total_l2_loss = 0
total_overlap = 0
total_overlap_score = 0
total_time = 0
num_of_images = 0
for fn in image_names:
if not (fn.endswith("jpg") or fn.endswith("JPG")):
continue
print(fn)
image = cv2.imread(os.path.join(test_path, fn), 3)
points, time = model.predict_points(image)
label = evaluator.get_label_by_image_name(fn)
# l1_loss, _ = evaluator.error_func['L1'](label, points)
# print( 'L1_loss = {}'.format(l1_loss))
# l2_loss, _ = evaluator.error_func['L2'](label, points)
# print( 'L2_loss = {}'.format(l2_loss))
try:
overlap, overlap_score = evaluator.error_func['overlap'](points,
label,
threshold=0.8)
except:
overlap = 0
overlap_score = 0
print('overlap = {}'.format(overlap))
