opencv python 实现ransac算法
时间: 2023-07-24 17:04:50 浏览: 90
RANSAC (Random Sample Consensus) 是一种用于拟合模型的迭代算法,常用于处理含有噪声和离群点的数据。下面是使用 OpenCV 和 Python 实现 RANSAC 算法的示例代码:
```python
import cv2
import numpy as np
def ransac(data, model, n, k, t, d):
"""
:param data: 输入数据
:param model: 用于拟合的模型
:param n: 从数据中随机选择的最小样本数
:param k: 最大迭代次数
:param t: 阈值,用于判断一个样本是否适合模型
:param d: 拟合出的模型与样本的距离
:return:
"""
bestfit = None # 最佳拟合解(返回值)
besterr = np.inf # 最佳拟合解的误差
for i in range(k):
maybeinliers = np.random.choice(data, n, replace=False)
maybeoutliers = np.delete(data, maybeinliers, axis=0)
maybemodel = model.fit(maybeinliers)
alsoinliers = []
for j in range(len(maybeoutliers)):
if model.distance(maybeoutliers[j], maybemodel) < t:
alsoinliers.append(maybeoutliers[j])
if len(alsoinliers) > d:
betterdata = np.concatenate((maybeinliers, alsoinliers))
bettermodel = model.fit(betterdata)
thiserr = model.error(bettermodel, betterdata)
if thiserr < besterr:
bestfit = bettermodel
besterr = thiserr
return bestfit
class LineModel:
def fit(self, data):
x = data[:, 0]
y = data[:, 1]
A = np.vstack([x, np.ones(len(x))]).T
k, b = np.linalg.lstsq(A, y, rcond=None)[0]
return k, b
def distance(self, point, model):
k, b = model
x0, y0 = point
d = np.abs(k*x0 - y0 + b) / np.sqrt(k**2 + 1)
return d
def error(self, model, data):
return np.mean([self.distance(point, model) for point in data])
# 示例用法
data = np.array([[1, 2], [2, 4], [3, 6], [4, 8], [5, 10], [6, 12], [7, 14], [8, 16], [9, 18], [10, 20]])
model = LineModel()
best_fit_model = ransac(data, model, 2, 100, 1.0, 3)
print("Best fit line:", best_fit_model)
```
这个示例代码使用 RANSAC 算法拟合了一个简单的二维直线模型 LineModel。你可以根据需要修改模型和数据,以适应不同的应用场景。