def track_min_objects(video_info):
previous_frame = None
previous_num_obj = -1
tracked_video = []
frame_id = 0
min_rects = []
min_frame_id = None
min_num_obj = None
for frame_info in video_info:
if (min_num_obj is None) & (len(frame_info.rects) > 0):
min_num_obj = len(frame_info.rects)
min_frame_id = frame_id
if (len(frame_info.rects) < min_num_obj) & (len(frame_info.rects) > 0):
min_num_obj = len(frame_info.rects)
min_frame_id = frame_id
frame_id = frame_id + 1
min_rects = multiclass_rectangle.duplicate_rects(
video_info[min_frame_id].rects)
("Min num object video:%d" % min_num_obj)
for frame_info in video_info:
print("Tracking Frame Nr: %d" % frame_info.frame)
print("Len Rects Frame: %d" % len(frame_info.rects))
current_frame = frame.Frame_Info()
current_frame = frame_info.duplicate()
current_frame.rects = []
if previous_frame is not None:
print("Min num object video:%d" % min_num_obj)
print("Len Previous Rects Frame: %d" % len(previous_frame.rects))
for rect in previous_frame.rects:
print(len(current_frame.rects))
if len(current_frame.rects) <= min_num_obj:
current_rect = multiclass_rectangle.pop_max_iou(
frame_info.rects, rect)
current_rect.load_trackID(rect.trackID)
current_frame.append_labeled_rect(current_rect)
else:
trackID = 1
for rect in min_rects:
if len(current_frame.rects) < min_num_obj:
current_rect = multiclass_rectangle.pop_max_iou(
frame_info.rects, rect)
current_rect.load_trackID(trackID)
current_frame.append_labeled_rect(current_rect)
trackID = trackID + 1
previous_frame = current_frame.duplicate()
previous_frame.rects = multiclass_rectangle.duplicate_rects(
current_frame.rects)
print("Current Frame obj:%d" % len(current_frame.rects))
tracked_video.insert(len(tracked_video), current_frame)
return tracked_video
def track_min_objects(video_info):
previous_frame= None
previous_num_obj=-1
tracked_video=[]
frame_id=0
min_rects=[]
min_frame_id=None
min_num_obj=None
for frame_info in video_info:
if (min_num_obj is None) & (len(frame_info.rects) >0):
min_num_obj = len(frame_info.rects)
min_frame_id=frame_id
if (len(frame_info.rects) < min_num_obj ) & (len(frame_info.rects) >0):
min_num_obj = len(frame_info.rects)
min_frame_id=frame_id
frame_id=frame_id+1
min_rects = multiclass_rectangle.duplicate_rects(video_info[min_frame_id].rects)
print "Min num object video:%d"%min_num_obj
for frame_info in video_info:
print "Tracking Frame Nr: %d"%frame_info.frame
print "Len Rects Frame: %d"%len(frame_info.rects)
current_frame = frame.Frame_Info()
current_frame=frame_info.duplicate()
current_frame.rects=[]
if previous_frame is not None:
print "Min num object video:%d"%min_num_obj
print "Len Previous Rects Frame: %d"%len(previous_frame.rects)
for rect in previous_frame.rects:
print len(current_frame.rects)
if len(current_frame.rects)<=min_num_obj:
current_rect = multiclass_rectangle.pop_max_iou(frame_info.rects,rect)
current_rect.load_trackID(rect.trackID)
current_frame.append_labeled_rect(current_rect)
else:
trackID=1
for rect in min_rects:
if len(current_frame.rects)<min_num_obj:
current_rect = multiclass_rectangle.pop_max_iou(frame_info.rects,rect)
current_rect.load_trackID(trackID)
current_frame.append_labeled_rect(current_rect)
trackID=trackID+1
previous_frame=current_frame.duplicate()
previous_frame.rects= multiclass_rectangle.duplicate_rects(current_frame.rects)
print "Current Frame obj:%d"%len(current_frame.rects)
tracked_video.insert(len(tracked_video), current_frame)
return tracked_video
def save_best_iou(val_bbox, output_bbox):
progress = progressbar.ProgressBar(widgets=[
progressbar.Bar('=', '[', ']'), ' ',
progressbar.Percentage(), ' ',
progressbar.ETA()
])
count_best_bbox = 0
len_val_bbox = len(val_bbox)
len_output_bbox = len(output_bbox)
count_missing_boxes = 0
with open("best_iou.txt", 'a') as d:
for i in progress(range(0, len(val_bbox))):
for rect in val_bbox[i].rects:
if (len(output_bbox[i].rects) > 0):
selected = multiclass_rectangle.pop_max_iou(
output_bbox[i].rects, rect)
count_best_bbox = count_best_bbox + 1
d.write(
str(val_bbox[i].frame) + ' ' + str(rect.label_chall) +
' 0.5 ' + str(selected.x1) + ' ' + str(selected.y1) +
' ' + str(selected.x2) + ' ' + str(selected.y2) +
os.linesep)
else:
count_missing_boxes = count_missing_boxes + 1
def track_objects(video_info):
previous_frame = None
previous_num_obj = -1
tracked_video = []
for frame_info in video_info:
print("Tracking Frame Nr: %d" % frame_info.frame)
print("Len Rects Frame: %d" % len(frame_info.rects))
current_frame = frame.Frame_Info()
current_frame = frame_info.duplicate()
current_frame.rects = []
if previous_frame is not None:
print("Len Previous Rects Frame: %d" % len(previous_frame.rects))
for rect in previous_frame.rects:
print(len(current_frame.rects))
current_rect = multiclass_rectangle.pop_max_iou(
frame_info.rects, rect)
current_rect.load_trackID(rect.trackID)
current_frame.append_labeled_rect(current_rect)
else:
trackID = 1
picked_rect = Utils_Tensorbox.NMS(frame_info.rects)
for rect in picked_rect:
current_rect = rect.duplicate()
current_rect.load_trackID(trackID)
current_frame.append_labeled_rect(current_rect)
trackID = trackID + 1
previous_frame = current_frame.duplicate()
previous_frame.rects = multiclass_rectangle.duplicate_rects(
current_frame.rects)
print("Current Frame obj:%d" % len(current_frame.rects))
tracked_video.insert(len(tracked_video), current_frame)
return tracked_video
def track_objects(video_info):
previous_frame= None
previous_num_obj=-1
tracked_video=[]
for frame_info in video_info:
print "Tracking Frame Nr: %d"%frame_info.frame
print "Len Rects Frame: %d"%len(frame_info.rects)
current_frame = frame.Frame_Info()
current_frame=frame_info.duplicate()
current_frame.rects=[]
if previous_frame is not None:
print "Len Previous Rects Frame: %d"%len(previous_frame.rects)
for rect in previous_frame.rects:
print len(current_frame.rects)
current_rect = multiclass_rectangle.pop_max_iou(frame_info.rects,rect)
current_rect.load_trackID(rect.trackID)
current_frame.append_labeled_rect(current_rect)
else:
trackID=1
picked_rect=Utils_Tensorbox.NMS(frame_info.rects)
for rect in picked_rect:
current_rect = rect.duplicate()
current_rect.load_trackID(trackID)
current_frame.append_labeled_rect(current_rect)
trackID=trackID+1
previous_frame=current_frame.duplicate()
previous_frame.rects= multiclass_rectangle.duplicate_rects(current_frame.rects)
print "Current Frame obj:%d"%len(current_frame.rects)
tracked_video.insert(len(tracked_video), current_frame)
return tracked_video
def save_best_iou(val_bbox, output_bbox):
progress = progressbar.ProgressBar(widgets=[
progressbar.Bar('=', '[', ']'), ' ',
progressbar.Percentage(), ' ',
progressbar.ETA()
])
count_best_bbox = 0
len_val_bbox = len(val_bbox)
len_output_bbox = len(output_bbox)
count_missing_boxes = 0
with open("best_iou.txt", 'a') as d:
for i in progress(range(0, len(val_bbox))):
for rect in val_bbox[i].rects:
if (len(output_bbox[i].rects) > 0):
selected = multiclass_rectangle.pop_max_iou(
output_bbox[i].rects, rect)
count_best_bbox = count_best_bbox + 1
d.write(
str(val_bbox[i].frame) + ' ' + str(rect.label_chall) +
' 0.5 ' + str(selected.x1) + ' ' + str(selected.y1) +
' ' + str(selected.x2) + ' ' + str(selected.y2) +
os.linesep)
else:
count_missing_boxes = count_missing_boxes + 1
print "Total Frame Number: " + str(len_val_bbox)
print "Total Output Bounding Boxes: " + str(len_output_bbox)
print "Total Best Bounding Boxes: " + str(count_best_bbox)
print "Total Missing Bounding Boxes: " + str(count_missing_boxes)
print "Total False Positive Bounding Boxes: " + str(len_output_bbox -
count_best_bbox)
print "BBox/Frame Number: " + str(
float(count_best_bbox) / float(len_val_bbox))
print "Missing BBox/Frame Number: " + str(
float(float(count_missing_boxes) / float(len_val_bbox)))
print "False Positive BBox/Frame Number: " + str(
float(float(len_output_bbox - count_best_bbox) / float(len_val_bbox)))
def save_best_iou(val_bbox, output_bbox):
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
count_best_bbox=0
len_val_bbox=len(val_bbox)
len_output_bbox=len(output_bbox)
count_missing_boxes=0
with open("best_iou.txt", 'a') as d:
for i in progress(range(0, len(val_bbox))):
for rect in val_bbox[i].rects:
if(len(output_bbox[i].rects)>0):
selected=multiclass_rectangle.pop_max_iou(output_bbox[i].rects,rect)
count_best_bbox=count_best_bbox+1
d.write(str(val_bbox[i].frame)+' '+str(rect.label_chall)+ ' 0.5 '+str(selected.x1)+' '+str(selected.y1)+' '+str(selected.x2)+' '+str(selected.y2) + os.linesep)
else:
count_missing_boxes=count_missing_boxes+1
print "Total Frame Number: "+ str(len_val_bbox)
print "Total Output Bounding Boxes: "+ str(len_output_bbox)
print "Total Best Bounding Boxes: "+ str(count_best_bbox)
print "Total Missing Bounding Boxes: "+ str(count_missing_boxes)
print "Total False Positive Bounding Boxes: "+ str(len_output_bbox-count_best_bbox)
print "BBox/Frame Number: "+ str(float(count_best_bbox)/float(len_val_bbox))
print "Missing BBox/Frame Number: "+ str(float(float(count_missing_boxes)/float(len_val_bbox)))
print "False Positive BBox/Frame Number: "+ str(float(float(len_output_bbox-count_best_bbox)/float(len_val_bbox)))
def recurrent_track_objects(video_info):
previous_frame= None
previous_num_obj=-1
tracked_video=[]
deltas_video=[]
deltas_frame=[]
dx1,dx2,dy1,dy2=0,0,0,0
for frame_info in video_info:
print "Tracking Frame Nr: %d"%frame_info.frame
print "Len Rects Frame: %d"%len(frame_info.rects)
current_frame = frame.Frame_Info()
current_frame=frame_info.duplicate()
current_frame.rects=[]
if previous_frame is not None:
deltas_frame=[]
if frame_info.frame>1:
print "Len Previous Rects Frame: %d"%len(previous_frame.rects)
rect_idx=0
for rect in previous_frame.rects:
print len(current_frame.rects)
rect.add_delta(deltas_video[frame_info.frame-2][rect_idx][0],deltas_video[frame_info.frame-2][rect_idx][1],deltas_video[frame_info.frame-2][rect_idx][2],deltas_video[frame_info.frame-2][rect_idx][3])
current_rect = multiclass_rectangle.pop_max_iou(frame_info.rects,rect)
if current_rect is not None:
current_rect.load_trackID(rect.trackID)
current_rect.check_rects_motion(frame_info.filename, rect, deltas_video[frame_info.frame-2][rect_idx][0],deltas_video[frame_info.frame-2][rect_idx][1],deltas_video[frame_info.frame-2][rect_idx][2],deltas_video[frame_info.frame-2][rect_idx][3])
current_frame.append_labeled_rect(current_rect)
dx1=current_rect.x1-rect.x1
dx2=current_rect.x2-rect.x2
dy1=current_rect.y1-rect.y1
dy2=current_rect.y2-rect.y2
deltas_frame.append((dx1,dx2,dy1,dy2))
else: break
else:
print "Len Previous Rects Frame: %d"%len(previous_frame.rects)
for rect in previous_frame.rects:
print len(current_frame.rects)
current_rect = multiclass_rectangle.pop_max_iou(frame_info.rects,rect)
if current_rect is not None:
dx1=current_rect.x1-rect.x1
dx2=current_rect.x2-rect.x2
dy1=current_rect.y1-rect.y1
dy2=current_rect.y2-rect.y2
deltas_frame.append((dx1,dx2,dy1,dy2))
current_rect.load_trackID(rect.trackID)
current_frame.append_labeled_rect(current_rect)
else: break
deltas_video.append(deltas_frame)
else:
trackID=1
picked_rect=Utils_Tensorbox.NMS(frame_info.rects)
for rect in picked_rect:
current_rect = rect.duplicate()
current_rect.load_trackID(trackID)
current_frame.append_labeled_rect(current_rect)
trackID=trackID+1
previous_frame=current_frame.duplicate()
previous_frame.rects= multiclass_rectangle.duplicate_rects(current_frame.rects)
print "Current Frame obj:%d"%len(current_frame.rects)
tracked_video.insert(len(tracked_video), current_frame)
return tracked_video
def recurrent_track_objects(video_info):
previous_frame = None
previous_num_obj = -1
tracked_video = []
deltas_video = []
deltas_frame = []
dx1, dx2, dy1, dy2 = 0, 0, 0, 0
for frame_info in video_info:
print("Tracking Frame Nr: %d" % frame_info.frame)
print("Len Rects Frame: %d" % len(frame_info.rects))
current_frame = frame.Frame_Info()
current_frame = frame_info.duplicate()
current_frame.rects = []
if previous_frame is not None:
deltas_frame = []
if frame_info.frame > 1:
print("Len Previous Rects Frame: %d" %
len(previous_frame.rects))
rect_idx = 0
for rect in previous_frame.rects:
print(len(current_frame.rects))
rect.add_delta(
deltas_video[frame_info.frame - 2][rect_idx][0],
deltas_video[frame_info.frame - 2][rect_idx][1],
deltas_video[frame_info.frame - 2][rect_idx][2],
deltas_video[frame_info.frame - 2][rect_idx][3])
current_rect = multiclass_rectangle.pop_max_iou(
frame_info.rects, rect)
if current_rect is not None:
current_rect.load_trackID(rect.trackID)
current_rect.check_rects_motion(
frame_info.filename, rect,
deltas_video[frame_info.frame - 2][rect_idx][0],
deltas_video[frame_info.frame - 2][rect_idx][1],
deltas_video[frame_info.frame - 2][rect_idx][2],
deltas_video[frame_info.frame - 2][rect_idx][3])
current_frame.append_labeled_rect(current_rect)
dx1 = current_rect.x1 - rect.x1
dx2 = current_rect.x2 - rect.x2
dy1 = current_rect.y1 - rect.y1
dy2 = current_rect.y2 - rect.y2
deltas_frame.append((dx1, dx2, dy1, dy2))
else:
break
else:
print("Len Previous Rects Frame: %d" %
len(previous_frame.rects))
for rect in previous_frame.rects:
print(len(current_frame.rects))
current_rect = multiclass_rectangle.pop_max_iou(
frame_info.rects, rect)
if current_rect is not None:
dx1 = current_rect.x1 - rect.x1
dx2 = current_rect.x2 - rect.x2
dy1 = current_rect.y1 - rect.y1
dy2 = current_rect.y2 - rect.y2
deltas_frame.append((dx1, dx2, dy1, dy2))
current_rect.load_trackID(rect.trackID)
current_frame.append_labeled_rect(current_rect)
else:
break
deltas_video.append(deltas_frame)
else:
trackID = 1
picked_rect = Utils_Tensorbox.NMS(frame_info.rects)
for rect in picked_rect:
current_rect = rect.duplicate()
current_rect.load_trackID(trackID)
current_frame.append_labeled_rect(current_rect)
trackID = trackID + 1
previous_frame = current_frame.duplicate()
previous_frame.rects = multiclass_rectangle.duplicate_rects(
current_frame.rects)
print("Current Frame obj:%d" % len(current_frame.rects))
tracked_video.insert(len(tracked_video), current_frame)
return tracked_video