def run_KCF_variant(tracker, seq, debug=False):
from keras.preprocessing import image
from visualisation_utils import plot_tracking_rect, show_precision
start_time = time.time()
start_frame = 0
tracker.res = []
for frame in range(start_frame, seq.endFrame - seq.startFrame+1):
image_no = seq.startFrame + frame
_id = seq.imgFormat.format(image_no)
image_path = seq.path + _id
img_rgb = image.load_img(image_path)
img_rgb = image.img_to_array(img_rgb)
if frame == start_frame:
tracker.train(img_rgb, seq.gtRect[start_frame], seq.name)
else:
tracker.detect(img_rgb, frame)
if debug and frame > start_frame:
print("Frame ==", frame)
print('horiz_delta: %.2f, vert_delta: %.2f' % (tracker.horiz_delta, tracker.vert_delta))
print("pos", np.array(tracker.res[-1]).astype(int))
print("gt", seq.gtRect[frame])
print("\n")
plot_tracking_rect(frame + seq.startFrame, img_rgb, tracker, seq.gtRect)
total_time = time.time() - start_time
tracker.fps = len(tracker.res) / total_time
print("Frames-per-second:", tracker.fps)
if debug:
tracker.precisions = show_precision(np.array(tracker.res), np.array(seq.gtRect), seq.name)
res = {'type': tracker.type, 'res': tracker.res, 'fps': tracker.fps}
return tracker, res
def main(debug=True):
tracker = KCFTracker(
feature_type='multi_cnn',
sub_feature_type='dsst',
sub_sub_feature_type='adapted_lr',
load_model=True,
vgglayer='',
model_path=
'./trained_models/CNN_Model_OBT100_multi_cnn_best_cifar_big_valid.h5',
name_suffix='_best_valid_CNN',
saliency_method=1,
spatial_reg=1)
from keras.preprocessing import image
from visualisation_utils import plot_tracking_rect, show_precision
img_list = sorted(os.listdir(img_dir))
start_time = time.time()
start_frame = 1
tracker.res = []
for frame in range(1, len(img_list)):
image_path = os.path.join(img_dir, img_list[frame])
img_rgb = image.load_img(image_path)
img_rgb = image.img_to_array(img_rgb)
img_saliency_path = os.path.join(saliency_dir, img_list[frame])
img_saliency = image.load_img(img_saliency_path)
img_saliency = image.img_to_array(img_saliency)
if frame == start_frame:
tracker.train(img_rgb, gtRect[start_frame], 'BMX', img_saliency)
else:
tracker.detect(img_rgb, frame, img_saliency)
if debug and frame > start_frame:
print("Frame ==", frame)
print('horiz_delta: %.2f, vert_delta: %.2f' %
(tracker.horiz_delta, tracker.vert_delta))
print("pos", np.array(tracker.res[-1]).astype(int))
print("gt", gtRect[frame])
print("\n")
plot_tracking_rect(frame + 0, img_rgb, tracker, gtRect[1:])
total_time = time.time() - start_time
tracker.fps = len(tracker.res) / total_time
print("Frames-per-second:", tracker.fps)
if True:
tracker.precisions = show_precision(np.array(tracker.res),
np.array(gtRect[1:]),
'BMX',
wait_second=0)
res = {'type': tracker.type, 'res': tracker.res, 'fps': tracker.fps}
return tracker, res