for cls_ind, cls in enumerate(self.classes):
if cls == '__background__':
continue
print 'Writing {} results file'.format(cls)
filename = os.path.join(path, self._image_set + '_' + cls + comp_id + '.txt')
with open(filename, 'wt') as f:
for im_ind, index in enumerate(self.image_index):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(index, 0, 0, 0, 0, 0))
continue
for k in xrange(dets.shape[0]):
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(index, dets[k, -1],
dets[k, 0], dets[k, 1],
dets[k, 2], dets[k, 3]))
return comp_id
def evaluate_detections(self, all_boxes, output_dir):
comp_id = self._write_results_file(all_boxes)
if __name__ == '__main__':
#d = datasets.eshops('all')
d = datasets.eshops('alza_test')
#d._load_annotation('xaa-433')
#print d.image_path_from_index('xaa-433')
res = d.roidb
print res
#from IPython import embed; embed()
if __name__ == "__main__":
args = parse_args()
print("Called with args:")
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
print("Using config:")
pprint.pprint(cfg)
while not os.path.exists(args.caffemodel) and args.wait:
print("Waiting for {} to exist...".format(args.caffemodel))
time.sleep(10)
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
net = caffe.Net(args.prototxt, args.caffemodel, caffe.TEST)
net.name = os.path.splitext(os.path.basename(args.caffemodel))[0]
imdb = datasets.eshops(args.imdb_name)
# imdb = get_imdb(args.imdb_name)
# imdb = datasets.eshops('alza_test')
# imdb = datasets.eshops('czc100')
imdb.competition_mode(args.comp_mode)
test_net(net, imdb)
def calculate_iou(box1, box2):
axmin, aymin, axmax, aymax = box1
bxmin, bymin, bxmax, bymax = box2
dx = int(min(axmax, bxmax)) - int(max(axmin, bxmin))
dy = int(min(aymax, bymax)) - int(max(aymin, bymin))
S12 = max(dx*dy, 0)
S1 = area(box1)
S2 = area(box2)
iou = (S12*1.0)/(S1+S2-S12)
return iou
imdb = sys.argv[1]
run = sys.argv[2]
d = datasets.eshops(imdb)
gt = [ x['boxes'].tolist() for x in d.gt_roidb()]
#gt = {}
#for index in d._image_index:
#gt[index] = d._load_annotation(index)['boxes']
n = len(gt)
classes = ('name', 'price', 'main_image')
IOU_THRESHOLD = 0.5
correct = {}
img_cnt = {}