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from typing import Dict
import numpy as np
from omegaconf import DictConfig, ListConfig
import torch
from torch.utils.data import Dataset
from pathlib import Path
import json
from PIL import Image
from torchvision import transforms
from einops import rearrange
from typing import Literal, Tuple, Optional, Any
import cv2
import random
import json
import os, sys
import math
from glob import glob
import PIL.Image
from .normal_utils import trans_normal, normal2img, img2normal
import pdb
from icecream import ic
import cv2
import numpy as np
def add_margin(pil_img, color=0, size=256):
width, height = pil_img.size
result = Image.new(pil_img.mode, (size, size), color)
result.paste(pil_img, ((size - width) // 2, (size - height) // 2))
return result
def scale_and_place_object(image, scale_factor):
assert np.shape(image)[-1]==4 # RGBA
# Extract the alpha channel (transparency) and the object (RGB channels)
alpha_channel = image[:, :, 3]
# Find the bounding box coordinates of the object
coords = cv2.findNonZero(alpha_channel)
x, y, width, height = cv2.boundingRect(coords)
# Calculate the scale factor for resizing
original_height, original_width = image.shape[:2]
if width > height:
size = width
original_size = original_width
else:
size = height
original_size = original_height
scale_factor = min(scale_factor, size / (original_size+0.0))
new_size = scale_factor * original_size
scale_factor = new_size / size
# Calculate the new size based on the scale factor
new_width = int(width * scale_factor)
new_height = int(height * scale_factor)
center_x = original_width // 2
center_y = original_height // 2
paste_x = center_x - (new_width // 2)
paste_y = center_y - (new_height // 2)
# Resize the object (RGB channels) to the new size
rescaled_object = cv2.resize(image[y:y+height, x:x+width], (new_width, new_height))
# Create a new RGBA image with the resized image
new_image = np.zeros((original_height, original_width, 4), dtype=np.uint8)
new_image[paste_y:paste_y + new_height, paste_x:paste_x + new_width] = rescaled_object
return new_image
class SingleImageDataset(Dataset):
def __init__(self,
root_dir: str,
num_views: int,
img_wh: Tuple[int, int],
bg_color: str,
crop_size: int = 224,
single_image: Optional[PIL.Image.Image] = None,
num_validation_samples: Optional[int] = None,
filepaths: Optional[list] = None,
cond_type: Optional[str] = None,
prompt_embeds_path: Optional[str] = None,
gt_path: Optional[str] = None
) -> None:
"""Create a dataset from a folder of images.
If you pass in a root directory it will be searched for images
ending in ext (ext can be a list)
"""
self.root_dir = root_dir
self.num_views = num_views
self.img_wh = img_wh
self.crop_size = crop_size
self.bg_color = bg_color
self.cond_type = cond_type
self.gt_path = gt_path
if single_image is None:
if filepaths is None:
# Get a list of all files in the directory
file_list = os.listdir(self.root_dir)
else:
file_list = filepaths
# Filter the files that end with .png or .jpg
self.file_list = [file for file in file_list if file.endswith(('.png', '.jpg', '.webp'))]
else:
self.file_list = None
# load all images
self.all_images = []
self.all_alphas = []
bg_color = self.get_bg_color()
if single_image is not None:
image, alpha = self.load_image(None, bg_color, return_type='pt', Imagefile=single_image)
self.all_images.append(image)
self.all_alphas.append(alpha)
else:
for file in self.file_list:
print(os.path.join(self.root_dir, file))
image, alpha = self.load_image(os.path.join(self.root_dir, file), bg_color, return_type='pt')
self.all_images.append(image)
self.all_alphas.append(alpha)
self.all_images = self.all_images[:num_validation_samples]
self.all_alphas = self.all_alphas[:num_validation_samples]
ic(len(self.all_images))
try:
self.normal_text_embeds = torch.load(f'{prompt_embeds_path}/normal_embeds.pt')
self.color_text_embeds = torch.load(f'{prompt_embeds_path}/clr_embeds.pt') # 4view
except:
self.color_text_embeds = torch.load(f'{prompt_embeds_path}/embeds.pt')
self.normal_text_embeds = None
def __len__(self):
return len(self.all_images)
def get_bg_color(self):
if self.bg_color == 'white':
bg_color = np.array([1., 1., 1.], dtype=np.float32)
elif self.bg_color == 'black':
bg_color = np.array([0., 0., 0.], dtype=np.float32)
elif self.bg_color == 'gray':
bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32)
elif self.bg_color == 'random':
bg_color = np.random.rand(3)
elif isinstance(self.bg_color, float):
bg_color = np.array([self.bg_color] * 3, dtype=np.float32)
else:
raise NotImplementedError
return bg_color
def load_image(self, img_path, bg_color, return_type='np', Imagefile=None):
# pil always returns uint8
if Imagefile is None:
image_input = Image.open(img_path)
else:
image_input = Imagefile
image_size = self.img_wh[0]
if self.crop_size!=-1:
alpha_np = np.asarray(image_input)[:, :, 3]
coords = np.stack(np.nonzero(alpha_np), 1)[:, (1, 0)]
min_x, min_y = np.min(coords, 0)
max_x, max_y = np.max(coords, 0)
ref_img_ = image_input.crop((min_x, min_y, max_x, max_y))
h, w = ref_img_.height, ref_img_.width
scale = self.crop_size / max(h, w)
h_, w_ = int(scale * h), int(scale * w)
ref_img_ = ref_img_.resize((w_, h_))
image_input = add_margin(ref_img_, size=image_size)
else:
image_input = add_margin(image_input, size=max(image_input.height, image_input.width))
image_input = image_input.resize((image_size, image_size))
# img = scale_and_place_object(img, self.scale_ratio)
img = np.array(image_input)
img = img.astype(np.float32) / 255. # [0, 1]
assert img.shape[-1] == 4 # RGBA
alpha = img[...,3:4]
img = img[...,:3] * alpha + bg_color * (1 - alpha)
if return_type == "np":
pass
elif return_type == "pt":
img = torch.from_numpy(img)
alpha = torch.from_numpy(alpha)
else:
raise NotImplementedError
return img, alpha
def __getitem__(self, index):
image = self.all_images[index%len(self.all_images)]
alpha = self.all_alphas[index%len(self.all_images)]
if self.file_list is not None:
filename = self.file_list[index%len(self.all_images)].replace(".png", "")
else:
filename = 'null'
img_tensors_in = [
image.permute(2, 0, 1)
] * self.num_views
alpha_tensors_in = [
alpha.permute(2, 0, 1)
] * self.num_views
img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W)
alpha_tensors_in = torch.stack(alpha_tensors_in, dim=0).float() # (Nv, 3, H, W)
if self.gt_path is not None:
gt_image = self.gt_images[index%len(self.all_images)]
gt_alpha = self.gt_alpha[index%len(self.all_images)]
gt_img_tensors_in = [gt_image.permute(2, 0, 1) ] * self.num_views
gt_alpha_tensors_in = [gt_alpha.permute(2, 0, 1) ] * self.num_views
gt_img_tensors_in = torch.stack(gt_img_tensors_in, dim=0).float()
gt_alpha_tensors_in = torch.stack(gt_alpha_tensors_in, dim=0).float()
normal_prompt_embeddings = self.normal_text_embeds if hasattr(self, 'normal_text_embeds') else None
color_prompt_embeddings = self.color_text_embeds if hasattr(self, 'color_text_embeds') else None
out = {
'imgs_in': img_tensors_in,
'alphas': alpha_tensors_in,
'normal_prompt_embeddings': normal_prompt_embeddings,
'color_prompt_embeddings': color_prompt_embeddings,
'filename': filename,
}
return out
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