def train_refined_SRClassifier(wcd):
"""A training function that takes in a WeightedCDataset and produces a weak BinaryClassifier.
This training function uses train_SRClassifier() to train but with 6 rectangles and 2 seconds.
Also, the threshold of the weak classifier is refined after training. The function to minimize
is classification error with lambda = 1.0
:Parameters:
wcd : WeightedCDataset
a weighted dataset of vector-integrated patches
:Returns:
fc: THaarClassifier
the best feature classifier obtained with
fc.b being the best threshold after refined
fc.err is the estimated 'error' corresponding to fc.b
fc.b2 is the best threshold estimated by train_SRClassifier()
fc.err2 is its corresponding 'estimated error'
fc.stats2e is its corresponding 1D statistics
"""
stats2 = compute_Stats2_integral(wcd, True)
fc = train_SRClassifier(stats2, 0, 1, 6, 2)[0]
# project to this newly found direction
feature_value_array = [project_Haar(x, fc.w, fc.p) for x in wcd.input_data]
wcd1 = WeightedCDataset(feature_value_array, wcd.weights)
# save results
fc.b2 = fc.b
fc.err2 = fc.err
fc.stats2e = project_SR_stats(stats2, fc.w, fc.p)
# refine the fc classifier
z = thresh_1d(0,1,wcd1,sort_1d(wcd1))
fc.b = z[0]
fc.err = z[1]
# store the histograms
# z = histogram_1d(wcd1, 32)
# fc.hist = z[0]
# fc.minValue = z[1][0,0]
# fc.maxValue = z[1][31,1]
# tprint("Hist=")
# tprint(fc.hist)
# tprint(fc.minValue)
# tprint(fc.maxValue)
# tprint(fc.stats2e.A)
return fc
def get_local_stats(cd,haars):
"""
Take a 2-class ClassificationDataset, obtain the global statistics,
then project using different Haar directions.
Input:
cd: a 2-class ClassificationDataset
haars: the generated Haar feature set of J features
Output: output of _project_Haar_stats() actually
B: a list of J*2-class Stats2e, each for each type of Haar features.
invert: a list of numpy.arrays, each array tells whether which feature
needs negating its projection direction
"""
tprint("Accumulating data...")
stats2 = compute_Stats2_integral(cd,True)
tprint("Projecting all the Haar features...")
return _project_Haar_stats(stats2,haars)
