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import torch
import torch.nn as nn
from torch.utils import data
import os
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
try:
from torchvision.transforms import InterpolationMode
BICUBIC = InterpolationMode.BICUBIC
except ImportError:
BICUBIC = Image.BICUBIC
import glob
import numpy as np
import open3d as o3d
import cv2
from datasets.taxonomy import category_map as category_ids
classname_map={
"chair":["chair","stool"],
"cabinet":["dishwasher","cabinet","oven","refrigerator",'storage'],
"sofa":["sofa"],
"table":["table"],
"bed":["bed"],
"shelf":["shelf"]
}
classname_remap={ #map small categories to six large categories
"chair":"chair",
"stool":"chair",
"dishwasher":"cabinet",
"cabinet":"cabinet",
"oven":"cabinet",
"refrigerator":"cabinet",
"storage":"cabinet",
"sofa":"sofa",
"table":"table",
"bed":"bed",
"shelf":"shelf"
}
def image_transform(n_px):
return Compose([
Resize(n_px, interpolation=BICUBIC),
CenterCrop(n_px),
ToTensor(),
Normalize((0.48145466, 0.4578275, 0.40821073),
(0.26862954, 0.26130258, 0.27577711)),
])
class Simple_InTheWild_dataset(data.Dataset):
def __init__(self,dataset_dir="/data1/haolin/data/real_scene_process_data",scene_id="letian-310",n_px=224):
self.dataset_dir=dataset_dir
self.preprocess = image_transform(n_px)
self.image_path = []
if scene_id=="all":
scene_list=os.listdir(self.dataset_dir)
for id in scene_list:
image_folder=os.path.join(self.dataset_dir,id,"6_images")
self.image_path+=glob.glob(image_folder+"/*/*jpg")
else:
image_folder = os.path.join(self.dataset_dir, scene_id, "6_images")
self.image_path += glob.glob(image_folder + "/*/*jpg")
def __len__(self):
return len(self.image_path)
def __getitem__(self,index):
path=self.image_path[index]
basename=os.path.basename(path)[:-4]
model_id=path.split(os.sep)[-2]
scene_id=path.split(os.sep)[-4]
image=Image.open(path)
image_tensor=self.preprocess(image)
return {"images":image_tensor,"image_name":basename,"model_id":model_id,"scene_id":scene_id}
class InTheWild_Dataset(data.Dataset):
def __init__(self,data_dir="/data1/haolin/data/real_scene_process_data/letian-310",scene_id="letian-310",
par_pc_size=2048,category="chair",max_n_imgs=5):
self.par_pc_size=par_pc_size
self.data_dir=data_dir
self.category=category
self.max_n_imgs=max_n_imgs
self.models=[]
category_list=classname_map[category]
modelid_list=[]
for cat in category_list:
if scene_id=="all":
scene_list=os.listdir(self.data_dir)
for id in scene_list:
data_folder=os.path.join(self.data_dir,id)
modelid_list+=glob.glob(data_folder+"/6_images/%s*"%(cat))
else:
data_folder=os.path.join(self.data_dir,scene_id)
modelid_list+=glob.glob(data_folder+"/6_images/%s*"%(cat))
sceneid_list = [item.split("/")[-3] for item in modelid_list]
modelid_list=[item.split("/")[-1] for item in modelid_list]
for idx,modelid in enumerate(modelid_list):
scene_id=sceneid_list[idx]
image_folder=os.path.join(self.data_dir,scene_id,"6_images",modelid)
image_list=os.listdir(image_folder)
if len(image_list)==0:
continue
imageid_list=[item[0:-4] for item in image_list]
imageid_list.sort(key=lambda x:int(x))
partial_path=os.path.join(self.data_dir,scene_id,"5_partial_points",modelid+".ply")
if os.path.exists(partial_path)==False: continue
self.models+=[
{'model_id':modelid,
"scene_id":scene_id,
"partial_path":partial_path,
"imageid_list":imageid_list,
}
]
def __len__(self):
return len(self.models)
def __getitem__(self,idx):
model = self.models[idx]['model_id']
scene_id=self.models[idx]['scene_id']
imageid_list = self.models[idx]['imageid_list']
partial_path=self.models[idx]['partial_path']
n_frames=min(len(imageid_list),self.max_n_imgs)
img_indexes=np.linspace(start=0,stop=len(imageid_list)-1,num=n_frames).astype(np.int32)
'''load partial points'''
par_point_o3d = o3d.io.read_point_cloud(partial_path)
par_points = np.asarray(par_point_o3d.points)
replace = par_points.shape[0] < self.par_pc_size
ind = np.random.default_rng().choice(par_points.shape[0], self.par_pc_size, replace=replace)
par_points=par_points[ind]
par_points=torch.from_numpy(par_points).float()
'''load image features'''
image_list=[]
valid_frames = []
image_namelist=[]
for img_index in img_indexes:
image_name = imageid_list[img_index]
image_feat_path = os.path.join(self.data_dir,scene_id, "7_img_feature", model,image_name + '.npz')
image = np.load(image_feat_path)["img_features"]
image_list.append(torch.from_numpy(image).float())
image_namelist.append(image_name)
valid_frames.append(True)
'''load original image'''
org_img_list=[]
for img_index in img_indexes:
image_name = imageid_list[img_index]
image_path = os.path.join(self.data_dir,scene_id, "6_images", model,image_name+".jpg")
org_image = cv2.imread(image_path)
org_image = cv2.resize(org_image, dsize=(224, 224), interpolation=cv2.INTER_LINEAR)
org_img_list.append(org_image)
'''load project matrix'''
proj_mat_list=[]
for img_index in img_indexes:
image_name = imageid_list[img_index]
proj_mat_path = os.path.join(self.data_dir,scene_id, "8_proj_matrix", model, image_name + ".npy")
proj_mat = np.load(proj_mat_path)
proj_mat_list.append(proj_mat)
'''load transformation matrix'''
tran_mat_path = os.path.join(self.data_dir,scene_id, "10_tran_matrix", model+".npy")
tran_mat = np.load(tran_mat_path)
'''category code, not used for category specific models'''
category_id = category_ids[self.category]
one_hot = torch.zeros((6)).float()
one_hot[category_id] = 1.0
ret_dict={
"model_id":model,
"scene_id":scene_id,
"par_points":par_points,
"proj_mat":torch.stack([torch.from_numpy(mat) for mat in proj_mat_list], dim=0),
"tran_mat":torch.from_numpy(tran_mat).float(),
"image":torch.stack(image_list,dim=0),
"org_image":org_img_list,
"valid_frames":torch.tensor(valid_frames).bool(),
"category_ids": category_id,
"category_code":one_hot,
}
return ret_dict
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