[Index]Search in Rotated Sorted Array series

本文介绍了一种在旋转数组中寻找特定目标值的搜索算法。该算法通过二分查找的方式实现,即使数组被旋转过也能有效地找到目标元素的位置,如果未找到则返回-1。文章详细解释了算法的实现思路,并提供了一个Java示例代码。

Good Luck !
//(i.e., 0 1 2 4 5 6 7 might become 4 5 6 7 0 1 2).

//You are given a target value to search. If found in the array return its index, otherwise return -1.
//Duplicate Allowed!
public class Solution {
    public int search(int[] nums, int target) {
        int l = 0;
        int r = nums.length - 1;
        while(l <= r){
            int m = (l+r)/2;
            if(nums[m] == target)
                return m;
            if(nums[m] > nums[r]){
                if(nums[l] <= target && target < nums[m]){
                    r = m - 1;
                }else{
                    l = m + 1;
                }
                }else if(nums[m] < nums[r]){
                    if(nums[m] < target  && target <= nums[r]){
                        l = m + 1;
                    }else{
                        r = m - 1;
                    }
                }else{
                    r -- ;
                }
        }
        return -1;
    }
}

//Find the minimum element.
//Duplicate Allowed
public class Solution {
    public int findMin(int[] nums) {
        public int findMin(int[] nums) {
        if(nums[0] < nums[nums.length-1])
            return nums[0];
        int r = nums.length-1;
        int l = 0;
        int MIN = nums[0];
        while(l <= r){
            int m = (l+r)/2;
            MIN = Math.min(MIN,nums[m]);
            if(nums[m] > nums[r]){
                l = m + 1;
                
            }else if(nums[m] < nums[r]){
                r = m - 1;
            }else{
                r -- ;
            }
        }
        return MIN;
    }
    }
}

个人防护装备实例分割数据集 一、基础信息 • 数据集名称:个人防护装备实例分割数据集 • 图片数量: 训练集:4524张图片 • 训练集:4524张图片 • 分类类别: 手套(Gloves) 头盔(Helmet) 未戴手套(No-Gloves) 未戴头盔(No-Helmet) 未穿鞋(No-Shoes) 未穿背心(No-Vest) 鞋子(Shoes) 背心(Vest) • 手套(Gloves) • 头盔(Helmet) • 未戴手套(No-Gloves) • 未戴头盔(No-Helmet) • 未穿鞋(No-Shoes) • 未穿背心(No-Vest) • 鞋子(Shoes) • 背心(Vest) • 标注格式:YOLO格式,适用于实例分割任务,包含边界框或多边形坐标。 • 数据格式:图片数据,来源于监控或相关场景。 二、适用场景 • 工业安全监控系统开发:用于自动检测工人是否佩戴必要的个人防护装备,提升工作场所安全性,减少工伤风险。 • 智能安防应用:集成到监控系统中,实时分析视频流,识别PPE穿戴状态,辅助安全预警。 • 合规性自动化检查:在建筑、制造等行业,自动检查个人防护装备穿戴合规性,支持企业安全审计。 • 计算机视觉研究:支持实例分割、目标检测等算法在安全领域的创新研究,促进AI模型优化。 三、数据集优势 • 类别全面:覆盖8种常见个人防护装备及其缺失状态,提供丰富的检测场景,确保模型能处理各种实际情况。 • 标注精准:采用YOLO格式,每个实例都经过精细标注,边界框或多边形坐标准确,提升模型训练质量。 • 真实场景数据:数据来源于实际环境,增强模型在真实世界中的泛化能力和实用性。 • 兼容性强:YOLO格式便于与主流深度学习框架(如YOLO、PyTorch等)集成,支持快速部署和实验。
### Rotated ROI Align in Object Detection and Image Processing In the context of object detection and image processing, rotated region-of-interest (ROI) alignment plays a crucial role when dealing with objects that are not aligned to the axis. Traditional methods like Fast R-CNN use standard ROIs which assume bounding boxes are upright rectangles. However, this assumption can lead to suboptimal performance on datasets where objects have arbitrary orientations. To address these limitations, researchers introduced **Rotated ROI Align**, an extension of the original ROI Align operation designed specifically for handling arbitrarily oriented proposals. This method allows more accurate feature extraction from regions defined by quadrilaterals rather than just rectangular areas[^1]. The key difference between regular ROI Align and its rotated counterpart lies in how features within each proposal box get sampled during forward propagation: - For conventional ROI Align, bilinear interpolation is applied over four grid points inside every bin. - In contrast, Rotated ROI Align applies adaptive sampling grids based on both position and orientation information associated with each proposed region. This approach ensures better spatial correspondence between input images and extracted features while maintaining computational efficiency through pooling operations tailored towards non-axis-aligned shapes. #### Implementation Details Implementing Rotated ROI Align involves several steps including defining appropriate data structures for storing rotation parameters alongside coordinates as well as modifying existing frameworks such as Faster R-CNN or Mask R-CNN to accommodate angled detections effectively. Below shows Python code snippet demonstrating part of what might be involved in implementing custom layers supporting rotated RoI alignments using PyTorch framework: ```python import torch.nn.functional as F from torchvision.ops import roi_align def rotated_roi_align(features, rois, output_size=(7, 7), spatial_scale=1./16., angle=None): """ Apply rotated ROI align operator Args: features (Tensor): Feature maps obtained after backbone network rois (list[tuple]): List containing tuples representing individual ROIs Each tuple consists of five elements: batch index, center_x, center_y, width, height followed optionally by `angle` parameter specifying degree of rotation ... Returns: Tensor: Aligned feature map corresponding to given set of ROIs """ if isinstance(rois[0], tuple) and len(rois[0]) >= 5: # Extract angles if provided along with other attributes per ROI _, cx, cy, w, h = zip(*rois) # Convert list into tensor format expected by underlying C++ implementation rois_tensor = torch.tensor([*zip(cx, cy, w, h)], dtype=torch.float32).to(device=features.device) # Perform actual transformation considering specified rotations transformed_features = transform_rotated_rois( features=features, rois=rois_tensor, angles=[r[-1] for r in rois], out_height=output_size[0], out_width=output_size[1], spatial_scale=spatial_scale ) return transformed_features else: raise ValueError("Invalid structure detected among supplied ROIs.") ```
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值