fromsahi.slicingimportslice_image# 输出文件名前缀output_file_name="slice"# 输出文件夹output_dir="result"# 切分图像slice_image_result=slice_image(image=image_path,output_file_name=output_file_name,output_dir=output_dir,slice_height=256,slice_width=256,overlap_height_ratio=0.2,overlap_width_ratio...
Slice an image: from sahi.slicing import slice_image slice_image_result = slice_image( image=image_path, output_file_name=output_file_name, output_dir=output_dir, slice_height=256, slice_width=256, overlap_height_ratio=0.2, overlap_width_ratio=0.2, ) Slice a COCO formatted dataset: fr...
* @param slice_num_h 水平部分切割数量 * @param slice_nms_v 垂直部分切割数量 * @param overlap_ratio 重叠区域比例,计算重叠区域时按照宽高最大值来计算 */voidslice(constuint8_t*image,std::vector<cv::Mat>&slice_images,constintwidth,constintheight,constintslice_num_h,constintslice_num_v,cons...
sahi coco slice --image_dir ./ --dataset_json_path annotations.json 然后再当前目录下生成runs文件夹 可以加参数--slice_size 640目前只能长宽相同 4.coco数据集可视化 参考链接https://blog.51cto.com/u_16213710/10144288 点击查看代码 importosfrompycocotools.cocoimportCOCOfromskimageimportiofrommatplotlibim...
image = read_image(image_dir) 最后,我们可以执行切片预测。在本例中,我们将在重叠率为 0.2 的 256x256 切片上执行预测: result = get_sliced_prediction( image, detection_model, slice_height = 256, slice_width = 256, overlap_height_ratio = 0.2, overlap_width_ratio = 0.2 ) 在原始图像上可视化...
slice_width = 256, overlap_height_ratio = 0.2, overlap_width_ratio = 0.2 ) 可视化预测对象 result.export_visuals(export_dir="demo_data/") Image("demo_data/prediction_visual.png") 预测的同一张图片进行对比,肉眼可见的是:“抓到的目标”变多了!当然,一张图片的预测时间也变长了,这属于典型的以...
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!pip install sahi !pip install -U scikit-image imagecodecs Step02: 基于SAHI切图。PP-YOLOE-SOD现支持的切图尺寸有三种: 模型数据集SLICE_SIZEOVERLAP_RATIO PP-YOLOE-P2-l DOTA 500 0.25 PP-YOLOE-P2-l Xview 400 0.25 PP-YOLOE-l VisDrone-DET 640 0.25 在上文我们提到,本项目数据集的尺寸是[...
slice_width = 320, overlap_height_ratio = 0.2, overlap_width_ratio = 0.2, ) 1. 2. 3. 4. 5. 6. 7. 8. 作为初步检查,我们可以将切片预测中的检测数量与原始预测中的检测数量进行比较: num_sliced_dets = len(sliced_result.to_fiftyone_detections()) ...
image= image_file1, detection_model= detection_model, slice_height=256, slice_width=256, overlap_height_ratio=0.25, overlap_width_ratio=0.25, postprocess_type="NMS", verbose=2, ) result.export_visuals( export_dir=r"D:\my_workspace\source\opencv\yolov8\WinFormsApp1", ...