1#!/bin/ipython 2 3import argparse 4import numpy as np 5import matplotlib.pyplot as plt 6import sys 7 8## general defines 9 10linecolor = "#%x%x%x" % ( 217, 234, 211 ) 11markercolor = "#%x%x%x" % ( 217/2, 234/2, 211/2 ) 12 13# Draw pretty plot 14def doc_plot(fig, x, y): 15 16 plt.figure(fig.number) 17 fig.clear() 18 19 lines, = plt.plot(x,y) 20 lines.set_color(linecolor) 21 lines.set_linewidth(4) 22 lines.set_marker('o') 23 lines.set_markeredgecolor(markercolor) 24 lines.set_markersize(6) 25 lines.set_markeredgewidth(2) 26 27 axes = fig.get_axes()[0] 28 axes.set_aspect(1) 29 axes.set_ybound(0,1) 30 axes.set_xbound(0,1) 31 axes.grid(True) 32 axes.xaxis.label.set_text(r'$P_{IN}$') 33 axes.xaxis.label.set_fontsize(14) 34 axes.yaxis.label.set_text(r'$P_{OUT}$') 35 axes.yaxis.label.set_fontsize(14) 36 37# Print out interleaved coefficients for HAL3 tonemap curve tags 38def doc_coeff(x,y): 39 coeffs = np.vstack((x, y)).reshape(-1,order='F') 40 coeff_str = "[ " 41 for val in coeffs[:-1]: 42 coeff_str += "%0.4f, " % val 43 44 coeff_str += "%0.4f ]" % coeffs[-1] 45 46 print coeff_str 47 48def doc_map(fig, imgMap, index): 49 plt.figure(fig.number) 50 fig.clear() 51 plt.imshow(imgMap - 1, interpolation='nearest') 52 for x in range(0, np.size(imgMap, 1)): 53 for y in range(0, np.size(imgMap, 0)): 54 plt.text(x,y, imgMap[y,x,index], color='white') 55 56 axes = fig.get_axes()[0] 57 axes.set_xticks(range(0, np.size(imgMap, 1))) 58 axes.set_yticks(range(0, np.size(imgMap, 0))) 59 60## Check arguments 61 62parser = argparse.ArgumentParser(description='Draw plots for camera HAL3.x implementation spec doc') 63parser.add_argument('--save_figures', default=False, action='store_true', 64 help='Save figures as pngs') 65 66args = parser.parse_args() 67 68## Linear mapping 69 70x_lin = np.linspace(0,1,2) 71y_lin = x_lin 72 73lin_fig = plt.figure(1) 74doc_plot(lin_fig, x_lin, y_lin) 75 76lin_title = 'Linear tonemapping curve' 77plt.title(lin_title) 78print lin_title 79doc_coeff(x_lin, y_lin) 80 81if args.save_figures: 82 plt.savefig('linear_tonemap.png',bbox_inches='tight') 83 84## Inverse mapping 85 86x_inv = x_lin 87y_inv = 1 - x_lin 88 89inv_fig = plt.figure(2) 90doc_plot(inv_fig, x_inv, y_inv) 91 92inv_title = 'Inverting tonemapping curve' 93plt.title(inv_title) 94print inv_title 95doc_coeff(x_inv, y_inv) 96 97if args.save_figures: 98 plt.savefig('inverse_tonemap.png',bbox_inches='tight') 99 100## Gamma 1/2.2 101 102x_gamma = np.linspace(0, 1, 16); 103 104y_gamma = x_gamma**(1/2.2) 105 106gamma_fig = plt.figure(3) 107doc_plot(gamma_fig, x_gamma, y_gamma) 108 109gamma_title = r'$\gamma=1/2.2$ tonemapping curve' 110plt.title(gamma_title) 111print gamma_title 112doc_coeff(x_gamma, y_gamma) 113 114if args.save_figures: 115 plt.savefig('gamma_tonemap.png',bbox_inches='tight') 116 117## sRGB curve 118 119x_srgb = x_gamma 120y_srgb = np.where(x_srgb <= 0.0031308, x_srgb * 12.92, 1.055*x_srgb**(1/2.4)-0.055) 121 122srgb_fig = plt.figure(4) 123doc_plot(srgb_fig, x_srgb, y_srgb) 124 125srgb_title = 'sRGB tonemapping curve' 126plt.title(srgb_title) 127print srgb_title 128doc_coeff(x_srgb, y_srgb) 129 130if args.save_figures: 131 plt.savefig('srgb_tonemap.png',bbox_inches='tight') 132 133## Sample lens shading map 134 135shadingMapSize = np.array([3, 4]) 136shadingMap1 = np.array( 137 [ 1.3, 1.2, 1.15, 1.2, 1.2, 1.2, 1.15, 1.2, 1.1, 1.2, 1.2, 1.2, 1.3, 1.2, 1.3, 1.3, 138 1.2, 1.2, 1.25, 1.1, 1.1, 1.1, 1.1, 1.0, 1.0, 1.0, 1.0, 1.0, 1.2, 1.3, 1.25, 1.2, 139 1.3, 1.2, 1.2, 1.3, 1.2, 1.15, 1.1, 1.2, 1.2, 1.1, 1.0, 1.2, 1.3, 1.15, 1.2, 1.3 ]) 140redMap = shadingMap1[0::4].reshape(shadingMapSize) 141greenEMap = shadingMap1[1::4].reshape(shadingMapSize) 142greenOMap = shadingMap1[2::4].reshape(shadingMapSize) 143blueMap = shadingMap1[3::4].reshape(shadingMapSize) 144 145rgbMap = np.dstack( (redMap, (greenEMap + greenOMap) / 2, blueMap) ) 146redMap = np.dstack( (redMap, np.zeros(shadingMapSize), np.zeros(shadingMapSize) ) ) 147greenEMap = np.dstack( (np.zeros(shadingMapSize), greenEMap, np.zeros(shadingMapSize) ) ) 148greenOMap = np.dstack( (np.zeros(shadingMapSize), greenOMap, np.zeros(shadingMapSize) ) ) 149blueMap = np.dstack( (np.zeros(shadingMapSize), np.zeros(shadingMapSize), blueMap ) ) 150 151redImg = plt.figure(5) 152doc_map(redImg, redMap, 0) 153plt.title('Red lens shading map') 154 155if args.save_figures: 156 plt.savefig('red_shading.png',bbox_inches='tight') 157 158greenEImg = plt.figure(6) 159doc_map(greenEImg, greenEMap, 1) 160plt.title('Green (even rows) lens shading map') 161 162if args.save_figures: 163 plt.savefig('green_e_shading.png',bbox_inches='tight') 164 165greenOImg = plt.figure(7) 166doc_map(greenOImg, greenOMap, 1) 167plt.title('Green (odd rows) lens shading map') 168 169if args.save_figures: 170 plt.savefig('green_o_shading.png',bbox_inches='tight') 171 172blueImg = plt.figure(8) 173doc_map(blueImg, blueMap, 2) 174plt.title('Blue lens shading map') 175 176if args.save_figures: 177 plt.savefig('blue_shading.png',bbox_inches='tight') 178 179rgbImg = plt.figure(9) 180 181rgbImg.clear() 182plt.imshow(1/rgbMap,interpolation='bicubic') 183 184axes = rgbImg.get_axes()[0] 185axes.set_xticks(range(0, np.size(rgbMap, 1))) 186axes.set_yticks(range(0, np.size(rgbMap, 0))) 187 188plt.title('Image of uniform white wall (inverse shading map)') 189 190if args.save_figures: 191 plt.savefig('inv_shading.png',bbox_inches='tight') 192 193# Rec. 709 194x_rec709 = x_gamma 195y_rec709 = np.where(x_rec709 <= 0.018, x_rec709 * 4.500, 1.099*x_rec709**0.45-0.099) 196 197rec709_fig = plt.figure(10) 198doc_plot(rec709_fig, x_rec709, y_rec709) 199 200rec709_title = 'Rec. 709 tonemapping curve' 201plt.title(rec709_title) 202print rec709_title 203doc_coeff(x_rec709, y_rec709) 204 205if args.save_figures: 206 plt.savefig('rec709_tonemap.png',bbox_inches='tight') 207 208 209# Show figures 210 211plt.show() 212