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| import gradio as gr | |
| import torch | |
| import json | |
| #import yolov7 | |
| import yolov7detect.helpers as yolov7d | |
| # 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') | |
| model_path = "kadirnar/yolov7-v0.1" #"kadirnar/yolov7-tiny-v0.1" | |
| image_size = 640 | |
| conf_threshold = 0.25 | |
| iou_threshold = 0.45 | |
| def yolov7_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, | |
| ): | |
| """ | |
| YOLOv7 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 = yolov7d.load_model(model_path, device="cpu", hf_model=True, trace=False) | |
| model.conf = conf_threshold | |
| model.iou = iou_threshold | |
| 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 = "Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors" | |
| examples = [['small-vehicles1.jpeg'], ['zidane.jpg']] | |
| demo_app = gr.Interface( | |
| fn=yolov7_inference, | |
| inputs=inputs, | |
| outputs=["text"], | |
| title=title, | |
| examples=examples, | |
| #cache_examples=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, server_port=8083, enable_queue=True) | |
| demo_app.launch(debug=True, enable_queue=True) | |