<jupyter_start><jupyter_text>Load ground truth and the match output from seq2single.ipynb Note that the ground truth image correspondences are based on their GPS locations. Due to opposing viewpoints, such a matched pair may not have any visual overlap at all. The actual match would be few frames apart. Please refer to [this paper](http://www.roboticsproceedings.org/rss14/p22.pdf) (Section-4B) for more details.<jupyter_code>gt = np.loadtxt("../gt/gt_1m-5s.txt")[:,1]
matches = np.loadtxt("../out/matchesOut.txt")[:,1]
matchesBase = np.loadtxt("../out/matchesBaseline.txt")[:,1]<jupyter_output><empty_output><jupyter_text>Compute recall<jupyter_code>recall = np.array([np.sum(abs(matches-gt) < i) for i in range(100) ]) / len(matches)
recallBase = np.array([np.sum(abs(matchesBase-gt) < i) for i in range(100) ]) / len(matchesBase)
plt.plot(recallBase,'-.',label='Baseline')
plt.plot(recall,'-',label='Proposed')

plt.legend(loc='lower right',fontsize=14)
plt.xlabel("Localization Radius (in meters)",fontsize=18)
plt.ylabel("Recall",fontsize=18)
plt.xticks(np.arange(0,120,10),2*np.arange(0,120,10),fontsize=14)
plt.yticks(fontsize=14)
plt.xlim(0,60)
plt.grid()
plt.tight_layout()
plt.show()<jupyter_output><empty_output>
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