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import io
import json
import os
import random
import warnings
import logging
from typing import Any
from copy import deepcopy
from distinctipy import distinctipy
import tqdm
import time
import numpy as np
from PIL import Image, ImageDraw
import cv2
import torch
from torch.utils.data import Dataset
import torchvision.transforms as tvT
from torchvision.transforms.functional import InterpolationMode
from datasets import Dataset as HFDataset
from datasets import DatasetDict, load_from_disk
from transformers import AutoConfig, AutoTokenizer
from pycocotools import mask
from mmdet.datasets.api_wrappers import COCO
from .utils import detection_utils as utils
from .utils.detectron2.data2 import transforms as T
from .utils.augmentation import build_pseudo_augmentation
from .utils import (expand2square, expand2square_mask)
from .process_functions import dynamic_preprocess
# https://www.exiv2.org/tags.html
_EXIF_ORIENT = 274 # exif 'Orientation' tag
def _apply_exif_orientation(image):
"""
Applies the exif orientation correctly.
This code exists per the bug:
https://github.com/python-pillow/Pillow/issues/3973
with the function `ImageOps.exif_transpose`. The Pillow source raises errors with
various methods, especially `tobytes`
Function based on:
https://github.com/wkentaro/labelme/blob/v4.5.4/labelme/utils/image.py#L59
https://github.com/python-pillow/Pillow/blob/7.1.2/src/PIL/ImageOps.py#L527
Args:
image (PIL.Image): a PIL image
Returns:
(PIL.Image): the PIL image with exif orientation applied, if applicable
"""
if not hasattr(image, "getexif"):
return image
try:
exif = image.getexif()
except Exception: # https://github.com/facebookresearch/detectron2/issues/1885
exif = None
if exif is None:
return image
orientation = exif.get(_EXIF_ORIENT)
method = {
2: Image.FLIP_LEFT_RIGHT,
3: Image.ROTATE_180,
4: Image.FLIP_TOP_BOTTOM,
5: Image.TRANSPOSE,
6: Image.ROTATE_270,
7: Image.TRANSVERSE,
8: Image.ROTATE_90,
}.get(orientation)
if method is not None:
return image.transpose(method)
return image
class SA1BPseudoVideoDataset(Dataset):
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def __init__(self,
model_path,
data_path=None,
image_folder=None,
dynamic_image_size=False,
pad_image_to_square=False,
num_dynamic_patch=None,
repeat_time=1,
ot_image_processor=None,
tokenizer=None,
vfm_name="RADIO",
):
super().__init__()
self.dynamic_image_size = dynamic_image_size
self.pad_image_to_square = pad_image_to_square
self.ot_image_processor = ot_image_processor
if vfm_name in ["DINOv2", "ConvNext"]:
self.ot_image_processor.do_center_crop=False
self.ot_image_processor.do_resize=False
self.cfg = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
if num_dynamic_patch is not None and len(num_dynamic_patch) == 2:
self.min_dynamic_patch = num_dynamic_patch[0]
self.max_dynamic_patch = num_dynamic_patch[1]
else:
self.min_dynamic_patch = self.cfg.min_dynamic_patch
self.max_dynamic_patch = self.cfg.max_dynamic_patch
self.image_size = self.cfg.force_image_size
self.use_thumbnail = self.cfg.use_thumbnail
with open(data_path, 'r') as f:
data_list = json.load(f)['images']
# self.data = data_list
left_data_list = []
for item in data_list:
if item['file_name'].startswith('sa_0000'):
continue
left_data_list.append(item)
self.data = left_data_list
# self.coco = COCO(data_path)
# img_ids = self.coco.get_img_ids()
# data_list = []
# print("Loading data list...")
# for img_id in tqdm.tqdm(img_ids):
# raw_img_info = self.coco.load_imgs([img_id])[0]
# raw_img_info["img_id"] = img_id
# ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
# raw_ann_info = self.coco.load_anns(ann_ids)
# parsed_data_info = self.parse_data_info(
# {'raw_ann_info': raw_ann_info, 'raw_img_info': raw_img_info}
# )
# data_list.append(parsed_data_info)
# self.data = data_list
if vfm_name == "DINOv2":
augs = build_pseudo_augmentation(True, force_image_size=512)
elif vfm_name in ["RADIO", "ConvNext"]:
augs = build_pseudo_augmentation(True, force_image_size=1024)
else:
raise NotImplementedError
self.augmentations = T.AugmentationList(augs)
self.transform = tvT.Compose([
tvT.Lambda(lambda img: img.convert('RGB')
if img.mode != 'RGB' else img),
tvT.Resize((self.image_size, self.image_size)),
tvT.ToTensor(),
tvT.Normalize(mean=self.IMAGENET_MEAN, std=self.IMAGENET_STD)
])
self.image_folder = image_folder
self._max_refetch = 100
def parse_data_info(self, img_info: dict):
data_info = {}
data_info["image"] = img_info["file_name"]
data_info["img_id"] = img_info["image_id"]
data_info["height"] = img_info["height"]
data_info["width"] = img_info["width"]
anno_file = os.path.join(self.image_folder, img_info["file_name"].replace('.jpg', '.json'))
with open(anno_file, 'r') as f:
json_data = json.load(f)
instances = []
for i, ann in enumerate(json_data['annotations']):
instance = {}
x1, y1, w, h = ann["bbox"]
inter_w = max(0, min(x1 + w, img_info["width"]) - max(x1, 0))
inter_h = max(0, min(y1 + h, img_info["height"]) - max(y1, 0))
if inter_w * inter_h == 0:
continue
if ann["area"] <= 0 or w < 1 or h < 1:
continue
bbox = [x1, y1, x1 + w, y1 + h]
if ann.get("iscrowd", False):
instance["ignore_flag"] = 1
else:
instance["ignore_flag"] = 0
instance["bbox"] = bbox
if ann.get("segmentation", None):
instance["segmentation"] = ann["segmentation"]
if "instance_id" in ann:
instance["instance_id"] = ann["instance_id"]
else:
instance["instance_id"] = i+1
instances.append(instance)
data_info["annotations"] = instances
return data_info
# def parse_data_info(self, raw_data_info: dict):
# img_info = raw_data_info["raw_img_info"]
# ann_info = raw_data_info["raw_ann_info"]
# data_info = {}
# data_info["image"] = img_info["file_name"]
# data_info["img_id"] = img_info["img_id"]
# data_info["height"] = img_info["height"]
# data_info["width"] = img_info["width"]
# instances = []
# for i, ann in enumerate(ann_info):
# instance = {}
# if ann.get("ignore", False):
# continue
# x1, y1, w, h = ann["bbox"]
# inter_w = max(0, min(x1 + w, img_info["width"]) - max(x1, 0))
# inter_h = max(0, min(y1 + h, img_info["height"]) - max(y1, 0))
# if inter_w * inter_h == 0:
# continue
# if ann["area"] <= 0 or w < 1 or h < 1:
# continue
# bbox = [x1, y1, x1 + w, y1 + h]
# if ann.get("iscrowd", False):
# instance["ignore_flag"] = 1
# else:
# instance["ignore_flag"] = 0
# instance["bbox"] = bbox
# if ann.get("segmentation", None):
# instance["segmentation"] = ann["segmentation"]
# if "instance_id" in ann:
# instance["instance_id"] = ann["instance_id"]
# else:
# instance["instance_id"] = ann["id"]
# instances.append(instance)
# data_info["annotations"] = instances
# return data_info
@property
def modality_length(self):
length_list = []
for data_dict in self.data:
cur_len = 100
length_list.append(cur_len)
return length_list
def _rand_another(self):
return np.random.randint(0, len(self.data))
def __len__(self):
return len(self.data)
def __getitem__(self, index) -> Any:
for _ in range(self._max_refetch + 1):
data = self.prepare_data(index)
if data is None:
index = self._rand_another()
continue
return data
def decode_mask(self, object_masks, ori_height, ori_width):
binary_masks = []
for object_mask in object_masks:
if isinstance(object_mask, dict):
if isinstance(object_mask["counts"], list):
# convert to compressed RLE
object_mask = mask.frPyObjects(object_mask, ori_height, ori_width)
m = mask.decode(object_mask)
m = m.astype(np.uint8).squeeze()
elif object_mask:
rles = mask.frPyObjects(object_mask, ori_height, ori_width)
rle = mask.merge(rles)
m = mask.decode(rle).astype(np.uint8).squeeze()
else:
m = np.zeros((ori_height, ori_width), dtype=np.uint8)
binary_masks.append(m)
if len(binary_masks) == 0:
binary_masks.append(np.zeros((ori_height, ori_width), dtype=np.uint8))
masks = np.stack(binary_masks, axis=0)
if self.pad_image_to_square:
masks = expand2square_mask(masks)
# masks = torch.from_numpy(masks)
return masks
def prepare_data(self, index):
data_dict = copy.deepcopy(self.parse_data_info(self.data[index]))
img_annos = data_dict.pop('annotations', None)
image_path = os.path.join(self.image_folder, data_dict['image'])
original_image = utils.read_image(image_path, "RGB")
sampling_frame_num = 2
image_list = []
annotations_list = []
for _ in range(sampling_frame_num):
utils.check_image_size(data_dict, original_image)
aug_input = T.AugInput(original_image)
transforms = self.augmentations(aug_input)
image = aug_input.image
image_shape = image.shape[:2]
image_list.append(Image.fromarray(image))
_img_annos = []
for anno in img_annos:
_anno = {}
for k, v in anno.items():
_anno[k] = copy.deepcopy(v)
_img_annos.append(_anno)
annos = [
utils.transform_instance_annotations(obj, transforms, image_shape)
for obj in _img_annos
if obj.get("iscrowd", 0) == 0
]
annotations_list.append(annos)
# sampled_frame_indices = random.sample(list(range(sampling_frame_num)), 2)
sampled_frame_indices = [0, 1]
# if random.random() < 0.2:
# images = [Image.open(image_path).convert('RGB'), image_list[sampled_frame_indices[0]]]
# annotations = [img_annos, annotations_list[sampled_frame_indices[0]]]
# else:
images = [image_list[sampled_frame_indices[0]], image_list[sampled_frame_indices[1]]]
annotations = [annotations_list[sampled_frame_indices[0]], annotations_list[sampled_frame_indices[1]]]
visual_prompts_list = []
region_ids_list = []
for fid, annotations_i in enumerate(annotations):
segms = [annotations_i[idx]['segmentation'] for idx in range(len(annotations_i))]
instance_ids = [annotations_i[idx]['instance_id'] for idx in range(len(annotations_i))]
if isinstance(segms[0], np.ndarray):
ori_width, ori_height = images[fid].size
regions = np.stack(segms, axis=0)
assert regions.shape[1] == ori_height, f"regions.shape[1]: {regions.shape[1]}, ori_height: {ori_height}"
assert regions.shape[2] == ori_width, f"regions.shape[2]: {regions.shape[2]}, ori_width: {ori_width}"
else:
ori_width, ori_height = images[fid].size
regions = self.decode_mask(segms, ori_height=ori_height, ori_width=ori_width)
visual_prompts_list.append(regions)
region_ids_list.append(instance_ids)
merged_visual_prompts = [image.copy() for image in images]
if self.dynamic_image_size:
num_patches_list, images_list, merged_regions_list, crop_regions_list, num_vprompts_list = [], [], [], [], []
for image, visual_prompts, merged_visual_prompt in zip(images, visual_prompts_list, merged_visual_prompts):
try:
_images, regions, merged_regions = dynamic_preprocess(
image, visual_prompts, merged_visual_prompt, min_num=self.min_dynamic_patch, max_num=self.max_dynamic_patch,
image_size=self.image_size, use_thumbnail=self.use_thumbnail
)
except Exception as e:
return None
images_list.extend(_images)
merged_regions_list.extend(merged_regions)
crop_regions_list.extend(regions)
num_patches_list.append(len(_images))
num_vprompts_list.append(len(regions))
else:
raise NotImplementedError
pixel_values = [self.transform(image) for image in images_list]
pixel_values = torch.stack(pixel_values) # num_patch, channels, h, w
merged_visual_prompts = [self.transform(merged_region) for merged_region in merged_regions_list]
merged_visual_prompts = torch.stack(merged_visual_prompts)
transformed_visual_prompts = []
for region in crop_regions_list:
transformed_regions = []
for _region in region:
resized_region = cv2.resize(
_region[:, :, np.newaxis], dsize=(self.image_size, self.image_size),
interpolation=cv2.INTER_NEAREST_EXACT)
transformed_regions.append(torch.from_numpy(resized_region).squeeze(-1))
transformed_visual_prompts.append(torch.stack(transformed_regions))
try:
visual_prompts = torch.stack(transformed_visual_prompts, dim=0) # num_prompts, num_patch, h, w
except:
print("num regions: ", len(crop_regions_list))
print("transformed_visual_prompts.shape: ", [ele.shape for ele in transformed_visual_prompts])
print("pixel_values.shape: ", pixel_values.shape)
exit(0)
assert merged_visual_prompts.shape[:2] == pixel_values.shape[:2]
ot_pixel_values = [self.ot_image_processor(images=image, return_tensors='pt').pixel_values for image in images]
ot_pixel_values = torch.cat(ot_pixel_values)
# vp_images_list = []
# for _visual_prompts in visual_prompts_list:
# for region in _visual_prompts:
# region_img = np.repeat(region[:, :, np.newaxis], 3, axis=2) * 255
# region_img = self.ot_image_processor(images=region_img, return_tensors='pt').pixel_values
# vp_images_list.append(region_img)
# ot_visual_prompts = torch.stack(vp_images_list)[:, 0, :, :] # num_prompts, h, w
ot_visual_prompts = torch.from_numpy(np.concatenate(visual_prompts_list, axis=0)).\
to(ot_pixel_values.dtype).to(ot_pixel_values.device) # num_prompts, h, w
assert ot_pixel_values.shape[-2:] == ot_visual_prompts.shape[-2:], f"ot_pixel_values.shape: {ot_pixel_values.shape[-2:]}, ot_visual_prompts.shape: {ot_visual_prompts.shape[-2:]}"
ret = dict(
input_ids=[1, 1, 1],
labels=[1, 1, 1],
attention_mask=[1, 1, 1],
pixel_values=pixel_values,
merged_visual_prompts=merged_visual_prompts,
num_patches=num_patches_list,
visual_prompts=visual_prompts.flatten(0, 1),
num_vprompts=num_vprompts_list,
num_images=len(num_vprompts_list),
ot_pixel_values=ot_pixel_values,
ot_visual_prompts=ot_visual_prompts,
region_ids=region_ids_list,
)
return ret
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