def val_to_data(source):
text_lines=[]
frames_list=[]
frame = None
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
with open(source, 'r') as s:
for line in s:
id_frame, id_class, conf, xmin, ymin, xmax, ymax = line.strip().split(' ')
text_lines.append((id_frame, id_class, conf, xmin, ymin, xmax, ymax))
for i in range(0, len(text_lines)):
if frame is None:
frame = fm.Frame_Info()
frame.frame= text_lines[i][0]
rect= multiclass_rectangle.Rectangle_Multiclass()
# Not all the inserted values are really used
rect.load_labeled_rect(0, text_lines[i][2], text_lines[i][2], text_lines[i][3], text_lines[i][4], text_lines[i][5], text_lines[i][6], text_lines[i][1], text_lines[i][1], text_lines[i][1])
frame.append_labeled_rect(rect)
else :
if frame.frame == text_lines[i][0]:
rect= multiclass_rectangle.Rectangle_Multiclass()
# Not all the inserted values are really used
rect.load_labeled_rect(0, text_lines[i][2], text_lines[i][2], text_lines[i][3], text_lines[i][4], text_lines[i][5], text_lines[i][6], text_lines[i][1], text_lines[i][1], text_lines[i][1])
frame.append_labeled_rect(rect)
else :
frames_list.append(frame)
frame = fm.Frame_Info()
frame.frame= text_lines[i][0]
rect= multiclass_rectangle.Rectangle_Multiclass()
# Not all the inserted values are really used
rect.load_labeled_rect(0, text_lines[i][2], text_lines[i][2], text_lines[i][3], text_lines[i][4], text_lines[i][5], text_lines[i][6], text_lines[i][1], text_lines[i][1], text_lines[i][1])
frame.append_labeled_rect(rect)
frames_list.append(frame)
return frames_list
def parse_XML_to_data(xml_list_video):
frames_list=[]
video_list=[]
# image_multi_class= None
# rectangle_multi = None
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
for i in progress(list(range(0, len(xml_list_video)))):
# print "Iterating on Video:"+ str(xml_list_video[i][0][0])
for j in range(0, len(xml_list_video[i])):
# print "Iterating on Frame:"+ str(xml_list_video[i][j][0])
with open(xml_list_video[i][j][0], 'rt') as f:
tree = ElementTree.parse(f)
for obj in tree.findall('object'):
name = obj.find('name').text
class_code= name
name = vid_classes.code_to_class_string(name)
if name in ["nothing"]:
continue
else:
#The files with the original data path are made in both: multiclass e single class
jump=0
image_multi_class= fm.Frame_Info()
image_multi_class.frame= xml_list_video[i][j][1]
# print image_multi_class.frame
rectangle_multi= multiclass_rectangle.Rectangle_Multiclass()
for node in tree.iter():
tag=str(node.tag)
if tag in ['name']:
if str(vid_classes.code_to_class_string(str(node.text))) in ["nothing"]:
jump = 1
else :
jump=0
rectangle_multi.label_chall=int(vid_classes.class_string_to_comp_code(str(vid_classes.code_to_class_string(str(node.text)))))
# print rectangle_multi.label_chall
rectangle_multi.label_code=str(node.text)
rectangle_multi.label=vid_classes.code_to_class_string(str(node.text))
if tag in ["xmax"]:
if jump == 0:
rectangle_multi.x2=float(node.text)
if tag in ["xmin"]:
if jump == 0:
rectangle_multi.x1=float(node.text)
if tag in ["ymax"]:
if jump == 0:
rectangle_multi.y2=float(node.text)
if tag in ["ymin"]:
if jump == 0:
rectangle_multi.y1=float(node.text)
image_multi_class.append_rect(rectangle_multi)
if jump == 0:
image_multi_class.append_labeled_rect(rectangle_multi)
break
frames_list.append(image_multi_class)
video_list.append(frames_list)
# frames_list=None
# frames_list=[]
return video_list
def get_multiclass_rectangles(H, confidences, boxes, rnn_len):
boxes_r = np.reshape(boxes, (-1,
H["grid_height"],
H["grid_width"],
rnn_len,
4))
confidences_r = np.reshape(confidences, (-1,
H["grid_height"],
H["grid_width"],
rnn_len,
H['num_classes']))
# print "boxes_r shape" + str(boxes_r.shape)
# print "confidences" + str(confidences.shape)
cell_pix_size = H['region_size']
all_rects = [[[] for _ in range(H["grid_width"])] for _ in range(H["grid_height"])]
for n in range(rnn_len):
for y in range(H["grid_height"]):
for x in range(H["grid_width"]):
bbox = boxes_r[0, y, x, n, :]
abs_cx = int(bbox[0]) + cell_pix_size/2 + cell_pix_size * x
abs_cy = int(bbox[1]) + cell_pix_size/2 + cell_pix_size * y
w = bbox[2]
h = bbox[3]
# conf = np.max(confidences_r[0, y, x, n, 1:])
index, conf = get_silhouette_confidence(confidences_r[0, y, x, n, 1:])
# print index, conf
# print np.max(confidences_r[0, y, x, n, 1:])
# print "conf" + str(conf)
# print "conf" + str(confidences_r[0, y, x, n, 1:])
new_rect=multiclass_rectangle.Rectangle_Multiclass()
new_rect.set_unlabeled_rect(abs_cx,abs_cy,w,h,conf)
all_rects[y][x].append(new_rect)
# print "confidences_r" + str(confidences_r.shape)
all_rects_r = [r for row in all_rects for cell in row for r in cell]
min_conf = get_higher_confidence(all_rects_r)
acc_rects=[rect for rect in all_rects_r if rect.true_confidence>min_conf]
rects = []
for rect in all_rects_r:
if rect.true_confidence>min_conf:
r = al.AnnoRect()
r.x1 = rect.cx - rect.width/2.
r.x2 = rect.cx + rect.width/2.
r.y1 = rect.cy - rect.height/2.
r.y2 = rect.cy + rect.height/2.
r.score = rect.true_confidence
r.silhouetteID=rect.label
rects.append(r)
print len(rects),len(acc_rects)
return rects, acc_rects
