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| 1 | import numpy | |
| 2 | import scipy.ndimage.interpolation | |
| 3 | ||
| 4 | exposures = [0, 1, 10, 20, 25] | |
| 5 | num_samples = 10 | |
| 6 | ||
| 7 | values2d = numpy.arange((20), dtype = numpy.float32).reshape(5, 2, 2) | |
| 8 | input_values = numpy.array([[10, 11], [12, 13]]) | |
| 9 | ||
| 10 | vmin2d, vmax2d = values2d.min(axis = 0), values2d.max(axis = 0) | |
| 11 | uniform_values2d = numpy.zeros((num_samples, 2, 2)) | |
| 12 | uniform_exposures2d = numpy.zeros((num_samples, 2, 2)) | |
| 13 | for i in xrange(2): | |
| 14 | for j in xrange(2): | |
| 15 | uniform_values2d[:, i, j] = numpy.linspace(vmin2d[i, j], | |
| 16 | vmax2d[i, j], num_samples) | |
| 17 | uniform_exposures2d[:, i, j] = numpy.interp(uniform_values2d[:, i, j], | |
| 18 | values2d[:, i, j], exposures) | |
| 19 | indices2d = (num_samples - 1) * (input_values - vmin2d) / (vmax2d - vmin2d) | |
| 20 | ||
| 21 | map_input = numpy.zeros((3, 2, 2)) | |
| 22 | map_input[:1] = indices2d | |
| 23 | map_input[1:] = numpy.indices((2, 2)) | |
| 24 | exposure_estimate2d = scipy.ndimage.interpolation.map_coordinates( | |
| 25 | uniform_exposures2d, map_input, order = 1, cval = -1) | |
| 26 | ||
| 27 | - | print uniform_values2d |
| 27 | + | print "Initial values:\n",values2d |
| 28 | - | print indices2d |
| 28 | + | print "\nUniformly-sampled:\n",uniform_values2d |
| 29 | - | print exposure_estimate2d |
| 29 | + | print "\nMapped to exposure-time indices:\n",indices2d |
| 30 | print "\nEstimated exposure times:\n",exposure_estimate2d |
