Python/OpenCV 如何处理.yaml 文件中的大量SIFT 特征？
Python/OpenCV how to deal with large number of SIFT feaure in .yml file?
我正在使用 OpenCv 并使用 yaml 来存储 SIFT 关键点和描述符。
我有一个包含 1659 张图片的数据库（.jpg，每张图片大约 95 KB）。对于每个图像，我创建了一个包含关键点和描述符的 .yml 文件。现在，对于单个图像，我最终得到了 700 个关键点和描述符，生成了一个 ca 文件。 4MB，我想避免使用二进制文件。
我的问题是：
- How can I know if the number of features is adequate to the image?
- There is any way to control the number of features? For example, setting a threshold for SIFT?
- Now storing a numpy matrix into a yamil file using
cv2.FileStorage.write, OpenCv writes each number with a 16 significant digits (ex. 1.9705572128295898e+00). Is there a problem if I reduce the significant digits? For example to 4?
- How can I know if the number of features is adequate to the image?
一定要看你的形象，你的任务要求。你应该比别人更清楚，或者做实验才能弄清楚。
- There is any way to control the number of features?
当然可以。创建时传入必要的参数即可。
cv2.xfeatures2d.SIFT_create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> retval
| . @param nfeatures The number of best features to retain. The features are ranked by their scores
| . (measured in SIFT algorithm as the local contrast)
| .
| . @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper.
| . The number of octaves is computed automatically from the image resolution.
| .
| . @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
| . (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
| .
| . @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
| . is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
| . filtered out (more features are retained).
| .
| . @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
| . is captured with a weak camera with soft lenses, you might want to reduce the number.
|
例如，我创建了一个具有 50 个关键点和 3 层的筛选检测器：
sift = cv2.xfeatures2d.SIFT_create(nfeatures = 50, nOctaveLayers=3)
这是检测结果：
- Too long. I know you stored large number keypoints and descriptors into .yml format in OpenCV-Python.
好的，当您要存储大量数据时，.yml 真的有用吗？真的合理吗？你真的需要 keypoint (points2f, size, response, octave, class_id) 的每一个元素吗？至于描述符，它是一个直方图，或者一个int数组。所以即使存为int，值也刚刚好。
我正在使用 OpenCv 并使用 yaml 来存储 SIFT 关键点和描述符。
我有一个包含 1659 张图片的数据库（.jpg，每张图片大约 95 KB）。对于每个图像，我创建了一个包含关键点和描述符的 .yml 文件。现在，对于单个图像，我最终得到了 700 个关键点和描述符，生成了一个 ca 文件。 4MB，我想避免使用二进制文件。
我的问题是：
- How can I know if the number of features is adequate to the image?
- There is any way to control the number of features? For example, setting a threshold for SIFT?
- Now storing a numpy matrix into a yamil file using
cv2.FileStorage.write, OpenCv writes each number with a 16 significant digits (ex. 1.9705572128295898e+00). Is there a problem if I reduce the significant digits? For example to 4?
- How can I know if the number of features is adequate to the image?
一定要看你的形象，你的任务要求。你应该比别人更清楚，或者做实验才能弄清楚。
- There is any way to control the number of features?
当然可以。创建时传入必要的参数即可。
cv2.xfeatures2d.SIFT_create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> retval
| . @param nfeatures The number of best features to retain. The features are ranked by their scores
| . (measured in SIFT algorithm as the local contrast)
| .
| . @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper.
| . The number of octaves is computed automatically from the image resolution.
| .
| . @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
| . (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
| .
| . @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
| . is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
| . filtered out (more features are retained).
| .
| . @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
| . is captured with a weak camera with soft lenses, you might want to reduce the number.
|
例如，我创建了一个具有 50 个关键点和 3 层的筛选检测器：
sift = cv2.xfeatures2d.SIFT_create(nfeatures = 50, nOctaveLayers=3)
这是检测结果：
- Too long. I know you stored large number keypoints and descriptors into .yml format in OpenCV-Python.
好的，当您要存储大量数据时，.yml 真的有用吗？真的合理吗？你真的需要 keypoint (points2f, size, response, octave, class_id) 的每一个元素吗？至于描述符，它是一个直方图，或者一个int数组。所以即使存为int，值也刚刚好。