static double SimulateCamera(ImageSensor sensor, double input)
{
Matrix <double> sensor_input = Matrix <double> .Build.Dense(sensor.Rows, sensor.Columns);
double[] sensor_input_arr = sensor_input.AsColumnMajorArray();
for (int k = 0; k < sensor_input_arr.Length; k++)
{
sensor_input_arr[k] = input;
}
Matrix <double> sensor_result = sensor.Simulate(sensor_input);
return(sensor_result[0, 0]);
}
public static Matrix <double> GenerateTestPattern(ImageSensor sensor, double incident_ph_flux)
{
Matrix <double> test_target = Matrix <double> .Build.Dense(sensor.Rows, sensor.Columns);
int fringe_width = 1;
int col_start = 1, col_end = col_start + fringe_width;
bool end = false;
while (!end)
{
for (int row = 0; row < test_target.RowCount / 2; row++)
{
for (int col = col_start; col < test_target.ColumnCount && col < col_end; col++)
{
test_target[row, col] = 1.0;
}
}
for (int row = test_target.RowCount / 2; row < test_target.RowCount; row++)
{
for (int col = col_start; col < test_target.ColumnCount && col < col_end; col++)
{
test_target[row, col] = ((double)col) / test_target.ColumnCount;
}
}
fringe_width++;
col_start += 2 * fringe_width;
col_end = col_start + fringe_width;
if (col_end >= test_target.ColumnCount)
{
end = true;
}
}
test_target *= incident_ph_flux;
test_target = test_target.Transpose();
return(sensor.Simulate(test_target));
}
