def func_co(x):
x.sort()
Eval = NuScenesEval('', '', args.format)
fused_data = cu.matching_and_fusion(cam_test_list[x[0]],cam_test_list[x[1]])
fused_gt = cu.filt_gt_labels_tuple(cam_gt_list[x[0]],cam_gt_list[x[1]])
mAP_temp = Eval.my_evaluate(fused_data,fused_gt)
return 1- mAP_temp
def fuse_constellation(x):
#根据x的维度进行融合
x.sort()
size_n = x.shape[0]
main_cam = x[0]
gt_list = []
gt_list.append(cam_gt_list[main_cam])
for i in x[1:]:
fused_data = cu.matching_and_fusion(cam_test_list[main_cam],cam_test_list[i]) #融合
gt_list.append(cam_gt_list[i])
fused_gt = cu.filt_gt_labels_tuple(*gt_list)
Eval = NuScenesEval('', '', args.format)
mAP_temp = Eval.my_evaluate(fused_data,fused_gt)
return 1- mAP_temp
# Eval = NuScenesEval('', '', args.format)
# fused_data = cu.matching_and_fusion(fused_data,cam_test_list[z]) #融合
# fused_gt = cu.filt_gt_labels_tuple(cam_gt_list[i],cam_gt_list[j],cam_gt_list[k],cam_gt_list[z])
# #评估
# mAP_temp = Eval.my_evaluate(fused_data,fused_gt)
# if mAP_temp > max_map:
# max_map = mAP_temp
# max_i,max_j = i,j
# print('temp max mAP: {}.......... time: ## i: {} j: {} k:{} z:{}'.format(max_map,i,j,k,z))
#print(mAP_temp)
max_map = 0
max_i, max_j = 0, 0
fused_gt = cu.filt_gt_labels_tuple(*cam_gt_list)
for i in range(34):
for j in range(i + 1, 34):
fused_data = cu.matching_and_fusion(cam_test_list[i],
cam_test_list[j]) #融合
Eval = NuScenesEval('', '', args.format)
#fused_gt = cu.filt_gt_labels_tuple(cam_gt_list[i],cam_gt_list[j],cam_gt_list[k])
#评估
mAP_temp = Eval.my_evaluate(fused_data, fused_gt)
if mAP_temp > max_map:
max_map = mAP_temp
max_i, max_j = i, j
print(
'temp max mAP: {}.......... time: ## i: {} j: {} '.
format(max_map, i, j))
#print(mAP_temp)
# max_map = 0
# max_i,max_j = 0,0
# fused_gt = cu.filt_gt_labels_tuple(*cam_gt_list)
# for i in range(34):
ry_world2cam1 = (rotation_y - yaw + 90) * np.pi / 180
tv = uti.CamVehicle(p_in_cam1[1][0], -p_in_cam1[2][0],
p_in_cam1[0][0], car[5], car[4], car[3],
ry_world2cam1)
ret.append(tv)
return ret
# filt_start_time = time.time()
# ret = cu.filt_gt_labels(cam_gt_list[0],cam_gt_list[1])
# filt_time = time.time() - filt_start_time
# print("filt gt for 1 iter, time: ",filt_time)
filt_start_time = time.time()
max_map = 0
max_i, max_j = 0, 0
fused_data = cu.matching_and_fusion([cam_test_list[2][44]],
[cam_test_list[19][44]]) #融合
fused_data = cu.matching_and_fusion(fused_data,
[cam_test_list[33][44]]) #融合
vehicls = vehicle_world2sensor(fused_data[0], cam_transform[2], -20)
box3d_list = [car.compute_box_3d() for car in vehicls]
#print(box3d_list)
bird_view = uti.add_bird_view(box3d_list)
cv2.imshow('bird', bird_view)
cv2.waitKey()
# max_map = 0
# max_i,max_j = 0,0
# for i in range(34):
# for j in range(i+1,34):
# fused_data = cu.matching_and_fusion(cam_test_list[i],cam_test_list[j]) #融合
# for k in range(j+1,34):