计算 FAST 特征 - openCV Python

Question

我在 Python 2.7.
中使用 openCV 3.1.0 我已经使用 FAST 检测器成功检测到关键点。现在我想使用 FAST.compute() 方法计算每个点的特征。
但是我没有设法拥有这个功能运行ning，它 returns :

 Traceback (most recent call last):

  File "<ipython-input-82-1b8e23b25e3b>", line 1, in <module>
    Fast.compute(Image8,Kp)

error: ..\..\..\modules\features2d\src\feature2d.cpp:144: error: (-213)  in function cv::Feature2D::detectAndCompute

我还没有设法运行 detectAndCompute 方法也不起作用，即使通过播放关键点属性（如 class_id...）也是如此。感谢您的帮助，

完整代码如下：

# -*- coding: utf-8 -*-
import cv2
import matplotlib.pyplot as plt
import numpy as np

# Function for 16-bit to 8-bit conversion
def map_uint16_to_uint8(img, lower_bound=None, upper_bound=None):
    '''
    Map a 16-bit image trough a lookup table to convert it to 8-bit.

    Parameters
    ----------
    img: numpy.ndarray[np.uint16]
        image that should be mapped
    lower_bound: int, optional
        lower bound of the range that should be mapped to ``[0, 255]``,
        value must be in the range ``[0, 65535]`` and smaller than `upper_bound`
        (defaults to ``numpy.min(img)``)
    upper_bound: int, optional
       upper bound of the range that should be mapped to ``[0, 255]``,
       value must be in the range ``[0, 65535]`` and larger than `lower_bound`
       (defaults to ``numpy.max(img)``)

    Returns
    -------
    numpy.ndarray[uint8]
    '''
    if not(0 <= lower_bound < 2**16) and lower_bound is not None:
        raise ValueError(
            '"lower_bound" must be in the range [0, 65535]')
    if not(0 <= upper_bound < 2**16) and upper_bound is not None:
        raise ValueError(
            '"upper_bound" must be in the range [0, 65535]')
    if lower_bound is None:
        lower_bound = np.min(img)
    if upper_bound is None:
        upper_bound = np.max(img)
    if lower_bound >= upper_bound:
        raise ValueError(
            '"lower_bound" must be smaller than "upper_bound"')
    lut = np.concatenate([
        np.zeros(lower_bound, dtype=np.uint16),
        np.linspace(0, 255, upper_bound - lower_bound).astype(np.uint16),
        np.ones(2**16 - upper_bound, dtype=np.uint16) * 255
    ])
    return lut[img].astype(np.uint8)



# Main
Image    = cv2.imread("C:\Users\Laurent Thomas\Documents\Acquifer\DataSet\IM03_BISCHOFF_DORSAL_2ndGO\BrightField\WE00006---A006--PO01--LO001--CO6--SL001--PX32500--PW0040--IN0020--TM246--X059308--Y011163--Z215816--T1373998365.tif",-1)
Image8   = map_uint16_to_uint8(Image)              # Conversion to 8-bit for FAST detection
ImageRGB = cv2.cvtColor(Image8,cv2.COLOR_GRAY2RGB) # RGB necessary if we want to see the detected points in color

# Get Keypoints
Fast = cv2.FastFeatureDetector_create()
Kp = Fast.detect(Image8)

# Overlay Keypoints on image
ImagePoint  = cv2.drawKeypoints(ImageRGB,Kp,ImageRGB,color=(255,0,0),flags=4) # flag 4 to be able to plaw with the size of the detected spot

# View Overlay
plt.imshow(ImagePoint)

# Compute features
Features = Fast.compute(Image8,Kp)

Answer 1

显然，FAST 描述符没有像 STAR 描述符那样实现。只有检测有效：

计算 FAST 特征 - openCV Python

Compute FAST features - openCV Python

python

opencv

feature-detection