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fishui/MainViewModel.py

+from PySide2.QtCore import QObject, Signal, Slot
+import numpy as np
+from fishui import projections
+from fishui import coordinate_conversion
+
+
+class MainViewModel(QObject):
+
+    didChange = Signal()
+
+    def __init__(self, parent=None):
+        super().__init__(parent)
+
+        self.sensor_size_mm = (5.2, 5.2)
+        self.sensor_size_px = (1088, 1088)
+        self.focal_length_mm = 1.8
+
+        self.origin = (256, 256)
+        self.blocksize = (64, 64)
+
+        self.affineModel = False
+        self.motion_vector_cp0 = (0, 0)
+        self.motion_vector_cp1 = (0, 0)
+
+        self.perspective_scale = 2
+
+        # Values used for drawing
+        self.samples_per_edge = 100
+        self.projection = None
+        self.perspective = None
+        self.block_xf = None
+        self.block_yf = None
+        self.block_xp = None
+        self.block_yp = None
+        self.block_xpm = None
+        self.block_ypm = None
+        self.block_xfm = None
+        self.block_yfm = None
+        self.block_ctr_px = None
+        self.block_ctr_py = None
+        self.block_ctr_pxm = None
+        self.block_ctr_pym = None
+        self.uwc_sign = None
+        self.updateProjections()
+
+    def setSensorSize(self, sensor_size):
+        self.sensor_size_mm = sensor_size
+        self.updateProjections()
+
+    def setSensorResolution(self, resolution):
+        self.sensor_size_px = resolution
+        self.updateProjections()
+
+    def setFocalLength(self, focal_length_mm):
+        self.focal_length_mm = focal_length_mm
+        self.updateProjections()
+
+    def setOrigin(self, origin):
+        self.origin = origin
+        self.updateProjections()
+
+    def setBlocksize(self, blocksize):
+        self.blocksize = blocksize
+        self.updateProjections()
+
+    def setAffineModel(self, enabled):
+        if not self.affineModel and enabled:
+            self.motion_vector_cp1 = self.motion_vector_cp0
+        self.affineModel = enabled
+        self.updateProjections()
+
+    def setMotionVectorCP0(self, motion_vector):
+        self.motion_vector_cp0 = motion_vector
+        self.updateProjections()
+
+    def setMotionVectorCP1(self, motion_vector):
+        self.motion_vector_cp1 = motion_vector
+        self.updateProjections()
+
+    def setPerspectiveScale(self, scale):
+        self.perspective_scale = scale
+        self.updateProjections()
+
+    def updateProjections(self):
+        self.projection = projections.EquisolidProjection.init_with(self.sensor_size_mm, self.sensor_size_px, self.focal_length_mm, fov=185)
+        self.perspective = projections.PerspectiveProjection(self.projection.focal_length_px, fov=185)
+        self.blockSignals(True)
+        self.updateFisheyeBlockpoints()
+        self.updatePerspectiveBlockpoints()
+        self.updatePerspectiveMovedBlockpoints()
+        self.updateReprojectedBlockpoints()
+        self.blockSignals(False)
+        self.didChange.emit()
+
+    def updateFisheyeBlockpoints(self):
+        block_x = np.zeros(4 * self.samples_per_edge)
+        block_y = np.zeros(4 * self.samples_per_edge)
+
+        # Top edge.
+        block_x[0:self.samples_per_edge] = np.linspace(self.origin[0], self.origin[0] + self.blocksize[0], self.samples_per_edge)
+        block_y[0:self.samples_per_edge] = self.origin[1]
+
+        # Right edge.
+        block_x[self.samples_per_edge:2 * self.samples_per_edge] = self.origin[0] + self.blocksize[0]
+        block_y[self.samples_per_edge:2 * self.samples_per_edge] = np.linspace(self.origin[1], self.origin[1] + self.blocksize[1], self.samples_per_edge)
+
+        # Bottom edge.
+        block_x[2 * self.samples_per_edge:3 * self.samples_per_edge] = np.linspace(self.origin[0] + self.blocksize[0], self.origin[0],
+                                                                         self.samples_per_edge)
+        block_y[2 * self.samples_per_edge:3 * self.samples_per_edge] = self.origin[1] + self.blocksize[1]
+
+        # Left edge.
+        block_x[3 * self.samples_per_edge:4 * self.samples_per_edge] = self.origin[0]
+        block_y[3 * self.samples_per_edge:4 * self.samples_per_edge] = np.linspace(self.origin[1] + self.blocksize[1], self.origin[1],
+                                                                         self.samples_per_edge)
+        self.block_xf = block_x
+        self.block_yf = block_y
+        self.didChange.emit()
+
+    def updatePerspectiveBlockpoints(self):
+        center = np.asarray(self.sensor_size_px)/2
+        block_yfc = self.block_yf - center[0]
+        block_xfc = self.block_xf - center[1]
+
+        r_f, phi = coordinate_conversion.cartesian_to_polar(block_yfc, block_xfc)
+        r_p = self.projection.transform_to(self.perspective, r_f)
+        self.uwc_sign = np.sign(r_p)
+        block_ypc, block_xpc = coordinate_conversion.polar_to_cartesian(r_p, phi)
+
+        self.block_yp = block_ypc
+        self.block_xp = block_xpc
+        self.didChange.emit()
+
+    def updatePerspectiveMovedBlockpoints(self):
+        if self.affineModel:
+            self.updateControlBlockpoints()
+            self.updatePerspectiveMovedBlockpointsAffine()
+        else:
+            self.block_ypm = self.block_yp + self.motion_vector_cp0[1] * self.uwc_sign
+            self.block_xpm = self.block_xp + self.motion_vector_cp0[0] * self.uwc_sign
+            self.didChange.emit()
+
+    def updateReprojectedBlockpoints(self):
+        r_pm, phi_m = coordinate_conversion.cartesian_to_polar(self.block_ypm, self.block_xpm)
+        phi_m = phi_m - (self.uwc_sign < 0) * np.pi
+        r_fm = self.perspective.transform_to(self.projection, r_pm)
+        r_fm = r_fm + (self.uwc_sign < 0) * 2 * (self.projection.radius(np.deg2rad(90)) - r_fm)
+        block_yfm, block_xfm = coordinate_conversion.polar_to_cartesian(r_fm, phi_m)
+        center = np.asarray(self.sensor_size_px) / 2
+        self.block_yfm = block_yfm + center[0]
+        self.block_xfm = block_xfm + center[1]
+        self.didChange.emit()
+
+    def updateControlBlockpoints(self):
+        center = np.asarray(self.sensor_size_px) / 2
+        block_center = np.asarray(self.origin) + (np.asarray(self.blocksize) - 1) / 2
+        r_f, phi = coordinate_conversion.cartesian_to_polar(block_center[1] - center[0], block_center[0] - center[1])
+        r_p = self.projection.transform_to(self.perspective, r_f)
+        cp0_y, cp0_x = coordinate_conversion.polar_to_cartesian(r_p, phi)
+        cp0_y = cp0_y - self.blocksize[1] / 2
+        cp0_x = cp0_x - self.blocksize[0] / 2
+        self.block_ctr_px = np.array([cp0_x, cp0_x + self.blocksize[0], cp0_x + self.blocksize[0], cp0_x])
+        self.block_ctr_py = np.array([cp0_y, cp0_y, cp0_y + self.blocksize[1], cp0_y + self.blocksize[1]])
+        self.didChange.emit()
+
+    def updatePerspectiveMovedBlockpointsAffine(self):
+        cp0_position = (self.block_ctr_py[0], self.block_ctr_px[0])
+        cps_motion_vector = np.array([self.motion_vector_cp0[0], self.motion_vector_cp1[0],
+                                      self.motion_vector_cp0[1], self.motion_vector_cp1[1]])
+
+        block_yp_cp0 = self.block_yp - cp0_position[0]
+        block_xp_cp0 = self.block_xp - cp0_position[1]
+
+        block_ypm = np.empty_like(self.block_yp)
+        block_xpm = np.empty_like(self.block_xp)
+        for i in range(len(self.block_xp)):
+            m = block_yp_cp0[i] / (self.blocksize[0] - 1)
+            n = block_xp_cp0[i] / (self.blocksize[1] - 1)
+            affine_matrix = np.array([[-m, m, 1 - n, n],
+                                      [1 - n, n, m, -m]])
+            mv = affine_matrix.dot(cps_motion_vector)
+            block_ypm[i] = self.block_yp[i] + mv[0]
+            block_xpm[i] = self.block_xp[i] + mv[1]
+
+        block_ctr_pxm = np.empty(4)
+        block_ctr_pym = np.empty(4)
+        for i in range(4):
+            m = (self.block_ctr_py[i] - cp0_position[0]) / (self.blocksize[0] - 1)
+            n = (self.block_ctr_px[i] - cp0_position[1]) / (self.blocksize[1] - 1)
+            affine_matrix = np.array([[-m, m, 1 - n, n],
+                                      [1 - n, n, m, -m]])
+            mv = affine_matrix.dot(cps_motion_vector)
+            block_ctr_pym[i] = self.block_ctr_py[i] + mv[0]
+            block_ctr_pxm[i] = self.block_ctr_px[i] + mv[1]
+
+        self.block_ypm = block_ypm
+        self.block_xpm = block_xpm
+        self.block_ctr_pym = block_ctr_pym
+        self.block_ctr_pxm = block_ctr_pxm
+        self.didChange.emit()
+
+
+# Preview
+if __name__ == '__main__':
+
+    @Slot()
+    def didChange():
+        print("Did change")
+
+    viewModel = MainViewModel()
+    viewModel.didChange.connect(didChange)
+    viewModel.setBlocksize((64, 64))
+    viewModel.setOrigin((256, 256))
+

fishui/MainWindow.py

+from PySide2.QtWidgets import *
+import fishui.widgets as widgets
+from fishui.MainViewModel import MainViewModel
+
+
+class MainWindow(QMainWindow):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.viewModel = MainViewModel(self)
+        self.propertiesWidget = widgets.PropertiesWidget(self, self.viewModel)
+        self.distortionWidget = widgets.DistortionVisualizationWidget(self, self.viewModel)
+
+        # layout
+        splitter = QSplitter()
+        splitter.addWidget(self.propertiesWidget)
+        splitter.addWidget(self.distortionWidget)
+        splitter.setHandleWidth(2)
+        splitter.setChildrenCollapsible(False)
+        splitter.setStyleSheet("QSplitter:handle { background-color: gray; }")
+
+        # widget = QWidget()
+        # widget.setLayout(layout)
+        self.setCentralWidget(splitter)
+        self.setFocus()
+        self.show()

fishui/coordinate_conversion.py

+import numpy as np
+from typing import Union, Tuple
+
+
+def cartesian_to_polar(y: Union[float, np.ndarray], x: Union[float, np.ndarray]) -> Tuple[Union[float, np.ndarray], Union[float, np.ndarray]]:
+    """Convert cartesian coordinates [y, x] to polar coordinates [r, phi]."""
+    r = np.sqrt(np.square(y) + np.square(x))
+    phi = np.arctan2(y, x)
+    return r, phi
+
+
+def polar_to_cartesian(r: Union[float, np.ndarray], phi: Union[float, np.ndarray]) -> Tuple[Union[float, np.ndarray], Union[float, np.ndarray]]:
+    """Convert polar coordinates [r, phi] to cartesian coordinates [y, x]."""
+    y = r * np.sin(phi)
+    x = r * np.cos(phi)
+    return y, x
+

fishui/projections/CalibratedProjection.py

+from typing import Union, Tuple
+import warnings
+import numpy as np
+from .Projection import Projection
+
+
+class CalibratedProjection(Projection):
+    """
+    Defines a calibrated projection model.
+    """
+
+    def __init__(self,
+                 polynom_coeffs: Union[np.ndarray, Tuple],
+                 sensor_size_mm: Tuple[float, float],
+                 sensor_size_px: Tuple[int, int],
+                 focal_length_mm: float,
+                 fov: float):
+        """
+        Initialize a new calibrated projection.
+
+        WE OVERRIDE INIT DESPITE ADVISING AGAINST IT IN Projection.py
+
+        :param polynom_coeffs: coefficients in decreasing powers of the
+        polynomial relating incident angle to sensor radius in mm
+        :param sensor_size_mm: sensor size in mm
+        :param sensor_size_px: sensor size in pixels
+        :param focal_length_mm: focal length in mm
+        :param fov: field of view in degrees
+        """
+        if not np.isclose(sensor_size_px[0]/sensor_size_mm[0], sensor_size_px[1]/sensor_size_mm[1]):
+            raise Exception("Pixel shape must be square.")
+        self._px_per_mm = sensor_size_px[0]/sensor_size_mm[0]
+        focal_length_px = self._px_per_mm * focal_length_mm
+        super().__init__(focal_length_px, fov)
+
+        # Get forward polynomial directly from calibrated coefficients.
+        self._forward_poly = np.poly1d(polynom_coeffs)
+
+        # Get backward polynomial by matching a polynomial inversely to the
+        # results of the forward polynomial.
+        theta_help = np.linspace(0, np.deg2rad(fov/2), 10000)
+        radius_help = self._forward_poly(theta_help)
+        warnings.filterwarnings("ignore", category=np.RankWarning)  # Filter rank warnings.
+        backward_coeffs = np.polyfit(radius_help, theta_help, (len(polynom_coeffs) - 1) * 2)
+        del warnings.filters[0]  # Remove rank warnings filter.
+        self._backward_poly = np.poly1d(backward_coeffs)
+
+    def radius(self, theta: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
+        return self._px_per_mm * self._forward_poly(theta)
+
+    def theta(self, radius: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
+        return self._backward_poly(radius / self._px_per_mm)

fishui/projections/EquidistantProjection.py

+from .Projection import Projection
+from typing import Union, Tuple
+import numpy as np
+
+
+class EquidistantProjection(Projection):
+    """
+    Defines the equidistant projection model.
+    """
+
+    @classmethod
+    def init_with(cls, sensor_size_mm: Tuple[float, float], sensor_size_px: Tuple[int, int], focal_length_mm: float, fov: float):
+        """
+        Specify new equidistant projection model with
+
+        :param sensor_size_mm: sensor size in mm [height, width]
+        :param sensor_size_px: sensor size in pixels [height, width]
+        :param focal_length_mm: focal length in mm
+        :param fov: field of view in degrees
+        """
+        if sensor_size_px[0]/sensor_size_mm[0] != sensor_size_px[1]/sensor_size_mm[1]:
+            raise Exception("Pixel shape must be square.")
+        focal_length_px = sensor_size_px[0]/sensor_size_mm[0] * focal_length_mm
+        return cls(focal_length_px, fov)
+
+    def radius(self, theta: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
+        return self.focal_length_px * theta
+
+    def theta(self, radius: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
+        return radius / self.focal_length_px

fishui/projections/EquisolidProjection.py

+from .Projection import Projection
+from typing import Union, Tuple
+import numpy as np
+
+
+class EquisolidProjection(Projection):
+    """
+    Defines the equisolid projection model.
+    """
+
+    @classmethod
+    def init_with(cls, sensor_size_mm: Tuple[float, float], sensor_size_px: Tuple[int, int], focal_length_mm: float, fov: float):
+        """
+        Specify new equisolid projection model with
+
+        :param sensor_size_mm: sensor size in mm [height, width]
+        :param sensor_size_px: sensor size in pixels [height, width]
+        :param focal_length_mm: focal length in mm
+        :param fov: field of view in degrees
+        """
+        if sensor_size_px[0]/sensor_size_mm[0] != sensor_size_px[1]/sensor_size_mm[1]:
+            raise Exception("Pixel shape must be square.")
+        focal_length_px = sensor_size_px[0]/sensor_size_mm[0] * focal_length_mm
+        return cls(focal_length_px, fov)
+
+    def radius(self, theta: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
+        return 2 * self.focal_length_px * np.sin(theta / 2)
+
+    def theta(self, radius: Union[float, np.ndarray]) -> Union[float, np.ndarray]:
+        return 2 * np.arcsin(radius / (2 * self.focal_length_px))

fishui/projections/OrthographicProjection.py

+from .Projection import Projection
+from typing import Union, Tuple
+import numpy as np
+