汐知
app
280eee9
import json
import cv2
import numpy as np
import os
from torch.utils.data import Dataset
from PIL import Image
import cv2
from .data_utils import *
from .base import BaseDataset
from pycocotools import mask as mask_utils
from lvis import LVIS
class LvisDataset(BaseDataset):
def __init__(self, image_dir, json_path):
self.image_dir = image_dir
self.json_path = json_path
lvis_api = LVIS(json_path)
img_ids = sorted(lvis_api.imgs.keys())
imgs = lvis_api.load_imgs(img_ids)
anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
self.data = imgs
self.annos = anns
self.lvis_api = lvis_api
self.size = (512,512)
self.clip_size = (224,224)
self.dynamic = 0
def register_subset(self, path):
data = os.listdir(path)
data = [ os.path.join(path, i) for i in data if '.json' in i]
self.data = self.data + data
def get_sample(self, idx):
# ==== get pairs =====
image_name = self.data[idx]['coco_url'].split('/')[-1]
image_path = os.path.join(self.image_dir, image_name)
image = cv2.imread(image_path)
ref_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
anno = self.annos[idx]
obj_ids = []
for i in range(len(anno)):
obj = anno[i]
area = obj['area']
if area > 3600:
obj_ids.append(i)
assert len(anno) > 0
obj_id = np.random.choice(obj_ids)
anno = anno[obj_id]
ref_mask = self.lvis_api.ann_to_mask(anno)
tar_image, tar_mask = ref_image.copy(), ref_mask.copy()
item_with_collage = self.process_pairs(ref_image, ref_mask, tar_image, tar_mask)
sampled_time_steps = self.sample_timestep()
item_with_collage['time_steps'] = sampled_time_steps
return item_with_collage
def __len__(self):
return 20000
def check_region_size(self, image, yyxx, ratio, mode = 'max'):
pass_flag = True
H,W = image.shape[0], image.shape[1]
H,W = H * ratio, W * ratio
y1,y2,x1,x2 = yyxx
h,w = y2-y1,x2-x1
if mode == 'max':
if h > H or w > W:
pass_flag = False
elif mode == 'min':
if h < H or w < W:
pass_flag = False
return pass_flag