def ball_only_classifier(circles, color_image, bonus_radius):
model = svmlight.read_model("./output/best_single_cup_model_for_ball")
ff = find_features()
# TODO: fix
label = 0
best_classification = 0.5
best_circle = None
best_circle_pixels = None
for c in circles[:6]:
pixels, circle = find_pixels(c, color_image, bonus_radius)
# create features for that circle
features = ff.generate_features(pixels, label)
features = parse_one_line(features)
print features
# run the classifier on that circle
classification = svmlight.classify(model, [features])
print classification
if classification[0] > best_classification:
best_classification = classification
best_circle = [c]
best_circle_pixels = pixels
# make a decision about whether that circle is circly enough
# cv2.imshow("Image processed", circle)
# cv2.waitKey()
# for the strict form of the classifier, I require that all of the detected circles
# are in fact circles. other classifiers may be more lenient
return best_circle, best_classification, best_circle_pixels
def simple_classifier(circles, color_image, bonus_radius):
model = svmlight.read_model("./output/best_single_cup_model")
ff = find_features()
# TODO: fix
label = 0
new_circles = []
for c in circles[:6]:
circle = find_pixels(c, color_image, bonus_radius)
new_circles.append(circle)
# create features for that circle
features = ff.generate_features(circle[0], label)
features = parse_one_line(features)
print features
# run the classifier on that circle
classification = svmlight.classify(model, [features])
print classification
# make a decision about whether that circle is circly enough
# cv2.imshow("Image processed", circle)
# cv2.waitKey()
# for the strict form of the classifier, I require that all of the detected circles
# are in fact circles. other classifiers may be more lenient
return new_circles