import gradio as gr import torch from PIL import Image import os cwd = os.getcwd() # !pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio # 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://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg') # Model #model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update model = torch.hub.load(cwd+'/yolov5', 'custom', path=cwd+'/saved_model/s1000_best.pt', source='local') # local model def yolo(im, size=640): g = (size / max(im.size)) # gain im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize results = model(im) # inference results.render() # updates results.imgs with boxes and labels return Image.fromarray(results.imgs[0]) inputs = gr.inputs.Image(type='pil', label="Original Image") outputs = gr.outputs.Image(type="pil", label="Output Image") title = "S1000 Detection" description = "YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use." article = "
YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes " \ "simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, " \ "and export to ONNX, CoreML and TFLite. Source code |" \ "iOS App | PyTorch Hub
" path_folder = cwd+'/datasets/s1000/' examples = [[path_folder+'s1000_50_metre.jpg'], [path_folder+'s1000_100_metre.jpg'],[path_folder+'s1000_150_metre.jpg'],[path_folder+'s1000_200_metre.jpg'],[path_folder+'s1000_250_metre.jpg']] gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch( debug=True) ''' git init git config user.name bulentsofttech git config user.email bulent.softtech@gmail.com git add * git commit -m "WriteCommit" git push origin master '''