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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
class YoutubeVOSDataset(BaseDataset):
def __init__(self, image_dir, anno, meta):
self.image_root = image_dir
self.anno_root = anno
self.meta_file = meta
video_dirs = []
with open(self.meta_file) as f:
records = json.load(f)
records = records["videos"]
for video_id in records:
video_dirs.append(video_id)
self.records = records
self.data = video_dirs
self.size = (512,512)
self.clip_size = (224,224)
self.dynamic = 1
def __len__(self):
return 40000
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 and w > W:
pass_flag = False
elif mode == 'min':
if h < H and w < W:
pass_flag = False
return pass_flag
def get_sample(self, idx):
video_id = list(self.records.keys())[idx]
objects_id = np.random.choice( list(self.records[video_id]["objects"].keys()) )
frames = self.records[video_id]["objects"][objects_id]["frames"]
# Sampling frames
min_interval = len(frames) // 10
start_frame_index = np.random.randint(low=0, high=len(frames) - min_interval)
end_frame_index = start_frame_index + np.random.randint(min_interval, len(frames) - start_frame_index )
end_frame_index = min(end_frame_index, len(frames) - 1)
# Get image path
ref_image_name = frames[start_frame_index]
tar_image_name = frames[end_frame_index]
ref_image_path = os.path.join(self.image_root, video_id, ref_image_name) + '.jpg'
tar_image_path = os.path.join(self.image_root, video_id, tar_image_name) + '.jpg'
ref_mask_path = ref_image_path.replace('JPEGImages','Annotations').replace('.jpg', '.png')
tar_mask_path = tar_image_path.replace('JPEGImages','Annotations').replace('.jpg', '.png')
# Read Image and Mask
ref_image = cv2.imread(ref_image_path)
ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB)
tar_image = cv2.imread(tar_image_path)
tar_image = cv2.cvtColor(tar_image, cv2.COLOR_BGR2RGB)
ref_mask = Image.open(ref_mask_path ).convert('P')
ref_mask= np.array(ref_mask)
ref_mask = ref_mask == int(objects_id)
tar_mask = Image.open(tar_mask_path ).convert('P')
tar_mask= np.array(tar_mask)
tar_mask = tar_mask == int(objects_id)
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
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