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import json
import os
from collections import defaultdict
import numpy as np
import torch
import torch.multiprocessing
import torch.utils.data
import torchvision.transforms.functional as F
from PIL import Image
from torch.utils.data import Dataset
from util.data_utils import l1_dist
from util.graph_utils import graph_to_tensor
from util.image_id_dict import d
from util.mean_std import mean, std
from util.semantics_dict import semantics_dict
torch.multiprocessing.set_sharing_strategy("file_system")
class MyDataset(Dataset):
def __init__(self, img_path, annot_path, extract_roi, image_size=512):
self.img_path = img_path
self.quadtree_path = "/".join(img_path.split("/")[:-1]) + "/annot_npy"
self.mode = img_path.split("/")[-1]
self.image_size = image_size
# load annotation
with open(annot_path, "r") as f:
dataset = json.load(f)
# images
self.imgs = {}
for img in dataset["images"]:
self.imgs[img["id"]] = img
self.imgToAnns = defaultdict(list)
for ann in dataset["annotations"]:
self.imgToAnns[ann["image_id"]].append(ann)
self.ids = list(sorted(self.imgs.keys()))
if "0c-10-c468a57377ff8ef63d3b26a6d1fa-0002" in self.ids:
self.ids.remove("0c-10-c468a57377ff8ef63d3b26a6d1fa-0002")
if "0c-10-8486f08035ba152d5244ac54099c-0001" in self.ids:
self.ids.remove("0c-10-8486f08035ba152d5244ac54099c-0001")
def __getitem__(self, index):
img_id = self.ids[index]
img_file_name = self.imgs[img_id]["file_name"].replace(".jpg", ".png")
img = Image.open(os.path.join(self.img_path, img_file_name)).convert("RGB")
image_scale = self.image_size / img.size[0]
if img.size[0] != self.image_size or img.size[1] != self.image_size:
img = img.resize((self.image_size, self.image_size), Image.BILINEAR)
if 1:
# get structure annotations
anns = self.imgToAnns[img_id]
new_anns = []
for ann in anns:
new_ann = copy.deepcopy(ann)
new_ann["point"] = [int(ann["point"][0] * image_scale), int(ann["point"][1] * image_scale)]
new_anns.append(new_ann)
target = {"image_id": img_id, "annotations": new_anns}
orig_quadtree = np.load(
os.path.join(self.quadtree_path, img_file_name[:-4] + ".npy"), allow_pickle=True
).item()["quatree"][0]
quadtree = {}
for k, v in orig_quadtree.items():
new_k = k
new_v = []
for pos in v:
new_pos = (int(pos[0] * image_scale), int(pos[1] * image_scale))
new_v.append(new_pos)
quadtree[new_k] = new_v
orig_graph = np.load(
os.path.join(self.quadtree_path, img_file_name[:-4] + ".npy"), allow_pickle=True
).item()
del orig_graph["quatree"]
new_graph = {}
for k, v in orig_graph.items():
new_k = (int(k[0] * image_scale), int(k[1] * image_scale))
new_v = []
for adj in v:
if adj == (-1, -1):
new_v.append((-1, -1))
else:
new_v.append((int(adj[0] * image_scale), int(adj[1] * image_scale)))
new_graph[new_k] = new_v
target_layers = []
for layer, layer_points in quadtree.items():
target_layer = []
for layer_point in layer_points:
for target_i in target["annotations"]:
if l1_dist(target_i["point"], list(layer_point)) <= 2:
target_layer.append(target_i)
break
target_layers.extend(target_layer)
layer_indices = []
count = 0
for k, v in quadtree.items():
if k == 0:
layer_indices.append(0)
else:
layer_indices.append(count)
count += len(v)
image_id = torch.tensor([d[img_id]])
points = [obj["point"] for obj in target_layers]
points = torch.as_tensor(points, dtype=torch.int64).reshape(-1, 2)
edges = [obj["edge_code"] for obj in target_layers]
edges = torch.tensor(edges, dtype=torch.int64)
# get semantic annotations
semantic_left_up = [semantics_dict[obj["semantic"][0]] for obj in target_layers]
semantic_right_up = [semantics_dict[obj["semantic"][1]] for obj in target_layers]
semantic_right_down = [semantics_dict[obj["semantic"][2]] for obj in target_layers]
semantic_left_down = [semantics_dict[obj["semantic"][3]] for obj in target_layers]
semantic_left_up = torch.tensor(semantic_left_up, dtype=torch.int64)
semantic_right_up = torch.tensor(semantic_right_up, dtype=torch.int64)
semantic_right_down = torch.tensor(semantic_right_down, dtype=torch.int64)
semantic_left_down = torch.tensor(semantic_left_down, dtype=torch.int64)
# annotations
target = {}
target["edges"] = edges
target["file_name"] = img_file_name
target["image_id"] = image_id
target["size"] = torch.as_tensor([img.size[1], img.size[0]])
target["semantic_left_up"] = semantic_left_up
target["semantic_right_up"] = semantic_right_up
target["semantic_right_down"] = semantic_right_down
target["semantic_left_down"] = semantic_left_down
# get image
img = F.to_tensor(img)
img = F.normalize(img, mean=mean, std=std)
target["unnormalized_points"] = points
# normalize
points = points / torch.tensor([img.shape[2], img.shape[1]], dtype=torch.float32)
target["points"] = points
target["layer_indices"] = torch.tensor(layer_indices)
target["graph"] = graph_to_tensor(new_graph)
return img, target
def __len__(self):
return len(self.ids)
class MyDataset2(Dataset):
def __init__(self, img_path, annot_path, extract_roi, disable_sem_info=False):
self.disable_sem_info = disable_sem_info
self.img_path = img_path
self.quadtree_path = "/".join(img_path.split("/")[:-1]) + "/annotations_npy/" + img_path.split("/")[-1]
self.edgecode_path = "/".join(img_path.split("/")[:-1]) + "/annotations_edge/" + img_path.split("/")[-1]
self.mode = img_path.split("/")[-1]
available_ids = {int(x.replace(".npy", "")) for x in os.listdir(self.quadtree_path)}
# load annotation
with open(annot_path, "r") as f:
dataset = json.load(f)
# images
self.imgs = {}
for img in dataset["images"]:
if img["id"] not in available_ids:
continue
self.imgs[img["id"]] = img
self.imgToAnns = defaultdict(list)
for ann in dataset["annotations"]:
if ann["image_id"] not in available_ids:
continue
self.imgToAnns[ann["image_id"]].append(ann)
self.ids = list(sorted(self.imgs.keys()))
def __getitem__(self, index):
img_id = self.ids[index]
img_file_name = self.imgs[int(img_id)]["file_name"]
img = Image.open(os.path.join(self.img_path, img_file_name)).convert("RGB")
if 1:
# get structure annotations
# anns = self.imgToAnns[int(img_id)]
data = np.load(os.path.join(self.quadtree_path, img_file_name[:-4] + ".npy"), allow_pickle=True).item()
orig_quadtree = data["quadtree"]
orig_graph = data["graph"]
image_points = data["points"]
new_anns = []
for pt in image_points:
new_ann = {
"point": [int(pt[0]), int(pt[1])],
}
new_anns.append(new_ann)
target = {"image_id": img_id, "annotations": new_anns}
quadtree = {}
for k, v in orig_quadtree.items():
new_k = k
new_v = []
for pos in v:
new_pos = (int(pos[0]), int(pos[1]))
new_v.append(new_pos)
quadtree[new_k] = new_v
new_graph = {}
for k, v in orig_graph.items():
new_k = (int(k[0]), int(k[1]))
new_v = []
for adj in v:
if adj == (-1, -1):
new_v.append((-1, -1))
else:
new_v.append((int(adj[0]), int(adj[1])))
new_graph[new_k] = new_v
target_layers = []
for layer, layer_points in quadtree.items():
target_layer = []
for layer_point in layer_points:
for target_i in target["annotations"]:
if l1_dist(target_i["point"], list(layer_point)) <= 2:
target_layer.append(target_i)
break
target_layers.extend(target_layer)
layer_indices = []
count = 0
for k, v in quadtree.items():
if k == 0:
layer_indices.append(0)
else:
layer_indices.append(count)
count += len(v)
image_id = torch.tensor([int(img_id)])
points = [obj["point"] for obj in target_layers]
with open(os.path.join(self.edgecode_path, img_file_name[:-4] + ".json"), "r") as f:
edge2code = json.load(f)
edge2code = {
tuple(map(lambda x: int(float(x)), key.strip("()").split(", "))): value
for key, value in edge2code.items()
}
edges = [edge2code[(int(pt[0]), int(pt[1]))] for pt in points]
points = torch.as_tensor(points, dtype=torch.int64).reshape(-1, 2)
edges = torch.tensor(edges, dtype=torch.int64)
# annotations
target = {}
target["edges"] = edges
target["image_id"] = image_id
target["file_name"] = img_file_name
target["size"] = torch.as_tensor([img.size[1], img.size[0]])
# get semantic annotations
if not self.disable_sem_info:
semantic_left_up = [semantics_dict[obj["semantic"][0]] for obj in target_layers]
semantic_right_up = [semantics_dict[obj["semantic"][1]] for obj in target_layers]
semantic_right_down = [semantics_dict[obj["semantic"][2]] for obj in target_layers]
semantic_left_down = [semantics_dict[obj["semantic"][3]] for obj in target_layers]
semantic_left_up = torch.tensor(semantic_left_up, dtype=torch.int64)
semantic_right_up = torch.tensor(semantic_right_up, dtype=torch.int64)
semantic_right_down = torch.tensor(semantic_right_down, dtype=torch.int64)
semantic_left_down = torch.tensor(semantic_left_down, dtype=torch.int64)
target["semantic_left_up"] = semantic_left_up
target["semantic_right_up"] = semantic_right_up
target["semantic_right_down"] = semantic_right_down
target["semantic_left_down"] = semantic_left_down
# get image
img = F.to_tensor(img)
img = F.normalize(img, mean=mean, std=std)
target["unnormalized_points"] = points
# normalize
points = points / torch.tensor([img.shape[2], img.shape[1]], dtype=torch.float32)
target["points"] = points
target["layer_indices"] = torch.tensor(layer_indices)
# padding (-1,-1) if not enough 4 neighbors
for pt, neighbors in new_graph.items():
if len(neighbors) < 4:
new_graph[pt].extend([(-1, -1)] * (4 - len(neighbors)))
elif len(neighbors) > 4:
new_graph[pt] = neighbors[:4]
target["graph"] = graph_to_tensor(new_graph)
return img, target
def __len__(self):
return len(self.ids)
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