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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 MVImageNetDataset(BaseDataset):
def __init__(self, txt, image_dir):
with open(txt,"r") as f:
data = f.read().split('\n')[:-1]
self.image_dir = image_dir
self.data = data
self.size = (512,512)
self.clip_size = (224,224)
self.dynamic = 2
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_alpha_mask(self, mask_path):
image = cv2.imread( mask_path, cv2.IMREAD_UNCHANGED)
mask = (image[:,:,-1] > 128).astype(np.uint8)
return mask
def get_sample(self, idx):
object_dir = self.data[idx].replace('MVDir/', self.image_dir)
frames = os.listdir(object_dir)
frames = [ i for i in frames if '.png' in i]
# Sampling frames
min_interval = len(frames) // 8
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_mask_name = frames[start_frame_index]
tar_mask_name = frames[end_frame_index]
ref_image_name = ref_mask_name.split('_')[0] + '.jpg'
tar_image_name = tar_mask_name.split('_')[0] + '.jpg'
ref_mask_path = os.path.join(object_dir, ref_mask_name)
tar_mask_path = os.path.join(object_dir, tar_mask_name)
ref_image_path = os.path.join(object_dir, ref_image_name)
tar_image_path = os.path.join(object_dir, tar_image_name)
# Read Image and Mask
ref_image = cv2.imread(ref_image_path).astype(np.uint8)
ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB)
tar_image = cv2.imread(tar_image_path).astype(np.uint8)
tar_image = cv2.cvtColor(tar_image, cv2.COLOR_BGR2RGB)
ref_mask = self.get_alpha_mask(ref_mask_path)
tar_mask = self.get_alpha_mask(tar_mask_path)
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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