用特征金字塔实现目标检测的代码
时间: 2024-02-16 11:00:13 浏览: 22
以下是使用特征金字塔实现目标检测的示例代码:
```python
# 导入必要的库
import cv2
import numpy as np
# 定义图像金字塔函数
def pyramid(image, scale=1.5, minSize=(30, 30)):
# yield the original image
yield image
# keep looping over the pyramid
while True:
# compute the new dimensions of the image and resize it
w = int(image.shape[1] / scale)
image = cv2.resize(image, (w, w))
# if the resized image does not meet the supplied minimum
# size, then stop constructing the pyramid
if image.shape[0] < minSize[1] or image.shape[1] < minSize[0]:
break
# yield the next image in the pyramid
yield image
# 定义滑动窗口函数
def sliding_window(image, stepSize, windowSize):
# slide a window across the image
for y in range(0, image.shape[0] - windowSize[1], stepSize):
for x in range(0, image.shape[1] - windowSize[0], stepSize):
# yield the current window
yield (x, y, image[y:y + windowSize[1], x:x + windowSize[0]])
# 加载图像
image = cv2.imread("example.jpg")
# 定义窗口大小和步长
(winW, winH) = (128, 128)
stepSize = 10
# 遍历图像金字塔
for resized in pyramid(image, scale=1.5):
# 遍历滑动窗口
for (x, y, window) in sliding_window(resized, stepSize=stepSize, windowSize=(winW, winH)):
# 如果窗口大小不符合要求,则忽略
if window.shape[0] != winH or window.shape[1] != winW:
continue
# 在窗口上进行目标检测
# ...
# 显示窗口和检测结果
cv2.rectangle(resized, (x, y), (x + winW, y + winH), (0, 255, 0), 2)
cv2.imshow("Window", resized)
cv2.waitKey(1)
```
在上面的代码中,我们定义了 `pyramid` 函数用于生成图像金字塔,该函数接受一个图像、缩放因子和最小尺寸作为参数,返回一系列缩放后的图像。然后我们定义了 `sliding_window` 函数用于在图像上滑动窗口,该函数接受一个图像、步长和窗口大小作为参数,返回一系列滑动窗口。最后,在使用上述函数遍历图像金字塔和滑动窗口时,我们可以在每个窗口上进行目标检测并显示检测结果。