def evaluate(): # avoid printing TF debugging information os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # TODO: allow parameters from command line or leave everything in json files? hp, evaluation, run, env, design = parse_arguments() # Set size for use with tf.image.resize_images with align_corners=True. # For example, # [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1) # instead of # [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3) final_score_sz = hp.response_up * (design.score_sz - 1) + 1 # build TF graph once for all gt, frame_name_list, _, _ = _init_video(env, evaluation, evaluation.video) frame_sz = [i for i in cv2.imread(frame_name_list[0]).shape] siamNet = siam.Siamese(batch_size = 1); image, z_crops, x_crops, templates_z, scores, loss, _, distance_to_gt, summary, templates_x, max_pos_x, max_pos_y = siamNet.build_tracking_graph_train(final_score_sz, design, env, hp, frame_sz) pos_x, pos_y, target_w, target_h = region_to_bbox(gt[evaluation.start_frame]) bboxes, speed = tracker(hp, run, design, frame_name_list, pos_x, pos_y, target_w, target_h, final_score_sz, image, templates_z, scores, evaluation.start_frame, path_ckpt = os.path.join(design.saver_folder, design.path_ckpt), siamNet = siamNet) _, precision, precision_auc, iou = _compile_results(gt, bboxes, evaluation.dist_threshold) print(evaluation.video + \ ' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % precision +\ ' -- Precision AUC: ' + "%.2f" % precision_auc + \ ' -- IOU: ' + "%.2f" % iou + \ ' -- Speed: ' + "%.2f" % speed + ' --')
def main(im, bbox):
# TODO: allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments()
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
siam = SiameseNet(env.root_pretrained, design.net)
with Image.fromarray(im) as img:
frame_sz = np.asarray(img.size)
frame_sz[1], frame_sz[0] = frame_sz[0], frame_sz[1]
im = Image.fromarray(im)
torch.save(siam.state_dict(), '/home/nvidia/jlaplaza/siamfc_pytorch_test/siamfc_pretrained.pt')
#gt, frame_name_list, _, _ = _init_video(env, evaluation, evaluation.video)
pos_x, pos_y, target_w, target_h = region_to_bbox(bbox)
print(target_w, target_h)
# bboxes, speed = tracker(hp, run, design, frame_name_list, pos_x, pos_y, target_w, target_h, final_score_sz,
# filename, image, templates_z, scores, evaluation.start_frame)
tracker(hp, run, design, im, pos_x, pos_y, target_w, target_h, final_score_sz,
siam, evaluation.start_frame)
"""
def _compile_results(gt, bboxes, dist_threshold):
l = np.size(bboxes, 0)
gt4 = np.zeros((l, 4))
new_distance = np.zeros(l)
new_ious = np.zeros(l)
n_thresholds = 50
precisions_ths = np.zeros(n_thresholds)
for i in range(l):
gt4[i, :] = region_to_bbox(gt[i, :], center=False)
new_distance[i] = _compute_distance(bboxes[i, :], gt4[i, :])
new_ious = _compute_iou(bboxes[i, :], gt4[i, :])
precision = sum(
new_distance < dist_threshold) / np.size(new_distance) * 100
# find above result for many thresholds, then report the AUC
thresholds = np.linspace(0, 25, n_thresholds + 1)
thresholds = thresholds[-n_thresholds:]
# reverse it so that higher values of precision goes at the begainning
thresholds = thresholds[::-1]
for i in range(n_thresholds):
precisions_ths[i] = sum(
new_distance < thresholds[i]) / np.size(new_distance)
precision_auc = np.trapz(precisions_ths)
iou = np.mean(new_ious) * 100
return l, precision, precision_auc, iou
def _compile_results(gt, bboxes, dist_threshold): l = np.size(bboxes, 0) gt4 = np.zeros((l, 4)) new_distances = np.zeros(l) new_ious = np.zeros(l) n_thresholds = 50 precisions_ths = np.zeros(n_thresholds) for i in range(l): gt4[i, :] = region_to_bbox(gt[i, :], center=False) new_distances[i] = _compute_distance(bboxes[i, :], gt4[i, :]) new_ious[i] = _compute_iou(bboxes[i, :], gt4[i, :]) # what's the percentage of frame in which center displacement is inferior to given threshold? (OTB metric) precision = sum(new_distances < dist_threshold)/np.size(new_distances) * 100 # find above result for many thresholds, then report the AUC thresholds = np.linspace(0, 25, n_thresholds+1) thresholds = thresholds[-n_thresholds:] # reverse it so that higher values of precision goes at the beginning thresholds = thresholds[::-1] for i in range(n_thresholds): precisions_ths[i] = sum(new_distances < thresholds[i])/np.size(new_distances) # integrate over the thresholds precision_auc = np.trapz(precisions_ths) # per frame averaged intersection over union (OTB metric) iou = np.mean(new_ious) * 100 return l, precision, precision_auc, iou
def train_siam_net(design,hp,frame_name_list,num_frames,gt,filename,conv_W,conv_b,siam_net_z,loss,train_op):
#-------------------------------------------------------------------------
#index_z:the index of template in the frame_name_list
#-------------------------------------------------------------------------
with tf.Session() as sess:
#tf.global_variables_initializer().run()
sess.run(tf.global_variables_initializer())
#tf.local_variables_initializer().run()
#Coordinate the loading of image files
coord=tf.train.Coordinator()
threads=tf.train.start_queue_runners(coord=coord)
#TB
merged=tf.summary.merge_all()
writer=tf.summary.FileWriter('/tmp/tensorlogs/siamtf',sess.graph)
for i in range(0,num_frames-1):
pos_x,pos_y,target_w,target_h=region_to_bbox(gt[i])
#connect the context to get the size of x and z crops
t_sz=(target_w+target_h)*design.context_amount
w_crop_z=target_w+t_sz
h_crop_z=target_h+t_sz
sz_z=np.sqrt(float(w_crop_z)*float(h_crop_z))
sz_x=float(design.instacneSize)/float(design.exemplarSize)*sz_z
siam_net_z_ = sess.run([siam_net_z],feed_dict={
#sess.run([train_op],feed_dict={
siam.pos_x:pos_x,
siam.pos_y:pos_y,
siam.z_size:sz_z,
filename:frame_name_list[i]})
#t_start=time.time()
#print('begin')
#train the image which is the pair of siam_net_z
result,train_op_=sess.run([merged,train_op],feed_dict={
siam.pos_x:pos_x,
siam.pos_y:pos_y,
siam.z_size:sz_z,
siam.x_size:float(sz_x),
siam_net_z:siam_net_z_[0],
filename:frame_name_list[i+1] })
writer.add_summary(result,i)
#print('loss end')
#train --back propagation
#tf.train.AdamOptimizer(hp.learning_rate).minimize(loss_)
coord.request_stop()
coord.join(threads)
def _compile_results(gt, bboxes, dist_threshold):
""" Computes the results for one sequence based on the tracking bounding
boxes.
Args:
gt: Nx4 array: ground truth bounding boxes.
bboxes: Nx4 array: predicted bounding boxes.
dist_threshold: int: threshold in pixels to calculate the precision.
Returns:
int: number of boxes/frames in the sequence.
float: precision of the results.
float: precision AuC of the results.
float: IoU of the results.
float: success rate of the results.
"""
l = np.size(bboxes, 0)
gt4 = np.zeros((l, 4))
new_distances = np.zeros(l)
new_ious = np.zeros(l)
n_thresholds = 50
precisions_ths = np.zeros(n_thresholds)
for i in range(l):
gt4[i, :] = region_to_bbox(gt[i, :], center=False)
new_distances[i] = _compute_distance(bboxes[i, :], gt4[i, :])
new_ious[i] = _compute_iou(bboxes[i, :], gt4[i, :])
# what's the percentage of frame in which center displacement is inferior
# to given threshold? (OTB metric)
precision = (sum(new_distances < dist_threshold) / np.size(new_distances) *
100)
success = sum(new_ious > 0.5) / np.size(new_ious) * 100
# find above result for many thresholds, then report the AUC
thresholds = np.linspace(0, 25, n_thresholds + 1)
thresholds = thresholds[-n_thresholds:]
# reverse it so that higher values of precision goes at the beginning
thresholds = thresholds[::-1]
for i in range(n_thresholds):
precisions_ths[i] = (sum(new_distances < thresholds[i]) /
np.size(new_distances))
# integrate over the thresholds
precision_auc = np.trapz(precisions_ths)
# per frame averaged intersection over union (OTB metric)
iou = np.mean(new_ious) * 100
return l, precision, precision_auc, iou, success
def main():
#avoid printing TF debugging information(only show error log)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
hp, evaluation, run, env, design = parse_arguments()
#build TF graph in siamese once for all
#siam.init_create_net()
filename, siam_net_z, loss, train_op = siam.make_siameseFC(env, design, hp)
#iterate through all videos of evaluation.dataset
if evaluation.video == 'all':
#the path of folder of all videos
train_data_folder = os.path.join(env.root_train_dataset,
evaluation.dataset)
videos_list = [v for v in os.listdir(train_data_folder)]
videos_list.sort()
num_v = len(videos_list)
for i in range(num_v):
gt, frame_name_list, frame_sz, n_frames = _init_train_video(
env, evaluation, videos_list[i])
start_frame = evaluation.start_frame
#not sure
#gt_=gt[start_frame:,:]
gt_ = gt[start_frame:]
frame_name_list_ = frame_name_list[start_frame:]
num_frames = np.size(frame_name_list_)
for j in range(num_frames - 1):
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[j])
#train_siam_net(design,hp,frame_name_list,z_index,pos_x,pos_y,target_w,target_h,filename,siam_net_z,loss)
train_siam_net(design, hp, frame_name_list, j, pos_x, pos_y,
target_w, target_h, filename, siam_net_z, loss,
train_op)
else:
gt, frame_name_list, _, _ = _init_train_video(env, evaluation,
evaluation.video)
start_frame = evaluation.start_frame
gt_ = gt[start_frame:]
frame_name_list_ = frame_name_list[start_frame:]
num_frames = np.size(frame_name_list_)
train_siam_net(design, hp, frame_name_list, num_frames, gt, filename,
siam_net_z, loss, train_op)
'''for i in range(num_frames-1):
def main():
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# TODO: allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments()
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
# build TF graph once for all
image, templates_z, scores = siam.build_tracking_graph_2(
final_score_sz, design, env)
# iterate through all videos of evaluation.dataset
gt, frame_name_list, _, _ = _init_video(env, evaluation, evaluation.video)
pos_x, pos_y, target_w, target_h = region_to_bbox(
gt[evaluation.start_frame])
track_cam(hp, run, design, final_score_sz, image, templates_z, scores,
evaluation.start_frame)
def _compile_results(gt, bboxes, dist_threshold):
l = np.size(bboxes, 0)
#np.zeros(shape=(1,4),dtype=float, order='C')
gt4 = np.zeros((l, 4))
new_distances = np.zeros(l)
new_ious = np.zeros(l)
n_thresholds = 50
precisions_ths = np.zeros(n_thresholds)
for i in range(l):
gt4[i, :] = region_to_bbox(gt[i, :], center=False)
new_distances[i] = _compute_distance(bboxes[i, :], gt4[i, :])
#计算重叠率
new_ious[i] = _compute_iou(bboxes[i, :], gt4[i, :])
#what's the percentage of from in which center displacement is inferior to given threshold?(OTB metric)
#sum(new_distances<dist_threshold):get the number of (new_distances<dist_threshold)
precision = sum(
new_distances < dist_threshold) / np.size(new_distances) * 100
#find above result for many thresholds,then report the AUC
thresholds = np.linspace(0, 25, n_thresholds + 1)
#get the number from the index of 1
thresholds = thresholds[-n_thresholds:]
#!!!reverse it so that higer values of precision goes at the beginning
thresholds = thresholds[::-1]
for i in range(n_thresholds):
precisions_ths[i] = sum(
new_distances < thresholds[i]) / np.size(new_distances)
#integrate over the thresholds
#AUC（Area Under Curve）被定义为ROC曲线下的面积
precision_auc = np.trapz(precisions_ths)
#per frame averaged interseciton over union (OTB metric)
iou = np.mean(new_ious) * 100
return l, precision, precision_auc, iou
def initialize(self, image_file, box):
pos_x, pos_y, target_w, target_h = region_to_bbox(box)
self.tracker = SiamMcfTracker(
self.design.context,
self.design.exemplar_sz,
self.design.search_sz,
self.hp.scale_step,
self.hp.scale_num,
self.hp.scale_penalty,
self.hp.scale_lr,
self.hp.window_influence,
self.design.tot_stride,
self.hp.response_up,
self.final_score_sz,
pos_x,
pos_y,
target_w,
target_h,
image_file,
self.sess,
self.templates_z,
self.filename,
)
def evaluate():
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# TODO: allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments()
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
# build the computational graph of Siamese fully-convolutional network
siamNet = siam.Siamese(batch_size=1)
# get tensors that will be used during tracking
image, z_crops, x_crops, templates_z, scores, loss, _, distance_to_gt, summary = siamNet.build_tracking_graph_train(
final_score_sz, design, env, hp)
# iterate through all videos of evaluation.dataset
if evaluation.video == 'all':
dataset_folder = os.path.join(env.root_dataset, evaluation.dataset)
videos_list = [v for v in os.listdir(dataset_folder)]
videos_list.sort()
nv = np.size(videos_list)
speed = np.zeros(nv)
precisions = np.zeros(nv)
precisions_auc = np.zeros(nv)
ious = np.zeros(nv)
lengths = np.zeros(nv)
for i in range(nv):
gt, frame_name_list, frame_sz, n_frames = _init_video(
env, evaluation, videos_list[i])
gt_ = gt[0:, :]
frame_name_list_ = frame_name_list[0:]
pos_x, pos_y, target_w, target_h = region_to_bbox(
gt_[0]
) # coordinate of gt is the bottom left point of the bbox
idx = i
bboxes, speed[idx] = tracker(hp,
run,
design,
frame_name_list,
pos_x,
pos_y,
target_w,
target_h,
final_score_sz,
image,
templates_z,
scores,
path_ckpt=os.path.join(
design.saver_folder,
design.path_ckpt),
siamNet=siamNet)
lengths[idx], precisions[idx], precisions_auc[idx], ious[
idx] = _compile_results(gt_, bboxes, evaluation.dist_threshold)
print(str(i) + ' -- ' + videos_list[i] + \
' -- Precision: ' + "%.2f" % precisions[idx] + \
' -- Precisions AUC: ' + "%.2f" % precisions_auc[idx] + \
' -- IOU: ' + "%.2f" % ious[idx] + \
' -- Speed: ' + "%.2f" % speed[idx] + ' --')
tot_frames = np.sum(lengths)
mean_precision = np.sum(precisions * lengths) / tot_frames
mean_precision_auc = np.sum(precisions_auc * lengths) / tot_frames
mean_iou = np.sum(ious * lengths) / tot_frames
mean_speed = np.sum(speed * lengths) / tot_frames
print('-- Overall stats (averaged per frame) on ' + str(nv) +
' videos (' + str(tot_frames) + ' frames) --')
print(' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % mean_precision +\
' -- Precisions AUC: ' + "%.2f" % mean_precision_auc +\
' -- IOU: ' + "%.2f" % mean_iou +\
' -- Speed: ' + "%.2f" % mean_speed + ' --')
#evaluate only one vedio
else:
gt, frame_name_list, frame_sz, n_frames = _init_video(
env, evaluation, evaluation.video)
pos_x, pos_y, target_w, target_h = region_to_bbox(gt[0])
bboxes, speed = tracker(hp,
run,
design,
frame_name_list,
pos_x,
pos_y,
target_w,
target_h,
final_score_sz,
image,
templates_z,
scores,
path_ckpt=os.path.join(design.saver_folder,
design.path_ckpt),
siamNet=siamNet)
_, precision, precision_auc, iou = _compile_results(
gt, bboxes, evaluation.dist_threshold)
print(evaluation.video + \
' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % precision +\
' -- Precision AUC: ' + "%.2f" % precision_auc + \
' -- IOU: ' + "%.2f" % iou + \
' -- Speed: ' + "%.2f" % speed + ' --')
return precision, precision_auc, iou, speed
def main():
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# TODO: allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments()
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
# build TF graph once for all
filename, image, templates_z, scores = siam.build_tracking_graph(
final_score_sz, design, env)
# iterate through all videos of evaluation.dataset
if evaluation.video == 'all':
dataset_folder = os.path.join(env.root_dataset, evaluation.dataset)
videos_list = [v for v in os.listdir(dataset_folder)]
videos_list.sort()
nv = np.size(videos_list)
speed = np.zeros(nv * evaluation.n_subseq)
precisions = np.zeros(nv * evaluation.n_subseq)
precisions_auc = np.zeros(nv * evaluation.n_subseq)
ious = np.zeros(nv * evaluation.n_subseq)
lengths = np.zeros(nv * evaluation.n_subseq)
for i in range(nv):
gt, frame_name_list, frame_sz, n_frames = _init_video(
env, evaluation, videos_list[i])
starts = np.rint(
np.linspace(0, n_frames - 1, evaluation.n_subseq + 1))
starts = starts[0:evaluation.n_subseq]
for j in range(evaluation.n_subseq):
start_frame = int(starts[j])
gt_ = gt[start_frame:, :]
frame_name_list_ = frame_name_list[start_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
idx = i * evaluation.n_subseq + j
# Call Tracker for the selected sequence
print("Tracking started!")
bboxes, speed[idx] = tracker(hp, run, design, frame_name_list_,
pos_x, pos_y, target_w, target_h,
final_score_sz, filename, image,
templates_z, scores, start_frame)
lengths[idx], precisions[idx], precisions_auc[idx], ious[
idx] = _compile_results(gt_, bboxes,
evaluation.dist_threshold)
print( str(i) + ' -- ' + videos_list[i] + \
' -- Precision: ' + "%.2f" % precisions[idx] + \
' -- Precisions AUC: ' + "%.2f" % precisions_auc[idx] + \
' -- IOU: ' + "%.2f" % ious[idx] + \
' -- Speed: ' + "%.2f" % speed[idx] + ' --' )
tot_frames = np.sum(lengths)
mean_precision = np.sum(precisions * lengths) / tot_frames
mean_precision_auc = np.sum(precisions_auc * lengths) / tot_frames
mean_iou = np.sum(ious * lengths) / tot_frames
mean_speed = np.sum(speed * lengths) / tot_frames
print('-- Overall stats (averaged per frame) on ' + str(nv) +
' videos (' + str(tot_frames) + ' frames) --')
print( ' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % mean_precision +\
' -- Precisions AUC: ' + "%.2f" % mean_precision_auc +\
' -- IOU: ' + "%.2f" % mean_iou +\
' -- Speed: ' + "%.2f" % mean_speed + ' --' )
else:
gt, frame_name_list, _, n_frames = _init_video(env, evaluation,
evaluation.video)
#pos_x, pos_y, target_w, target_h = region_to_bbox(gt[evaluation.start_frame])
# np.size(frame_name_list) = Amount of frames
# ott = amount of Objects To Track
ott = len(gt) if evaluation.multi_object else 1
objects = np.zeros((ott, 4))
for i in range(ott):
objects[i, :] = region_to_bbox(gt[i])
# Call Tracker for the selected sequence.
print("Tracking started!")
bboxes, speed = tracker(hp, run, design, frame_name_list, objects,
final_score_sz, filename, image, templates_z,
scores, evaluation.start_frame)
if evaluation.multi_object:
print('No Ground Truth available for multi object, just printing speed result....\n' + \
evaluation.video + \
' -- Speed: ' + "%.2f" % speed + ' --' )
else:
_, precision, precision_auc, iou = _compile_results(
gt, bboxes, evaluation.dist_threshold)
print( evaluation.video + \
' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % precision +\
' -- Precision AUC: ' + "%.2f" % precision_auc + \
' -- IOU: ' + "%.2f" % iou + \
' -- Speed: ' + "%.2f" % speed + ' --' )
print("Tracking finished!")
def main():
# TODO: allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments()
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
siam = SiameseNet(env.root_pretrained, design.net)
# iterate through all videos of evaluation.dataset
if evaluation.video == 'all':
dataset_folder = os.path.join(env.root_dataset, evaluation.dataset)
videos_list = [v for v in os.listdir(dataset_folder) if not v[0] == '.']
videos_list.sort()
nv = np.size(videos_list)
speed = np.zeros(nv * evaluation.n_subseq)
precisions = np.zeros(nv * evaluation.n_subseq)
precisions_auc = np.zeros(nv * evaluation.n_subseq)
ious = np.zeros(nv * evaluation.n_subseq)
lengths = np.zeros(nv * evaluation.n_subseq)
for i in range(nv): # -- Iterate through all videos
print('video: %d' % (i + 1))
gt, frame_name_list, frame_sz, n_frames = _init_video(env, evaluation, videos_list[i])
starts = np.rint(np.linspace(0, n_frames - 1, evaluation.n_subseq + 1))
starts = starts[0:evaluation.n_subseq]
for j in range(evaluation.n_subseq): # -- Iterate through a single video
start_frame = int(starts[j])
gt_ = gt[start_frame:, :]
frame_name_list_ = frame_name_list[start_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
idx = i * evaluation.n_subseq + j
# bboxes, speed[idx] = tracker(hp, run, design, frame_name_list_, pos_x, pos_y,
# target_w, target_h, final_score_sz, filename,
# image, templates_z, scores, start_frame)
bboxes, speed[idx] = tracker(hp, run, design, frame_name_list_, pos_x, pos_y,
target_w, target_h, final_score_sz, siam, start_frame) # -- here is where tracker.py is called
lengths[idx], precisions[idx], precisions_auc[idx], ious[idx] = _compile_results(gt_, bboxes, evaluation.dist_threshold)
print(str(i) + ' -- ' + videos_list[i] + \
' -- Precision: ' + "%.2f" % precisions[idx] + \
' -- Precisions AUC: ' + "%.2f" % precisions_auc[idx] + \
' -- IOU: ' + "%.2f" % ious[idx] + \
' -- Speed: ' + "%.2f" % speed[idx] + ' --\n')
tot_frames = np.sum(lengths)
mean_precision = np.sum(precisions * lengths) / tot_frames
mean_precision_auc = np.sum(precisions_auc * lengths) / tot_frames
mean_iou = np.sum(ious * lengths) / tot_frames
mean_speed = np.sum(speed * lengths) / tot_frames
print('-- Overall stats (averaged per frame) on ' + str(nv) + ' videos (' + str(tot_frames) + ' frames) --')
print(' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % mean_precision +\
' -- Precisions AUC: ' + "%.2f" % mean_precision_auc +\
' -- IOU: ' + "%.2f" % mean_iou +\
' -- Speed: ' + "%.2f" % mean_speed + ' --\n')
else:
gt, frame_name_list, _, _ = _init_video(env, evaluation, evaluation.video)
pos_x, pos_y, target_w, target_h = region_to_bbox(gt[evaluation.start_frame])
# bboxes, speed = tracker(hp, run, design, frame_name_list, pos_x, pos_y, target_w, target_h, final_score_sz,
# filename, image, templates_z, scores, evaluation.start_frame)
bboxes, speed = tracker(hp, run, design, frame_name_list, pos_x, pos_y, target_w, target_h, final_score_sz,
siam, evaluation.start_frame)
_, precision, precision_auc, iou = _compile_results(gt, bboxes, evaluation.dist_threshold)
print(evaluation.video + \
' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % precision +\
' -- Precision AUC: ' + "%.2f" % precision_auc + \
' -- IOU: ' + "%.2f" % iou + \
' -- Speed: ' + "%.2f" % speed + ' --\n')
def main():
#avoid printing TF debugging information
#仅显示error log
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
#TODO:allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments()
#gt_,frame_name_list_,_,_=_init_video(env,evaluation,evaluation.video)
#pos_x,pos_y,target_w,target_h=region_to_bbox(gt_[0])
#print('---target_w---'+"%d"%target_w+'--target_h---'+"%d"%target_h)
#why?????????????
#Set size for use with tf.image.resize_images with align_corners=True
#For example:
# [1,4,7]=>[1 2 3 4 5 6 7] (length 3*(3-1)+1)
#instead of
#[1,4,7]=>[1 1 2 3 4 5 6 7 7](length 3*3)
#Why hp.response_up???
#design.score_sz=33
#hp.response_up=8
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
#build TF graph once for all
#filename,image,templates_z,scores are only processes.!!!
#真正返回信息需要用sess去执行（tracker中执行）
#return filename, image, templates_z, scores_up
filename, image, templates_z, scores = siam.build_tracking_graph(
final_score_sz, design, env)
#iterate through all videos of evaluation dataset
if evaluation.video == 'all':
dataset_folder = os.path.join(env.root_dataset, evaluation.dataset)
#os.listdir(path):返回指定路径下的文件和文件夹
videos_list = [v for v in os.listdir(dataset_folder)]
videos_list.sort()
nv = np.size(videos_list)
speed = np.zeros(nv * evaluation.n_subseq)
precisions = np.zeros(nv * evaluation.n_subseq)
precisions_auc = np.zeros(nv * evaluation.n_subseq)
ious = np.zeros(nv * evaluation.n_subseq)
lengths = np.zeros(nv * evaluation.n_subseq)
#遍历不同的视频样本
for i in range(nv):
#frame_name_list:each image of a video sequence
gt, frame_name_list, frame_sz, n_frames = _init_video(
env, evaluation, videos_list[i])
#np.rint():对浮点数取整但不改变浮点数类型
#n_subseq=3
starts = np.rint(
np.linspace(0, n_frame - 1, evaluation.n_subseq + 1))
#分成n_subseq+1份，将数组赋给starts
starts = starts[0:evaluation.n_subseq]
for j in range(evaluation.n_subseq):
start_frame = int(starts[j])
#start_frame:指start_frame及以后(选取了n_subseq中的一份)
gt_ = gt[start_frame:, :]
frame_name_list_ = frame_name_list[start_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
idx = i * evaluation.n_subseq + j
#Update
bboxes, speed[idx] = tracker(
hp,
run,
design,
env,
evaluation,
frame_name_list_,
pos_x,
pos_y,
#bboxes,speed[idx]=tracker(hp,run,design,frame_name_list_,pos_x,pos_y,
target_w,
target_h,
final_score_sz,
filename,
image,
templates_z,
scores,
start_frame)
#gt_:ground truth
#bboxes:the result of tracking
lengths[idx], precisions[idx], precisions_auc[idx], ious[
idx] = _compile_results(gt_, bboxes,
evaluation.dist_threshold)
print(
str(i) + '--' + videos_list[i] + '--Precision: ' +
"%.2f" % precisions[idx] + '--Precisions AUC: ' +
"%.2f" % precisions_auc[idx] + '--IOU: ' +
"%.2f" % ious[idx] + '--Speed: ' + "%.2f" % speed[idx] +
'--')
else:
#evaluation.video='all'
print(evaluation.video)
gt, frame_name_list, _, _ = _init_video(env, evaluation,
evaluation.video)
#evaluation.start_frame=0
pos_x, pos_y, target_w, target_h = region_to_bbox(
gt[evaluation.start_frame])
#Update
#bboxes,speed=tracker(hp,run,design,frame_name_list,pos_x,pos_y,target_w,target_h,final_score_sz,
bboxes, speed = tracker(hp, run, design, env, evaluation,
frame_name_list, pos_x, pos_y, target_w,
target_h, final_score_sz, filename, image,
templates_z, scores, evaluation.start_frame)
_, precision, precisions_auc, iou = _compile_results(
gt, bboxes, evaluation.dist_threshold)
#print(evaluation.video+
print(evaluation.video + '--Precision: ' +
"(%d px)" % evaluation.dist_threshold + ': ' +
"%.2f" % precision + '--Precisions AUC: ' +
"%.2f" % precisions_auc + '--IOU: ' + "%.2f" % iou +
'--Speed: ' + "%.2f" % speed + '--')
def main(argv):
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
hp, evaluation, env, design = parse_arguments(root_dir)
cmd_args = parse_command_line_arguments()
if 'otb13' in cmd_args.dataset_name:
dataset_type = 'otb13'
elif 'otb15' in cmd_args.dataset_name:
dataset_type = 'otb15'
elif 'vot16' in cmd_args.dataset_name:
dataset_type = 'vot16'
elif 'vot17' in cmd_args.dataset_name:
dataset_type = 'vot17'
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
# build TF graph once for all
filename, image, templates_x, templates_z, scores_list =\
siam.build_tracking_graph(
root_dir, final_score_sz, design, env, hp)
# iterate through all videos of dataset_name
videos_folder = os.path.join(root_dir, env.root_dataset,
cmd_args.dataset_name)
videos_list = [
v for v in os.listdir(videos_folder)
if os.path.isdir(os.path.join(videos_folder, v))
]
videos_list.sort()
nv = np.size(videos_list)
speed = np.zeros(nv * evaluation.n_subseq)
precisions = np.zeros(nv * evaluation.n_subseq)
precisions_auc = np.zeros(nv * evaluation.n_subseq)
ious = np.zeros(nv * evaluation.n_subseq)
lengths = np.zeros(nv * evaluation.n_subseq)
successes = np.zeros(nv * evaluation.n_subseq)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
tf.global_variables_initializer().run()
vars_to_load = []
for v in tf.global_variables():
if 'postnorm' not in v.name:
vars_to_load.append(v)
siam_ckpt_name = 'pretrained/siam_mcf.ckpt-50000'
siam_saver = tf.train.Saver(vars_to_load)
siam_saver.restore(sess, siam_ckpt_name)
for i in range(nv):
gt, frame_name_list, frame_sz, n_frames = _init_video(
videos_list[i], videos_folder, dataset_type)
starts = np.rint(
np.linspace(0, n_frames - 1, evaluation.n_subseq + 1))
starts = starts[0:evaluation.n_subseq]
for j in range(evaluation.n_subseq):
start_frame = int(starts[j])
gt_ = gt[start_frame:, :]
frame_name_list_ = frame_name_list[start_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
idx = i * evaluation.n_subseq + j
bboxes, speed[idx] = track_one_sequence(hp,
design,
frame_name_list_,
pos_x,
pos_y,
target_w,
target_h,
final_score_sz,
filename,
image,
templates_x,
templates_z,
scores_list,
videos_list[i],
dataset_type,
sess,
cmd_args.visualize,
cmd_args.save_images,
cmd_args.save_bboxes,
vot_handle=None,
gt=gt_)
(lengths[idx], precisions[idx], precisions_auc[idx], ious[idx],
successes[idx]) = _compile_results(gt_, bboxes,
evaluation.dist_threshold)
print(
str(i) + ' -- ' + videos_list[i] + ' -- Precision: ' +
"%.2f" % precisions[idx] + ' -- Precisions AUC: ' +
"%.2f" % precisions_auc[idx] + ' -- IOU: ' +
"%.2f" % ious[idx] + ' -- [email protected]: ' +
"%.2f" % successes[idx] + ' -- Speed: ' +
"%.2f" % speed[idx] + ' --')
tot_frames = np.sum(lengths)
mean_precision = np.sum(precisions * lengths) / tot_frames
mean_precision_auc = np.sum(precisions_auc * lengths) / tot_frames
mean_iou = np.sum(ious * lengths) / tot_frames
mean_speed = np.sum(speed * lengths) / tot_frames
mean_success = np.sum(successes * lengths) / tot_frames
print('-- Overall stats (averaged per frame) on ' + str(nv) + ' videos (' +
str(tot_frames) + ' frames) --')
print(' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' +
'%.2f' % mean_precision + ' -- Precisions AUC: ' +
"%.2f" % mean_precision_auc + ' -- IOU: ' + "%.2f" % mean_iou +
' -- [email protected]: ' + "%.2f" % mean_success + ' -- Speed: ' +
"%.2f" % mean_speed + ' --')
def main():
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# TODO: allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments(mode="conv2")
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
final_score_sz = hp.response_up * design.score_sz
# build TF graph once for all
image, templates_z, scores = siam.build_tracking_graph(
final_score_sz, design, env)
# iterate through all videos of evaluation.dataset
if evaluation.video == 'all':
dataset_folder = os.path.join(env.root_dataset, evaluation.dataset)
videos_list = [v for v in os.listdir(dataset_folder)]
videos_list.sort()
# videos_list = videos_list[91:][:] #only use vot 2016
nv = np.size(videos_list)
speed = np.zeros(nv * evaluation.n_subseq)
precisions = np.zeros(nv * evaluation.n_subseq)
precisions_auc = np.zeros(nv * evaluation.n_subseq)
ious = np.zeros(nv * evaluation.n_subseq)
success_auc = np.zeros(nv * evaluation.n_subseq)
lengths = np.zeros(nv * evaluation.n_subseq)
for i in range(nv):
# gt, frame_name_list, frame_sz, n_frames, img_mode = _init_video(env, evaluation, videos_list[i])
gt, frame_name_list, frame_sz, n_frames, img_mode = _init_video_OTB(
env, evaluation, videos_list[i])
starts = np.rint(
np.linspace(0, n_frames - 1, evaluation.n_subseq + 1))
starts = starts[0:evaluation.n_subseq]
for j in range(evaluation.n_subseq):
start_frame = int(starts[j])
gt_ = gt[start_frame:, :]
frame_name_list_ = frame_name_list[start_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
idx = i * evaluation.n_subseq + j
bboxes, speed[idx] = tracker(hp, run, design, frame_name_list_,
pos_x, pos_y, target_w, target_h,
final_score_sz, image,
templates_z, scores, start_frame)
lengths[idx], precisions[idx], precisions_auc[idx], ious[
idx], success_auc[idx] = _compile_results(
gt_, bboxes, evaluation.dist_threshold)
print str(i) + ' -- ' + videos_list[i] + \
' -- Precision: ' + "%.2f" % precisions[idx] + \
' -- Precisions AUC: ' + "%.2f" % precisions_auc[idx] + \
' -- IOU: ' + "%.2f" % ious[idx] + \
' -- Success AUC: ' + "%.2f" % success_auc[idx] + \
' -- Speed: ' + "%.2f" % speed[idx] + ' --'
print
tot_frames = np.mean(lengths)
mean_precision = np.mean(precisions)
mean_precision_auc = np.mean(precisions_auc)
mean_iou = np.mean(ious)
mean_success_auc = np.mean(success_auc)
mean_speed = np.mean(speed)
print 'data set ' + evaluation.dataset + ' z_lr %f scale_step %f scale_penalty %f scale_lr %f window_influence %f' % (
hp.z_lr, hp.scale_step, hp.scale_penalty, hp.scale_lr,
hp.window_influence)
print '-- Overall stats (averaged per frame) on ' + str(
nv) + ' videos (' + str(tot_frames) + ' frames) --'
print ' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % mean_precision +\
' -- Precisions AUC: ' + "%.2f" % mean_precision_auc +\
' -- IOU: ' + "%.2f" % mean_iou +\
' -- Success AUC: ' + "%.2f" % mean_success_auc +\
' -- Speed: ' + "%.2f" % mean_speed + ' --'
print
with open('log_test.txt', 'a+') as f:
f.write(time.asctime(time.localtime(time.time())) + '\r\n')
f.write(
'data set ' + evaluation.dataset +
' z_lr %f scale_step %f scale_penalty %f scale_lr %f window_influence %f \r\n'
% (hp.z_lr, hp.scale_step, hp.scale_penalty, hp.scale_lr,
hp.window_influence))
f.write('-- Overall stats (averaged per frame) on ' + str(nv) +
' videos (' + str(tot_frames) + ' frames) --\r\n')
f.write(' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % mean_precision + \
' -- Precisions AUC: ' + "%.2f" % mean_precision_auc + \
' -- IOU: ' + "%.2f" % mean_iou + \
' -- AUC: ' + "%.3f" % mean_success_auc + \
' -- Speed: ' + "%.2f" % mean_speed + ' --\r\n')
f.write('\r\n')
else:
gt, frame_name_list, _, _ = _init_video(env, evaluation,
evaluation.video)
pos_x, pos_y, target_w, target_h = region_to_bbox(
gt[evaluation.start_frame])
bboxes, speed = tracker(hp, run, design, frame_name_list, pos_x, pos_y,
target_w, target_h, final_score_sz, filename,
image, templates_z, scores,
evaluation.start_frame)
_, precision, precision_auc, iou = _compile_results(
gt, bboxes, evaluation.dist_threshold)
print evaluation.video + \
' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % precision +\
' -- Precision AUC: ' + "%.2f" % precision_auc + \
' -- IOU: ' + "%.2f" % iou + \
' -- Speed: ' + "%.2f" % speed + ' --'
print
def track_one_sequence(hp,
design,
frame_name_list,
pos_x,
pos_y,
target_w,
target_h,
final_score_sz,
filename,
image,
templates_x,
templates_z,
scores_list,
vid_name,
dataset_type,
sess,
visualize_results,
save_images,
save_bboxes,
vot_handle,
gt=None):
""" Handles tracking for one whole sequence. Inputs are fed to the network
and the results are collected and can be shown on the screen and saved to
the disk.
Args:
hp: namespace: hyperparameters.
design: namespace: design parameters.
frame_name_list: string list: list of sorted image paths to be read.
pos_x: int: horizontal center of the target.
pos_y: int: vertical center of the target.
target_w: int: target width.
target_h: int: target height.
final_score_sz: int: size of the score map after upsampling.
filename: string tensor: placeholder for the image path to be read.
image: 3D tensor: the image read from the path.
templates_x: 4D tensor: instance features from one or more layers
concatenated by channels. See siam_mcf_net.inference comments for more
details.
templates_z: 4D tensor: exemplar features from one or more layers
concatenated by channels. See siam_mcf_net.inference comments for more
details.
scores_list: 5D tensor: batch of score heatmaps for each of the selected
layers.
vid_name: string: name of this sequence (only for saving purposes).
dataset_type: string: name of this dataset (only for saving purposes).
sess: an open tf.Session to execute the graph.
visualize_results: boolean: whether to show the results on the screen.
save_images: boolean: whether to save image results to the disk.
save_bboxes: boolean: whether to save bounding boxes to the disk.
vot_handle: vot handle for running the VOT toolkit.
gt: Nx4 array: optional ground truth bounding boxes (only for
visualization purposes).
Returns:
Nx4 array: the resulting bounding boxes from the tracking.
float: the tracking speed in frames per second.
"""
num_frames = np.size(frame_name_list)
# stores tracker's output for evaluation
bboxes = np.zeros((num_frames, 4))
if save_images:
res_dir = 'results/%s/frames/%s' % (
dataset_type, vid_name)
if not os.path.exists(res_dir):
os.makedirs(res_dir)
if save_bboxes:
bb_res_dir = 'results/%s/bboxes' % (dataset_type)
if not os.path.exists(bb_res_dir):
os.makedirs(bb_res_dir)
# save first frame position (from ground-truth)
bboxes[0, :] = pos_x-target_w/2, pos_y-target_h/2, target_w, target_h
if vot_handle is not None:
frame_path = vot_handle.frame()
else:
frame_path = frame_name_list[0]
tracker = SiamMcfTracker(
design.context, design.exemplar_sz, design.search_sz, hp.scale_step,
hp.scale_num, hp.scale_penalty, hp.scale_lr, hp.window_influence,
design.tot_stride, hp.response_up, final_score_sz, pos_x, pos_y,
target_w, target_h, frame_path, sess, templates_z, filename)
t_start = time.time()
# Get an image from the queue
for i in range(1, num_frames):
if vot_handle is not None:
frame_path = vot_handle.frame()
else:
frame_path = frame_name_list[i]
if save_images or visualize_results:
image_ = sess.run(image, feed_dict={filename: frame_path})
bbox = tracker.track(
frame_path, sess, templates_z, templates_x, scores_list, filename)
# convert <cx,cy,w,h> to <x,y,w,h> and save output
bboxes[i, :] = bbox
if vot_handle is not None:
vot_rect = vot.Rectangle(bbox[0], bbox[1], bbox[2], bbox[3])
vot_handle.report(vot_rect)
if visualize_results:
show_frame(image_, bboxes[i, :], 1)
if save_images:
out_img = Image.fromarray(image_.copy().astype(np.uint8))
out_draw = ImageDraw.Draw(out_img)
if gt is not None:
gt_rect = np.array(region_to_bbox(gt[i, :], False)).astype(
np.int32)
gt_rect[2:] = gt_rect[:2] + gt_rect[2:]
rect = bboxes[i].copy()
rect[2:] = rect[:2] + rect[2:]
rect = rect.astype(np.int32)
pillow_version = [int(x) for x in PIL.__version__.split('.')]
if (pillow_version[0] > 5 or
(pillow_version[0] == 5 and pillow_version[1] >= 3)):
if gt is not None:
out_draw.rectangle(
[tuple(gt_rect[:2]), tuple(gt_rect[2:])],
outline=(0, 0, 255),
width=2)
out_draw.rectangle(
[tuple(rect[:2]), tuple(rect[2:])],
outline=(255, 0, 0),
width=3)
else:
if gt is not None:
out_draw.rectangle(
[tuple(gt_rect[:2]), tuple(gt_rect[2:])],
outline=(0, 0, 255))
out_draw.rectangle(
[tuple(rect[:2]), tuple(rect[2:])],
outline=(255, 0, 0))
out_img.save(os.path.join(res_dir, '%05d.jpg' % (i + 1)))
t_elapsed = time.time() - t_start
speed = num_frames/t_elapsed
if save_bboxes:
with open(os.path.join(bb_res_dir, vid_name+'.txt'), 'w') as f:
for bb in bboxes:
f.write('%.02f,%.02f,%.02f,%.02f\n' % tuple(bb))
return bboxes, speed
def main():
hp, evaluation, run, env, design = parse_arguments()
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
siam = SiameseNet(env.root_pretrained, design.net)
if torch.cuda.is_available():
siam = siam.cuda()
if evaluation.video == 'all':
dataset_folder = os.path.join(env.root_dataset, evaluation.dataset)
video_list = [v for v in os.listdir(dataset_folder) if not v[0] == '.']
video_list.sort()
nv = np.size(video_list)
speed = np.zeros(nv * evaluation.n_subseq)
precisions = np.zeros(nv * evaluation.n_subseq)
precision_auc = np.zeros(nv * evaluation.n_subseq)
ious = np.zeros(nv * evaluation.n_subseq)
lengths = np.zeros(nv * evaluation.n_subseq)
for i in range(nv):
print('video: %d' % (i + 1))
gt, frame_name_list, frame_sz, n_frame = _init_video(
env, evaluation, video_list[i])
starts = np.rint(
np.linspace(0, n_frame - 1, evaluation.n_subseq + 1))
starts = starts[0:evaluation.n_subseq]
for j in range(evaluation.n_subseq):
starts_frame = int(starts[j])
gt_ = gt[starts_frame:, :]
frame_name_list = frame_name_list[starts_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
idx = i * evaluation.n_subseq + j
bboxes, speed[idx] = tracker(hp, run, design, frame_name_list,
pos_x, pos_y, target_w, target_h,
final_score_sz, siam,
starts_frame)
lengths[idx], precisions[idx], precision_auc[idx], ious[
idx] = _compile_results(gt_, bboxes,
evaluation.dist_threshold)
print(str(i) + ' -- ' + video_list[i] + \
' -- Precision: ' + "%.2f" %precisions[idx] + \
' -- Precisions AUC: ' + "%.2f" %precision_auc[idx] + \
' -- IOU: ' + "%.2f" % ious[idx] + \
'-- Speed: ' + "%.2f" % speed[idx] + ' --\n')
tot_frames = np.sum(lengths)
mean_precision = np.sum(precisions * lengths) / tot_frames
mean_iou = np.sum(ious * lengths) / tot_frames
mean_precision_auc = np.sum(precision_auc * lengths) / tot_frames
mean_iou = np.sum(ious * lengths) / tot_frames
mean_speed = np.sum(speed * lengths) / tot_frames
print(' -- Overall stats (averaged per frame) on ' + str(nv) +
'videos(' + str(tot_frames) + 'frames) --')
print(' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" %mean_precision +\
' -- Precisions AUC: ' + "%.2f" % mean_precision_auc +\
' -- IOU: ' + "%.2f" %mean_iou +\
' -- Speed: ' + "%.2f" %mean_speed + '-- \n')
else:
gt, frame_name_list, _, _ = _init_video(env, evaluation,
evaluation.video)
pos_x, pos_y, target_w, target_h = region_to_bbox(
gt[evaluation.start_frame])
bboxes, speed = tracker(hp, run, design, frame_name_list, pos_x, pos_y,
target_w, target_h, final_score_sz, siam,
evaluation.start_frame)
_, precision, precision_auc, iou = _compile_results(
gt, bboxes, evaluation.dist_threshold)
print(evaluation.video + \
' -- precision ' + "(%d px)" %evaluation.dist_threshold + ': ' + "%.2f" % precision +\
' -- precision AUC: ' + "%.2f" %precision_auc + \
' -- IOU: ' + "%.2f" %iou +\
'-- Speed: ' + "%.2f" %speed + ' -- \n')
def get_gt_bbox_cv(bbox):
x, y, width, height = region_to_bbox(bbox, False)
return (int(x), int(y)), (int(x + width), int(y + height))
def main():
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Load hyperparameter (hp), evaluation, run, environment (env) and design parameters from
# parameters/ directory.
hp, evaluation, run, env, design = parse_arguments()
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
# build TF graph once for all
filename, image, templates_z, scores = siam.build_tracking_graph(
final_score_sz, design, env)
# iterate through all videos of evaluation.dataset
if evaluation.video == 'all':
dataset_folder = os.path.join(env.root_dataset, evaluation.dataset)
videos_list = [v for v in os.listdir(dataset_folder)]
videos_list.sort()
nv = np.size(videos_list)
speed = np.zeros(nv * evaluation.n_subseq)
precisions = np.zeros(nv * evaluation.n_subseq)
precisions_auc = np.zeros(nv * evaluation.n_subseq)
ious = np.zeros(nv * evaluation.n_subseq)
lengths = np.zeros(nv * evaluation.n_subseq)
for i in range(nv):
gt, frame_name_list, frame_sz, n_frames = _init_video(
env, evaluation, videos_list[i])
starts = np.rint(
np.linspace(0, n_frames - 1, evaluation.n_subseq + 1))
starts = starts[0:evaluation.n_subseq]
for j in range(evaluation.n_subseq):
start_frame = int(starts[j])
gt_ = gt[start_frame:, :]
frame_name_list_ = frame_name_list[start_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
idx = i * evaluation.n_subseq + j
bboxes, speed[idx] = tracker(hp, run, design, frame_name_list_,
pos_x, pos_y, target_w, target_h,
final_score_sz, filename, image,
templates_z, scores, start_frame)
lengths[idx], precisions[idx], precisions_auc[idx], ious[
idx] = _compile_results(gt_, bboxes,
evaluation.dist_threshold)
print str(i) + ' -- ' + videos_list[i] + \
' -- Precision: ' + "%.2f" % precisions[idx] + \
' -- Precisions AUC: ' + "%.2f" % precisions_auc[idx] + \
' -- IOU: ' + "%.2f" % ious[idx] + \
' -- Speed: ' + "%.2f" % speed[idx] + ' --'
print
tot_frames = np.sum(lengths)
mean_precision = np.sum(precisions * lengths) / tot_frames
mean_precision_auc = np.sum(precisions_auc * lengths) / tot_frames
mean_iou = np.sum(ious * lengths) / tot_frames
mean_speed = np.sum(speed * lengths) / tot_frames
print '-- Overall stats (averaged per frame) on ' + str(
nv) + ' videos (' + str(tot_frames) + ' frames) --'
print ' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % mean_precision +\
' -- Precisions AUC: ' + "%.2f" % mean_precision_auc +\
' -- IOU: ' + "%.2f" % mean_iou +\
' -- Speed: ' + "%.2f" % mean_speed + ' --'
print
else:
gt, frame_name_list, _, _ = _init_video(env, evaluation,
evaluation.video)
pos_x, pos_y, target_w, target_h = region_to_bbox(
gt[evaluation.start_frame])
bboxes, speed = tracker(hp, run, design, frame_name_list, pos_x, pos_y,
target_w, target_h, final_score_sz, filename,
image, templates_z, scores,
evaluation.start_frame)
_, precision, precision_auc, iou = _compile_results(
gt, bboxes, evaluation.dist_threshold)
print evaluation.video + \
' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % precision +\
' -- Precision AUC: ' + "%.2f" % precision_auc + \
' -- IOU: ' + "%.2f" % iou + \
' -- Speed: ' + "%.2f" % speed + ' --'
print
def main():
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# TODO: allow parameters from command line or leave everything in json files?
# hp, evaluation, run, env, design = parse_arguments()
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
# final_score_sz = hp.response_up * (design.score_sz - 1) + 1
# build TF graph once for all
# filename, image, templates_z, templates_x, scores, scores_original = siam.build_tracking_graph(final_score_sz, design, env)
# create dataset
dataset = DatasetFactory.create_dataset(name=args_dataset,
dataset_root=dataset_root,
load_img=False)
# iterate through all videos of evaluation.dataset
videos_list = list(dataset.videos.keys())
videos_list.sort()
nv = np.size(videos_list)
tracker = None
for i in range(nv):
current_key = sorted(list(dataset.videos.keys()))[i]
gt, frame_name_list, frame_sz, n_frames = _init_video(
dataset, current_key)
rect = region_to_bbox(gt[0], False)
# tracker = SiamFCTracker(frame_name_list[0], vot.Rectangle(rect[0],rect[1],rect[2],rect[3]))
# tracker = ColorizationTracker(frame_name_list[0], vot.Rectangle(rect[0], rect[1], rect[2], rect[3]))
if tracker is None:
tracker = HybridTracker(
frame_name_list[0],
vot.Rectangle(rect[0], rect[1], rect[2], rect[3]))
else:
tracker.HotInit(frame_name_list[0],
vot.Rectangle(rect[0], rect[1], rect[2], rect[3]))
bboxes = []
for i in range(0, n_frames):
bbox, confidence = tracker.track(frame_name_list[i])
bboxes.append(bbox)
#Visualize
if visualize:
for i, (bbox, groundt,
frame_name) in enumerate(zip(bboxes, gt, frame_name_list)):
image = cv2.imread(frame_name)
bbox_pt1, bbox_pt2 = get_bbox_rect(bbox)
bbox_gt1, bbox_gt2 = get_gt_bbox_cv(groundt)
#Draw result
cv2.rectangle(image, bbox_pt1, bbox_pt2, (0, 255, 0))
#Draw ground truth
cv2.rectangle(image, bbox_gt1, bbox_gt2, (0, 0, 0))
cv2.imshow("Results:", image)
if save_vis:
cv2.imwrite(os.path.join(vis_output,
str(i) + ".jpg"), image)
cv2.waitKey()
#Reset the tracker, get prepared for next subset
tracker.reset()
# Write the results to disc for evaluation
bboxes_n = []
for bbox in bboxes:
bboxes_n.append([bbox.x, bbox.y, bbox.width, bbox.height])
bboxes_n[0] = [1]
target_dir = os.path.join(result_output, current_key)
if not os.path.exists(target_dir):
os.mkdir(target_dir)
results_file = current_key + "_" + "{:03d}".format(1) + ".txt"
results_abs_file = os.path.join(target_dir, results_file)
with open(results_abs_file, "w") as f:
for bbox in bboxes_n:
if len(bbox) == 1:
f.write('%d\n' % (bbox[0]))
else:
f.write('%.2f, %.2f, %.2f, %.2f\n' %
(bbox[0], bbox[1], bbox[2], bbox[3]))
def main():
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# TODO: allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments()
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
# build TF graph once for all
filename, image, templates_z, scores = siam.build_tracking_graph(final_score_sz, design, env)
# iterate through all videos of evaluation.dataset
if evaluation.video == 'all':
dataset_folder = os.path.join(env.root_dataset, evaluation.dataset)
videos_list = [v for v in os.listdir(dataset_folder)]
videos_list.sort()
nv = np.size(videos_list)
speed = np.zeros(nv * evaluation.n_subseq)
precisions = np.zeros(nv * evaluation.n_subseq)
precisions_auc = np.zeros(nv * evaluation.n_subseq)
ious = np.zeros(nv * evaluation.n_subseq)
lengths = np.zeros(nv * evaluation.n_subseq)
for i in range(nv):
images_arr, gt, frame_name_list, frame_sz, n_frames = _init_video(env, evaluation, videos_list[i])
starts = np.rint(np.linspace(0, n_frames - 1, evaluation.n_subseq + 1))
starts = starts[0:evaluation.n_subseq]
for j in range(evaluation.n_subseq):
start_frame = int(starts[j])
gt_ = gt[start_frame:, :]
frame_name_list_ = frame_name_list[start_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
idx = i * evaluation.n_subseq + j
bboxes, speed[idx] = tracker(hp, run, design, frame_name_list_, pos_x, pos_y,
target_w, target_h, final_score_sz, filename,
image, templates_z, scores, start_frame)
lengths[idx], precisions[idx], precisions_auc[idx], ious[idx] = compile_results(gt, bboxes, evaluation.dist_threshold)
print(str(i) + ' -- ' + videos_list[i] + ' -- Precision: ' + "%.2f" % precisions[idx] + ' -- Precisions AUC: ' + "%.2f" % precisions_auc[idx] + ' -- IOU: ' + "%.2f" % ious[idx] + ' -- Speed: ' + "%.2f" % speed[idx] + ' --')
tot_frames = np.sum(lengths)
mean_precision = np.sum(precisions * lengths) / tot_frames
mean_precision_auc = np.sum(precisions_auc * lengths) / tot_frames
mean_iou = np.sum(ious * lengths) / tot_frames
mean_speed = np.sum(speed * lengths) / tot_frames
print('-- Overall stats (averaged per frame) on ' + str(nv) + ' videos (' + str(tot_frames) + ' frames) --')
print(' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % mean_precision + ' -- Precisions AUC: ' + "%.2f" % mean_precision_auc + ' -- IOU: ' + "%.2f" % mean_iou + ' -- Speed: ' + "%.2f" % mean_speed + ' --')
else:
images_arr, gt, frame_name_list, _, _ = _init_video(env, evaluation, evaluation.video)
pos_x, pos_y, target_w, target_h = region_to_bbox(gt[evaluation.start_frame])
bboxes, speed = tracker(hp, run, design, frame_name_list, pos_x, pos_y, target_w, target_h, final_score_sz,
filename, image, templates_z, scores, evaluation.start_frame)
num_frames = np.size(frame_name_list)
bboxes_final = np.zeros((num_frames,4))
lk_params = dict( winSize = (5,5),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 1, 0.03))
bboxes_final = np.zeros((num_frames, 4))
for i in range(1,len(images_arr)-1):
# Create some random colors
color = np.random.randint(0,255,(100,3))
# Take first frame and find corners in it
#ret, old_frame = cap.read()
frame = images_arr[i+1]
old_frame = images_arr[i]
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = np.zeros((1,1,2), dtype=np.float32)
bbox_i = bboxes[i]
c, r, w, h = int(bbox_i[0]), int(bbox_i[1]), int(bbox_i[2]), int(bbox_i[3])
p0[0,0,0] = c
p0[0,0,1] = r
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
good_new = p1[st==1]
bboxes_final[i,:] = p1[0][0][0], p1[0][0][1], w, h
good_old = p0[st==1]
for i,(new,old) in enumerate(zip(good_new,good_old)):
a,b = new.ravel()
c,d = old.ravel()
fig = plt.figure(1)
ax = fig.add_subplot(111)
r1 = patches.ConnectionPatch((a,b),(c,d),'data','data',arrowstyle="-|>")
r2 = patches.Circle((a,b),5,color='r')#((x,y), w, h, linewidth=2, edgecolor='r', fill=False)
ax.imshow(np.uint8(frame))
ax.add_patch(r2)
ax.add_patch(r1)
plt.ion()
plt.show()
plt.pause(0.001)
plt.clf()
old_gray = frame_gray.copy()
p0 = good_new.reshape(-1,1,2)
_, precision, precision_auc, iou = _compile_results(gt, bboxes_final, evaluation.dist_threshold)
print(evaluation.video + ' -- Precision ' + "(%d px)" % evaluation.dist_threshold + ': ' + "%.2f" % precision + ' -- Precision AUC: ' + "%.2f" % precision_auc + ' -- IOU: ' + "%.2f" % iou + ' -- Speed: ' + "%.2f" % speed + ' --')
def cozmo_program(robot: cozmo.robot.Robot):
global angle
angle = 25.
robot.set_head_angle(degrees(angle)).wait_for_completed()
robot.set_lift_height(0.0).wait_for_completed()
robot.camera.image_stream_enabled = True
robot.camera.color_image_enabled = True
robot.camera.enable_auto_exposure = True
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
frame = os.path.join(directory, "current.jpeg")
print("Starting Tensorflow...")
with tf.Session() as sess:
print("Session successfully started")
model = load_model('modelv1.07-0.96.hdf5')
while True:
global X, Y, W, H
global result
X = 245.
Y = 165.
W = 150.
H = 150.
gt = [X, Y, W, H]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt)
frame = os.path.join(directory, "current.jpeg")
result = 0
dog_counter = 0
cat_counter = 0
background_counter = 0
next_state = 0
current_state = 0 #Background: 0, Cat:1, Dog:2
while True:
latest_img = robot.world.latest_image
if latest_img is not None:
pilImage = latest_img.raw_image
pilImage.resize((640,480), Image.ANTIALIAS).save(os.path.join(directory, "current.jpeg"), "JPEG")
show_frame(np.asarray(Image.open(frame)), [900.,900.,900.,900.], 1)
img = load_image(frame)
[result,out_relu,global_average_pooling2d] = sess.run([model.outputs,model.get_layer('out_relu').output\
,model.get_layer('global_average_pooling2d').output ], feed_dict={model.input.name:img})
next_state = np.argmax(result)
print('Arg max: ',next_state)
# Initial Current State is Background
if current_state == 0:
print('Background')
if next_state == 1: # Detected a Cat
current_state = 1 # Transition to Cat State
background_counter = 0
cat_counter = 1
dog_counter = 0
elif next_state == 2: # Detected a Dog
current_state = 2 # Transition to Dog state
background_counter = 0
cat_counter = 0
dog_counter = 1
# Current State is Cat
elif current_state == 1:
print('\t\t\t\t\t\tCat')
if next_state == 0: # Detected Background
background_counter += 1
if background_counter >= 6: # Transition to Background only if Background appeared for more than 6 times
background_counter = 0
current_state = 0
cat_counter = 0
elif next_state == 1: # Detected Cat itself
cat_counter +=1
if cat_counter >= 30:
print('Cozmo sees a cat')
dense = model.get_layer('dense').get_weights()
weights = dense[0].T
testing_counter = 0
detected_centroid = 0
xmin_avg = 0
xmax_avg = 0
ymin_avg = 0
ymax_avg = 0
frame_average = 2
frame_count = 0
while True:
latest_img = robot.world.latest_image
if latest_img is not None:
pilImage = latest_img.raw_image
pilImage.resize((640,480), Image.ANTIALIAS).save(os.path.join(directory, "current.jpeg"), "JPEG")
img = load_image(frame)
[result,out_relu,global_average_pooling2d] = sess.run([model.outputs,model.get_layer('out_relu').output\
,model.get_layer('global_average_pooling2d').output ], feed_dict={model.input.name:img})
kernels = out_relu.reshape(7,7,1280)
final = np.dot(kernels,weights[result[0].argmax()])
final1 = array_to_img(final.reshape(7,7,1))
final1 = final1.resize((224,224), Image.ANTIALIAS)
box = img_to_array(final1).reshape(224,224)
#box = cv2.blur(box,(30,30))
temp = (box > box.max()*.8) *1
temp_adjusted = np.ndarray(shape=np.shape(temp), dtype=np.dtype(np.uint8))
temp_adjusted[:,:] = np.asarray(temp)*255
contours, hierarchy = cv2.findContours(temp_adjusted, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[-2:]
contours = np.array(contours)
max_area = [0,0] # contours index and area
for index, contour in enumerate(contours):
if(max_area[1]< len(contour)):
max_area = [index,len(contour)]
contours_adjusted = contours[max_area[0]].squeeze(axis=1).T
xmin = contours_adjusted[0].min() * (640./224.)
ymin = contours_adjusted[1].min() * (480./224.)
xmax = contours_adjusted[0].max() * (640./224.)
ymax = contours_adjusted[1].max() * (480./224.)
if result[0].argmax() == 1:
# Frame smoothing
frame_count = frame_count + 1
xmin_avg = xmin_avg + xmin
xmax_avg = xmax_avg + xmax
ymin_avg = ymin_avg + ymin
ymax_avg = ymax_avg + ymax
if frame_count % frame_average == 0:
frame_count = 0
xmin_avg = xmin_avg/frame_average
xmax_avg = xmax_avg/frame_average
ymin_avg = ymin_avg/frame_average
ymax_avg = ymax_avg/frame_average
print(xmin_avg, end=",")
print(ymin_avg, end=",")
print(xmax_avg, end=",")
print(ymax_avg, end="\n")
ymin_avg = ymin_avg + (ymax_avg - ymin_avg)/2. - H/2.
xmin_avg = xmin_avg + (xmax_avg - xmin_avg)/2. - W/2.
print("150: ",xmin_avg, end=",")
print("150: ",ymin_avg, end="\n")
gt = [xmin_avg, ymin_avg, W, H]
xmin_avg = 0
xmax_avg = 0
ymin_avg = 0
ymax_avg = 0
pos_x, pos_y, target_w, target_h = region_to_bbox(gt)
bboxes = np.zeros((1, 4))
#bboxes[0,:] = pos_x-target_w/2, pos_y-target_h/2, target_w, target_h
bboxes[0,:] = pos_x-W/2, pos_y-H/2, W, H
print(len(contours))
testing_counter = testing_counter + 1
print("Testing_counter: ",testing_counter)
show_frame(np.asarray(Image.open(frame)), gt, 1)
print("Cat is detected")
print("Starting the tracker ...")
if (bboxes[0,1] + bboxes[0,3]/2) < (Y + H/2 - 40):
print("Command: Raise the head")
angle = angle + 0.5
if angle > 44.5:
angle = 44.5
elif (bboxes[0,1] + bboxes[0,3]/2) > (Y + H/2 + 40):
print("Command: Lower the head")
angle = angle - 0.5
if angle < 0:
angle = 0
else:
pass
set_head_angle_action = robot.set_head_angle(degrees(angle), max_speed=20, in_parallel=True)
if straight(bboxes[0,:])[0] != 0 and turn(bboxes[0,:])[0] != 0:
robot.drive_wheel_motors(straight(bboxes[0,:])[0] + turn(bboxes[0,:])[0], straight(bboxes[0,:])[1] + turn(bboxes[0,:])[1])
detected_centroid = 0
elif straight(bboxes[0,:])[0] == 0 and turn(bboxes[0,:])[0] == 0:
robot.stop_all_motors()
detected_centroid = detected_centroid + 1
elif straight(bboxes[0,:])[0] == 0:
robot.drive_wheel_motors(turn(bboxes[0,:])[0], turn(bboxes[0,:])[1])
detected_centroid = 0
elif turn(bboxes[0,:])[0] == 0:
robot.drive_wheel_motors(straight(bboxes[0,:])[0], straight(bboxes[0,:])[1])
detected_centroid = 0
else:
robot.stop_all_motors()
detected_centroid = detected_centroid + 1
if detected_centroid > 20//frame_average:
detected_centroid = 0
print("Reached a stable state.........\t\t\t\t\t\t\t\t STABLE")
# Go near the object
set_head_angle_action.wait_for_completed()
robot.abort_all_actions(log_abort_messages=True)
robot.wait_for_all_actions_completed()
robot.set_head_angle(degrees(0.5)).wait_for_completed()
print("Robot's head angle: ",robot.head_angle)
target_frame_count = 1
while True:
latest_img = None
while latest_img is None:
latest_img = robot.world.latest_image
target_frame1 = latest_img.raw_image
target_frame1 = target_frame1.resize((640,480), Image.ANTIALIAS)
#target_frame1 = target_frame1.convert('L')
target_frame1 = np.asarray(target_frame1)
#orb1 = cv2.ORB_create(500)
#kp1 = orb1.detect(target_frame1,None)
#kp1, des1 = orb1.compute(target_frame1, kp1)
#features_img1 = cv2.drawKeypoints(target_frame1, kp1, None, color=(255,0,0), flags=0)
#plt.imsave("target_frame1_"+str(target_frame_count)+".jpeg",features_img1)
plt.imsave("target_frame1_"+str(target_frame_count)+".jpeg",target_frame1)
drive_straight_action = robot.drive_straight(distance=cozmo.util.distance_mm(distance_mm=10),speed=cozmo.util.speed_mmps(10), in_parallel=True)
drive_straight_action.wait_for_completed()
robot.set_head_angle(degrees(0.5)).wait_for_completed()
print("Robot's head angle: ",robot.head_angle)
latest_img = None
while latest_img is None:
latest_img = robot.world.latest_image
target_frame2 = latest_img.raw_image
target_frame2 = target_frame2.resize((640,480), Image.ANTIALIAS)
#target_frame2 = target_frame2.convert('L')
target_frame2 = np.asarray(target_frame2)
#orb2 = cv2.ORB_create(500)
#kp2 = orb2.detect(target_frame2,None)
#kp2, des2 = orb2.compute(target_frame2, kp2)
#features_img2 = cv2.drawKeypoints(target_frame2, kp2, None, color=(255,0,0), flags=0)
#plt.imsave("target_frame2_"+str(target_frame_count)+".jpeg",features_img2)
plt.imsave("target_frame2_"+str(target_frame_count)+".jpeg",target_frame2)
target_frame_count = target_frame_count + 1
'''
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
matches = matcher.match(des1, des2, None)
matches.sort(key=lambda x: x.distance, reverse=False)
matches = matches[:10]
imMatches = cv2.drawMatches(target_frame1, kp1, target_frame2, kp2, matches, None)
cv2.imwrite("matches_tf1_tf2.jpg", imMatches)
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points1[i, :] = kp1[match.queryIdx].pt
points2[i, :] = kp2[match.trainIdx].pt
print("Points1 [{}]: {}".format(i,points1[i][0]), points1[i][1],"\tPoints2: ",points2[i][0], points2[i][1])
index = None
dist1_x = []
dist2_x = []
for index in range(len(points1)):
dist1_x.append((W/2.)-points1[index][0]) # Extract only the x-coordinate
dist2_x.append((W/2.)-points2[index][0]) # Extract only the x-coordinate
fw_x = 1./((1./np.array(dist2_x)) - (1./np.array(dist1_x))) # Calculate the image plane to obj plane mapping in x direction
pt1_x = []
pt2_x = []
for index in range(len(points1)):
pt1_x.append(fw_x[index]/(W/2. - points1[index][0]))
pt2_x.append(fw_x[index]/(W/2. - points2[index][0]))
print("Approx. distance[{}]: {}".format(index, pt1_x[index]))
if len(pt2_x) < 10:
break
'''
sys.exit(0)
else: # Detected Dog
dog_counter += 1
if dog_counter >= 6: # Transition to Dog only if Dog appeared for more than 6 times
cat_counter = 0
current_state = 2
# Current State is Dog
elif current_state == 2:
print('\t\t\t\t\t\t\t\t\t\t\t\tDog')
if next_state == 0: # Detected Background
background_counter += 1
if background_counter >= 6: # Transition to Background only if Background appeared for more than 6 times
background_counter = 0
current_state = 0
dog_counter = 0
elif next_state == 2: # Detected Dog itself
dog_counter +=1
if dog_counter >= 30:
print('Cozmo sees a Dog')
robot.drive_wheels(-50, -50)
time.sleep(3)
robot.drive_wheels(70, -70)
time.sleep(2.8)
robot.drive_wheels(0, 0)
break
else: # Detected Cat
cat_counter += 1
if cat_counter >= 6: # Transition to Cat only if Cat appeared for more than 6 times
dog_counter = 0
current_state = 1
def main():
hp, evaluation, run, env, design = parse_arguments()
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
print("final_score_sz is:%d" % (final_score_sz))
gt, frame_name_list, frame_sz, n_frames = _init_video(
env, evaluation, videos_path)
num_frames = np.size(frame_name_list)
scale_factors = hp.scale_step**np.linspace(-np.ceil(hp.scale_num / 2),
np.ceil(hp.scale_num / 2),
hp.scale_num)
# cosine window to penalize large displacements
hann_1d = np.expand_dims(np.hanning(final_score_sz), axis=0)
penalty = np.transpose(hann_1d) * hann_1d
penalty = penalty / np.sum(penalty)
pos_x, pos_y, target_w, target_h = region_to_bbox(gt[start_frame])
context = design.context * (target_w + target_h)
z_sz = np.sqrt(np.prod((target_w + context) * (target_h + context)))
x_sz = float(design.search_sz) / design.exemplar_sz * z_sz
scaled_exemplar = z_sz * scale_factors
scaled_search_area = x_sz * scale_factors
scaled_target_w = target_w * scale_factors
scaled_target_h = target_h * scale_factors
# thresholds to saturate patches shrinking/growing
min_z = hp.scale_min * z_sz
max_z = hp.scale_max * z_sz
min_x = hp.scale_min * x_sz
max_x = hp.scale_max * x_sz
#search size
x_sz0_ph = scaled_search_area[0]
x_sz1_ph = scaled_search_area[1]
x_sz2_ph = scaled_search_area[2]
image = Image.open(frame_name_list[0])
image.show()
image = np.array(image)
# used to pad the crops
if design.pad_with_image_mean:
avg_chan = np.mean(image, axis=(0, 1))
else:
avg_chan = None
# pad with if necessary
frame_padded_z, npad_z = pad_frame_numpy(image, frame_sz, pos_y, pos_x,
z_sz, avg_chan)
# extract tensor of z_crops
# print type(design.exemplar_sz)
z_crops = extract_crops_z_numpy(frame_padded_z, npad_z, pos_y, pos_x, z_sz,
design.exemplar_sz)
print 'the shape of the img z_crops is :' + ' ' + str(np.shape(z_crops))
z_crops = np.squeeze(z_crops)
img = Image.fromarray(z_crops.astype('uint8'), 'RGB')
img.show()
frame_padded_x, npad_x = pad_frame_numpy(image, frame_sz, pos_y, pos_x,
x_sz2_ph, avg_chan)
# extract tensor of x_crops (3 scales)
x_crops = extract_crops_x_numpy(frame_padded_x, npad_x, pos_y, pos_x,
x_sz0_ph, x_sz1_ph, x_sz2_ph,
design.search_sz)
print 'the shape of the img x_crops is :' + ' ' + str(np.shape(x_crops))
x_crops_1 = np.squeeze(x_crops[0, :, :])
img_1 = Image.fromarray(x_crops_1.astype('uint8'), 'RGB')
img_1.show()
x_crops_2 = np.squeeze(x_crops[1, :, :])
img_2 = Image.fromarray(x_crops_2.astype('uint8'), 'RGB')
img_2.show()
x_crops_3 = np.squeeze(x_crops[2, :, :])
img_3 = Image.fromarray(x_crops_3.astype('uint8'), 'RGB')
img_3.show()
def main():
# avoid printing TF debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# TODO: allow parameters from command line or leave everything in json files?
hp, evaluation, run, env, design = parse_arguments()
# Set size for use with tf.image.resize_images with align_corners=True.
# For example,
# [1 4 7] => [1 2 3 4 5 6 7] (length 3*(3-1)+1)
# instead of
# [1 4 7] => [1 1 2 3 4 5 6 7 7] (length 3*3)
final_score_sz = hp.response_up * (design.score_sz - 1) + 1
# build TF graph once for all
filename, image, templates_z, templates_x, scores, scores_original = siam.build_tracking_graph(
final_score_sz, design, env)
# create dataset
dataset = DatasetFactory.create_dataset(name=args_dataset,
dataset_root=dataset_root,
load_img=False)
# iterate through all videos of evaluation.dataset
videos_list = list(dataset.videos.keys())
videos_list.sort()
nv = np.size(videos_list)
for i in range(nv):
current_key = sorted(list(dataset.videos.keys()))[i]
gt, frame_name_list, frame_sz, n_frames = _init_video(
dataset, current_key)
for j in range(1):
start_frame = 0
gt_ = gt[start_frame:, :]
frame_name_list_ = frame_name_list[start_frame:]
pos_x, pos_y, target_w, target_h = region_to_bbox(gt_[0])
bboxes, _ = tracker(videos_list[i], hp, run, design,
frame_name_list_, pos_x, pos_y, target_w,
target_h, final_score_sz, filename, image,
templates_z, templates_x, scores,
scores_original, start_frame)
#Visualize
if visualize:
for bbox, groundt, frame_name in zip(bboxes, gt_,
frame_name_list_):
image = cv2.imread(frame_name)
bbox_pt1, bbox_pt2 = get_bbox_cv(bbox)
bbox_gt1, bbox_gt2 = get_gt_bbox_cv(groundt)
#Draw result
cv2.rectangle(image, bbox_pt1, bbox_pt2, (0, 255, 0))
#Draw ground truth
cv2.rectangle(image, bbox_gt1, bbox_gt2, (0, 0, 0))
cv2.imshow("Results:", image)
cv2.waitKey()
bboxes = bboxes.tolist()
bboxes[0] = [1]
target_dir = os.path.join(result_output, current_key)
if not os.path.exists(target_dir):
os.mkdir(target_dir)
results_file = current_key + "_" + "{:03d}".format(1) + ".txt"
results_abs_file = os.path.join(target_dir, results_file)
with open(results_abs_file, "w") as f:
for bbox in bboxes:
if len(bbox) == 1:
f.write('%d\n' % (bbox[0]))
else:
f.write('%.2f, %.2f, %.2f, %.2f\n' %
(bbox[0], bbox[1], bbox[2], bbox[3]))
