from sahi import utils, predict, AutoDetectionModel from PIL import Image import gradio as gr import numpy import torch model_id_list = ['deprem-ml/Binafarktespit-yolo5x-v1-xview', 'SerdarHelli/deprem_satellite_labeled_yolov8', 'kadirnar/yolov7-v0.1', 'kadirnar/UNet-EfficientNet-b6-Istanbul'] current_device = "cuda" if torch.cuda.is_available() else "cpu" model_types = ["YOLOv5", "YOLOv5 + SAHI", "YOLOv8", "YOLOv7", "Unet-Istanbul"] def sahi_yolov5_inference( image, model_id, model_type, image_size, slice_height=512, slice_width=512, overlap_height_ratio=0.1, overlap_width_ratio=0.1, postprocess_type="NMS", postprocess_match_metric="IOU", postprocess_match_threshold=0.25, postprocess_class_agnostic=False, ): rect_th = None or max(round(sum(image.size) / 2 * 0.0001), 1) text_th = None or max(rect_th - 2, 1) if model_type == "YOLOv5": # standard inference model = AutoDetectionModel.from_pretrained( model_type="yolov5", model_path=model_id, device=current_device, confidence_threshold=0.5, image_size=image_size, ) prediction_result_1 = predict.get_prediction( image=image, detection_model=model ) visual_result_1 = utils.cv.visualize_object_predictions( image=numpy.array(image), object_prediction_list=prediction_result_1.object_prediction_list, rect_th=rect_th, text_th=text_th, ) output = Image.fromarray(visual_result_1["image"]) return output elif model_type == "YOLOv5 + SAHI": model = AutoDetectionModel.from_pretrained( model_type="yolov5", model_path=model_id, device=current_device, confidence_threshold=0.5, image_size=image_size, ) prediction_result_2 = predict.get_sliced_prediction( image=image, detection_model=model, slice_height=int(slice_height), slice_width=int(slice_width), overlap_height_ratio=overlap_height_ratio, overlap_width_ratio=overlap_width_ratio, postprocess_type=postprocess_type, postprocess_match_metric=postprocess_match_metric, postprocess_match_threshold=postprocess_match_threshold, postprocess_class_agnostic=postprocess_class_agnostic, ) visual_result_2 = utils.cv.visualize_object_predictions( image=numpy.array(image), object_prediction_list=prediction_result_2.object_prediction_list, rect_th=rect_th, text_th=text_th, ) output = Image.fromarray(visual_result_2["image"]) return output elif model_type == "YOLOv8": from ultralyticsplus import YOLO, render_result model = YOLO('SerdarHelli/deprem_satellite_labeled_yolov8') result = model.predict(image, imgsz=image_size)[0] render = render_result(model=model, image=image, result=result, rect_th=rect_th, text_th=text_th) return render elif model_type == "YOLOv7": import yolov7 model = yolov7.load(model_id, device="cuda:0", hf_model=True, trace=False) results = model([image], size=image_size) return results.render()[0] elif model_type == "Unet-Istanbul": from utils.istanbul_unet import unet_prediction output = unet_prediction(input_path=image, model_path=model_id) return output inputs = [ gr.Image(type="pil", label="Original Image"), gr.Dropdown(choices=model_id_list,label="Choose Model",value=model_id_list[0]), gr.Dropdown( choices=model_types, label="Choose Model Type", value=model_types[1]), gr.Slider(minimum=128, maximum=2048, value=640, step=32, label="Image Size"), gr.Slider(minimum=128, maximum=2048, value=512, step=32, label="Slice Height"), gr.Slider(minimum=128, maximum=2048, value=512, step=32, label="Slice Width"), gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.1, label="Overlap Height Ratio"), gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.1, label="Overlap Width Ratio"), gr.Dropdown(["NMS", "GREEDYNMM"], type="value", value="NMS", label="Postprocess Type"), gr.Dropdown(["IOU", "IOS"], type="value", value="IOU", label="Postprocess Type"), gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.1, label="Postprocess Match Threshold"), gr.Checkbox(value=True, label="Postprocess Class Agnostic"), ] outputs = [gr.outputs.Image(type="pil", label="Output")] title = "Building Detection from Satellite Images with SAHI + YOLOv5" description = "SAHI + YOLOv5 demo for building detection from satellite images. Upload an image or click an example image to use." article = "

SAHI is a lightweight vision library for performing large scale object detection/ instance segmentation.. SAHI Github | SAHI Blog | YOLOv5 Github

" examples = [ ["data/26.jpg", 'deprem-ml/Binafarktespit-yolo5x-v1-xview', "YOLOv5 + SAHI", 640, 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False], ["data/27.jpg", 'deprem-ml/Binafarktespit-yolo5x-v1-xview', "YOLOv5 + SAHI", 640, 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False], ["data/28.jpg", 'deprem-ml/Binafarktespit-yolo5x-v1-xview', "YOLOv5 + SAHI", 640, 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False], ["data/31.jpg", 'deprem-ml/SerdarHelli-yolov8-v1-xview', "YOLOv8", 640, 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False], ["data/Istanbul.jpg", 'kadirnar/UNet-EfficientNet-b6-Istanbul', "Unet-Istanbul", 512, 512, 512, 0.1, 0.1, "NMS", "IOU", 0.25, False], ] demo = gr.Interface( sahi_yolov5_inference, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface", cache_examples=True, ) demo.launch(debug=True, enable_queue=True)