xyxy_list.append(xyxy)

`xyxy_list.append(xyxy)` 是 Python 中列表（list）操作的一个常见方法，用于在列表末尾添加新的元素。在这个上下文中，`xyxy_list` 是一个列表类型变量，`append` 是列表对象自带的方法，`xyxy` 是你想要插入的单个元素，通常是一个包含四个值（x, y, x, y）的元组或字典，用于表示二维空间中的一个边界框。

具体来说，这行代码执行后，`xyxy` 的值会被添加到 `xyxy_list` 的最后一个位置，如果 `xyxy_list` 为空，那么 `xyxy` 就会作为第一个元素存在；如果已经有一些元素，`xyxy` 会在它们后面增加一个元素。

相关问题

translation this code to c:def filter_box(org_box, conf_thres, iou_thres): org_box = np.squeeze(org_box) conf = org_box[..., 4] > conf_thres box = org_box[conf == True] print('box:') print(box.shape) cls_cinf = box[..., 5:] cls = [] for i in range(len(cls_cinf)): cls.append(int(np.argmax(cls_cinf[i]))) all_cls = list(set(cls)) output = [] for i in range(len(all_cls)): curr_cls = all_cls[i] curr_cls_box = [] curr_out_box = [] for j in range(len(cls)): if cls[j] == curr_cls: box[j][5] = curr_cls curr_cls_box.append(box[j][:6]) curr_cls_box = np.array(curr_cls_box) curr_cls_box = xywh2xyxy(curr_cls_box) curr_out_box = nms(curr_cls_box, iou_thres) for k in curr_out_box: output.append(curr_cls_box[k]) output = np.array(output) return output

以下是该代码的 C 语言版本：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define MAX_BOX_NUM 1000

typedef struct {
    float x, y, w, h, score;
    int cls;
} Box;

float max(float a, float b) {
    return a > b ? a : b;
}

float min(float a, float b) {
    return a < b ? a : b;
}

float iou(Box a, Box b) {
    float inter_xmin = max(a.x - a.w / 2, b.x - b.w / 2);
    float inter_ymin = max(a.y - a.h / 2, b.y - b.h / 2);
    float inter_xmax = min(a.x + a.w / 2, b.x + b.w / 2);
    float inter_ymax = min(a.y + a.h / 2, b.y + b.h / 2);
    float inter_w = max(inter_xmax - inter_xmin, 0.f);
    float inter_h = max(inter_ymax - inter_ymin, 0.f);
    float inter_area = inter_w * inter_h;
    float a_area = a.w * a.h;
    float b_area = b.w * b.h;
    float union_area = a_area + b_area - inter_area;
    return inter_area / union_area;
}

void xywh2xyxy(float* box) {
    float x = box[0], y = box[1], w = box[2], h = box[3];
    box[0] = x - w / 2;
    box[1] = y - h / 2;
    box[2] = x + w / 2;
    box[3] = y + h / 2;
}

void nms(Box* boxes, int box_num, float iou_thres, Box* out_boxes, int* out_box_num) {
    int* mask = (int*)malloc(sizeof(int) * box_num);
    int i, j, k;
    for (i = 0; i < box_num; ++i) {
        mask[i] = 1;
    }
    for (i = 0; i < box_num; ++i) {
        if (!mask[i]) {
            continue;
        }
        out_boxes[(*out_box_num)++] = boxes[i];
        for (j = i + 1; j < box_num; ++j) {
            if (!mask[j]) {
                continue;
            }
            float iou_val = iou(boxes[i], boxes[j]);
            if (iou_val > iou_thres) {
                mask[j] = 0;
            }
        }
    }
    free(mask);
}

Box* filter_box(float* org_box, float conf_thres, float iou_thres, int* box_num) {
    int i, j;
    float* box = (float*)malloc(sizeof(float) * MAX_BOX_NUM * 6);
    int conf_box_num = 0;
    int cls[MAX_BOX_NUM];
    int cls_num = 0;
    for (i = 0; i < MAX_BOX_NUM; ++i) {
        float* cur_box = org_box + i * 6;
        if (cur_box[4] <= conf_thres) {
            continue;
        }
        for (j = 0; j < 5; ++j) {
            box[conf_box_num * 6 + j] = cur_box[j];
        }
        cls[conf_box_num] = (int)round(cur_box[5]);
        ++conf_box_num;
    }
    for (i = 0; i < conf_box_num; ++i) {
        int cur_cls = cls[i];
        int is_new_cls = 1;
        for (j = 0; j < cls_num; ++j) {
            if (cur_cls == cls[j]) {
                is_new_cls = 0;
                break;
            }
        }
        if (is_new_cls) {
            cls[cls_num++] = cur_cls;
        }
    }
    Box* output = (Box*)malloc(sizeof(Box) * MAX_BOX_NUM);
    int output_box_num = 0;
    for (i = 0; i < cls_num; ++i) {
        int cur_cls = cls[i];
        float curr_cls_box[MAX_BOX_NUM][6];
        int curr_cls_box_num = 0;
        for (j = 0; j < conf_box_num; ++j) {
            if (cls[j] == cur_cls) {
                box[j * 6 + 5] = cur_cls;
                int k;
                for (k = 0; k < 6; ++k) {
                    curr_cls_box[curr_cls_box_num][k] = box[j * 6 + k];
                }
                ++curr_cls_box_num;
            }
        }
        for (j = 0; j < curr_cls_box_num; ++j) {
            xywh2xyxy(curr_cls_box[j]);
        }
        Box curr_out_box[MAX_BOX_NUM];
        int curr_out_box_num = 0;
        nms((Box*)curr_cls_box, curr_cls_box_num, iou_thres, curr_out_box, &curr_out_box_num);
        for (j = 0; j < curr_out_box_num; ++j) {
            output[output_box_num++] = curr_out_box[j];
        }
    }
    free(box);
    *box_num = output_box_num;
    return output;
}

代码解释# Process detections for i, det in enumerate(pred): # detections per image if webcam: # batch_size >= 1 p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() else: p, s, im0 = path, '', im0s save_path = str(Path(out) / Path(p).name) s += '%gx%g ' % img.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if det is not None and len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += '%g %ss, ' % (n, names[int(c)]) # add to string # Write results for *xyxy, conf, cls in det: if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file: file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format if save_img or view_img: # Add bbox to image label = '%s %.2f' % (names[int(cls)], conf) if label is not None: if (label.split())[0] == 'person': people_coords.append(xyxy) # plot_one_box(xyxy, im0, line_thickness=3) plot_dots_on_people(xyxy, im0) # Plot lines connecting people distancing(people_coords, im0, dist_thres_lim=(100, 150)) # Print time (inference + NMS) print('%sDone. (%.3fs)' % (s, t2 - t1)) # Stream results if 1: ui.showimg(im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration # Save results (image with detections) if save_img: if dataset.mode == 'images': cv2.imwrite(save_path, im0) else: if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h)) vid_writer.write(im0)

这段代码是目标检测算法的输出结果处理部分。主要包括以下几个步骤：

1. 对每张图片的检测结果进行处理，包括将检测框从输入图像的尺寸缩放到输出图像的尺寸，并将结果写入文本文件中。

2. 对每个类别的检测结果统计数量，并将数量和类别名称添加到输出字符串中。

3. 对每个检测到的目标绘制边界框，并在边界框上标注类别和置信度。

4. 如果检测到的目标是人，则将其坐标保存在列表中，并在图像上绘制点和连线进行社交距离监测。

5. 将处理后的图像展示出来，并将图像保存到文件中。