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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
class SAMDataset(BaseDataset):
def __init__(self, sub1, sub2, sub3, sub4):
image_mask_dict = {}
self.data = []
self.register_subset(sub1)
self.register_subset(sub2)
self.register_subset(sub3)
self.register_subset(sub4)
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 =====
json_path = self.data[idx]
image_path = json_path.replace('.json', '.jpg')
with open(json_path, 'r') as json_file:
data = json.load(json_file)
annotation = data['annotations']
valid_ids = []
for i in range(len(annotation)):
area = annotation[i]['area']
if area > 100 * 100 * 5:
valid_ids.append(i)
chosen_id = np.random.choice(valid_ids)
mask = mask_utils.decode(annotation[chosen_id]["segmentation"] )
# ======================
image = cv2.imread(image_path)
ref_image = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB)
tar_image = ref_image
ref_mask = mask
tar_mask = mask
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
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