1 files changed, 41 insertions, 45 deletions
diff --git a/data/face-recognition-accuracy.py b/data/face-recognition-accuracy.py
index 00feb8f..8b3584f 100755
--- a/data/face-recognition-accuracy.py
+++ b/data/face-recognition-accuracy.py
@@ -1,53 +1,49 @@
 #!/usr/bin/python
-import os
+import matplotlib.pyplot as plt
+import matplotlib.mlab as ml
+import numpy as np
 import sys
-import pickle
 
-prun = 0
-runs = {}
-labels = {}
-users = pickle.load((open(sys.argv[3])))
-recs = map(lambda x:0,users)
+numbins_distance = 15
+numbins_brightness = 10
 
-for line in open(sys.argv[1]):
-    line = line.split(',')
-    try:
-        run = int(line[2])
-    except:
-        continue
-    user = line[1]
-    if run not in runs:
-        runs[run] = 0
-        labels[run] = users.index(user) + 1
+test_filename = sys.argv[1]
+bfile = open(sys.argv[2])
+distance=[]
+brightness=[]
+detected=[]
 
-for line in open(sys.argv[2]):
-    line = line.split(',')
-    try:
-        run = int(line[2])
-    except:
-        continue
-    user = line[1]
-    rec = ' '.join(line[6].split('@')[0].split('_'))
-    if run != prun and prun > 0:
-        runs[prun] = recs.index(max(recs))+1
-        recs = map(lambda x:0,users)
-    recs[users.index(user)] += 1
-    prun = run
+for line in open(test_filename):
+    values = line.rstrip().split(',')
+    subdir,name,run,frame,zv = values[0:5]
+    name = '_'.join(name.split())
+    detected_name = values[6].split('@')[0]
+    bline = bfile.readline().rstrip().split(',')
+    while bline[3] != frame:
+        bline = bfile.readline().rstrip().split(',')
 
-t=0.0
-tp=0.0
-fp=0.0
-fn=0.0
-for (k,v) in runs.items():
-    if v == 0:
-        fn += 1
-    elif v != labels[k]:
-        fp += 1
-    else:
-        tp += 1
-    t += 1
+    br = bline[4]
+    distance.append(float(zv))
+    brightness.append(float(br))
+    detected.append(detected_name==name)
 
-print("True positives: ",tp/t)
-print("False positives: ",fp/t)
-print("False negatives: ",fn/t)
+distance = np.array(distance)
+brightness = np.array(brightness)
+detected = np.array(detected)
 
+distance_detected = distance[detected>0]
+brightness_detected = brightness[detected>0]
+range_distance = (min(distance),max(distance))
+range_brightness = (min(brightness),max(brightness))
+hist_detected,bins = np.histogram(distance_detected,bins=numbins_distance,range=range_distance)
+hist,bins = np.histogram(distance, bins=numbins_distance, range=range_distance)
+brightness_hist_detected,bins_brightness = np.histogram(brightness_detected,bins=numbins_brightness, range=range_brightness)
+brightness_hist,bins_brightness = np.histogram(brightness,bins=numbins_brightness, range=range_brightness)
+
+result1 = hist_detected*1./hist
+result2 = brightness_hist_detected*1./brightness_hist
+plt.subplot(211)
+plt.bar(bins[:-1],result1,width=np.diff(bins))
+plt.subplot(212)
+plt.bar(bins_brightness[:-1],result2,width=np.diff(bins_brightness))
+plt.show()