mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-10 21:27:17 +08:00
Finished the docs, and caught a couple more warts.
This commit is contained in:
sccolbert
@@ -13,7 +13,7 @@ class cvdoc(object):
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base_url = 'http://opencv.willowgarage.com/documentation/'
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branch_urls = {'cv': {'image': 'image_processing',
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'structural': 'structural_analysis',
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'calibration': 'camera_calibration_and_3D_reconstruction'
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'calibration': 'camera_calibration_and_3d_reconstruction'
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},
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'cxcore': {},
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'highgui': {}
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+1449
-881
File diff suppressed because it is too large
Load Diff
@@ -1576,7 +1576,10 @@ CV_BILATERAL:
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param1 x param2 define the neighborhood.
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param3 defines the color stddev.
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param4 defines the space stddev.
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in_place operation is not supported.''')
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in_place operation is not supported.
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Using standard sigma for small kernels (3x3 to 7x7)
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gives better speed.''')
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def cvSmooth(np.ndarray src, int smoothtype=CV_GAUSSIAN, int param1=3,
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int param2=0, double param3=0, double param4=0,
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bool in_place=False):
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@@ -1636,6 +1639,42 @@ def cvSmooth(np.ndarray src, int smoothtype=CV_GAUSSIAN, int param1=3,
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else:
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return out
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#-----------
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# cvFilter2D
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#-----------
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@cvdoc(package='cv', group='image', doc=\
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'''Convolve an image with the given kernel.
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Signature
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---------
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cvFilter2D(src, kernel, anchor=None, in_place=False)
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Parameters
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----------
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src : ndarray
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The source image.
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kernel : ndarray, 2D, dtype=float32
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The kernel with which to convolve the image.
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anchor : 2-tuple, (x, y)
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The kernel anchor.
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in_place : bool
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If True, perform the operation in_place.
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Returns
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-------
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out/None : ndarray or None
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If in_place is True, returns None.
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Otherwise a new array is returned which is the result
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of the convolution.
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Notes
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-----
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This is a high performance function. OpenCV automatically
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determines, based on the size of the image and the kernel,
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whether it will faster to do the convolution in the spatial
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or the frequency domain, and behaves accordingly.''')
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def cvFilter2D(np.ndarray src, np.ndarray kernel, anchor=None, in_place=False):
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validate_array(src)
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@@ -1682,6 +1721,35 @@ def cvFilter2D(np.ndarray src, np.ndarray kernel, anchor=None, in_place=False):
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else:
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return out
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#-----------
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# cvIntegral
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#-----------
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@cvdoc(package='cv', group='image', doc=\
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'''Calculate the integral of an image.
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Signature
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---------
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cvIntegral(src, square_sum=False, titled_sum=False)
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Parameters
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----------
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src : ndarray, dtyp=[uint8, float32, float64]
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The source image.
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square_sum : bool
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If True, also returns the square sum.
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tilted_sum : bool
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If True, also returns the titled sum (45 degree tilt)
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Returns
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-------
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[out1, out2, out3] : list of ndarray's
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Returns a list consisting at least of:
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out1: the integral image, and optionally:
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out2: the square sum image
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out3: the titled sum image,
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or any combination of these two.''')
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def cvIntegral(np.ndarray src, square_sum=False, tilted_sum=False):
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validate_array(src)
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@@ -1736,6 +1804,56 @@ def cvIntegral(np.ndarray src, square_sum=False, tilted_sum=False):
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return out
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#-----------
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# cvCvtColor
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#-----------
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@cvdoc(package='cv', group='image', doc=\
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'''Convert an image to another color space.
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Signature
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---------
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cvCvtColor(src, code)
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Parameters
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----------
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src : ndarray, dtype=[uint8, uint16, float32]
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The source image.
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code : integer
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A flag representing which color conversion to perform.
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Valid flags are the following:
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CV_BGR2BGRA, CV_RGB2RGBA, CV_BGRA2BGR, CV_RGBA2RGB,
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CV_BGR2RGBA, CV_RGB2BGRA, CV_RGBA2BGR, CV_BGRA2RGB,
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CV_BGR2RGB, CV_RGB2BGR, CV_BGRA2RGBA, CV_RGBA2BGRA,
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CV_BGR2GRAY, CV_RGB2GRAY, CV_GRAY2BGR, CV_GRAY2RGB,
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CV_GRAY2BGRA, CV_GRAY2RGBA, CV_BGRA2GRAY, CV_RGBA2GRAY,
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CV_BGR2BGR565, CV_RGB2BGR565, CV_BGR5652BGR, CV_BGR5652RGB,
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CV_BGRA2BGR565, CV_RGBA2BGR565, CV_BGR5652BGRA, CV_BGR5652RGBA,
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CV_GRAY2BGR565, CV_BGR5652GRAY, CV_BGR2BGR555, CV_RGB2BGR555,
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CV_BGR5552BGR, CV_BGR5552RGB, CV_BGRA2BGR555, CV_RGBA2BGR555,
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CV_BGR5552BGRA, CV_BGR5552RGBA, CV_GRAY2BGR555, CV_BGR5552GRAY,
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CV_BGR2XYZ, CV_RGB2XYZ, CV_XYZ2BGR, CV_XYZ2RGB,
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CV_BGR2YCrCb, CV_RGB2YCrCb, CV_YCrCb2BGR, CV_YCrCb2RGB,
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CV_BGR2HSV, CV_RGB2HSV, CV_BGR2Lab, CV_RGB2Lab,
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CV_BayerBG2BGR, CV_BayerGB2BGR, CV_BayerRG2BGR, CV_BayerGR2BGR,
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CV_BayerBG2RGB, CV_BayerGB2RGB, CV_BayerRG2RGB, CV_BayerGR2RGB,
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CV_BGR2Luv, CV_RGB2Luv, CV_BGR2HLS, CV_RGB2HLS,
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CV_HSV2BGR, CV_HSV2RGB, CV_Lab2BGR, CV_Lab2RGB,
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CV_Luv2BGR, CV_Luv2RGB, CV_HLS2BGR, CV_HLS2RGB
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Returns
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-------
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out : ndarray
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A new image in the requested color-space, with
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an appropriate dtype.
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Notes
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-----
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Not all conversion types support all dtypes.
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An exception will be raise if the dtype is not supported.
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See the OpenCV documentation for more details
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about the specific color conversions.''')
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def cvCvtColor(np.ndarray src, int code):
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validate_array(src)
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@@ -1780,6 +1898,45 @@ def cvCvtColor(np.ndarray src, int code):
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return out
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#------------
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# cvThreshold
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#------------
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@cvdoc(package='cv', group='image', doc=\
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'''Threshold an image.
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Signature
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---------
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cvThreshold(src, threshold, max_value=255, threshold_type=CV_THRESH_BINARY,
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use_otsu=False)
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Parameters
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----------
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src : ndarray, 2D, dtype=[uint8, float32]
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threshold : float
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The threshold value. (decision value)
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max_value : float
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The maximum value.
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threshold_type : integer
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The flag representing which type of thresholding to apply.
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Valid flags are:
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CV_THRESH_BINARY (max_value if src(x,y) > threshold else 0)
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CV_THRESH_BINARY_INV (0 if src(x,y) > threshold else max_value)
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CV_THRESH_TRUNC (threshold if src(x,y) > threshold else src(x,y))
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CV_THRESH_TOZERO (src(x,y) if src(x,y) > threshold else 0)
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CV_THRESH_TOZERO_INV (0 if src(x,y) > threshold else src(x,y))
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use_otsu : bool
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If true, the optimum threshold is automatically computed
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and the passed in threshold value is ignored.
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Only implemented for uint8 source images.
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Returns
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-------
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out/(out, threshold) : ndarray or (ndarray, float)
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If use_otsu is True, then the computed threshold value is
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returned in addition to the thresholded image. Otherwise
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just the thresholded image is returned.''')
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def cvThreshold(np.ndarray src, double threshold, double max_value=255,
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int threshold_type=CV_THRESH_BINARY, use_otsu=False):
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@@ -1806,6 +1963,45 @@ def cvThreshold(np.ndarray src, double threshold, double max_value=255,
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else:
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return out
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#--------------------
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# cvAdaptiveThreshold
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#--------------------
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@cvdoc(package='cv', group='image', doc=\
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'''Apply an adaptive threshold to an image.
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Signature
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---------
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cvAdaptiveThreshold(src, max_value,
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adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C,
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threshold_type=CV_THRESH_BINARY,
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block_size=3, param1=5)
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Parameters
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----------
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src : ndarray, 2D, dtype=uint8
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max_value : float
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The maximum value.
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adaptive_method : integer
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The flag representing the adaptive method.
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Valid flags are:
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CV_ADAPTIVE_THRESH_MEAN_C (uses mean of the neighborhood)
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CV_ADAPTIVE_THRESH_GAUSSIAN_C (uses gaussian of the neighborhood)
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threshold_type : integer
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The flag representing which type of thresholding to apply.
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Valid flags are:
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CV_THRESH_BINARY (max_value if src(x,y) > threshold else 0)
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CV_THRESH_BINARY_INV (0 if src(x,y) > threshold else max_value)
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block_size : integer
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Defines a block_size x block_size neighborhood
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param1 : float
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The weight to be subtracted from the neighborhood computation.
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Returns
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-------
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out : ndarray
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The thresholded image.''')
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def cvAdaptiveThreshold(np.ndarray src, double max_value,
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int adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C,
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int threshold_type=CV_THRESH_BINARY,
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@@ -1838,6 +2034,27 @@ def cvAdaptiveThreshold(np.ndarray src, double max_value,
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return out
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#----------
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# cvPyrDown
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#----------
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@cvdoc(package='cv', group='image', doc=\
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'''Downsample an image.
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Signature
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---------
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cvPyrDown(src)
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Parameters
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----------
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src : ndarray, dtype=[uint8, uint16, float32, float64]
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Returns
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-------
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out : ndarray
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Downsampled image half the size of the original
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in each dimension.''')
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def cvPyrDown(np.ndarray src):
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validate_array(src)
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@@ -1861,6 +2078,27 @@ def cvPyrDown(np.ndarray src):
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return out
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#--------
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# cvPyrUp
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#--------
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@cvdoc(package='cv', group='image', doc=\
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'''Upsample an image.
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Signature
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---------
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cvPyrUp(src)
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Parameters
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----------
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src : ndarray, dtype=[uint8, uint16, float32, float64]
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Returns
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-------
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out : ndarray
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Upsampled image twice the size of the original
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in each dimension.''')
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def cvPyrUp(np.ndarray src):
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validate_array(src)
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@@ -1884,6 +2122,37 @@ def cvPyrUp(np.ndarray src):
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return out
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#-------------------
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# cvCalibrateCamera2
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#-------------------
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@cvdoc(package='cv', group='calibration', doc=\
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'''Finds the intrinsic and extrinsic camera parameters
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using a calibration pattern.
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Signature
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---------
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cvCalibrateCamera2(object_points, image_points, point_counts, image_size)
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Parameters
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----------
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object_points : ndarray, Nx3
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An array representing the (X, Y, Z) known coordinates of the
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calibration object.
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image_points : ndarry, Nx2
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An array representing the pixel image coordinate of the
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points in object_points.
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point_counts : ndarry, 1D, dtype=int32
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Vector containing the number of points in each particular view.
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image_size : 2-tuple, (height, width)
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The height and width of the images used.
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Returns
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-------
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(intrinsics, distortion) : ndarray 3x3, ndarray 5-vector
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Intrinsics is the 3x3 camera instrinsics matrix.
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Distortion is the 5-vector of distortion coefficients.''')
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def cvCalibrateCamera2(np.ndarray object_points, np.ndarray image_points,
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np.ndarray point_counts, image_size):
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@@ -1947,19 +2216,42 @@ def cvCalibrateCamera2(np.ndarray object_points, np.ndarray image_points,
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return intrinsics, distortion
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#------------------------
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# cvFindChessboardCorners
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#------------------------
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@cvdoc(package='cv', group='calibration', doc=\
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'''Finds the position of the internal corners of a chessboard.
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Signature
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---------
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cvFindChessboardCorners(src, pattern_size, flag=CV_CALIB_CB_ADAPTIVE_THRESH)
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Parameters
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----------
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src : ndarray, dtype=uint8
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Image to search for chessboard corners.
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pattern_size : 2-tuple of inner corners (h,w)
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flag : integer
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CV_CALIB_CB_ADAPTIVE_THRESH - use adaptive thresholding
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to convert the image to black and white,
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rather than a fixed threshold level
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(computed from the average image brightness).
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CV_CALIB_CB_NORMALIZE_IMAGE - normalize the image using
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cvNormalizeHist() before applying fixed or adaptive
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thresholding.
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CV_CALIB_CB_FILTER_QUADS - use additional criteria
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(like contour area, perimeter, square-like shape) to
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filter out false quads that are extracted at the contour
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retrieval stage.
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Returns
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-------
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out : ndarray Nx2
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An nx2 array of the corners found.''')
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def cvFindChessboardCorners(np.ndarray src, pattern_size,
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int flags = CV_CALIB_CB_ADAPTIVE_THRESH):
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"""
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Wrapper around the OpenCV cvFindChessboardCorners function.
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src - Image to search for chessboard corners
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pattern_size - Tuple of inner corners (h,w)
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flags - see appropriate flags in opencv docs
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http://opencv.willowgarage.com/documentation/cvreference.html
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returns - an nx2 array of the corners found.
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"""
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int flag=CV_CALIB_CB_ADAPTIVE_THRESH):
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validate_array(src)
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@@ -1983,27 +2275,44 @@ def cvFindChessboardCorners(np.ndarray src, pattern_size,
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cdef int ncorners_found
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c_cvFindChessboardCorners(&srcimg, cvpattern_size, cvpoints,
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&ncorners_found, flags)
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&ncorners_found, flag)
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return out[:ncorners_found]
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#------------------------
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# cvFindChessboardCorners
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#------------------------
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@cvdoc(package='cv', group='calibration', doc=\
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'''Renders found chessboard corners into an image.
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Signature
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---------
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cvDrawChessboardCorners(src, pattern_size, corners, in_place=False)
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Parameters
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----------
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src : ndarray, dim 3, dtype: uint8
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Image to draw into.
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pattern_size : 2-tuple, (h, w)
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Number of inner corners (h,w)
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corners : ndarray, nx2, dtype=float32
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Corners found in the image. See cvFindChessboardCorners and
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cvFindCornerSubPix
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in_place: bool
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If true, perform the drawing on the submitted
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image. If false, a copy of the image will be made and drawn to.
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Returns
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-------
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out/None : ndarray or none
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If in_place is True, the function returns None.
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Otherwise, the function returns a new image with
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the corners drawn into it.''')
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def cvDrawChessboardCorners(np.ndarray src, pattern_size, np.ndarray corners,
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in_place=False):
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"""
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Wrapper around the OpenCV cvDrawChessboardCorners function.
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Parameters
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----------
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src : ndarray, dim 3, dtype: uint8
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Image to draw into.
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pattern_size : array_like, shape (2,)
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Number of inner corners (h,w)
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corners : ndarray, shape (n,2), dtype: float32
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Corners found in the image. See cvFindChessboardCorners and
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cvFindCornerSubPix
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in_place: True/False (default=True) perform the drawing on the submitted
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image. If false, a copy of the image will be made and drawn to.
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"""
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validate_array(src)
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assert_nchannels(src, [3])
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Reference in New Issue
Block a user