def getMatches(data):
from models.model_wrap import PointTracker
desc = data['desc']
warped_desc = data['warped_desc']
nn_thresh = 1.2
print("nn threshold: ", nn_thresh)
tracker = PointTracker(max_length=2, nn_thresh=nn_thresh)
# matches = tracker.nn_match_two_way(desc, warped_desc, nn_)
tracker.update(keypoints.T, desc.T)
tracker.update(warped_keypoints.T, warped_desc.T)
matches = tracker.get_matches().T
mscores = tracker.get_mscores().T
# mAP
# matches = data['matches']
print("matches: ", matches.shape)
print("mscores: ", mscores.shape)
try:
print("mscore max: ", mscores.max(axis=0))
print("mscore min: ", mscores.min(axis=0))
except ValueError:
pass
return matches, mscores
def export_descriptor(config, output_dir, args):
"""
# input 2 images, output keypoints and correspondence
save prediction:
pred:
'image': np(320,240)
'prob' (keypoints): np (N1, 2)
'desc': np (N2, 256)
'warped_image': np(320,240)
'warped_prob' (keypoints): np (N2, 2)
'warped_desc': np (N2, 256)
'homography': np (3,3)
'matches': np [N3, 4]
"""
from utils.loader import get_save_path
from utils.var_dim import squeezeToNumpy
# basic settings
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logging.info("train on device: %s", device)
with open(os.path.join(output_dir, "config.yml"), "w") as f:
yaml.dump(config, f, default_flow_style=False)
writer = SummaryWriter(getWriterPath(task=args.command, date=True))
save_path = get_save_path(output_dir)
save_output = save_path / "../predictions"
os.makedirs(save_output, exist_ok=True)
## parameters
outputMatches = True
subpixel = config["model"]["subpixel"]["enable"]
patch_size = config["model"]["subpixel"]["patch_size"]
# data loading
from utils.loader import dataLoader_test as dataLoader
task = config["data"]["dataset"]
data = dataLoader(config, dataset=task)
test_set, test_loader = data["test_set"], data["test_loader"]
from utils.print_tool import datasize
datasize(test_loader, config, tag="test")
# model loading
from utils.loader import get_module
Val_model_heatmap = get_module("", config["front_end_model"])
## load pretrained
val_agent = Val_model_heatmap(config["model"], device=device)
val_agent.loadModel()
## tracker
tracker = PointTracker(max_length=2, nn_thresh=val_agent.nn_thresh)
###### check!!!
count = 0
for i, sample in tqdm(enumerate(test_loader)):
img_0, img_1 = sample["image"], sample["warped_image"]
# first image, no matches
# img = img_0
def get_pts_desc_from_agent(val_agent, img, device="cpu"):
"""
pts: list [numpy (3, N)]
desc: list [numpy (256, N)]
"""
heatmap_batch = val_agent.run(
img.to(device)
) # heatmap: numpy [batch, 1, H, W]
# heatmap to pts
pts = val_agent.heatmap_to_pts()
# print("pts: ", pts)
if subpixel:
pts = val_agent.soft_argmax_points(pts, patch_size=patch_size)
# heatmap, pts to desc
desc_sparse = val_agent.desc_to_sparseDesc()
# print("pts[0]: ", pts[0].shape, ", desc_sparse[0]: ", desc_sparse[0].shape)
# print("pts[0]: ", pts[0].shape)
outs = {"pts": pts[0], "desc": desc_sparse[0]}
return outs
def transpose_np_dict(outs):
for entry in list(outs):
outs[entry] = outs[entry].transpose()
outs = get_pts_desc_from_agent(val_agent, img_0, device=device)
pts, desc = outs["pts"], outs["desc"] # pts: np [3, N]
if outputMatches == True:
tracker.update(pts, desc)
# save keypoints
pred = {"image": squeezeToNumpy(img_0)}
pred.update({"prob": pts.transpose(), "desc": desc.transpose()})
# second image, output matches
outs = get_pts_desc_from_agent(val_agent, img_1, device=device)
pts, desc = outs["pts"], outs["desc"]
if outputMatches == True:
tracker.update(pts, desc)
pred.update({"warped_image": squeezeToNumpy(img_1)})
# print("total points: ", pts.shape)
pred.update(
{
"warped_prob": pts.transpose(),
"warped_desc": desc.transpose(),
"homography": squeezeToNumpy(sample["homography"]),
}
)
if outputMatches == True:
matches = tracker.get_matches()
print("matches: ", matches.transpose().shape)
pred.update({"matches": matches.transpose()})
print("pts: ", pts.shape, ", desc: ", desc.shape)
# clean last descriptor
tracker.clear_desc()
filename = str(count)
path = Path(save_output, "{}.npz".format(filename))
np.savez_compressed(path, **pred)
# print("save: ", path)
count += 1
print("output pairs: ", count)