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eval_linemod_json.py
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from pope_model_api import *
if __name__ == "__main__":
ckpt, model_type = get_model_info("h")
sam = sam_model_registry[model_type](checkpoint=ckpt)
DEVICE = "cuda"
sam.to(device=DEVICE)
MASK_GEN = SamAutomaticMaskGenerator(sam)
logger.info(f"load SAM model from {ckpt}")
crop_tool = CropImage()
dinov2_model = load_dinov2_model()
dinov2_model.to("cuda:0")
metrics = dict()
metrics.update({'R_errs': [], 't_errs': [], 'inliers': [] , "identifiers":[] })
ROOT_DIR = "data/LM_dataset/"
dir_list = os.listdir(ROOT_DIR)
id2name_dict = {
1: "ape",
2: "benchvise",
4: "camera",
5: "can",
6: "cat",
8: "driller",
9: "duck",
10: "eggbox",
11: "glue",
12: "holepuncher",
13: "iron",
14: "lamp",
15: "phone",
}
from tqdm import tqdm
import torch.nn.functional as F
res_table = []
import json
with open("data/pairs/LINEMOD-test.json") as f:
dir_list = json.load(f)
for label_idx , test_dict in enumerate(dir_list):
logger.info(f"LINEMOD: {label_idx}")
metrics = dict()
metrics.update({'R_errs': [], 't_errs': [], 'inliers': [] , "identifiers":[] })
sample_data = dir_list[label_idx]["0"][0]
label = sample_data.split("/")[0]
name = label.split("-")[1]
dir_name = os.path.dirname(sample_data)
FULL_ROOT_DIR = os.path.join(ROOT_DIR, dir_name)
recall_image,all_image = 0,0
for rotation_key, rotation_list in zip(test_dict.keys(), test_dict.values()):
for pair_idx,pair_name in enumerate(tqdm(rotation_list)):
all_image = all_image + 1
base_name = os.path.basename(pair_name)
idx0_name = base_name.split("-")[0]
idx1_name = base_name.split("-")[1]
image0_name = os.path.join( FULL_ROOT_DIR, idx0_name )
image1_name = os.path.join( FULL_ROOT_DIR.replace("color", "color_full"), idx1_name )
intrinsic_path = image0_name.replace("color", "intrin_ba").replace("png","txt")
K0 = np.loadtxt(intrinsic_path, delimiter=' ')
intrinsic_path = image1_name.replace("color_full", "intrin").replace("png","txt")
K1 = np.loadtxt(intrinsic_path, delimiter=' ')
image0 = cv2.imread(image0_name)
ref_torch_image = set_torch_image(image0, center_crop=True)
ref_fea = get_cls_token_torch(dinov2_model, ref_torch_image)
image1 = cv2.imread(image1_name)
image_h,image_w,_ = image1.shape
t1 = time.time()
masks = MASK_GEN.generate(image1)
t2 = time.time()
similarity_score, top_images = np.array([0,0,0],np.float32) , [[],[],[]]
t3 = time.time()
compact_percent = 0.3
for xxx, mask in enumerate(masks):
object_mask = np.expand_dims(mask["segmentation"], -1)
x0, y0, w, h = mask["bbox"]
x1, y1 = x0+w,y0+h
x0 -= int(w * compact_percent)
y0 -= int(h * compact_percent)
x1 += int(w * compact_percent)
y1 += int(h * compact_percent)
box = np.array([x0, y0, x1, y1])
resize_shape = np.array([y1 - y0, x1 - x0])
K_crop, K_crop_homo = get_K_crop_resize(box, K1, resize_shape)
image_crop, _ = get_image_crop_resize(image1, box, resize_shape)
# object_mask,_ = get_image_crop_resize(object_mask, box, resize_shape)
box_new = np.array([0, 0, x1 - x0, y1 - y0])
resize_shape = np.array([256, 256])
K_crop, K_crop_homo = get_K_crop_resize(box_new, K_crop, resize_shape)
image_crop, _ = get_image_crop_resize(image_crop, box_new, resize_shape)
crop_tensor = set_torch_image(image_crop, center_crop=True)
with torch.no_grad():
fea = get_cls_token_torch(dinov2_model, crop_tensor)
score = F.cosine_similarity(ref_fea, fea, dim=1, eps=1e-8)
if (score.item() > similarity_score).any():
mask["crop_image"] = image_crop
mask["K"] = K_crop
mask["bbox"] = box
min_idx = np.argmin(similarity_score)
similarity_score[min_idx] = score.item()
top_images[min_idx] = mask.copy()
img0 = cv2.cvtColor(image0, cv2.COLOR_BGR2GRAY)
img0 = torch.from_numpy(img0).float()[None] / 255.
img0 = img0.unsqueeze(0).cuda()
matching_score = [ [0] for _ in range(len(top_images)) ]
for top_idx in range(len(top_images)):
img1 = cv2.cvtColor(top_images[top_idx]["crop_image"], cv2.COLOR_BGR2GRAY)
img1 = torch.from_numpy(img1).float()[None] / 255.
img1 = img1.unsqueeze(0).cuda()
batch = {'image0': img0, 'image1': img1}
with torch.no_grad():
matcher(batch)
mkpts0 = batch['mkpts0_f'].cpu().numpy()
mkpts1 = batch['mkpts1_f'].cpu().numpy()
confidences = batch["mconf"].cpu().numpy()
conf_mask = np.where(confidences > 0.9)
matching_score[top_idx] = conf_mask[0].shape[0]
top_images[top_idx]["mkpts0"] = mkpts0
top_images[top_idx]["mkpts1"] = mkpts1
top_images[top_idx]["mconf"] = confidences
#---------------------------------------------------
# crop_image = cv2.resize(top_images[np.argmax(matching_score)]["crop_image"],(256,256))
# que_image = cv2.resize(image0,(256,256))
# image = np.hstack((que_image, crop_image))
# for top_idx in range(len(top_images)):
# crop_image = top_images[top_idx]["crop_image"]
# score = matching_score[top_idx]
# crop_image = cv2.resize(crop_image,(256,256))
# cv2.putText(crop_image,f'{score}',(100,100),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),1)
# image = np.hstack((image, crop_image))
# cv2.imwrite(f"segment_anything/crop_images/{idx}.jpg", image)
#---------------------------------------------------
t4 = time.time()
# print(f"t4-t3: object detection:{1000*(t4-t3)} ms")
pose0_name = image0_name.replace("color", "poses_ba").replace("png","txt")
pose1_name = image1_name.replace("color_full", "poses_ba").replace("png","txt")
pose0 = np.loadtxt(pose0_name)
pose1 = np.loadtxt(pose1_name)
pose0 = np.vstack((pose0, np.array([[0,0,0,1]])))
pose1 = np.vstack((pose1, np.array([[0,0,0,1]])))
relative_pose = np.matmul(pose1, inv(pose0))
t = relative_pose[:3,-1].reshape(1,3)
max_match_idx = np.argmax(matching_score)
pre_bbox = top_images[max_match_idx]["bbox"]
mkpts0 = top_images[max_match_idx]["mkpts0"]
mkpts1 = top_images[max_match_idx]["mkpts1"]
pre_K = top_images[max_match_idx]["K"]
_3d_bbox = np.loadtxt(f"{os.path.join(ROOT_DIR, label)}/box3d_corners.txt")
bbox_pts_3d, _ = project_points(_3d_bbox, pose1[:3,:4], K1)
bbox_pts_3d = bbox_pts_3d.astype(np.int32)
x0, y0, w, h = cv2.boundingRect(bbox_pts_3d)
x1,y1 = x0+w, y0+h
gt_bbox = np.array([x0, y0, x1, y1])
is_recalled = recall_object(pre_bbox , gt_bbox)
recall_image = recall_image + int(is_recalled>0.5)
ret = estimate_pose(mkpts0, mkpts1 , K0 , pre_K , 0.5, 0.99)
if ret is not None:
Rot, t, inliers = ret
t_err, R_err = relative_pose_error(relative_pose, Rot, t, ignore_gt_t_thr=0.0)
metrics['R_errs'].append(R_err)
metrics['t_errs'].append(t_err)
else:
metrics['R_errs'].append(90)
metrics['t_errs'].append(90)
metrics["identifiers"].append( pair_name )
import pprint
from src.utils.metrics import (
aggregate_metrics
)
from loguru import logger
val_metrics_4tb = aggregate_metrics(metrics, 5e-4)
val_metrics_4tb["AP50"] = recall_image/all_image
logger.info('\n' + pprint.pformat(val_metrics_4tb))
obj_name = int(name[2:])
res_table.append( [f"{id2name_dict[obj_name] }"] + list(val_metrics_4tb.values()) )
from tabulate import tabulate
headers = ["Category"] + list(val_metrics_4tb.keys())
all_data = np.array(res_table)[:,1:].astype(np.float32)
res_table.append( ["Avg"] + all_data.mean(0).tolist() )
print(tabulate(res_table, \
headers=headers, tablefmt='fancy_grid'))