cls_score.shape[1]
时间: 2024-01-29 21:03:16 浏览: 34
This code will return the number of columns in the variable `cls_score`.
Assuming `cls_score` is a NumPy array or a Pandas DataFrame, the code will look like this:
```
cls_score.shape[1]
```
The `.shape` attribute returns a tuple with the dimensions of the array or DataFrame, where the first element is the number of rows and the second element is the number of columns. By accessing the second element of the tuple (`[1]`), we get the number of columns.
For example, if `cls_score` is a NumPy array with 100 rows and 5 columns, `cls_score.shape[1]` will return `5`.
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:
```c
#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;
}
```
xyxy=yolov8_results.boxes.xyxy.cpu().numpy(), confidence=yolov8_results.boxes.conf.cpu().numpy(), class_id=yolov8_results.boxes.cls.cpu().numpy().astype(int), mask=extract_yolov8_masks(yolov8_results), 怎么用 boxes = detections.xyxy conf = detections.confidence class_ids = detections.class_id results = [] if polygon.shape[0] > 0: for label, score, seg in zip(class_ids, conf, polygon): # 将多边形的点坐标转换为列表形式 points = [[float(coord[0]), float(coord[1])] for coord in seg] results.append({ "confidence": str(score), "label": labels.get(label, "unknown"), "points": points, "type": "polygon",})
根据您提供的代码,您可以将以下代码作为替代:
```python
xyxy = detections.xyxy
confidence = detections.confidence
class_id = detections.class_id
results = []
if polygon.shape[0] > 0:
for label, score, seg in zip(class_id, confidence, polygon):
# 将多边形的点坐标转换为列表形式
points = [[float(coord[0]), float(coord[1])] for coord in seg]
results.append({
"confidence": str(score),
"label": labels.get(label, "unknown"),
"points": points,
"type": "polygon",
})
```
在这个修改后的代码中,我们使用 `detections` 对象的属性来获取边界框的坐标、置信度和类别ID,并将它们分别赋值给 `xyxy`、`confidence` 和 `class_id` 变量。
然后,我们使用 `zip` 函数来将 `class_id`、`confidence` 和 `polygon` 配对,然后在循环中处理每个配对。我们将多边形的点坐标转换为列表形式,并将结果添加到 `results` 列表中。
请确保根据您的实际情况修改代码,并根据需要调整变量名和逻辑。