def get_user_data(db, userid):
cvd_data_query = """
select
users.uniqueid as userid,
target_r, target_g, target_b,
specimen_r, specimen_g, specimen_b,
correct, target_h, specimen_h,
target_lab_l, target_lab_a, target_lab_b,
specimen_lab_l, specimen_lab_a, specimen_lab_b
from
selectionevents inner join users on selectionevents.userid = users.id
where users.uniqueid = "%s"
"""
selections = db.execute_adhoc(cvd_data_query % userid)
selections['target_hue_bucket'] = s_utils.munsell_buckets(
selections['target_h'], labels=True)[1]
selections['specimen_hue_bucket'] = s_utils.munsell_buckets(
selections['specimen_h'], labels=True)[1]
selections['hue_pair'] = selections[[
'target_hue_bucket', 'specimen_hue_bucket'
]].apply(tuple, axis=1).map(s_utils.munsell_pair_map)
selections['dist'] = get_LAB_distance(selections)
# make sure we type cast to boolean, to avoid issues with pandas and integer rep (sqlite) of boolean
selections['correct'] = (selections['correct'] == 1)
return selections
def wrong_hue(df):
""" Fraction of selections in wrong hue bucket vs target (all selections, not just wrong) """
df['target_hue_bucket'] = s_utils.munsell_buckets(df['target_h'],
labels=True)[1]
df['specimen_hue_bucket'] = s_utils.munsell_buckets(df['specimen_h'],
labels=True)[1]
df['wrong_hue'] = df['target_hue_bucket'] != df['specimen_hue_bucket']
return df.groupby('userid')[['wrong_hue']].mean()
def _score_samples(self, dists, colors1, colors2):
# TODO: this is very slow!
rgb_to_hue = lambda x: colorsys.rgb_to_hsv(*x)[0]
# Note that we need RGB to be in [0.0, 1.0], so divide by 255
hue1 = np.apply_along_axis(rgb_to_hue, 1, colors1 / 255.0)
hue2 = np.apply_along_axis(rgb_to_hue, 1, colors2 / 255.0)
hue_bucket1 = s_utils.munsell_buckets(hue1, labels=True)[1]
hue_bucket2 = s_utils.munsell_buckets(hue2, labels=True)[1]
data = np.vstack([dists, hue_bucket1, hue_bucket2])
# get appropriate estimator, or fall back on to general one
get_kde = lambda h1, h2: self.hue_kdes.get(
s_utils.munsell_pair_map[(h1, h2)], self.kde)
# get actual density estimate for each observation
get_density = np.vectorize(
lambda d, h1, h2: get_kde(h1, h2).score_samples(d))
get_density_vec = np.vectorize(get_density)
return get_density_vec(dists, hue_bucket1, hue_bucket2)
def images_to_specimen(target_path, specimen_path):
print "Comparing %s[target] vs %s[specimen]" % (target_path, specimen_path)
target = Image.open(target_path)
specimen = Image.open(specimen_path)
pixels_t = image_to_pixels(target)
pixels_s = image_to_pixels(specimen)
correct = (pixels_t == pixels_s).all(axis=1)
rgb_to_hue = lambda x: colorsys.rgb_to_hsv(*x)[0]
# divide pixels (which are in RGB) by 255 to be within 0 and 1.0
hues_t = np.apply_along_axis(rgb_to_hue, 1, pixels_t / 255.)
hue_bucket = s_utils.munsell_buckets(hues_t, labels=True)[1]
df = pd.DataFrame({'hue': hue_bucket, 'correct': correct})
return df.groupby('hue')['correct'].agg({'ct': len, 'correct': np.sum}).reset_index()
def _fit(self, df, kernel='gaussian', plot=False, **kwargs):
""" Estimate density for errors as a function of perceptual distance """
df = df.copy()
# add hue pairs
df['target_hue'] = s_utils.munsell_buckets(df['target_h'],
labels=True)[1]
df['specimen_hue'] = s_utils.munsell_buckets(df['specimen_h'],
labels=True)[1]
df['hue_pair'] = df[['target_hue', 'specimen_hue'
]].apply(tuple,
axis=1).map(s_utils.munsell_pair_map)
errors = df[~df['correct']]
hue_pair_cts = errors.groupby('hue_pair')['target_h'].agg({
'ct': np.size
}).reset_index()
enough_cts = hue_pair_cts[hue_pair_cts['ct'] >= self.min_obs]
for pair in enough_cts['hue_pair']:
pair_errors = errors[errors['hue_pair'] == pair]
pair_kde = KernelDensity(kernel=kernel)
pair_kde.fit(pair_errors['distance'].values.reshape(-1, 1))
self.hue_kdes[pair] = pair_kde
# fit a global kde to fall back on
self.kde = KernelDensity(kernel=kernel)
self.kde.fit(errors['distance'].values.reshape(-1, 1))
