Commit 5bda5cc5 authored by Jay's avatar Jay Committed by Jason R Laura
Browse files

Clean up and removal of unused print / variables.

parent 534c30fa

autocnet/camera/camera.py

+31 −25
Original line number Diff line number Diff line 
import numpy as np

import pandas as pd



import cv2





def compute_epipoles(f):

    """
@@ -91,29 +93,26 @@ def triangulate(pt, pt1, p, p1): 
    Returns

    -------

    coords : ndarray

             (n, 4) array of triangulated coordinates in the form (x, y, z, a)

             (4, n) projection matrix



    """

    npts = len(pt)

    a = np.zeros((4, 4))

    coords = np.empty((npts, 4))



    if isinstance(pt, pd.DataFrame):

        pt = pt.values

    if isinstance(pt1, pd.DataFrame):

        pt1 = pt.values



    for i in range(npts):

        # Compute AX = 0

        a[0] = pt[i][0] * p[2] - p[0]

        a[1] = pt[i][1] * p[2] - p[1]

        a[2] = pt1[i][0] * p1[2] - p1[0]

        a[3] = pt1[i][1] * p1[2] - p1[1]

        # v.T is a least squares solution that minimizes the error residual

        u, s, vh = np.linalg.svd(a)

        v = vh.T

        coords[i] = v[:, 3] / (v[:, 3][-1])

    return coords.T

    # Transpose for the openCV call if needed

    if pt.shape[0] != 3:

        pt = pt.T

    if pt1.shape[0] != 3:

        pt1 = pt1.T



    X = cv2.triangulatePoints(p, p1, pt[:2], pt1[:2])



    # Homogenize

    X /= X[3]



    return X





def projection_error(p1, p, pt, pt1):
@@ -142,22 +141,29 @@ def projection_error(p1, p, pt, pt1): 


    Returns

    -------

    reproj_error : ndarray

    residuals : ndarray

                (n, 1) residuals for each correspondence



    cumulative_error : float

                       sum of the residuals





    """

    if p1.shape != (3,4):

        p1 = p1.reshape(3,4)



    # Triangulate the correspondences

    xw_est = triangulate(pt, pt1, p, p1)



    xhat = p.dot(xw_est).T

    xhat /= xhat[:, -1][:, np.newaxis]

    x2hat = p1.dot(xw_est).T

    x2hat /= x2hat[:, -1][:, np.newaxis]

    # Back project and homogenize

    xhat = np.dot(p, xw_est)

    xhat /= xhat[2]

    x2hat = np.dot(p1, xw_est)

    x2hat /= x2hat[2]



    # Compute residuals

    dist = (pt - xhat)**2 + (pt1 - x2hat)**2

    reproj_error = np.sqrt(np.sum(dist, axis=1) / len(pt))

    dist = (pt.T - xhat)**2 + (pt1.T - x2hat)**2

    residuals = np.sum(dist, axis=0)

    reproj_error = np.sum(dist)



    return reproj_error
 
    return residuals, reproj_error

autocnet/fileio/io_gdal.py

+0 −5
Original line number Diff line number Diff line
@@ -214,11 +214,6 @@ class GeoDataset(object): 
                xy_extent = self.xy_extent

                lowerlat, lowerlon = self.pixel_to_latlon(xy_extent[0][0], xy_extent[0][1])

                upperlat, upperlon = self.pixel_to_latlon(xy_extent[1][0], xy_extent[1][1])

                geom = {"type": "Polygon", "coordinates": [[[lowerlat, lowerlon],

                                                           [lowerlat, upperlon],

                                                           [upperlat, upperlon],

                                                           [upperlat, lowerlon],

                                                           [lowerlat, lowerlon]]]}

            self._latlon_extent = [(lowerlat, lowerlon), (upperlat, upperlon)]

        return self._latlon_extent

autocnet/graph/network.py

+1 −1
Original line number Diff line number Diff line
@@ -276,7 +276,7 @@ class CandidateGraph(nx.Graph): 


        hdf = None



    def match_features(self, **kwargs):

    def match_features(self, *args, **kwargs):

        """

        For all connected edges in the graph, apply feature matching

autocnet/matcher/matcher.py

+2 −2
Original line number Diff line number Diff line
@@ -55,8 +55,8 @@ def pattern_match(template, image, upsampling=16, func=cv2.TM_CCOEFF_NORMED, err 
    if upsampling < 1:

        raise ValueError



    u_template = zoom(template, upsampling, order=1)

    u_image = zoom(image, upsampling, order=1)

    u_template = zoom(template, upsampling, order=3)

    u_image = zoom(image, upsampling, order=3)



    result = cv2.matchTemplate(u_image, u_template, method=func)

    min_corr, max_corr, min_loc, max_loc = cv2.minMaxLoc(result)

autocnet/utils/isis_serial_numbers.py

+3 −3
Original line number Diff line number Diff line
@@ -86,15 +86,14 @@ def generate_serial_number(label): 


            sub_group = find_nested_in_dict(label, search_position)

            serial_entry = sub_group[search_key]

            print(serial_entry)

            if serial_entry in search_translation.keys():

                serial_entry = search_translation[serial_entry]

            elif '*' in search_translation.keys() and search_translation['*'] != '*':

                serial_entry = search_translation['*']



            serial_number.append(serial_entry)

        except:pass

    print('HERE!')

        except:

            pass

    return '/'.join(serial_number)
@@ -103,6 +102,7 @@ class SerialNumberDecoder(pvl.decoder.PVLDecoder): 
    A PVL Decoder class to handle cube label parsing for the purpose of creating a valid ISIS

    serial number. Inherits from the PVLDecoder in planetarypy's pvl module.

    """



    def cast_unquoated_string(self, value):

        """

        Overrides the parent class's method so that any un-quoted string type value found in the