def perform_tracking(vidreader,smoothMasks,num_blocks,num_prev_frames,num_objects=1):
start = time.time();
tracker = tracker_instance(TrackerMethods.MIXTURE_BASED); tracker.numMixtures = num_objects;
prev_rects = [np.array([vidreader.width/4,vidreader.height/4,\
vidreader.width/2,vidreader.height/2],\
dtype=np.float32) for idx in range(num_objects)];
prev_centers = [np.array([vidreader.width/2,vidreader.height/2],
dtype=np.float32) for idx in range(num_objects)];
vidreader.__reset__(); N = vidreader.frames;
skip_frames = num_prev_frames + num_blocks/2;
frame_idx = 0; numFrames= len(smoothMasks);
frame_all_window_center = []; window_size = np.zeros((num_objects,2));
while(frame_idx < numFrames):
print 'Tracking ... {0}%\r'.format((frame_idx*100/N)),
(cnt,frames) = vidreader.read(frame_idx+skip_frames,num_blocks);
if cnt > 1:
window_frames = tracker.track_object(frames,smoothMasks[frame_idx:frame_idx+cnt]);
s = frame_idx; e = s + cnt;
for window_frame in window_frames:
all_window_center = [];
for (idx,_window) in enumerate(window_frame):
(window,lbl) = _window
rect = np.array([window[0],window[1],window[2]-window[0],window[3]-window[1]],dtype=np.float32);
cv2.accumulateWeighted(rect,prev_rects[idx],0.1,None);
window_center = prev_rects[idx][:2]+(prev_rects[idx][2:]/2);
cv2.accumulateWeighted(window_center,prev_centers[idx],0.3,None);
window_center = prev_centers[idx].copy();
window_size[idx] = np.maximum(window_size[idx],prev_rects[idx][2:]);
all_window_center.extend([window_center]);
frame_all_window_center.extend([all_window_center]);
frame_idx += num_blocks;
time_taken = time.time()-start;
print "Tracking .... [DONE] in ",time_taken," seconds"
return frame_all_window_center,window_size
def perform_tracking(vidreader,
smoothMasks,
num_blocks,
num_prev_frames,
num_objects=1):
start = time.time()
tracker = tracker_instance(TrackerMethods.MIXTURE_BASED)
tracker.numMixtures = num_objects
prev_rects = [np.array([vidreader.width/4,vidreader.height/4,\
vidreader.width/2,vidreader.height/2],\
dtype=np.float32) for idx in range(num_objects)]
prev_centers = [
np.array([vidreader.width / 2, vidreader.height / 2], dtype=np.float32)
for idx in range(num_objects)
]
vidreader.__reset__()
N = vidreader.frames
skip_frames = num_prev_frames + num_blocks / 2
frame_idx = 0
numFrames = len(smoothMasks)
frame_all_window_center = []
window_size = np.zeros((num_objects, 2))
while (frame_idx < numFrames):
print 'Tracking ... {0}%\r'.format((frame_idx * 100 / N)),
(cnt, frames) = vidreader.read(frame_idx + skip_frames, num_blocks)
if cnt > 1:
window_frames = tracker.track_object(
frames, smoothMasks[frame_idx:frame_idx + cnt])
s = frame_idx
e = s + cnt
for window_frame in window_frames:
all_window_center = []
for (idx, _window) in enumerate(window_frame):
(window, lbl) = _window
rect = np.array([
window[0], window[1], window[2] - window[0],
window[3] - window[1]
],
dtype=np.float32)
cv2.accumulateWeighted(rect, prev_rects[idx], 0.1, None)
window_center = prev_rects[idx][:2] + (
prev_rects[idx][2:] / 2)
cv2.accumulateWeighted(window_center, prev_centers[idx],
0.3, None)
window_center = prev_centers[idx].copy()
window_size[idx] = np.maximum(window_size[idx],
prev_rects[idx][2:])
all_window_center.extend([window_center])
frame_all_window_center.extend([all_window_center])
frame_idx += num_blocks
time_taken = time.time() - start
print "Tracking .... [DONE] in ", time_taken, " seconds"
return frame_all_window_center, window_size
from dnn_predict import get_instance as dnn_instance
from io_module.video_reader import VideoReader
from io_module.video_writer import VideoWriter;
from io_module.block_reader import BlockReader
from saliency import SaliencyProps,SaliencyMethods,get_instance as sal_instance
from bg_sub import get_instance as bg_instance,BGMethods
from tracker import get_instance as tracker_instance,TrackerMethods
from smoothing import get_instance as smooth_instance,SmoothMethods
from features.pixellete import allFeats as feats
from features.color import GRAY
from utils import normalize
sal = sal_instance(SaliencyMethods.REGION_CONTRAST,SaliencyProps())
bg = bg_instance(BGMethods.FRAME_DIFFERENCING);
smoothner = smooth_instance(feats,SmoothMethods.GMM_BASED);
tracker = tracker_instance(TrackerMethods.MIXTURE_BASED);
KERNEL = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3));
def __morphologicalOps__(mask):
#_mask = binary_fill_holes(mask)
_mask = cv2.medianBlur(np.uint8(mask),3)
_mask = cv2.morphologyEx(_mask, cv2.MORPH_CLOSE,KERNEL)
_mask = binary_fill_holes(_mask)
_mask = remove_small_objects(_mask,min_size=128,connectivity=2)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
_mask = cv2.dilate(np.uint8(_mask),kernel,iterations = 1)
return _mask;
def load_labels(fileName):
idx = 0; labels ={}
with open(fileName,'r') as f:
