def __init__(self, params):
self.params = params
self.pause_mode = False
self.step = False
self.visdom = None
if self.params.debug > 0 and self.params.visdom_info.get(
'use_visdom', True):
try:
self.visdom = Visdom(self.params.debug, {
'handler': self.visdom_ui_handler,
'win_id': 'Tracking'
},
visdom_info=self.params.visdom_info)
# Show help
help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \
'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \
'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \
'block list.'
self.visdom.register(help_text, 'text', 1, 'Help')
except:
time.sleep(0.5)
print(
'!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n'
'!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!'
)
class Tracker:
"""Wraps the tracker for evaluation and running purposes.
args:
name: Name of tracking method.
parameter_name: Name of parameter file.
run_id: The run id.
display_name: Name to be displayed in the result plots.
"""
def __init__(self, name: str, parameter_name: str, run_id: int = None, display_name: str = None):
assert run_id is None or isinstance(run_id, int)
self.name = name
self.parameter_name = parameter_name
self.run_id = run_id
self.display_name = display_name
env = env_settings()
if self.run_id is None:
self.results_dir = '{}/{}/{}'.format(env.results_path, self.name, self.parameter_name)
self.segmentation_dir = '{}/{}/{}'.format(env.segmentation_path, self.name, self.parameter_name)
else:
self.results_dir = '{}/{}/{}_{:03d}'.format(env.results_path, self.name, self.parameter_name, self.run_id)
self.segmentation_dir = '{}/{}/{}_{:03d}'.format(env.segmentation_path, self.name, self.parameter_name, self.run_id)
tracker_module_abspath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tracker', self.name))
if os.path.isdir(tracker_module_abspath):
tracker_module = importlib.import_module('pytracking.tracker.{}'.format(self.name))
self.tracker_class = tracker_module.get_tracker_class()
else:
self.tracker_class = None
self.visdom = None
def _init_visdom(self, visdom_info, debug):
visdom_info = {} if visdom_info is None else visdom_info
self.pause_mode = False
self.step = False
if debug > 0 and visdom_info.get('use_visdom', True):
try:
self.visdom = Visdom(debug, {'handler': self._visdom_ui_handler, 'win_id': 'Tracking'},
visdom_info=visdom_info)
# Show help
help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \
'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \
'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \
'block list.'
self.visdom.register(help_text, 'text', 1, 'Help')
except:
time.sleep(0.5)
print('!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n'
'!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!')
def _visdom_ui_handler(self, data):
if data['event_type'] == 'KeyPress':
if data['key'] == ' ':
self.pause_mode = not self.pause_mode
elif data['key'] == 'ArrowRight' and self.pause_mode:
self.step = True
def create_tracker(self, params):
tracker = self.tracker_class(params)
tracker.visdom = self.visdom
return tracker
def run_sequence(self, seq, visualization=None, debug=None, visdom_info=None, multiobj_mode=None):
"""Run tracker on sequence.
args:
seq: Sequence to run the tracker on.
visualization: Set visualization flag (None means default value specified in the parameters).
debug: Set debug level (None means default value specified in the parameters).
visdom_info: Visdom info.
multiobj_mode: Which mode to use for multiple objects.
"""
params = self.get_parameters()
visualization_ = visualization
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
if visualization is None:
if debug is None:
visualization_ = getattr(params, 'visualization', False)
else:
visualization_ = True if debug else False
params.visualization = visualization_
params.debug = debug_
self._init_visdom(visdom_info, debug_)
if visualization_ and self.visdom is None:
self.init_visualization()
# Get init information
init_info = seq.init_info()
is_single_object = not seq.multiobj_mode
if multiobj_mode is None:
multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default'))
if multiobj_mode == 'default' or is_single_object:
tracker = self.create_tracker(params)
elif multiobj_mode == 'parallel':
tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom)
else:
raise ValueError('Unknown multi object mode {}'.format(multiobj_mode))
output = self._track_sequence(tracker, seq, init_info)
return output
def _track_sequence(self, tracker, seq, init_info):
# Define outputs
# Each field in output is a list containing tracker prediction for each frame.
# In case of single object tracking mode:
# target_bbox[i] is the predicted bounding box for frame i
# time[i] is the processing time for frame i
# segmentation[i] is the segmentation mask for frame i (numpy array)
# In case of multi object tracking mode:
# target_bbox[i] is an OrderedDict, where target_bbox[i][obj_id] is the predicted box for target obj_id in
# frame i
# time[i] is either the processing time for frame i, or an OrderedDict containing processing times for each
# object in frame i
# segmentation[i] is the multi-label segmentation mask for frame i (numpy array)
output = {'target_bbox': [],
'time': [],
'segmentation': []}
def _store_outputs(tracker_out: dict, defaults=None):
defaults = {} if defaults is None else defaults
for key in output.keys():
val = tracker_out.get(key, defaults.get(key, None))
if key in tracker_out or val is not None:
output[key].append(val)
# Initialize
image = self._read_image(seq.frames[0])
if tracker.params.visualization and self.visdom is None:
self.visualize(image, init_info.get('init_bbox'))
start_time = time.time()
out = tracker.initialize(image, init_info)
if out is None:
out = {}
prev_output = OrderedDict(out)
init_default = {'target_bbox': init_info.get('init_bbox'),
'time': time.time() - start_time,
'segmentation': init_info.get('init_mask')}
_store_outputs(out, init_default)
for frame_num, frame_path in enumerate(seq.frames[1:], start=1):
while True:
if not self.pause_mode:
break
elif self.step:
self.step = False
break
else:
time.sleep(0.1)
image = self._read_image(frame_path)
start_time = time.time()
info = seq.frame_info(frame_num)
info['previous_output'] = prev_output
out = tracker.track(image, info)
prev_output = OrderedDict(out)
_store_outputs(out, {'time': time.time() - start_time})
segmentation = out['segmentation'] if 'segmentation' in out else None
if self.visdom is not None:
tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation)
elif tracker.params.visualization:
self.visualize(image, out['target_bbox'], segmentation)
for key in ['target_bbox', 'segmentation']:
if key in output and len(output[key]) <= 1:
output.pop(key)
return output
def run_video(self, videofilepath, optional_box=None, debug=None, visdom_info=None, save_results=False):
"""Run the tracker with the vieofile.
args:
debug: Debug level.
"""
params = self.get_parameters()
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
params.debug = debug_
params.tracker_name = self.name
params.param_name = self.parameter_name
self._init_visdom(visdom_info, debug_)
multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default'))
if multiobj_mode == 'default':
tracker = self.create_tracker(params)
if hasattr(tracker, 'initialize_features'):
tracker.initialize_features()
elif multiobj_mode == 'parallel':
tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True)
else:
raise ValueError('Unknown multi object mode {}'.format(multiobj_mode))
assert os.path.isfile(videofilepath), "Invalid param {}".format(videofilepath)
", videofilepath must be a valid videofile"
output_boxes = []
cap = cv.VideoCapture(videofilepath)
display_name = 'Display: ' + tracker.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
success, frame = cap.read()
cv.imshow(display_name, frame)
def _build_init_info(box):
return {'init_bbox': OrderedDict({1: box}), 'init_object_ids': [1, ], 'object_ids': [1, ],
'sequence_object_ids': [1, ]}
if success is not True:
print("Read frame from {} failed.".format(videofilepath))
exit(-1)
if optional_box is not None:
assert isinstance(optional_box, (list, tuple))
assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]"
tracker.initialize(frame, _build_init_info(optional_box))
output_boxes.append(optional_box)
else:
while True:
# cv.waitKey()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL,
1.5, (0, 0, 0), 1)
x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False)
init_state = [x, y, w, h]
tracker.initialize(frame, _build_init_info(init_state))
output_boxes.append(init_state)
break
while True:
ret, frame = cap.read()
if frame is None:
break
frame_disp = frame.copy()
# Draw box
out = tracker.track(frame)
state = [int(s) for s in out['target_bbox'][1]]
output_boxes.append(state)
cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]),
(0, 255, 0), 5)
font_color = (0, 0, 0)
cv.putText(frame_disp, 'Tracking!', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 80), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
ret, frame = cap.read()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
(0, 0, 0), 1)
cv.imshow(display_name, frame_disp)
x, y, w, h = cv.selectROI(display_name, frame_disp, fromCenter=False)
init_state = [x, y, w, h]
tracker.initialize(frame, _build_init_info(init_state))
output_boxes.append(init_state)
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
if save_results:
if not os.path.exists(self.results_dir):
os.makedirs(self.results_dir)
video_name = Path(videofilepath).stem
base_results_path = os.path.join(self.results_dir, 'video_{}'.format(video_name))
tracked_bb = np.array(output_boxes).astype(int)
bbox_file = '{}.txt'.format(base_results_path)
np.savetxt(bbox_file, tracked_bb, delimiter='\t', fmt='%d')
def run_webcam(self, debug=None, visdom_info=None):
"""Run the tracker with the webcam.
args:
debug: Debug level.
"""
params = self.get_parameters()
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
params.debug = debug_
params.tracker_name = self.name
params.param_name = self.parameter_name
self._init_visdom(visdom_info, debug_)
multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default'))
if multiobj_mode == 'default':
tracker = self.create_tracker(params)
elif multiobj_mode == 'parallel':
tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True)
else:
raise ValueError('Unknown multi object mode {}'.format(multiobj_mode))
class UIControl:
def __init__(self):
self.mode = 'init' # init, select, track
self.target_tl = (-1, -1)
self.target_br = (-1, -1)
self.new_init = False
def mouse_callback(self, event, x, y, flags, param):
if event == cv.EVENT_LBUTTONDOWN and self.mode == 'init':
self.target_tl = (x, y)
self.target_br = (x, y)
self.mode = 'select'
elif event == cv.EVENT_MOUSEMOVE and self.mode == 'select':
self.target_br = (x, y)
elif event == cv.EVENT_LBUTTONDOWN and self.mode == 'select':
self.target_br = (x, y)
self.mode = 'init'
self.new_init = True
def get_tl(self):
return self.target_tl if self.target_tl[0] < self.target_br[0] else self.target_br
def get_br(self):
return self.target_br if self.target_tl[0] < self.target_br[0] else self.target_tl
def get_bb(self):
tl = self.get_tl()
br = self.get_br()
bb = [min(tl[0], br[0]), min(tl[1], br[1]), abs(br[0] - tl[0]), abs(br[1] - tl[1])]
return bb
ui_control = UIControl()
cap = cv.VideoCapture(0)
display_name = 'Display: ' + self.name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
cv.setMouseCallback(display_name, ui_control.mouse_callback)
next_object_id = 1
sequence_object_ids = []
prev_output = OrderedDict()
while True:
# Capture frame-by-frame
ret, frame = cap.read()
frame_disp = frame.copy()
info = OrderedDict()
info['previous_output'] = prev_output
if ui_control.new_init:
ui_control.new_init = False
init_state = ui_control.get_bb()
info['init_object_ids'] = [next_object_id, ]
info['init_bbox'] = OrderedDict({next_object_id: init_state})
sequence_object_ids.append(next_object_id)
next_object_id += 1
# Draw box
if ui_control.mode == 'select':
cv.rectangle(frame_disp, ui_control.get_tl(), ui_control.get_br(), (255, 0, 0), 2)
if len(sequence_object_ids) > 0:
info['sequence_object_ids'] = sequence_object_ids
out = tracker.track(frame, info)
prev_output = OrderedDict(out)
if 'segmentation' in out:
frame_disp = overlay_mask(frame_disp, out['segmentation'])
if 'target_bbox' in out:
for obj_id, state in out['target_bbox'].items():
state = [int(s) for s in state]
cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]),
_tracker_disp_colors[obj_id], 5)
# Put text
font_color = (0, 0, 0)
cv.putText(frame_disp, 'Select target', (20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 85), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
next_object_id = 1
sequence_object_ids = []
prev_output = OrderedDict()
info = OrderedDict()
info['object_ids'] = []
info['init_object_ids'] = []
info['init_bbox'] = OrderedDict()
tracker.initialize(frame, info)
ui_control.mode = 'init'
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
def run_vot2020(self, debug=None, visdom_info=None):
params = self.get_parameters()
params.tracker_name = self.name
params.param_name = self.parameter_name
params.run_id = self.run_id
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
if debug is None:
visualization_ = getattr(params, 'visualization', False)
else:
visualization_ = True if debug else False
params.visualization = visualization_
params.debug = debug_
self._init_visdom(visdom_info, debug_)
tracker = self.create_tracker(params)
tracker.initialize_features()
output_segmentation = tracker.predicts_segmentation_mask()
import pytracking.evaluation.vot2020 as vot
def _convert_anno_to_list(vot_anno):
vot_anno = [vot_anno[0], vot_anno[1], vot_anno[2], vot_anno[3]]
return vot_anno
def _convert_image_path(image_path):
return image_path
"""Run tracker on VOT."""
if output_segmentation:
handle = vot.VOT("mask")
else:
handle = vot.VOT("rectangle")
vot_anno = handle.region()
image_path = handle.frame()
if not image_path:
return
image_path = _convert_image_path(image_path)
image = self._read_image(image_path)
if output_segmentation:
vot_anno_mask = vot.make_full_size(vot_anno, (image.shape[1], image.shape[0]))
bbox = masks_to_bboxes(torch.from_numpy(vot_anno_mask), fmt='t').squeeze().tolist()
else:
bbox = _convert_anno_to_list(vot_anno)
vot_anno_mask = None
out = tracker.initialize(image, {'init_mask': vot_anno_mask, 'init_bbox': bbox})
if out is None:
out = {}
prev_output = OrderedDict(out)
# Track
while True:
image_path = handle.frame()
if not image_path:
break
image_path = _convert_image_path(image_path)
image = self._read_image(image_path)
info = OrderedDict()
info['previous_output'] = prev_output
out = tracker.track(image, info)
prev_output = OrderedDict(out)
if output_segmentation:
pred = out['segmentation'].astype(np.uint8)
else:
state = out['target_bbox']
pred = vot.Rectangle(*state)
handle.report(pred, 1.0)
segmentation = out['segmentation'] if 'segmentation' in out else None
if self.visdom is not None:
tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation)
elif tracker.params.visualization:
self.visualize(image, out['target_bbox'], segmentation)
def run_vot(self, debug=None, visdom_info=None):
params = self.get_parameters()
params.tracker_name = self.name
params.param_name = self.parameter_name
params.run_id = self.run_id
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
if debug is None:
visualization_ = getattr(params, 'visualization', False)
else:
visualization_ = True if debug else False
params.visualization = visualization_
params.debug = debug_
self._init_visdom(visdom_info, debug_)
tracker = self.create_tracker(params)
tracker.initialize_features()
import pytracking.evaluation.vot as vot
def _convert_anno_to_list(vot_anno):
vot_anno = [vot_anno[0][0][0], vot_anno[0][0][1], vot_anno[0][1][0], vot_anno[0][1][1],
vot_anno[0][2][0], vot_anno[0][2][1], vot_anno[0][3][0], vot_anno[0][3][1]]
return vot_anno
def _convert_image_path(image_path):
image_path_new = image_path[20:- 2]
return "".join(image_path_new)
"""Run tracker on VOT."""
handle = vot.VOT("polygon")
vot_anno_polygon = handle.region()
vot_anno_polygon = _convert_anno_to_list(vot_anno_polygon)
init_state = convert_vot_anno_to_rect(vot_anno_polygon, tracker.params.vot_anno_conversion_type)
image_path = handle.frame()
if not image_path:
return
image_path = _convert_image_path(image_path)
image = self._read_image(image_path)
tracker.initialize(image, {'init_bbox': init_state})
# Track
while True:
image_path = handle.frame()
if not image_path:
break
image_path = _convert_image_path(image_path)
image = self._read_image(image_path)
out = tracker.track(image)
state = out['target_bbox']
handle.report(vot.Rectangle(state[0], state[1], state[2], state[3]))
segmentation = out['segmentation'] if 'segmentation' in out else None
if self.visdom is not None:
tracker.visdom_draw_tracking(image, out['target_bbox'], segmentation)
elif tracker.params.visualization:
self.visualize(image, out['target_bbox'], segmentation)
def get_parameters(self):
"""Get parameters."""
param_module = importlib.import_module('pytracking.parameter.{}.{}'.format(self.name, self.parameter_name))
params = param_module.parameters()
return params
def init_visualization(self):
self.pause_mode = False
self.fig, self.ax = plt.subplots(1)
self.fig.canvas.mpl_connect('key_press_event', self.press)
plt.tight_layout()
def visualize(self, image, state, segmentation=None):
self.ax.cla()
self.ax.imshow(image)
if segmentation is not None:
self.ax.imshow(segmentation, alpha=0.5)
if isinstance(state, (OrderedDict, dict)):
boxes = [v for k, v in state.items()]
else:
boxes = (state,)
for i, box in enumerate(boxes, start=1):
col = _tracker_disp_colors[i]
col = [float(c) / 255.0 for c in col]
rect = patches.Rectangle((box[0], box[1]), box[2], box[3], linewidth=1, edgecolor=col, facecolor='none')
self.ax.add_patch(rect)
if getattr(self, 'gt_state', None) is not None:
gt_state = self.gt_state
rect = patches.Rectangle((gt_state[0], gt_state[1]), gt_state[2], gt_state[3], linewidth=1, edgecolor='g', facecolor='none')
self.ax.add_patch(rect)
self.ax.set_axis_off()
self.ax.axis('equal')
draw_figure(self.fig)
if self.pause_mode:
keypress = False
while not keypress:
keypress = plt.waitforbuttonpress()
def reset_tracker(self):
pass
def press(self, event):
if event.key == 'p':
self.pause_mode = not self.pause_mode
print("Switching pause mode!")
elif event.key == 'r':
self.reset_tracker()
print("Resetting target pos to gt!")
def _read_image(self, image_file: str):
im = cv.imread(image_file)
return cv.cvtColor(im, cv.COLOR_BGR2RGB)
class BaseTracker:
"""Base class for all trackers."""
def visdom_ui_handler(self, data):
if data['event_type'] == 'KeyPress':
if data['key'] == ' ':
self.pause_mode = not self.pause_mode
elif data['key'] == 'ArrowRight' and self.pause_mode:
self.step = True
def __init__(self, params):
self.params = params
self.pause_mode = False
self.step = False
self.visdom = None
if self.params.debug > 0 and self.params.visdom_info.get(
'use_visdom', True):
try:
self.visdom = Visdom(self.params.debug, {
'handler': self.visdom_ui_handler,
'win_id': 'Tracking'
},
visdom_info=self.params.visdom_info)
# Show help
help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \
'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \
'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \
'block list.'
self.visdom.register(help_text, 'text', 1, 'Help')
except:
time.sleep(0.5)
print(
'!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n'
'!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!'
)
def initialize(self, image, info: dict) -> dict:
"""Overload this function in your tracker. This should initialize the model."""
raise NotImplementedError
def track(self, image) -> dict:
"""Overload this function in your tracker. This should track in the frame and update the model."""
raise NotImplementedError
def track_sequence(self, sequence):
"""Run tracker on a sequence."""
output = {'target_bbox': [], 'time': []}
def _store_outputs(tracker_out: dict, defaults=None):
defaults = {} if defaults is None else defaults
for key in tracker_out.keys():
if key not in output:
raise RuntimeError('Unknown output from tracker.')
for key in output.keys():
val = tracker_out.get(key, defaults.get(key, None))
if val is not None:
output[key].append(val)
# Initialize
image = self._read_image(sequence.frames[0])
if self.params.visualization and self.visdom is None:
self.init_visualization()
self.visualize(image, sequence.get('init_bbox'), sequence.name, 0)
start_time = time.time()
out = self.initialize(image, sequence.init_info())
if out is None:
out = {}
_store_outputs(
out, {
'target_bbox': sequence.get('init_bbox'),
'time': time.time() - start_time
})
if self.visdom is not None:
self.visdom.register((image, sequence.get('init_bbox')),
'Tracking', 1, 'Tracking')
# Track
for i, frame in enumerate(sequence.frames[1:]):
while True:
if not self.pause_mode:
break
elif self.step:
self.step = False
break
else:
time.sleep(0.1)
image = self._read_image(frame)
start_time = time.time()
out = self.track(image)
_store_outputs(out, {'time': time.time() - start_time})
if self.visdom is not None:
self.visdom.register((image, out['target_bbox']), 'Tracking',
1, 'Tracking')
elif self.params.visualization:
self.visualize(image, out['target_bbox'], sequence.name, i + 1)
return output
def track_videofile(self, videofilepath, optional_box=None):
"""Run track with a video file input."""
assert os.path.isfile(videofilepath), "Invalid param {}".format(
videofilepath)
", videofilepath must be a valid videofile"
if hasattr(self, 'initialize_features'):
self.initialize_features()
cap = cv.VideoCapture(videofilepath)
display_name = 'Display: ' + self.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
success, frame = cap.read()
cv.imshow(display_name, frame)
if success is not True:
print("Read frame from {} failed.".format(videofilepath))
exit(-1)
if optional_box is not None:
assert isinstance(optional_box, list, tuple)
assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]"
self.initialize(frame, {'init_bbox': optional_box})
else:
while True:
# cv.waitKey()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER',
(20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
(0, 0, 0), 1)
x, y, w, h = cv.selectROI(display_name,
frame_disp,
fromCenter=False)
init_state = [x, y, w, h]
self.initialize(frame, {'init_bbox': init_state})
break
while True:
ret, frame = cap.read()
if frame is None:
return
frame_disp = frame.copy()
# Draw box
out = self.track(frame)
state = [int(s) for s in out['target_bbox']]
cv.rectangle(frame_disp, (state[0], state[1]),
(state[2] + state[0], state[3] + state[1]),
(0, 255, 0), 5)
font_color = (0, 0, 0)
cv.putText(frame_disp, 'Tracking!', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 80),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
ret, frame = cap.read()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER',
(20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
(0, 0, 0), 1)
cv.imshow(display_name, frame_disp)
x, y, w, h = cv.selectROI(display_name,
frame_disp,
fromCenter=False)
init_state = [x, y, w, h]
self.initialize(frame, {'init_bbox': init_state})
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
def track_webcam(self):
"""Run tracker with webcam."""
class UIControl:
def __init__(self):
self.mode = 'init' # init, select, track
self.target_tl = (-1, -1)
self.target_br = (-1, -1)
self.mode_switch = False
def mouse_callback(self, event, x, y, flags, param):
if event == cv.EVENT_LBUTTONDOWN and self.mode == 'init':
self.target_tl = (x, y)
self.target_br = (x, y)
self.mode = 'select'
self.mode_switch = True
elif event == cv.EVENT_MOUSEMOVE and self.mode == 'select':
self.target_br = (x, y)
elif event == cv.EVENT_LBUTTONDOWN and self.mode == 'select':
self.target_br = (x, y)
self.mode = 'track'
self.mode_switch = True
def get_tl(self):
return self.target_tl if self.target_tl[0] < self.target_br[
0] else self.target_br
def get_br(self):
return self.target_br if self.target_tl[0] < self.target_br[
0] else self.target_tl
def get_bb(self):
tl = self.get_tl()
br = self.get_br()
bb = [
min(tl[0], br[0]),
min(tl[1], br[1]),
abs(br[0] - tl[0]),
abs(br[1] - tl[1])
]
return bb
ui_control = UIControl()
cap = cv.VideoCapture(0)
display_name = 'Display: ' + self.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
cv.setMouseCallback(display_name, ui_control.mouse_callback)
if hasattr(self, 'initialize_features'):
self.initialize_features()
while True:
# Capture frame-by-frame
ret, frame = cap.read()
frame_disp = frame.copy()
if ui_control.mode == 'track' and ui_control.mode_switch:
ui_control.mode_switch = False
init_state = ui_control.get_bb()
self.initialize(frame, {'init_bbox': init_state})
# Draw box
if ui_control.mode == 'select':
cv.rectangle(frame_disp, ui_control.get_tl(),
ui_control.get_br(), (255, 0, 0), 2)
elif ui_control.mode == 'track':
out = self.track(frame)
state = [int(s) for s in out['target_bbox']]
cv.rectangle(frame_disp, (state[0], state[1]),
(state[2] + state[0], state[3] + state[1]),
(0, 255, 0), 5)
# Put text
font_color = (0, 0, 0)
if ui_control.mode == 'init' or ui_control.mode == 'select':
cv.putText(frame_disp, 'Select target', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
elif ui_control.mode == 'track':
cv.putText(frame_disp, 'Tracking!', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 80),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
ui_control.mode = 'init'
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
def track_vot(self):
"""Run tracker on VOT."""
def _convert_anno_to_list(vot_anno):
vot_anno = [
vot_anno[0][0][0], vot_anno[0][0][1], vot_anno[0][1][0],
vot_anno[0][1][1], vot_anno[0][2][0], vot_anno[0][2][1],
vot_anno[0][3][0], vot_anno[0][3][1]
]
return vot_anno
def _convert_image_path(image_path):
image_path_new = image_path[20:-2]
return "".join(image_path_new)
handle = vot.VOT("polygon")
vot_anno_polygon = handle.region()
vot_anno_polygon = _convert_anno_to_list(vot_anno_polygon)
init_state = convert_vot_anno_to_rect(
vot_anno_polygon, self.params.vot_anno_conversion_type)
image_path = handle.frame()
if not image_path:
return
image_path = _convert_image_path(image_path)
image = self._read_image(image_path)
self.initialize(image, {'init_bbox': init_state})
if self.visdom is not None:
self.visdom.register((image, init_state), 'Tracking', 1,
'Tracking')
# Track
while True:
while True:
if not self.pause_mode:
break
elif self.step:
self.step = False
break
else:
time.sleep(0.1)
image_path = handle.frame()
if not image_path:
break
image_path = _convert_image_path(image_path)
image = self._read_image(image_path)
out = self.track(image)
state = out['target_bbox']
if self.visdom is not None:
self.visdom.register((image, state), 'Tracking', 1, 'Tracking')
handle.report(vot.Rectangle(state[0], state[1], state[2],
state[3]))
def reset_tracker(self):
pass
def press(self, event):
if event.key == 'p':
self.pause_mode = not self.pause_mode
print("Switching pause mode!")
elif event.key == 'r':
self.reset_tracker()
print("Resetting target pos to gt!")
def init_visualization(self):
# plt.ion()
self.pause_mode = False
self.fig, self.ax = plt.subplots(1)
self.fig.canvas.mpl_connect('key_press_event', self.press)
plt.tight_layout()
def visualize(self, image, state, seq_name, frame_no):
self.ax.cla()
self.ax.imshow(image)
rect = patches.Rectangle((state[0], state[1]),
state[2],
state[3],
linewidth=1,
edgecolor='r',
facecolor='none')
self.ax.add_patch(rect)
if hasattr(self, 'gt_state') and False:
gt_state = self.gt_state
rect = patches.Rectangle((gt_state[0], gt_state[1]),
gt_state[2],
gt_state[3],
linewidth=1,
edgecolor='g',
facecolor='none')
self.ax.add_patch(rect)
self.ax.set_axis_off()
self.ax.axis('equal')
#draw_figure(self.fig)
if not isdir(seq_name):
os.makedirs(seq_name)
self.fig.savefig(
join(seq_name, 'frame' + str(frame_no).zfill(5) + '.png'))
if self.pause_mode:
keypress = False
while not keypress:
keypress = plt.waitforbuttonpress()
def show_image(self, im, plot_name=None, ax=None):
if isinstance(im, torch.Tensor):
im = torch_to_numpy(im)
# plot_id = sum([ord(x) for x in list(plot_name)])
if ax is None:
plot_fig_name = 'debug_fig_' + plot_name
plot_ax_name = 'debug_ax_' + plot_name
if not hasattr(self, plot_fig_name):
fig, ax = plt.subplots(1)
setattr(self, plot_fig_name, fig)
setattr(self, plot_ax_name, ax)
plt.tight_layout()
ax.set_title(plot_name)
else:
fig = getattr(self, plot_fig_name, None)
ax = getattr(self, plot_ax_name, None)
ax.cla()
ax.imshow(im)
ax.set_axis_off()
ax.axis('equal')
ax.set_title(plot_name)
draw_figure(fig)
def _read_image(self, image_file: str):
return cv.cvtColor(cv.imread(image_file), cv.COLOR_BGR2RGB)
class BaseTracker:
"""Base class for all trackers."""
def visdom_ui_handler(self, data):
if data['event_type'] == 'KeyPress':
if data['key'] == ' ':
self.pause_mode = not self.pause_mode
elif data['key'] == 'ArrowRight' and self.pause_mode:
self.step = True
def __init__(self, params):
self.params = params
self.pause_mode = False
self.step = False
self.visdom = None
if self.params.debug > 0 and self.params.visdom_info.get(
'use_visdom', True):
try:
self.visdom = Visdom(self.params.debug, {
'handler': self.visdom_ui_handler,
'win_id': 'Tracking'
},
visdom_info=self.params.visdom_info)
# Show help
help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \
'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \
'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \
'block list.'
self.visdom.register(help_text, 'text', 1, 'Help')
except:
time.sleep(0.5)
print(
'!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n'
'!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!'
)
def initialize(self, image, info: dict) -> dict:
"""Overload this function in your tracker. This should initialize the model."""
raise NotImplementedError
def track(self, image) -> dict:
"""Overload this function in your tracker. This should track in the frame and update the model."""
raise NotImplementedError
def track_sequence(self, sequence):
"""Run tracker on a sequence."""
output = {'target_bbox': [], 'time': [], 'scores': []}
def _store_outputs(tracker_out: dict, defaults=None):
defaults = {} if defaults is None else defaults
for key in tracker_out.keys():
if key not in output:
raise RuntimeError('Unknown output from tracker.')
for key in output.keys():
val = tracker_out.get(key, defaults.get(key, None))
if val is not None:
output[key].append(val)
# Initialize
image = self._read_image(sequence.frames[0])
#['image'] (480, 640, 3)
if hasattr(self.params, 'use_depth_channel'):
if self.params.use_depth_channel:
print('have %d depth frames' % (len(sequence.depth_frames)))
depth = self._read_depth(sequence.depth_frames[0])
#depth = depth/1000.0 #from mm to m
depth = np.repeat(np.expand_dims(depth, axis=2), 3,
axis=2) #.astype(np.uint8)
if image.shape[0] != depth.shape[0]:
depth = depth[1:image.shape[0] + 1, :, :]
#image = np.concatenate((image,np.expand_dims(depth,axis=2)),axis=2).astype(np.uint8)
#print(['image'],image.shape)
#['image'] (480, 640, 4)
#print(['depth', depth.shape, np.mean(depth, (0,1)), np.std(depth, (0,1))])
#['depth', (480, 640), 22.48,19.60]
if self.params.visualization and self.visdom is None:
self.init_visualization()
self.visualize(image[:, :, 0:3], sequence.get('init_bbox'))
start_time = time.time()
if hasattr(self.params, 'use_depth_channel'):
out = self.initialize(image, depth, sequence.init_info())
else:
out = self.initialize(image, sequence.init_info())
if out is None:
out = {}
_store_outputs(
out, {
'target_bbox': sequence.get('init_bbox'),
'time': time.time() - start_time,
'scores': 1.0
})
if self.visdom is not None:
self.visdom.register((image, sequence.get('init_bbox')),
'Tracking', 1, 'Tracking')
# Track
ind_frame = 0
for frame in sequence.frames[1:]:
ind_frame = ind_frame + 1
self.ind_frame = ind_frame
while True:
if not self.pause_mode:
break
elif self.step:
self.step = False
break
else:
time.sleep(0.1)
image = self._read_image(frame)
if hasattr(self.params, 'use_depth_channel'):
#print(['depth image',sequence.depth_frames[ind_frame]])
depth = self._read_depth(sequence.depth_frames[ind_frame])
#depth = depth/1000.0 #from mm to m
depth = np.repeat(np.expand_dims(depth, axis=2), 3,
axis=2) #.astype(np.uint8)
start_time = time.time()
if hasattr(self.params, 'use_depth_channel'):
out = self.track(image, depth)
else:
out = self.track(image)
_store_outputs(
out, {
'time': time.time() - start_time,
'scores': self.debug_info['max_score']
})
#get gt_state if the gt_state for the whole sequence is provided
if sequence.ground_truth_rect.shape[0] > 1:
self.gt_state = sequence.ground_truth_rect[ind_frame]
if self.visdom is not None:
self.visdom.register((image, out['target_bbox']), 'Tracking',
1, 'Tracking')
elif self.params.visualization:
# if hasattr(self.params, 'use_depth_channel'):
# self.visualize(image, out['target_bbox'], out_rgb['target_bbox'], out_depth['target_bbox'])
# else:
self.visualize(image, out['target_bbox'])
#visualize the depth
if hasattr(self.params, 'use_depth_channel'):
if os.path.exists(sequence.depth_frames[ind_frame]):
#dimage=self._read_image(sequence.depth_frames[ind_frame])
self.visualize_depth(
np.uint8(255 * depth / np.max(depth)),
out['target_bbox'])
#print(depth.shape)
pass
return output
def track_videofile(self, videofilepath, optional_box=None):
"""Run track with a video file input."""
assert os.path.isfile(videofilepath), "Invalid param {}".format(
videofilepath)
", videofilepath must be a valid videofile"
if hasattr(self, 'initialize_features'):
self.initialize_features()
cap = cv.VideoCapture(videofilepath)
display_name = 'Display: ' + self.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
success, frame = cap.read()
cv.imshow(display_name, frame)
if success is not True:
print("Read frame from {} failed.".format(videofilepath))
exit(-1)
if optional_box is not None:
assert isinstance(optional_box, list, tuple)
assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]"
self.initialize(frame, {'init_bbox': optional_box})
else:
while True:
# cv.waitKey()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER',
(20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
(0, 0, 0), 1)
x, y, w, h = cv.selectROI(display_name,
frame_disp,
fromCenter=False)
init_state = [x, y, w, h]
self.initialize(frame, {'init_bbox': init_state})
break
while True:
ret, frame = cap.read()
if frame is None:
return
frame_disp = frame.copy()
# Draw box
out = self.track(frame)
state = [int(s) for s in out['target_bbox']]
cv.rectangle(frame_disp, (state[0], state[1]),
(state[2] + state[0], state[3] + state[1]),
(0, 255, 0), 5)
font_color = (0, 0, 0)
cv.putText(frame_disp, 'Tracking!', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 80),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
ret, frame = cap.read()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER',
(20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
(0, 0, 0), 1)
cv.imshow(display_name, frame_disp)
x, y, w, h = cv.selectROI(display_name,
frame_disp,
fromCenter=False)
init_state = [x, y, w, h]
self.initialize(frame, {'init_bbox': init_state})
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
def track_webcam(self):
"""Run tracker with webcam."""
class UIControl:
def __init__(self):
self.mode = 'init' # init, select, track
self.target_tl = (-1, -1)
self.target_br = (-1, -1)
self.mode_switch = False
def mouse_callback(self, event, x, y, flags, param):
if event == cv.EVENT_LBUTTONDOWN and self.mode == 'init':
self.target_tl = (x, y)
self.target_br = (x, y)
self.mode = 'select'
self.mode_switch = True
elif event == cv.EVENT_MOUSEMOVE and self.mode == 'select':
self.target_br = (x, y)
elif event == cv.EVENT_LBUTTONDOWN and self.mode == 'select':
self.target_br = (x, y)
self.mode = 'track'
self.mode_switch = True
def get_tl(self):
return self.target_tl if self.target_tl[0] < self.target_br[
0] else self.target_br
def get_br(self):
return self.target_br if self.target_tl[0] < self.target_br[
0] else self.target_tl
def get_bb(self):
tl = self.get_tl()
br = self.get_br()
bb = [
min(tl[0], br[0]),
min(tl[1], br[1]),
abs(br[0] - tl[0]),
abs(br[1] - tl[1])
]
return bb
ui_control = UIControl()
cap = cv.VideoCapture(0)
display_name = 'Display: ' + self.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
cv.setMouseCallback(display_name, ui_control.mouse_callback)
if hasattr(self, 'initialize_features'):
self.initialize_features()
while True:
# Capture frame-by-frame
ret, frame = cap.read()
frame_disp = frame.copy()
if ui_control.mode == 'track' and ui_control.mode_switch:
ui_control.mode_switch = False
init_state = ui_control.get_bb()
self.initialize(frame, {'init_bbox': init_state})
# Draw box
if ui_control.mode == 'select':
cv.rectangle(frame_disp, ui_control.get_tl(),
ui_control.get_br(), (255, 0, 0), 2)
elif ui_control.mode == 'track':
out = self.track(frame)
state = [int(s) for s in out['target_bbox']]
cv.rectangle(frame_disp, (state[0], state[1]),
(state[2] + state[0], state[3] + state[1]),
(0, 255, 0), 5)
# Put text
font_color = (0, 0, 0)
if ui_control.mode == 'init' or ui_control.mode == 'select':
cv.putText(frame_disp, 'Select target', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
elif ui_control.mode == 'track':
cv.putText(frame_disp, 'Tracking!', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 80),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
ui_control.mode = 'init'
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
def track_vot(self):
"""Run tracker on VOT."""
def _convert_anno_to_list(vot_anno):
vot_anno = [
vot_anno[0][0][0], vot_anno[0][0][1], vot_anno[0][1][0],
vot_anno[0][1][1], vot_anno[0][2][0], vot_anno[0][2][1],
vot_anno[0][3][0], vot_anno[0][3][1]
]
return vot_anno
def _convert_image_path(image_path):
image_path_new = image_path[20:-2]
return "".join(image_path_new)
handle = vot.VOT("polygon")
vot_anno_polygon = handle.region()
vot_anno_polygon = _convert_anno_to_list(vot_anno_polygon)
init_state = convert_vot_anno_to_rect(
vot_anno_polygon, self.params.vot_anno_conversion_type)
image_path = handle.frame()
if not image_path:
return
image_path = _convert_image_path(image_path)
image = self._read_image(image_path)
self.initialize(image, {'init_bbox': init_state})
if self.visdom is not None:
self.visdom.register((image, init_state), 'Tracking', 1,
'Tracking')
# Track
while True:
while True:
if not self.pause_mode:
break
elif self.step:
self.step = False
break
else:
time.sleep(0.1)
image_path = handle.frame()
if not image_path:
break
image_path = _convert_image_path(image_path)
image = self._read_image(image_path)
out = self.track(image)
state = out['target_bbox']
if self.visdom is not None:
self.visdom.register((image, state), 'Tracking', 1, 'Tracking')
handle.report(vot.Rectangle(state[0], state[1], state[2],
state[3]))
def reset_tracker(self):
pass
def press(self, event):
if event.key == 'p':
self.pause_mode = not self.pause_mode
print("Switching pause mode!")
elif event.key == 'r':
self.reset_tracker()
print("Resetting target pos to gt!")
def init_visualization(self):
# plt.ion()
self.pause_mode = False
self.fig, self.ax = plt.subplots(1)
self.fig2, self.ax2 = plt.subplots(1)
self.fig.canvas.mpl_connect('key_press_event', self.press)
plt.tight_layout()
def visualize(self, image, state, *var):
self.ax.cla()
self.ax.imshow(image)
if (state[2] != 0 and state[3] != 0):
self.ax.text(10,
30,
'FOUND',
fontsize=14,
bbox=dict(facecolor='green', alpha=0.2))
else:
self.ax.text(10,
30,
'NOT FOUND',
fontsize=14,
bbox=dict(facecolor='red', alpha=0.2))
pass
if len(var) == 0:
rect = patches.Rectangle((state[0], state[1]),
state[2],
state[3],
linewidth=1,
edgecolor='r',
facecolor='none')
self.ax.add_patch(rect)
if len(var) > 0: #state_rgb, state_depth, provided
state_rgb = var[0]
state_depth = var[1]
#draw one dot for the center of state_rgb
# self.ax.plot(state_rgb[0]+state_rgb[2]/2, state_rgb[1]+state_rgb[3]/2,'ro')
# self.ax.plot(state_depth[0]+state_depth[2]/2, state_depth[1]+state_depth[3]/2, 'bo')
# self.ax.plot(state[0]+state[2]/2, state[1]+state[3]/2,'wo')
#another dot for the center of state_depth
# rect_rgb= patches.Rectangle((state_rgb[0], state_rgb[1]), state_rgb[2], state_rgb[3], linewidth=2, edgecolor='r', facecolor='none')
# self.ax.add_patch(rect_rgb)
rect_depth = patches.Rectangle((state_depth[0], state_depth[1]),
state_depth[2],
state_depth[3],
linewidth=2,
edgecolor='b',
facecolor='none')
self.ax.add_patch(rect_depth)
rect = patches.Rectangle((state[0], state[1]),
state[2],
state[3],
linewidth=1,
edgecolor='w',
facecolor='none')
self.ax.add_patch(rect)
#print(['var', var])
#['var', (tensor([263.5000, 266.5000]), tensor([263.5000, 266.5000]), tensor([263.6045, 271.1568]))]
if hasattr(self, 'gt_state') and True:
gt_state = self.gt_state
self.ax.plot(gt_state[0] + gt_state[2] / 2,
gt_state[1] + gt_state[3] / 2, 'go')
rect = patches.Rectangle((gt_state[0], gt_state[1]),
gt_state[2],
gt_state[3],
linewidth=2,
edgecolor='g',
facecolor='none')
self.ax.add_patch(rect)
self.ax.set_axis_off()
self.ax.axis('equal')
draw_figure(self.fig)
if hasattr(self, 'ind_frame'):
if os.path.exists('./tracking_results/imgs'):
self.fig.savefig('./tracking_results/imgs/img_%d.png' %
self.ind_frame)
if self.pause_mode:
keypress = False
while not keypress:
keypress = plt.waitforbuttonpress()
def visualize_depth(self, image, state):
self.ax2.cla()
self.ax2.imshow(image)
self.ax2.set_axis_off()
self.ax2.axis('equal')
plt.draw()
plt.pause(0.001)
if hasattr(self, 'ind_frame'):
if os.path.exists('./tracking_results/imgs'):
self.fig2.savefig('./tracking_results/imgs/depth_%d.png' %
self.ind_frame)
if self.pause_mode:
plt.waitforbuttonpress()
def show_image(self, im, plot_name=None, ax=None):
if isinstance(im, torch.Tensor):
im = torch_to_numpy(im)
# plot_id = sum([ord(x) for x in list(plot_name)])
if ax is None:
plot_fig_name = 'debug_fig_' + plot_name
plot_ax_name = 'debug_ax_' + plot_name
if not hasattr(self, plot_fig_name):
fig, ax = plt.subplots(1)
setattr(self, plot_fig_name, fig)
setattr(self, plot_ax_name, ax)
plt.tight_layout()
ax.set_title(plot_name)
else:
fig = getattr(self, plot_fig_name, None)
ax = getattr(self, plot_ax_name, None)
ax.cla()
ax.imshow(im)
ax.set_axis_off()
ax.axis('equal')
ax.set_title(plot_name)
draw_figure(fig)
def _read_depth(self, image_file: str):
# Full kernels
FULL_KERNEL_3 = np.ones((3, 3), np.uint8)
FULL_KERNEL_5 = np.ones((5, 5), np.uint8)
FULL_KERNEL_7 = np.ones((7, 7), np.uint8)
FULL_KERNEL_9 = np.ones((9, 9), np.uint8)
FULL_KERNEL_31 = np.ones((31, 31), np.uint8)
depth = cv.imread(image_file, cv.COLOR_BGR2GRAY)
#print(['_read_depth', depth.min(), depth.max(), depth.mean(), depth.std()])
if 'Princeton' in image_file: #depth.max()>=60000: # bug found, we need to bitshift depth.
depth = np.bitwise_or(np.right_shift(depth, 3),
np.left_shift(depth, 13))
depth = depth / 1000.0
depth[depth >= 8.0] = 8.0
depth[depth <= 0.0] = 8.0
#depth=8.0-depth
# Hole closing
depth = cv.morphologyEx(depth, cv.MORPH_CLOSE, FULL_KERNEL_7)
#depth = 255.0*depth/(np.max(depth)+1e-3)
return depth
def _read_image(self, image_file: str):
return cv.cvtColor(cv.imread(image_file), cv.COLOR_BGR2RGB)
class TrackerYolo:
"""Wraps the tracker for evaluation and running purposes.
args:
name: Name of tracking method.
parameter_name: Name of parameter file.
run_id: The run id.
display_name: Name to be displayed in the result plots.
"""
def __init__(self, name: str, parameter_name: str, run_id: int = None, display_name: str = None):
assert run_id is None or isinstance(run_id, int)
self.name = name
self.parameter_name = parameter_name
self.run_id = run_id
self.display_name = display_name
env = env_settings()
if self.run_id is None:
self.results_dir = '{}/{}/{}'.format(env.results_path, self.name, self.parameter_name)
self.segmentation_dir = '{}/{}/{}'.format(env.segmentation_path, self.name, self.parameter_name)
else:
self.results_dir = '{}/{}/{}_{:03d}'.format(env.results_path, self.name, self.parameter_name, self.run_id)
self.segmentation_dir = '{}/{}/{}_{:03d}'.format(env.segmentation_path, self.name, self.parameter_name, self.run_id)
tracker_module_abspath = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'tracker', self.name))
if os.path.isdir(tracker_module_abspath):
tracker_module = importlib.import_module('pytracking.tracker.{}'.format(self.name))
self.tracker_class = tracker_module.get_tracker_class()
else:
self.tracker_class = None
self.visdom = None
def _init_visdom(self, visdom_info, debug):
visdom_info = {} if visdom_info is None else visdom_info
self.pause_mode = False
self.step = False
if debug > 0 and visdom_info.get('use_visdom', True):
try:
self.visdom = Visdom(debug, {'handler': self._visdom_ui_handler, 'win_id': 'Tracking'},
visdom_info=visdom_info)
# Show help
help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \
'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \
'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \
'block list.'
self.visdom.register(help_text, 'text', 1, 'Help')
except:
time.sleep(0.5)
print('!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n'
'!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!')
def _visdom_ui_handler(self, data):
if data['event_type'] == 'KeyPress':
if data['key'] == ' ':
self.pause_mode = not self.pause_mode
elif data['key'] == 'ArrowRight' and self.pause_mode:
self.step = True
def create_tracker(self, params):
tracker = self.tracker_class(params)
tracker.visdom = self.visdom
return tracker
def run_video(self, videofilepath, optional_box=None, debug=None, visdom_info=None, save_results=False):
"""Run the tracker with the vieofile.
args:
debug: Debug level.
"""
def yolo_search(W, H, frame_yolo):
# if the frame dimensions are empty, grab them
if W is None or H is None:
(H, W) = frame_yolo.shape[:2]
# construct a blob from the input frame and then perform a forward
# pass of the YOLO object detector, giving us our bounding boxes
# and associated probabilities
blob = cv.dnn.blobFromImage(frame_yolo, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
layerOutputs = net.forward(ln)
# initialize our lists of detected bounding boxes, confidences,
# and class IDs, respectively
boxes = []
confidences = []
classIDs = []
# loop over each of the layer outputs
for output in layerOutputs:
# loop over each of the detections
for detection in output:
# extract the class ID and confidence (i.e., probability)
# of the current object detection
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# filter weak prediction and unrelated classes
if classID not in outdoor_classes and confidence > 0.5:
# scale the bounding box coordinates back relative to
# the size of the image, keeping in mind that YOLO
# actually returns the center (x, y)-coordinates of
# the bounding box followed by the boxes' width and
# height
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
# use the center (x, y)-coordinates to derive the top
# and and left corner of the bounding box
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
# update our list of bounding box coordinates,
# confidences, and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# apply non-maxima suppression to suppress weak, overlapping
# bounding boxes
idxs = cv.dnn.NMSBoxes(boxes, confidences, 0.5, 0.3)
# ensure at least one detection exists
if len(idxs) > 0:
# loop over the indexes we are keeping
for i in idxs.flatten():
# extract the bounding box coordinates
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
# draw a bounding box rectangle and label on the frame
color = [int(c) for c in COLORS[classIDs[i]]]
cv.rectangle(frame_yolo, (x, y), (x + w, y + h), color, 2)
text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
cv.putText(frame_yolo, text, (x, y - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
if classIDs[i] == 45: # 0 - person, 65 - remote 45 - bowl
detection_flag = 1
tl_coor = (x, y) # top left coordinates
br_coor = ((x + w), (y + h)) # bottom right coordinates
cv.rectangle(frame_yolo, tl_coor, br_coor, (255, 255, 255), 2)
return tl_coor, br_coor, detection_flag, frame_yolo
return (0, 0), (0, 0), 0, frame_yolo
# load the COCO class labels our YOLO model was trained on
# and the classes that wont be used (coco.names contains the names)
# Init a detection flag
det_flag = 0
stop_yolo = 0
labelsPath = "/home/ebo/Parrot_CV_Project/local_yolo/yolo-coco/coco.names"
LABELS = open(labelsPath).read().strip().split("\n")
outdoor_classes = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
17, 18, 19, 20, 21, 22, 23,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38]
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
# derive the paths to the YOLO weights and model configuration
weightsPath = "/home/ebo/Parrot_CV_Project/local_yolo/yolo-coco/yolov3.weights"
configPath = "/home/ebo/Parrot_CV_Project/local_yolo/yolo-coco/yolov3.cfg"
# load our YOLO object detector trained on COCO dataset (80 classes)
print("[INFO] loading YOLO from disk...")
net = cv.dnn.readNetFromDarknet(configPath, weightsPath)
# determine only the output layers from yolo
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
(W, H) = (None, None)
params = self.get_parameters()
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
params.debug = debug_
params.tracker_name = self.name
params.param_name = self.parameter_name
self._init_visdom(visdom_info, debug_)
multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default'))
if multiobj_mode == 'default':
tracker = self.create_tracker(params)
if hasattr(tracker, 'initialize_features'):
tracker.initialize_features()
elif multiobj_mode == 'parallel':
tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True)
else:
raise ValueError('Unknown multi object mode {}'.format(multiobj_mode))
assert os.path.isfile(videofilepath), "Invalid param {}".format(videofilepath)
", videofilepath must be a valid videofile"
output_boxes = []
cap = cv.VideoCapture(videofilepath)
display_name = 'Display: ' + tracker.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
success, frame = cap.read()
cv.imshow(display_name, frame)
def _build_init_info(box):
return {'init_bbox': OrderedDict({1: box}), 'init_object_ids': [1, ], 'object_ids': [1, ],
'sequence_object_ids': [1, ]}
if success is not True:
print("Read frame from {} failed.".format(videofilepath))
exit(-1)
while True:
ret, frame = cap.read()
if frame is None:
break
frame_disp = frame.copy()
if W is None or H is None:
(H, W) = frame_disp.shape[:2]
if stop_yolo == 0:
tl_yolo, br_yolo, det_flag, frame_disp = yolo_search(W, H, frame.copy())
cv.putText(frame_disp, "Searching: BOWL", (50, 50), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
if det_flag == 1:
if stop_yolo == 0:
stop_yolo = 1
x = tl_yolo[0]
y = tl_yolo[1]
w = abs(br_yolo[0] - tl_yolo[0])
h = abs(br_yolo[1] - tl_yolo[1])
init_state = [x, y, w, h]
tracker.initialize(frame, _build_init_info(init_state))
output_boxes.append(init_state)
# Draw box
out = tracker.track(frame)
state = [int(s) for s in out['target_bbox'][1]]
output_boxes.append(state)
tl = (state[0], state[1])
br = (state[2] + state[0], state[3] + state[1])
w = state[2]
h = state[3]
cv.rectangle(frame_disp, tl, br, (0, 255, 0), 5)
center = (int(tl[0] + w/2), int(tl[1] + h/2))
cv.circle(frame_disp, center, 3, (0, 0, 255), -1)
cv.putText(frame_disp, "FOUND BOWL", (50, 50), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
if center[0] < W*0.40:
cv.putText(frame_disp, "MOVE LEFT", (50, 150), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
elif center[0] > W*0.60:
cv.putText(frame_disp, "MOVE RIGHT", (450, 150), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
if center[1] < H*0.40:
cv.putText(frame_disp, "MOVE UP", (200, 50), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
elif center[1] > H*0.60:
cv.putText(frame_disp, "MOVE DOWN", (200, 300), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
if w*h < W*H*0.05:
cv.putText(frame_disp, "MOVE FORWARD", (int(W/2), int(H/2)), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
elif w*h > W*H*0.15:
cv.putText(frame_disp, "MOVE BACK", (int(W/2), int(H/2)), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
if save_results:
if not os.path.exists(self.results_dir):
os.makedirs(self.results_dir)
video_name = Path(videofilepath).stem
base_results_path = os.path.join(self.results_dir, 'video_{}'.format(video_name))
tracked_bb = np.array(output_boxes).astype(int)
bbox_file = '{}.txt'.format(base_results_path)
np.savetxt(bbox_file, tracked_bb, delimiter='\t', fmt='%d')
def run_webcam(self, debug=None, visdom_info=None):
"""Run the tracker with the webcam.
args:
debug: Debug level.
"""
def yolo_search(W, H, frame_yolo):
fl = 0
# if the frame dimensions are empty, grab them
if W is None or H is None:
(H, W) = frame_yolo.shape[:2]
# construct a blob from the input frame and then perform a forward
# pass of the YOLO object detector, giving us our bounding boxes
# and associated probabilities
blob = cv.dnn.blobFromImage(frame_yolo, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
layerOutputs = net.forward(ln)
# initialize our lists of detected bounding boxes, confidences,
# and class IDs, respectively
boxes = []
confidences = []
classIDs = []
# loop over each of the layer outputs
for output in layerOutputs:
# loop over each of the detections
for detection in output:
# extract the class ID and confidence (i.e., probability)
# of the current object detection
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# filter weak prediction and unrelated classes
if classID not in outdoor_classes and confidence > 0.5:
# scale the bounding box coordinates back relative to
# the size of the image, keeping in mind that YOLO
# actually returns the center (x, y)-coordinates of
# the bounding box followed by the boxes' width and
# height
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
# use the center (x, y)-coordinates to derive the top
# and and left corner of the bounding box
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
# update our list of bounding box coordinates,
# confidences, and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# apply non-maxima suppression to suppress weak, overlapping
# bounding boxes
idxs = cv.dnn.NMSBoxes(boxes, confidences, 0.5, 0.3)
# ensure at least one detection exists
if len(idxs) > 0:
# loop over the indexes we are keeping
for i in idxs.flatten():
# extract the bounding box coordinates
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
# draw a bounding box rectangle and label on the frame
color = [int(c) for c in COLORS[classIDs[i]]]
cv.rectangle(frame_yolo, (x, y), (x + w, y + h), color, 2)
text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
cv.putText(frame_yolo, text, (x, y - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
if classIDs[i] == 0:
detection_flag = 1
tl_coor = (x, y) # top left coordinates
br_coor = ((x + w), (y + h)) # bottom right coordinates
coordinates_text = "{} {}".format(tl_coor, br_coor)
cv.rectangle(frame_yolo, tl_coor, br_coor, (255, 255, 255), 2)
fl = 1
return tl_coor, br_coor, detection_flag, frame_yolo
if fl == 0:
return (0, 0), (0, 0), 0, frame_yolo
# load the COCO class labels our YOLO model was trained on
# and the classes that wont be used (coco.names contains the names)
# Init a detection flag
det_flag = 0
labelsPath = "/home/ebo/Parrot_CV_Project/local_yolo/yolo-coco/coco.names"
LABELS = open(labelsPath).read().strip().split("\n")
outdoor_classes = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
17, 18, 19, 20, 21, 22, 23,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38]
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
# derive the paths to the YOLO weights and model configuration
weightsPath = "/home/ebo/Parrot_CV_Project/local_yolo/yolo-coco/yolov3.weights"
configPath = "/home/ebo/Parrot_CV_Project/local_yolo/yolo-coco/yolov3.cfg"
# load our YOLO object detector trained on COCO dataset (80 classes)
print("[INFO] loading YOLO from disk...")
net = cv.dnn.readNetFromDarknet(configPath, weightsPath)
# determine only the output layers from yolo
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
(W, H) = (None, None)
temp_flag = 0
params = self.get_parameters()
debug_ = debug
if debug is None:
debug_ = getattr(params, 'debug', 0)
params.debug = debug_
params.tracker_name = self.name
params.param_name = self.parameter_name
self._init_visdom(visdom_info, debug_)
multiobj_mode = getattr(params, 'multiobj_mode', getattr(self.tracker_class, 'multiobj_mode', 'default'))
if multiobj_mode == 'default':
tracker = self.create_tracker(params)
elif multiobj_mode == 'parallel':
tracker = MultiObjectWrapper(self.tracker_class, params, self.visdom, fast_load=True)
else:
raise ValueError('Unknown multi object mode {}'.format(multiobj_mode))
class UIControl:
def __init__(self):
self.mode = 'init' # init, select, track
self.new_init = False
def get_bb(self):
# yolo bb
if det_flag == 1:
tl = tl_yolo
br = br_yolo
bb = [min(tl[0], br[0]), min(tl[1], br[1]), abs(br[0] - tl[0]), abs(br[1] - tl[1])]
return bb
ui_control = UIControl()
cap = cv.VideoCapture(0)
display_name = 'Display: ' + self.name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
next_object_id = 1
sequence_object_ids = []
prev_output = OrderedDict()
while True:
# Capture frame-by-frame
ret, frame = cap.read()
frame_disp = frame.copy()
tl_yolo, br_yolo, det_flag, frame_yolo = yolo_search(W, H, frame.copy())
info = OrderedDict()
info['previous_output'] = prev_output
# If there's a human detection, show it
if det_flag == 1 and temp_flag == 0:
init_state = ui_control.get_bb()
info['init_object_ids'] = [next_object_id, ]
info['init_bbox'] = OrderedDict({next_object_id: init_state})
sequence_object_ids.append(next_object_id)
next_object_id += 1
temp_flag = 1
if len(sequence_object_ids) > 0:
info['sequence_object_ids'] = sequence_object_ids
out = tracker.track(frame, info)
prev_output = OrderedDict(out)
if 'segmentation' in out:
frame_disp = overlay_mask(frame_disp, out['segmentation'])
if 'target_bbox' in out:
for obj_id, state in out['target_bbox'].items():
state = [int(s) for s in state]
cv.rectangle(frame_disp, (state[0], state[1]), (state[2] + state[0], state[3] + state[1]),
_tracker_disp_colors[obj_id], 5)
# Put text
font_color = (0, 0, 0)
cv.putText(frame_disp, 'Press r to reset', (20, 25), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 55), cv.FONT_HERSHEY_COMPLEX_SMALL, 1,
font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
cv.imshow("YOLO", frame_yolo)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
next_object_id = 1
sequence_object_ids = []
prev_output = OrderedDict()
info = OrderedDict()
info['object_ids'] = []
info['init_object_ids'] = []
info['init_bbox'] = OrderedDict()
tracker.initialize(frame, info)
ui_control.mode = 'init'
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
def get_parameters(self):
"""Get parameters."""
param_module = importlib.import_module('pytracking.parameter.{}.{}'.format(self.name, self.parameter_name))
params = param_module.parameters()
return params
def init_visualization(self):
self.pause_mode = False
self.fig, self.ax = plt.subplots(1)
self.fig.canvas.mpl_connect('key_press_event', self.press)
plt.tight_layout()
def visualize(self, image, state, segmentation=None):
self.ax.cla()
self.ax.imshow(image)
if segmentation is not None:
self.ax.imshow(segmentation, alpha=0.5)
if isinstance(state, (OrderedDict, dict)):
boxes = [v for k, v in state.items()]
else:
boxes = (state,)
for i, box in enumerate(boxes, start=1):
col = _tracker_disp_colors[i]
col = [float(c) / 255.0 for c in col]
rect = patches.Rectangle((box[0], box[1]), box[2], box[3], linewidth=1, edgecolor=col, facecolor='none')
self.ax.add_patch(rect)
if getattr(self, 'gt_state', None) is not None:
gt_state = self.gt_state
rect = patches.Rectangle((gt_state[0], gt_state[1]), gt_state[2], gt_state[3], linewidth=1, edgecolor='g', facecolor='none')
self.ax.add_patch(rect)
self.ax.set_axis_off()
self.ax.axis('equal')
draw_figure(self.fig)
if self.pause_mode:
keypress = False
while not keypress:
keypress = plt.waitforbuttonpress()
def reset_tracker(self):
pass
def press(self, event):
if event.key == 'p':
self.pause_mode = not self.pause_mode
print("Switching pause mode!")
elif event.key == 'r':
self.reset_tracker()
print("Resetting target pos to gt!")
def _read_image(self, image_file: str):
im = cv.imread(image_file)
return cv.cvtColor(im, cv.COLOR_BGR2RGB)