Coverage for src/bob/bio/face/reports/arface.py: 0%

1import matplotlib.pyplot as plt 

2import numpy as np 

3 

4import bob.measure 

5 

6from bob.bio.base.score.load import get_split_dataframe 

7 

8 

9def arface_report( 

10 scores_dev, 

11 scores_eval, 

12 output_filename, 

13 titles, 

14 fmr_threshold=1e-3, 

15 figsize=(16, 8), 

16 y_abs_max=32, # Max absolute value for y axis 

17 colors=plt.cm.tab10.colors, 

18): 

19 occlusion_illumination = [ 

20 "illumination", 

21 "occlusion", 

22 "both", 

23 ] 

24 

25 occlusions = [ 

26 "scarf", 

27 "sunglasses", 

28 ] 

29 

30 # style_iterator = _get_colors_markers() 

31 # colors = plt.cm.tab20.colors 

32 

33 eval_fmr_fnmr_occlusion_illumination = dict() 

34 eval_fmr_fnmr_occlusion = dict() 

35 

36 for ( 

37 d_scores, 

38 e_scores, 

39 title, 

40 ) in zip(scores_dev, scores_eval, titles): 

41 eval_fmr_fnmr_occlusion_illumination[title] = [] 

42 eval_fmr_fnmr_occlusion[title] = [] 

43 

44 # Load the score files and fill in the angle associated to each camera 

45 impostors_dev, genuines_dev = get_split_dataframe(d_scores) 

46 impostors_eval, genuines_eval = get_split_dataframe(e_scores) 

47 

48 # loading the dask dataframes 

49 impostors_dev = impostors_dev.compute() 

50 genuines_dev = genuines_dev.compute() 

51 impostors_eval = impostors_eval.compute() 

52 genuines_eval = genuines_eval.compute() 

53 

54 # Computing the threshold combining all distances 

55 threshold = bob.measure.far_threshold( 

56 impostors_dev["score"].to_numpy(), 

57 genuines_dev["score"].to_numpy(), 

58 fmr_threshold, 

59 ) 

60 

61 def compute_fmr_fnmr(impostor_scores, genuine_scores): 

62 eval_fmr, eval_fnmr = bob.measure.farfrr( 

63 impostor_scores["score"].to_numpy(), 

64 genuine_scores["score"].to_numpy(), 

65 threshold, 

66 ) 

67 return eval_fmr, eval_fnmr 

68 

69 # EVALUATING OCCLUSION AND ILLUMINATION 

70 

71 # All illumination 

72 i_eval = impostors_eval.loc[ 

73 (impostors_eval.probe_illumination != "front") 

74 & (impostors_eval.probe_occlusion == "none") 

75 ] 

76 g_eval = genuines_eval.loc[ 

77 (genuines_eval.probe_illumination != "front") 

78 & (genuines_eval.probe_occlusion == "none") 

79 ] 

80 eval_fmr_fnmr_occlusion_illumination[title].append( 

81 compute_fmr_fnmr(i_eval, g_eval) 

82 ) 

83 

84 # All occlusions 

85 i_eval = impostors_eval.loc[ 

86 (impostors_eval.probe_occlusion != "none") 

87 & (impostors_eval.probe_illumination == "front") 

88 ] 

89 g_eval = genuines_eval.loc[ 

90 (genuines_eval.probe_occlusion != "none") 

91 & (genuines_eval.probe_illumination == "front") 

92 ] 

93 eval_fmr_fnmr_occlusion_illumination[title].append( 

94 compute_fmr_fnmr(i_eval, g_eval) 

95 ) 

96 

97 # BOTH 

98 overall_fmr_fnmr = compute_fmr_fnmr(impostors_eval, genuines_eval) 

99 eval_fmr_fnmr_occlusion_illumination[title].append(overall_fmr_fnmr) 

100 

101 # EVALUATING DIFFERENT TYPES OF OCCLUSION 

102 for occlusion in occlusions: 

103 i_eval = impostors_eval.loc[ 

104 impostors_eval.probe_occlusion == occlusion 

105 ] 

106 g_eval = genuines_eval.loc[ 

107 genuines_eval.probe_occlusion == occlusion 

108 ] 

109 

110 eval_fmr_fnmr_occlusion[title].append( 

111 compute_fmr_fnmr(i_eval, g_eval) 

112 ) 

113 

114 pass 

115 

116 # Plotting 

117 

118 # 

119 # EFFECT OF OCCLUSION TYPES 

120 

121 # Figure for eval plot 

122 fig = plt.figure(figsize=figsize) 

123 ax = fig.add_subplot(111) 

124 

125 width = 0.8 / len(titles) 

126 

127 X = np.arange(len(occlusions)) 

128 

129 for i, (title, color) in enumerate(zip(titles, colors)): 

130 fmrs = [-1 * fmr * 100 for fmr, _ in eval_fmr_fnmr_occlusion[title]] 

131 fnmrs = [fnmr * 100 for _, fnmr in eval_fmr_fnmr_occlusion[title]] 

132 

133 x_axis = X + (i + 1) * width - width / 2 

134 ax.bar( 

135 x_axis, 

136 fmrs, 

137 width, 

138 label=title, 

139 color=color, 

140 alpha=1, 

141 hatch="\\", 

142 ) 

143 ax.bar( 

144 x_axis, 

145 fnmrs, 

146 width, 

147 color=color, 

148 alpha=0.5, 

149 hatch="/", 

150 ) 

151 

152 # Writting the texts on top of the bar plots 

153 for i, fnmr, fmr in zip(x_axis, fnmrs, fmrs): 

154 plt.text( 

155 i - width / 2, fnmr + 0.5, str(round(fnmr, 1)), fontsize=15 

156 ) 

157 plt.text( 

158 i - width / 2, fmr - 2.3, str(round(abs(fmr), 1)), fontsize=15 

159 ) 

160 

161 # Plot finalization 

162 plt.title(f"FMR vs FNMR. at Dev. FMR@{fmr_threshold*100}%", fontsize=16) 

163 ax.set_xlabel("Occlusion types", fontsize=14) 

164 ax.set_ylabel("FMR(%) vs FNMR(%) ", fontsize=18) 

165 

166 ax.set_xticks(X + 0.5) 

167 ax.set_xticklabels(occlusions, fontsize=14) 

168 

169 yticks = np.array( 

170 [ 

171 -y_abs_max / 4, 

172 0, 

173 y_abs_max / 4, 

174 y_abs_max / 2, 

175 y_abs_max, 

176 ] 

177 ) 

178 

179 ax.set_yticks(yticks) 

180 ax.set_yticklabels([abs(int(y)) for y in yticks], fontsize=16) 

181 

182 plt.axhline(0, linestyle="-", color="k") 

183 plt.ylim([-y_abs_max / 4, y_abs_max + 1]) 

184 

185 plt.legend() 

186 plt.grid() 

187 

188 plt.savefig(fig) 

189 

190 # EFFECT OF ILLUMINATION AND OCCLUSION 

191 

192 # Figure for eval plot 

193 fig = plt.figure(figsize=figsize) 

194 ax = fig.add_subplot(111) 

195 

196 X = np.arange(len(occlusion_illumination)) 

197 

198 for i, (title, color) in enumerate(zip(titles, colors)): 

199 fmrs = [ 

200 -1 * fmr * 100 

201 for fmr, _ in eval_fmr_fnmr_occlusion_illumination[title] 

202 ] 

203 fnmrs = [ 

204 fnmr * 100 

205 for _, fnmr in eval_fmr_fnmr_occlusion_illumination[title] 

206 ] 

207 

208 x_axis = X + (i + 1) * width - width / 2 

209 ax.bar( 

210 x_axis, 

211 fmrs, 

212 width, 

213 label=title, 

214 color=color, 

215 alpha=1, 

216 hatch="\\", 

217 ) 

218 ax.bar( 

219 x_axis, 

220 fnmrs, 

221 width, 

222 color=color, 

223 alpha=0.5, 

224 hatch="/", 

225 ) 

226 # Writting the texts on top of the bar plots 

227 for i, fnmr, fmr in zip(x_axis, fnmrs, fmrs): 

228 plt.text( 

229 i - width / 2, 

230 fnmr + 0.4, 

231 str(int(fnmr)) if fnmr == 0 else str(round(fnmr, 1)), 

232 fontsize=15, 

233 ) 

234 plt.text( 

235 i - width / 2, 

236 fmr - 0.9, 

237 str(int(fmr)), 

238 fontsize=15, 

239 ) 

240 

241 plt.title(f"FMR vs FNMR. at Dev. FMR@{fmr_threshold*100}%", fontsize=16) 

242 ax.set_xlabel("Occlusion and illumination", fontsize=14) 

243 ax.set_ylabel("FMR(%) vs FNMR(%) ", fontsize=18) 

244 

245 ax.set_xticks(X + 0.5) 

246 ax.set_xticklabels(occlusion_illumination, fontsize=14) 

247 

248 yticks = np.array([-y_abs_max / 4, 0, y_abs_max / 4, y_abs_max / 2]) 

249 ax.set_yticks(yticks) 

250 ax.set_yticklabels([abs(int(y)) for y in yticks], fontsize=16) 

251 

252 plt.axhline(0, linestyle="-", color="k") 

253 plt.ylim([-y_abs_max / 4, y_abs_max / 2]) 

254 

255 plt.legend() 

256 plt.grid() 

257 plt.savefig(fig)