for i in range(bins_l-1,0,-1):
时间: 2024-06-02 10:08:30 浏览: 14
这是一个简单的 Python 循环语句。其中 `range(bins_l-1, 0, -1)` 意味着从 `bins_l-1` 到 1(不包含 1),步长为 -1,即倒序循环。循环变量 `i` 依次取到从 `bins_l-1` 到 1 的所有整数值。在循环体中,可以对 `i` 进行操作,实现对一个序列的倒序遍历。
相关问题
bbx_ls = [] for line in lines: obj = Object3d(line) center = np.array(obj.t) center[2]=center[2]+obj.h # ry=obj.ry heading_angle =-obj.ry - np.pi / 2 R = rotz((heading_angle)) # only boundingbox range = (obj.l, obj.w, obj.h) # all vertical range = (obj.l, obj.w, 10) # print(center,obj.ry,range) bbx = o3d.geometry.OrientedBoundingBox(center, R, range) bbx_ls.append(bbx) # o3d.visualization.draw_geometries([pcd, origin] + bbx_ls) for box in bbx_ls: cropped_cloud = pcd.crop(box) # if set colors # colors = [[0, 255, 0] for i in np.arange(len(cropped_cloud.points))] # cropped_cloud.colors = o3d.utility.Vector3dVector(colors) points = np.array(cropped_cloud.points) print(points.shape) o3d.visualization.draw_geometries([cropped_cloud,box])根据上述截取的部分代码,加代码,提取出目标区域点云数据的x,y,z坐标,不管x和y坐标,只要z坐标,将点汇聚到z轴上,画出直方图,统计高度,每隔0.1米进行划分,横坐标为高度,纵坐标为点的数量。
可以按照以下方式补充代码:
```
# 声明一个列表,存储所有点的z坐标
z_list = []
for box in bbx_ls:
cropped_cloud = pcd.crop(box)
points = np.array(cropped_cloud.points)
# 将所有点的z坐标加入列表
z_list.extend(points[:, 2])
# 统计直方图
hist, bin_edges = np.histogram(z_list, bins=np.arange(0, max(z_list) + 0.1, 0.1))
# 画出直方图
plt.bar(bin_edges[:-1], hist, width=0.1)
plt.xlabel("Height (m)")
plt.ylabel("Number of points")
plt.show()
```
这段代码会将所有目标区域点云数据中的点的z坐标加入一个列表中,然后使用 `numpy` 库的 `histogram` 函数统计直方图,步长为0.1米,最大高度为所有点中的最大z坐标。最后使用 `matplotlib` 库画出直方图。
DCP暗通道python
DCP暗通道python实现的代码可以参考以下示例:
```python
import cv2
import numpy as np
def get_min_channel(img):
return np.min(img, axis=2)
def guided_filter(I, p, win_size, eps):
mean_I = cv2.blur(I, (win_size, win_size))
mean_p = cv2.blur(p, (win_size, win_size))
corr_I = cv2.blur(I*I, (win_size, win_size))
corr_Ip = cv2.blur(I*p, (win_size, win_size))
var_I = corr_I - mean_I * mean_I
cov_Ip = corr_Ip - mean_I * mean_p
a = cov_Ip / (var_I + eps)
b = mean_p - a * mean_I
mean_a = cv2.blur(a, (win_size, win_size))
mean_b = cv2.blur(b, (win_size, win_size))
q = mean_a * I + mean_b
return q
def get_A(img_haze, dark_channel, bins_l):
hist, bins = np.histogram(dark_channel, bins=bins_l)
d = np.cumsum(hist) / float(dark_channel.size)
threshold = 0
for i in range(bins_l-1, 0, -1):
if d[i <= 0.999:
threshold = i
break
A = img_haze[dark_channel >= bins<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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