import gradio as gr import torch import json 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') torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt','yolov5s.pt') model_path = "yolov5x.pt" #"yolov5s.pt" #"yolov5m.pt", "yolov5l.pt", "yolov5x.pt", image_size = 640, conf_threshold = 0.25, iou_threshold = 0.45, model = yolov5.load(model_path, device="cpu") def yolov5_inference( image: gr.inputs.Image = None, ): """ 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 """ results = model([image], size=image_size) tensor = { "tensorflow": [ ] } if results.pred is not None: for i, element in enumerate(results.pred[0]): object = {} #print (element[0]) itemclass = round(element[5].item()) object["classe"] = itemclass object["nome"] = results.names[itemclass] object["score"] = element[4].item() object["x"] = element[0].item() object["y"] = element[1].item() object["w"] = element[2].item() object["h"] = element[3].item() tensor["tensorflow"].append(object) text = json.dumps(tensor) #print (text) return text #results.render()[0] inputs = [ gr.inputs.Image(type="pil", label="Input Image"), ] 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'], ['image3.jpg']] demo_app = gr.Interface( fn=yolov5_inference, inputs=inputs, outputs=["text"], title=title, examples=examples, #cache_examples=True, #live=True, #theme='huggingface', ) demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8080, enable_queue=True) demo_app.launch(debug=True, enable_queue=True) #demo_app.launch(debug=True, server_port=8083, enable_queue=True)