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) | |
| # import gradio as gr | |
| # import torch | |
| # import yolov5 | |
| # import subprocess | |
| # import tempfile | |
| # import time | |
| # from pathlib import Path | |
| # import uuid | |
| # import cv2 | |
| # import gradio as gr | |
| # # Images | |
| # #torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg') | |
| # #torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg') | |
| # def image_fn( | |
| # image: gr.inputs.Image = None, | |
| # model_path: gr.inputs.Dropdown = None, | |
| # image_size: gr.inputs.Slider = 640, | |
| # conf_threshold: gr.inputs.Slider = 0.25, | |
| # iou_threshold: gr.inputs.Slider = 0.45, | |
| # ): | |
| # """ | |
| # YOLOv5 inference function | |
| # Args: | |
| # image: Input image | |
| # model_path: Path to the model | |
| # image_size: Image size | |
| # conf_threshold: Confidence threshold | |
| # iou_threshold: IOU threshold | |
| # Returns: | |
| # Rendered image | |
| # """ | |
| # model = yolov5.load(model_path, device="cpu", hf_model=True, trace=False) | |
| # model.conf = conf_threshold | |
| # model.iou = iou_threshold | |
| # results = model([image], size=image_size) | |
| # return results.render()[0] | |
| # demo_app = gr.Interface( | |
| # fn=image_fn, | |
| # inputs=[ | |
| # gr.inputs.Image(type="pil", label="Input Image"), | |
| # gr.inputs.Dropdown( | |
| # choices=[ | |
| # "alshimaa/yolo5_epoch100", | |
| # #"kadirnar/yolov7-v0.1", | |
| # ], | |
| # default="alshimaa/yolo5_epoch100", | |
| # label="Model", | |
| # ) | |
| # #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size") | |
| # #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"), | |
| # #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold") | |
| # ], | |
| # outputs=gr.outputs.Image(type="filepath", label="Output Image"), | |
| # title="Object Detector: Identify People Without Mask", | |
| # examples=[['img1.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img2.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img3.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45]], | |
| # cache_examples=True, | |
| # live=True, | |
| # theme='huggingface', | |
| # ) | |
| # demo_app.launch(debug=True, enable_queue=True) | |