def main(params, train):
si = ScreenImage()
if train:
# Initialization
trainset = glob(join("face_training", "face*.png"))
t0 = time()
print_(verbosity, "Begin collecting training Samples")
Labels, Samples = get_training_samples(trainset, params)
print_(verbosity, "Success. Elapsed: %.2f s." % (time() - t0))
print_(verbosity, "Begin classifier training using %s..."
% (params["classifier"]))
if params["classifier"] == "NB":
clf = GaussianNB()
elif params["classifier"] == "RF":
clf = RandomForestClassifier()
clf.fit(Samples, Labels)
pickle.dump([clf, params], open(params["name"], "w"))
else:
testset = glob(join("face_testing", "face*.png"))
print_(verbosity, "Begin classifier prediction...")
score = np.zeros(len(testset),)
models = glob("._*")
for i, testname in enumerate(testset):
im_orig = imread(testname)
truthname = get_groundname(testname)
im_skin = [[] for k in models]
title = ["" for k in models]
for j, model in enumerate(models):
im_truth = rgb2gray(imread(truthname)).astype(np.uint8)*255
pkl = pickle.load(open(model, "r"))
clf = pkl[0]
params = pkl[1]
_, _, fvec = im2feature(testname, params)
im_skin[j] = clf.predict(fvec).reshape(im_truth.shape).astype(np.uint8)
score = jaccard_similarity_score(im_truth, im_skin[j], normalize=True)
title[j] = "%s\nClassifier: %s, Thresh: %.2f\nK: %d, Score: %.2f" \
% (params["classifier"], params["feature"], params["thresh"],
params["n_cluster"], score)
print_(verbosity, "\tTest %d of %d, Score %.2f\n" % (i+1, len(testset), score))
si.show(testname, [im_orig, im_skin[0], im_skin[1],
im_skin[2], im_skin[3], im_skin[4]],
["Original\n%s" % testname, title[0], title[1],
title[2], title[3], title[4]])
def get_training_samples(trainset, params):
Samples = np.zeros((200, len(params["feature"])))
Labels = np.ones(200,)
k = 0
for i, trainname in enumerate(trainset):
print_(verbosity, "\tBeginning training and truth image set %d of %d... "
% (i+1, len(trainset)))
truthname = get_groundname(trainname)
im_truth = imread(truthname)[:, :, 0].astype(np.uint8)
rgb_lab, kmeans, fvec = im2feature(trainname, params)
mask = rgb_lab * im_truth
overlap = get_truth_overlap(kmeans, rgb_lab, mask,
thresh=params["thresh"])
print_(verbosity, "\tCache Samples/Labels ...\n")
for lap in overlap:
Samples[k, :] = lap["Center"]
Labels[k] = lap["Class"]
k += 1
# Remove Missing Labels
keepers = Labels != 1
return Labels[keepers], Samples[keepers, :]