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BerkeleyAutomation/perception | perception/image.py | BinaryImage.add_frame | def add_frame(
self,
left_boundary,
right_boundary,
upper_boundary,
lower_boundary):
""" Adds a frame to the image, e.g. turns the boundaries white
Parameters
----------
left_boundary : int
the leftmost boundary of ... | python | def add_frame(
self,
left_boundary,
right_boundary,
upper_boundary,
lower_boundary):
""" Adds a frame to the image, e.g. turns the boundaries white
Parameters
----------
left_boundary : int
the leftmost boundary of ... | [
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the leftmost boundary of the frame
right_boundary : int
the rightmost boundary of the frame (must be greater than left_boundary)
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BerkeleyAutomation/perception | perception/image.py | BinaryImage.most_free_pixel | def most_free_pixel(self):
""" Find the black pixel with the largest distance from the white pixels.
Returns
-------
:obj:`numpy.ndarray`
2-vector containing the most free pixel
"""
dist_tf = self.to_distance_im()
max_px = np.where(dist_tf == np.max(d... | python | def most_free_pixel(self):
""" Find the black pixel with the largest distance from the white pixels.
Returns
-------
:obj:`numpy.ndarray`
2-vector containing the most free pixel
"""
dist_tf = self.to_distance_im()
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BerkeleyAutomation/perception | perception/image.py | BinaryImage.diff_with_target | def diff_with_target(self, binary_im):
""" Creates a color image to visualize the overlap between two images.
Nonzero pixels that match in both images are green.
Nonzero pixels of this image that aren't in the other image are yellow
Nonzero pixels of the other image that aren't in this i... | python | def diff_with_target(self, binary_im):
""" Creates a color image to visualize the overlap between two images.
Nonzero pixels that match in both images are green.
Nonzero pixels of this image that aren't in the other image are yellow
Nonzero pixels of the other image that aren't in this i... | [
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BerkeleyAutomation/perception | perception/image.py | BinaryImage.num_adjacent | def num_adjacent(self, i, j):
""" Counts the number of adjacent nonzero pixels to a given pixel.
Parameters
----------
i : int
row index of query pixel
j : int
col index of query pixel
Returns
-------
int
number of adj... | python | def num_adjacent(self, i, j):
""" Counts the number of adjacent nonzero pixels to a given pixel.
Parameters
----------
i : int
row index of query pixel
j : int
col index of query pixel
Returns
-------
int
number of adj... | [
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BerkeleyAutomation/perception | perception/image.py | BinaryImage.to_sdf | def to_sdf(self):
""" Converts the 2D image to a 2D signed distance field.
Returns
-------
:obj:`numpy.ndarray`
2D float array of the signed distance field
"""
# compute medial axis transform
skel, sdf_in = morph.medial_axis(self.data, return_distance... | python | def to_sdf(self):
""" Converts the 2D image to a 2D signed distance field.
Returns
-------
:obj:`numpy.ndarray`
2D float array of the signed distance field
"""
# compute medial axis transform
skel, sdf_in = morph.medial_axis(self.data, return_distance... | [
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BerkeleyAutomation/perception | perception/image.py | BinaryImage.to_color | def to_color(self):
"""Creates a ColorImage from the binary image.
Returns
-------
:obj:`ColorImage`
The newly-created color image.
"""
color_data = np.zeros([self.height, self.width, 3])
color_data[:, :, 0] = self.data
color_data[:, :, 1] = s... | python | def to_color(self):
"""Creates a ColorImage from the binary image.
Returns
-------
:obj:`ColorImage`
The newly-created color image.
"""
color_data = np.zeros([self.height, self.width, 3])
color_data[:, :, 0] = self.data
color_data[:, :, 1] = s... | [
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BerkeleyAutomation/perception | perception/image.py | BinaryImage.open | def open(filename, frame='unspecified'):
"""Creates a BinaryImage from a file.
Parameters
----------
filename : :obj:`str`
The file to load the data from. Must be one of .png, .jpg,
.npy, or .npz.
frame : :obj:`str`
A string representing the ... | python | def open(filename, frame='unspecified'):
"""Creates a BinaryImage from a file.
Parameters
----------
filename : :obj:`str`
The file to load the data from. Must be one of .png, .jpg,
.npy, or .npz.
frame : :obj:`str`
A string representing the ... | [
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BerkeleyAutomation/perception | perception/image.py | RgbdImage._check_valid_data | def _check_valid_data(self, data):
"""Checks that the given data is a float array with four channels.
Parameters
----------
data : :obj:`numpy.ndarray`
The data to check.
Raises
------
ValueError
If the data is invalid.
"""
... | python | def _check_valid_data(self, data):
"""Checks that the given data is a float array with four channels.
Parameters
----------
data : :obj:`numpy.ndarray`
The data to check.
Raises
------
ValueError
If the data is invalid.
"""
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BerkeleyAutomation/perception | perception/image.py | RgbdImage.from_color_and_depth | def from_color_and_depth(color_im, depth_im):
""" Creates an RGB-D image from a separate color and depth image. """
# check shape
if color_im.height != depth_im.height or color_im.width != depth_im.width:
raise ValueError('Color and depth images must have the same shape')
# ... | python | def from_color_and_depth(color_im, depth_im):
""" Creates an RGB-D image from a separate color and depth image. """
# check shape
if color_im.height != depth_im.height or color_im.width != depth_im.width:
raise ValueError('Color and depth images must have the same shape')
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BerkeleyAutomation/perception | perception/image.py | RgbdImage.color | def color(self):
""" Returns the color image. """
return ColorImage(self.raw_data[:, :, :3].astype(
np.uint8), frame=self.frame) | python | def color(self):
""" Returns the color image. """
return ColorImage(self.raw_data[:, :, :3].astype(
np.uint8), frame=self.frame) | [
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BerkeleyAutomation/perception | perception/image.py | RgbdImage.mask_binary | def mask_binary(self, binary_im):
"""Create a new image by zeroing out data at locations
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Parameters
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binary_im : :obj:`BinaryImage`
A BinaryImage of the same size as this image, with pixel values of either
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"""Create a new image by zeroing out data at locations
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BerkeleyAutomation/perception | perception/image.py | RgbdImage.resize | def resize(self, size, interp='bilinear'):
"""Resize the image.
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size : int, float, or tuple
* int - Percentage of current size.
* float - Fraction of current size.
* tuple - Size of the output image.
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"""Resize the image.
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size : int, float, or tuple
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BerkeleyAutomation/perception | perception/image.py | RgbdImage.crop | def crop(self, height, width, center_i=None, center_j=None):
"""Crop the image centered around center_i, center_j.
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height : int
The height of the desired image.
width : int
The width of the desired image.
center_i : int
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"""Crop the image centered around center_i, center_j.
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----------
height : int
The height of the desired image.
width : int
The width of the desired image.
center_i : int
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BerkeleyAutomation/perception | perception/image.py | RgbdImage.transform | def transform(self, translation, theta, method='opencv'):
"""Create a new image by translating and rotating the current image.
Parameters
----------
translation : :obj:`numpy.ndarray` of float
The XY translation vector.
theta : float
Rotation angle in rad... | python | def transform(self, translation, theta, method='opencv'):
"""Create a new image by translating and rotating the current image.
Parameters
----------
translation : :obj:`numpy.ndarray` of float
The XY translation vector.
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BerkeleyAutomation/perception | perception/image.py | RgbdImage.to_grayscale_depth | def to_grayscale_depth(self):
""" Converts to a grayscale and depth (G-D) image. """
gray = self.color.to_grayscale()
return GdImage.from_grayscale_and_depth(gray, self.depth) | python | def to_grayscale_depth(self):
""" Converts to a grayscale and depth (G-D) image. """
gray = self.color.to_grayscale()
return GdImage.from_grayscale_and_depth(gray, self.depth) | [
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BerkeleyAutomation/perception | perception/image.py | RgbdImage.combine_with | def combine_with(self, rgbd_im):
"""
Replaces all zeros in the source rgbd image with the values of a different rgbd image
Parameters
----------
rgbd_im : :obj:`RgbdImage`
rgbd image to combine with
Returns
-------
:obj:`RgbdImage`
... | python | def combine_with(self, rgbd_im):
"""
Replaces all zeros in the source rgbd image with the values of a different rgbd image
Parameters
----------
rgbd_im : :obj:`RgbdImage`
rgbd image to combine with
Returns
-------
:obj:`RgbdImage`
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height : int
The height of the desired image.
width : int
The width of the desired image.
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"""Crop the image centered around center_i, center_j.
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height : int
The height of the desired image.
width : int
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BerkeleyAutomation/perception | perception/image.py | GdImage.from_grayscale_and_depth | def from_grayscale_and_depth(gray_im, depth_im):
""" Creates an G-D image from a separate grayscale and depth image. """
# check shape
if gray_im.height != depth_im.height or gray_im.width != depth_im.width:
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""" Creates an G-D image from a separate grayscale and depth image. """
# check shape
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""" Returns the grayscale image. """
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self.raw_data[:, :, 0].astype(np.uint8), frame=self.frame) | python | def gray(self):
""" Returns the grayscale image. """
return GrayscaleImage(
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BerkeleyAutomation/perception | perception/image.py | GdImage.resize | def resize(self, size, interp='bilinear'):
"""Resize the image.
Parameters
----------
size : int, float, or tuple
* int - Percentage of current size.
* float - Fraction of current size.
* tuple - Size of the output image.
interp : :obj:`str... | python | def resize(self, size, interp='bilinear'):
"""Resize the image.
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----------
size : int, float, or tuple
* int - Percentage of current size.
* float - Fraction of current size.
* tuple - Size of the output image.
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BerkeleyAutomation/perception | perception/image.py | GdImage.crop | def crop(self, height, width, center_i=None, center_j=None):
"""Crop the image centered around center_i, center_j.
Parameters
----------
height : int
The height of the desired image.
width : int
The width of the desired image.
center_i : int
... | python | def crop(self, height, width, center_i=None, center_j=None):
"""Crop the image centered around center_i, center_j.
Parameters
----------
height : int
The height of the desired image.
width : int
The width of the desired image.
center_i : int
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BerkeleyAutomation/perception | perception/image.py | SegmentationImage.border_pixels | def border_pixels(
self,
grad_sigma=0.5,
grad_lower_thresh=0.1,
grad_upper_thresh=1.0):
"""
Returns the pixels on the boundary between all segments, excluding the zero segment.
Parameters
----------
grad_sigma : float
s... | python | def border_pixels(
self,
grad_sigma=0.5,
grad_lower_thresh=0.1,
grad_upper_thresh=1.0):
"""
Returns the pixels on the boundary between all segments, excluding the zero segment.
Parameters
----------
grad_sigma : float
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BerkeleyAutomation/perception | perception/image.py | SegmentationImage.segment_mask | def segment_mask(self, segnum):
""" Returns a binary image of just the segment corresponding to the given number.
Parameters
----------
segnum : int
the number of the segment to generate a mask for
Returns
-------
:obj:`BinaryImage`
bina... | python | def segment_mask(self, segnum):
""" Returns a binary image of just the segment corresponding to the given number.
Parameters
----------
segnum : int
the number of the segment to generate a mask for
Returns
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:obj:`BinaryImage`
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BerkeleyAutomation/perception | perception/image.py | SegmentationImage.mask_binary | def mask_binary(self, binary_im):
"""Create a new image by zeroing out data at locations
where binary_im == 0.0.
Parameters
----------
binary_im : :obj:`BinaryImage`
A BinaryImage of the same size as this image, with pixel values of either
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"""Create a new image by zeroing out data at locations
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BerkeleyAutomation/perception | perception/image.py | SegmentationImage.resize | def resize(self, size, interp='nearest'):
"""Resize the image.
Parameters
----------
size : int, float, or tuple
* int - Percentage of current size.
* float - Fraction of current size.
* tuple - Size of the output image.
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"""Resize the image.
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size : int, float, or tuple
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* float - Fraction of current size.
* tuple - Size of the output image.
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BerkeleyAutomation/perception | perception/image.py | SegmentationImage.open | def open(filename, frame='unspecified'):
""" Opens a segmentation image """
data = Image.load_data(filename)
return SegmentationImage(data, frame) | python | def open(filename, frame='unspecified'):
""" Opens a segmentation image """
data = Image.load_data(filename)
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BerkeleyAutomation/perception | perception/image.py | PointCloudImage.resize | def resize(self, size, interp='nearest'):
"""Resize the image.
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----------
size : int, float, or tuple
* int - Percentage of current size.
* float - Fraction of current size.
* tuple - Size of the output image.
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"""Resize the image.
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* int - Percentage of current size.
* float - Fraction of current size.
* tuple - Size of the output image.
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BerkeleyAutomation/perception | perception/image.py | PointCloudImage.to_mesh | def to_mesh(self, dist_thresh=0.01):
""" Convert the point cloud to a mesh.
Returns
-------
:obj:`trimesh.Trimesh`
mesh of the point cloud
"""
# init vertex and triangle buffers
vertices = []
triangles = []
vertex_indices = -1 * np.one... | python | def to_mesh(self, dist_thresh=0.01):
""" Convert the point cloud to a mesh.
Returns
-------
:obj:`trimesh.Trimesh`
mesh of the point cloud
"""
# init vertex and triangle buffers
vertices = []
triangles = []
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BerkeleyAutomation/perception | perception/image.py | PointCloudImage.to_point_cloud | def to_point_cloud(self):
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Returns
-------
:obj:`autolab_core.PointCloud`
The corresponding PointCloud.
"""
return PointCloud(
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self.height *
self.... | python | def to_point_cloud(self):
"""Convert the image to a PointCloud object.
Returns
-------
:obj:`autolab_core.PointCloud`
The corresponding PointCloud.
"""
return PointCloud(
data=self._data.reshape(
self.height *
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BerkeleyAutomation/perception | perception/image.py | PointCloudImage.normal_cloud_im | def normal_cloud_im(self, ksize=3):
"""Generate a NormalCloudImage from the PointCloudImage using Sobel filtering.
Parameters
----------
ksize : int
Size of the kernel to use for derivative computation
Returns
-------
:obj:`NormalCloudImage`
... | python | def normal_cloud_im(self, ksize=3):
"""Generate a NormalCloudImage from the PointCloudImage using Sobel filtering.
Parameters
----------
ksize : int
Size of the kernel to use for derivative computation
Returns
-------
:obj:`NormalCloudImage`
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BerkeleyAutomation/perception | perception/image.py | PointCloudImage.open | def open(filename, frame='unspecified'):
"""Creates a PointCloudImage from a file.
Parameters
----------
filename : :obj:`str`
The file to load the data from. Must be one of .png, .jpg,
.npy, or .npz.
frame : :obj:`str`
A string representing ... | python | def open(filename, frame='unspecified'):
"""Creates a PointCloudImage from a file.
Parameters
----------
filename : :obj:`str`
The file to load the data from. Must be one of .png, .jpg,
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BerkeleyAutomation/perception | perception/image.py | NormalCloudImage.to_normal_cloud | def to_normal_cloud(self):
"""Convert the image to a NormalCloud object.
Returns
-------
:obj:`autolab_core.NormalCloud`
The corresponding NormalCloud.
"""
return NormalCloud(
data=self._data.reshape(
self.height *
... | python | def to_normal_cloud(self):
"""Convert the image to a NormalCloud object.
Returns
-------
:obj:`autolab_core.NormalCloud`
The corresponding NormalCloud.
"""
return NormalCloud(
data=self._data.reshape(
self.height *
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BerkeleyAutomation/perception | perception/image.py | NormalCloudImage.open | def open(filename, frame='unspecified'):
"""Creates a NormalCloudImage from a file.
Parameters
----------
filename : :obj:`str`
The file to load the data from. Must be one of .png, .jpg,
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A string representing... | python | def open(filename, frame='unspecified'):
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BerkeleyAutomation/perception | perception/orthographic_intrinsics.py | OrthographicIntrinsics.S | def S(self):
""":obj:`numpy.ndarray` : The 3x3 scaling matrix for this projection
"""
S = np.array([[self._plane_width / self._vol_width, 0, 0],
[0, self._plane_height / self._vol_height, 0],
[0, 0, self._depth_scale / self._vol_depth]])
return... | python | def S(self):
""":obj:`numpy.ndarray` : The 3x3 scaling matrix for this projection
"""
S = np.array([[self._plane_width / self._vol_width, 0, 0],
[0, self._plane_height / self._vol_height, 0],
[0, 0, self._depth_scale / self._vol_depth]])
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BerkeleyAutomation/perception | perception/orthographic_intrinsics.py | OrthographicIntrinsics.t | def t(self):
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t = np.array([self._plane_width / 2,
self._plane_height / 2,
self._depth_scale / 2])
return t | python | def t(self):
""":obj:`numpy.ndarray` : The 3x1 translation matrix for this projection
"""
t = np.array([self._plane_width / 2,
self._plane_height / 2,
self._depth_scale / 2])
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BerkeleyAutomation/perception | perception/orthographic_intrinsics.py | OrthographicIntrinsics.P | def P(self):
""":obj:`numpy.ndarray` : The 4x4 projection matrix for this camera.
"""
P = np.r_[np.c_[self.S, self.t], np.array([0,0,0,1])]
return P | python | def P(self):
""":obj:`numpy.ndarray` : The 4x4 projection matrix for this camera.
"""
P = np.r_[np.c_[self.S, self.t], np.array([0,0,0,1])]
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BerkeleyAutomation/perception | perception/orthographic_intrinsics.py | OrthographicIntrinsics.project_to_image | def project_to_image(self, point_cloud, round_px=True):
"""Projects a point cloud onto the camera image plane and creates
a depth image. Zero depth means no point projected into the camera
at that pixel location (i.e. infinite depth).
Parameters
----------
point_cloud : ... | python | def project_to_image(self, point_cloud, round_px=True):
"""Projects a point cloud onto the camera image plane and creates
a depth image. Zero depth means no point projected into the camera
at that pixel location (i.e. infinite depth).
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----------
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BerkeleyAutomation/perception | perception/orthographic_intrinsics.py | OrthographicIntrinsics.deproject | def deproject(self, depth_image):
"""Deprojects a DepthImage into a PointCloud.
Parameters
----------
depth_image : :obj:`DepthImage`
The 2D depth image to projet into a point cloud.
Returns
-------
:obj:`autolab_core.PointCloud`
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"""Deprojects a DepthImage into a PointCloud.
Parameters
----------
depth_image : :obj:`DepthImage`
The 2D depth image to projet into a point cloud.
Returns
-------
:obj:`autolab_core.PointCloud`
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BerkeleyAutomation/perception | perception/orthographic_intrinsics.py | OrthographicIntrinsics.deproject_pixel | def deproject_pixel(self, depth, pixel):
"""Deprojects a single pixel with a given depth into a 3D point.
Parameters
----------
depth : float
The depth value at the given pixel location.
pixel : :obj:`autolab_core.Point`
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"""Deprojects a single pixel with a given depth into a 3D point.
Parameters
----------
depth : float
The depth value at the given pixel location.
pixel : :obj:`autolab_core.Point`
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BerkeleyAutomation/perception | perception/orthographic_intrinsics.py | OrthographicIntrinsics.save | def save(self, filename):
"""Save the CameraIntrinsics object to a .intr file.
Parameters
----------
filename : :obj:`str`
The .intr file to save the object to.
Raises
------
ValueError
If filename does not have the .intr extension.
... | python | def save(self, filename):
"""Save the CameraIntrinsics object to a .intr file.
Parameters
----------
filename : :obj:`str`
The .intr file to save the object to.
Raises
------
ValueError
If filename does not have the .intr extension.
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BerkeleyAutomation/perception | perception/orthographic_intrinsics.py | OrthographicIntrinsics.load | def load(filename):
"""Load a CameraIntrinsics object from a file.
Parameters
----------
filename : :obj:`str`
The .intr file to load the object from.
Returns
-------
:obj:`CameraIntrinsics`
The CameraIntrinsics object loaded from the fil... | python | def load(filename):
"""Load a CameraIntrinsics object from a file.
Parameters
----------
filename : :obj:`str`
The .intr file to load the object from.
Returns
-------
:obj:`CameraIntrinsics`
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BerkeleyAutomation/perception | perception/phoxi_sensor.py | PhoXiSensor.start | def start(self):
"""Start the sensor.
"""
if rospy.get_name() == '/unnamed':
raise ValueError('PhoXi sensor must be run inside a ros node!')
# Connect to the cameras
if not self._connect_to_sensor():
self._running = False
return False
... | python | def start(self):
"""Start the sensor.
"""
if rospy.get_name() == '/unnamed':
raise ValueError('PhoXi sensor must be run inside a ros node!')
# Connect to the cameras
if not self._connect_to_sensor():
self._running = False
return False
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BerkeleyAutomation/perception | perception/phoxi_sensor.py | PhoXiSensor.stop | def stop(self):
"""Stop the sensor.
"""
# Check that everything is running
if not self._running:
logging.warning('PhoXi not running. Aborting stop')
return False
# Stop the subscribers
self._color_im_sub.unregister()
self._depth_im_sub.unr... | python | def stop(self):
"""Stop the sensor.
"""
# Check that everything is running
if not self._running:
logging.warning('PhoXi not running. Aborting stop')
return False
# Stop the subscribers
self._color_im_sub.unregister()
self._depth_im_sub.unr... | [
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BerkeleyAutomation/perception | perception/phoxi_sensor.py | PhoXiSensor.frames | def frames(self):
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Returns
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:obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray`
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"""Retrieve a new frame from the PhoXi and convert it to a ColorImage,
a DepthImage, and an IrImage.
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-------
:obj:`tuple` of :obj:`ColorImage`, :obj:`DepthImage`, :obj:`IrImage`, :obj:`numpy.ndarray`
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BerkeleyAutomation/perception | perception/phoxi_sensor.py | PhoXiSensor._connect_to_sensor | def _connect_to_sensor(self):
"""Connect to the sensor.
"""
name = self._device_name
try:
# Check if device is actively in list
rospy.wait_for_service('phoxi_camera/get_device_list')
device_list = rospy.ServiceProxy('phoxi_camera/get_device_list', GetD... | python | def _connect_to_sensor(self):
"""Connect to the sensor.
"""
name = self._device_name
try:
# Check if device is actively in list
rospy.wait_for_service('phoxi_camera/get_device_list')
device_list = rospy.ServiceProxy('phoxi_camera/get_device_list', GetD... | [
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BerkeleyAutomation/perception | perception/phoxi_sensor.py | PhoXiSensor._color_im_callback | def _color_im_callback(self, msg):
"""Callback for handling textures (greyscale images).
"""
try:
data = self._bridge.imgmsg_to_cv2(msg)
if np.max(data) > 255.0:
data = 255.0 * data / 1200.0 # Experimentally set value for white
data = np.clip(d... | python | def _color_im_callback(self, msg):
"""Callback for handling textures (greyscale images).
"""
try:
data = self._bridge.imgmsg_to_cv2(msg)
if np.max(data) > 255.0:
data = 255.0 * data / 1200.0 # Experimentally set value for white
data = np.clip(d... | [
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BerkeleyAutomation/perception | perception/phoxi_sensor.py | PhoXiSensor._depth_im_callback | def _depth_im_callback(self, msg):
"""Callback for handling depth images.
"""
try:
self._cur_depth_im = DepthImage(self._bridge.imgmsg_to_cv2(msg) / 1000.0, frame=self._frame)
except:
self._cur_depth_im = None | python | def _depth_im_callback(self, msg):
"""Callback for handling depth images.
"""
try:
self._cur_depth_im = DepthImage(self._bridge.imgmsg_to_cv2(msg) / 1000.0, frame=self._frame)
except:
self._cur_depth_im = None | [
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BerkeleyAutomation/perception | perception/phoxi_sensor.py | PhoXiSensor._normal_map_callback | def _normal_map_callback(self, msg):
"""Callback for handling normal maps.
"""
try:
self._cur_normal_map = self._bridge.imgmsg_to_cv2(msg)
except:
self._cur_normal_map = None | python | def _normal_map_callback(self, msg):
"""Callback for handling normal maps.
"""
try:
self._cur_normal_map = self._bridge.imgmsg_to_cv2(msg)
except:
self._cur_normal_map = None | [
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BerkeleyAutomation/perception | perception/opencv_camera_sensor.py | OpenCVCameraSensor.start | def start(self):
""" Starts the OpenCVCameraSensor Stream
Raises:
Exception if unable to open stream
"""
self._sensor = cv2.VideoCapture(self._device_id)
if not self._sensor.isOpened():
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""" Starts the OpenCVCameraSensor Stream
Raises:
Exception if unable to open stream
"""
self._sensor = cv2.VideoCapture(self._device_id)
if not self._sensor.isOpened():
raise Exception("Unable to open OpenCVCameraSensor for id {0}".format(... | [
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BerkeleyAutomation/perception | perception/opencv_camera_sensor.py | OpenCVCameraSensor.frames | def frames(self, flush=True):
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Raises:
Exception if opencv sensor gives ret_val of 0
"""
self.flush()
ret_val, frame = self._sensor.read()
if not ret_val:
raise Exception("Unable to retrieve frame f... | python | def frames(self, flush=True):
""" Returns the latest color image from the stream
Raises:
Exception if opencv sensor gives ret_val of 0
"""
self.flush()
ret_val, frame = self._sensor.read()
if not ret_val:
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BerkeleyAutomation/perception | perception/detector.py | RgbdDetection.image | def image(self, render_mode):
""" Get the image associated with a particular render mode """
if render_mode == RenderMode.SEGMASK:
return self.query_im
elif render_mode == RenderMode.COLOR:
return self.color_im
elif render_mode == RenderMode.DEPTH:
ret... | python | def image(self, render_mode):
""" Get the image associated with a particular render mode """
if render_mode == RenderMode.SEGMASK:
return self.query_im
elif render_mode == RenderMode.COLOR:
return self.color_im
elif render_mode == RenderMode.DEPTH:
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BerkeleyAutomation/perception | perception/detector.py | RgbdForegroundMaskDetector.detect | def detect(self, color_im, depth_im, cfg, camera_intr=None,
T_camera_world=None, segmask=None):
"""
Detects all relevant objects in an rgbd image pair using foreground masking.
Parameters
----------
color_im : :obj:`ColorImage`
color image for detectio... | python | def detect(self, color_im, depth_im, cfg, camera_intr=None,
T_camera_world=None, segmask=None):
"""
Detects all relevant objects in an rgbd image pair using foreground masking.
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----------
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BerkeleyAutomation/perception | perception/detector.py | RgbdForegroundMaskQueryImageDetector._segment_color | def _segment_color(self, color_im, bounding_box, bgmodel, cfg, vis_segmentation=False):
""" Re-segments a color image to isolate an object of interest using foreground masking and kmeans """
# read params
foreground_mask_tolerance = cfg['foreground_mask_tolerance']
color_seg_rgb_weight =... | python | def _segment_color(self, color_im, bounding_box, bgmodel, cfg, vis_segmentation=False):
""" Re-segments a color image to isolate an object of interest using foreground masking and kmeans """
# read params
foreground_mask_tolerance = cfg['foreground_mask_tolerance']
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BerkeleyAutomation/perception | perception/detector.py | RgbdForegroundMaskQueryImageDetector.detect | def detect(self, color_im, depth_im, cfg, camera_intr=None,
T_camera_world=None,
vis_foreground=False, vis_segmentation=False, segmask=None):
"""
Detects all relevant objects in an rgbd image pair using foreground masking.
Parameters
----------
colo... | python | def detect(self, color_im, depth_im, cfg, camera_intr=None,
T_camera_world=None,
vis_foreground=False, vis_segmentation=False, segmask=None):
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Detects all relevant objects in an rgbd image pair using foreground masking.
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BerkeleyAutomation/perception | perception/detector.py | PointCloudBoxDetector.detect | def detect(self, color_im, depth_im, cfg, camera_intr,
T_camera_world,
vis_foreground=False, vis_segmentation=False, segmask=None):
"""Detects all relevant objects in an rgbd image pair using foreground masking.
Parameters
----------
color_im : :obj:`ColorI... | python | def detect(self, color_im, depth_im, cfg, camera_intr,
T_camera_world,
vis_foreground=False, vis_segmentation=False, segmask=None):
"""Detects all relevant objects in an rgbd image pair using foreground masking.
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BerkeleyAutomation/perception | perception/detector.py | RgbdDetectorFactory.detector | def detector(detector_type):
""" Returns a detector of the specified type. """
if detector_type == 'point_cloud_box':
return PointCloudBoxDetector()
elif detector_type == 'rgbd_foreground_mask_query':
return RgbdForegroundMaskQueryImageDetector()
elif detector_typ... | python | def detector(detector_type):
""" Returns a detector of the specified type. """
if detector_type == 'point_cloud_box':
return PointCloudBoxDetector()
elif detector_type == 'rgbd_foreground_mask_query':
return RgbdForegroundMaskQueryImageDetector()
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BerkeleyAutomation/perception | tools/capture_dataset.py | preprocess_images | def preprocess_images(raw_color_im,
raw_depth_im,
camera_intr,
T_camera_world,
workspace_box,
workspace_im,
image_proc_config):
""" Preprocess a set of color and depth images. """
... | python | def preprocess_images(raw_color_im,
raw_depth_im,
camera_intr,
T_camera_world,
workspace_box,
workspace_im,
image_proc_config):
""" Preprocess a set of color and depth images. """
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BerkeleyAutomation/perception | perception/cnn.py | conv | def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1):
"""
Convolution layer helper function
From https://github.com/ethereon/caffe-tensorflow
"""
c_i = input.get_shape()[-1]
assert c_i%group==0
assert c_o%group==0
convolve = lambda i, k: tf.nn.conv2d(i, k, ... | python | def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1):
"""
Convolution layer helper function
From https://github.com/ethereon/caffe-tensorflow
"""
c_i = input.get_shape()[-1]
assert c_i%group==0
assert c_o%group==0
convolve = lambda i, k: tf.nn.conv2d(i, k, ... | [
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BerkeleyAutomation/perception | perception/cnn.py | AlexNet._parse_config | def _parse_config(self, config):
""" Parses a tensorflow configuration """
self._batch_size = config['batch_size']
self._im_height = config['im_height']
self._im_width = config['im_width']
self._num_channels = config['channels']
self._output_layer = config['out_layer']
... | python | def _parse_config(self, config):
""" Parses a tensorflow configuration """
self._batch_size = config['batch_size']
self._im_height = config['im_height']
self._im_width = config['im_width']
self._num_channels = config['channels']
self._output_layer = config['out_layer']
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BerkeleyAutomation/perception | perception/cnn.py | AlexNet._load | def _load(self):
""" Loads a model into weights """
if self._model_filename is None:
raise ValueError('Model filename not specified')
# read the input image
self._graph = tf.Graph()
with self._graph.as_default():
# read in filenames
reader = t... | python | def _load(self):
""" Loads a model into weights """
if self._model_filename is None:
raise ValueError('Model filename not specified')
# read the input image
self._graph = tf.Graph()
with self._graph.as_default():
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BerkeleyAutomation/perception | perception/cnn.py | AlexNet._initialize | def _initialize(self):
""" Open from caffe weights """
self._graph = tf.Graph()
with self._graph.as_default():
self._input_node = tf.placeholder(tf.float32, (self._batch_size, self._im_height, self._im_width, self._num_channels))
weights = self.build_alexnet_weights()
... | python | def _initialize(self):
""" Open from caffe weights """
self._graph = tf.Graph()
with self._graph.as_default():
self._input_node = tf.placeholder(tf.float32, (self._batch_size, self._im_height, self._im_width, self._num_channels))
weights = self.build_alexnet_weights()
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BerkeleyAutomation/perception | perception/cnn.py | AlexNet.open_session | def open_session(self):
""" Open tensorflow session. Exposed for memory management. """
with self._graph.as_default():
init = tf.initialize_all_variables()
self._sess = tf.Session()
self._sess.run(init) | python | def open_session(self):
""" Open tensorflow session. Exposed for memory management. """
with self._graph.as_default():
init = tf.initialize_all_variables()
self._sess = tf.Session()
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BerkeleyAutomation/perception | perception/cnn.py | AlexNet.close_session | def close_session(self):
""" Close tensorflow session. Exposes for memory management. """
with self._graph.as_default():
self._sess.close()
self._sess = None | python | def close_session(self):
""" Close tensorflow session. Exposes for memory management. """
with self._graph.as_default():
self._sess.close()
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BerkeleyAutomation/perception | perception/cnn.py | AlexNet.predict | def predict(self, image_arr, featurize=False):
""" Predict a set of images in batches.
Parameters
----------
image_arr : NxHxWxC :obj:`numpy.ndarray`
input set of images in a num_images x image height x image width x image channels array (must match parameters of network)
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""" Predict a set of images in batches.
Parameters
----------
image_arr : NxHxWxC :obj:`numpy.ndarray`
input set of images in a num_images x image height x image width x image channels array (must match parameters of network)
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BerkeleyAutomation/perception | perception/cnn.py | AlexNet.build_alexnet_weights | def build_alexnet_weights(self):
""" Build a set of convnet weights for AlexNet """
net_data = self._net_data
#conv1
#conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4
conv1W = tf.Variable(net_data["conv1"][0])
co... | python | def build_alexnet_weights(self):
""" Build a set of convnet weights for AlexNet """
net_data = self._net_data
#conv1
#conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4
conv1W = tf.Variable(net_data["conv1"][0])
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BerkeleyAutomation/perception | perception/cnn.py | AlexNet.build_alexnet | def build_alexnet(self, weights, output_layer=None):
""" Connects graph of alexnet from weights """
if output_layer is None:
output_layer = self._output_layer
#conv1
#conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4... | python | def build_alexnet(self, weights, output_layer=None):
""" Connects graph of alexnet from weights """
if output_layer is None:
output_layer = self._output_layer
#conv1
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BerkeleyAutomation/perception | perception/feature_extractors.py | CNNBatchFeatureExtractor._forward_pass | def _forward_pass(self, images):
""" Forward pass a list of images through the CNN """
# form image array
num_images = len(images)
if num_images == 0:
return None
for image in images:
if not isinstance(image, Image):
new_images = []
... | python | def _forward_pass(self, images):
""" Forward pass a list of images through the CNN """
# form image array
num_images = len(images)
if num_images == 0:
return None
for image in images:
if not isinstance(image, Image):
new_images = []
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | Engine.repositories | def repositories(self):
"""
Returns a DataFrame with the data about the repositories found at
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>>> repos_df = engine.repositories
:rtype: RepositoriesDataFrame
"""
return RepositoriesDataFrame(self.__engi... | python | def repositories(self):
"""
Returns a DataFrame with the data about the repositories found at
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>>> repos_df = engine.repositories
:rtype: RepositoriesDataFrame
"""
return RepositoriesDataFrame(self.__engi... | [
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | Engine.blobs | def blobs(self, repository_ids=[], reference_names=[], commit_hashes=[]):
"""
Retrieves the blobs of a list of repositories, reference names and commit hashes.
So the result will be a DataFrame of all the blobs in the given commits that are
in the given references that belong to the give... | python | def blobs(self, repository_ids=[], reference_names=[], commit_hashes=[]):
"""
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | Engine.from_metadata | def from_metadata(self, db_path, db_name='engine_metadata.db'):
"""
Registers in the current session the views of the MetadataSource so the
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:param db_path: path to the folder ... | python | def from_metadata(self, db_path, db_name='engine_metadata.db'):
"""
Registers in the current session the views of the MetadataSource so the
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] | 79d05a0bcf0da435685d6118828a8884e2fe4b94 | https://github.com/src-d/jgit-spark-connector/blob/79d05a0bcf0da435685d6118828a8884e2fe4b94/python/sourced/engine/engine.py#L106-L121 | train | Loads the metadata from the database at the specified path. | GiP5xwVCF98Z,B3LV8Eo811Ma,bIsJhlpYrrU2,UtiWT6f6p9yZ,sY2ClS3bs_Vs,cXy7eDEmqBLX,WBIJpxagI_Bm,oiYQtqKByLVy,pKtZbyLPTF7M,N5KuUvtbiyqB,Y8CO_HpFZe1H,lFpHwHL3xiEO,QT_5wdIFQ3WX,gn988v5t9NEf,dVZxwLTOCtbO,RjQP07DYIdkf,Wun5u3i1rn23,m64e4RQAlmFd,zfo2Sgkz3IVJ,aWb0eXvJHTT7,s2y8nAB4S7UF,znAfcqx_89tO,ZnHlCcECsuOK,TdYHRT1SBW60,mBmBrDJU... |
src-d/jgit-spark-connector | python/sourced/engine/engine.py | SourcedDataFrame.__generate_method | def __generate_method(name):
"""
Wraps the DataFrame's original method by name to return the derived class instance.
"""
try:
func = getattr(DataFrame, name)
except AttributeError as e:
# PySpark version is too old
def func(self, *args, **kwarg... | python | def __generate_method(name):
"""
Wraps the DataFrame's original method by name to return the derived class instance.
"""
try:
func = getattr(DataFrame, name)
except AttributeError as e:
# PySpark version is too old
def func(self, *args, **kwarg... | [
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | RepositoriesDataFrame.references | def references(self):
"""
Returns the joined DataFrame of references and repositories.
>>> refs_df = repos_df.references
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getReferences(),
self._session, ... | python | def references(self):
"""
Returns the joined DataFrame of references and repositories.
>>> refs_df = repos_df.references
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getReferences(),
self._session, ... | [
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | RepositoriesDataFrame.remote_references | def remote_references(self):
"""
Returns a new DataFrame with only the remote references of the
current repositories.
>>> remote_refs_df = repos_df.remote_references
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getRemoteRefer... | python | def remote_references(self):
"""
Returns a new DataFrame with only the remote references of the
current repositories.
>>> remote_refs_df = repos_df.remote_references
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getRemoteRefer... | [
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current repositories. | GiP5xwVCF98Z,B3LV8Eo811Ma,bIsJhlpYrrU2,UtiWT6f6p9yZ,sY2ClS3bs_Vs,cXy7eDEmqBLX,WBIJpxagI_Bm,oiYQtqKByLVy,pKtZbyLPTF7M,N5KuUvtbiyqB,Y8CO_HpFZe1H,lFpHwHL3xiEO,QT_5wdIFQ3WX,gn988v5t9NEf,dVZxwLTOCtbO,RjQP07DYIdkf,Wun5u3i1rn23,m64e4RQAlmFd,zfo2Sgkz3IVJ,aWb0eXvJHTT7,s2y8nAB4S7UF,znAfcqx_89tO,ZnHlCcECsuOK,TdYHRT1SBW60,mBmBrDJU... |
src-d/jgit-spark-connector | python/sourced/engine/engine.py | RepositoriesDataFrame.master_ref | def master_ref(self):
"""
Filters the current DataFrame references to only contain those rows whose reference is master.
>>> master_df = repos_df.master_ref
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getReferences().getHEAD(),
... | python | def master_ref(self):
"""
Filters the current DataFrame references to only contain those rows whose reference is master.
>>> master_df = repos_df.master_ref
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getReferences().getHEAD(),
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | ReferencesDataFrame.head_ref | def head_ref(self):
"""
Filters the current DataFrame to only contain those rows whose reference is HEAD.
>>> heads_df = refs_df.head_ref
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getHEAD(),
self... | python | def head_ref(self):
"""
Filters the current DataFrame to only contain those rows whose reference is HEAD.
>>> heads_df = refs_df.head_ref
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getHEAD(),
self... | [
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] | 79d05a0bcf0da435685d6118828a8884e2fe4b94 | https://github.com/src-d/jgit-spark-connector/blob/79d05a0bcf0da435685d6118828a8884e2fe4b94/python/sourced/engine/engine.py#L346-L355 | train | Returns a new DataFrame containing only those rows whose reference is HEAD. | GiP5xwVCF98Z,B3LV8Eo811Ma,bIsJhlpYrrU2,UtiWT6f6p9yZ,sY2ClS3bs_Vs,cXy7eDEmqBLX,WBIJpxagI_Bm,oiYQtqKByLVy,pKtZbyLPTF7M,N5KuUvtbiyqB,Y8CO_HpFZe1H,lFpHwHL3xiEO,QT_5wdIFQ3WX,gn988v5t9NEf,dVZxwLTOCtbO,RjQP07DYIdkf,Wun5u3i1rn23,m64e4RQAlmFd,zfo2Sgkz3IVJ,aWb0eXvJHTT7,s2y8nAB4S7UF,znAfcqx_89tO,ZnHlCcECsuOK,TdYHRT1SBW60,mBmBrDJU... |
src-d/jgit-spark-connector | python/sourced/engine/engine.py | ReferencesDataFrame.master_ref | def master_ref(self):
"""
Filters the current DataFrame to only contain those rows whose reference is master.
>>> master_df = refs_df.master_ref
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getMaster(),
... | python | def master_ref(self):
"""
Filters the current DataFrame to only contain those rows whose reference is master.
>>> master_df = refs_df.master_ref
:rtype: ReferencesDataFrame
"""
return ReferencesDataFrame(self._engine_dataframe.getMaster(),
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | ReferencesDataFrame.ref | def ref(self, ref):
"""
Filters the current DataFrame to only contain those rows whose reference is the given
reference name.
>>> heads_df = refs_df.ref('refs/heads/HEAD')
:param ref: Reference to get
:type ref: str
:rtype: ReferencesDataFrame
"""
... | python | def ref(self, ref):
"""
Filters the current DataFrame to only contain those rows whose reference is the given
reference name.
>>> heads_df = refs_df.ref('refs/heads/HEAD')
:param ref: Reference to get
:type ref: str
:rtype: ReferencesDataFrame
"""
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>>> heads_df = refs_df.ref('refs/heads/HEAD')
:param ref: Reference to get
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | ReferencesDataFrame.all_reference_commits | def all_reference_commits(self):
"""
Returns the current DataFrame joined with the commits DataFrame, with all of the commits
in all references.
>>> commits_df = refs_df.all_reference_commits
Take into account that getting all the commits will lead to a lot of repeated tree
... | python | def all_reference_commits(self):
"""
Returns the current DataFrame joined with the commits DataFrame, with all of the commits
in all references.
>>> commits_df = refs_df.all_reference_commits
Take into account that getting all the commits will lead to a lot of repeated tree
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src-d/jgit-spark-connector | python/sourced/engine/engine.py | ReferencesDataFrame.commits | def commits(self):
"""
Returns the current DataFrame joined with the commits DataFrame. It just returns
the last commit in a reference (aka the current state).
>>> commits_df = refs_df.commits
If you want all commits from the references, use the `all_reference_commits` method,
... | python | def commits(self):
"""
Returns the current DataFrame joined with the commits DataFrame. It just returns
the last commit in a reference (aka the current state).
>>> commits_df = refs_df.commits
If you want all commits from the references, use the `all_reference_commits` method,
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If you want all commits from the references, use the `all_reference_commits` method,
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