IS_Net/data_loader.py

## data loader
## Ackownledgement:
## We would like to thank Dr. Ibrahim Almakky (https://scholar.google.co.uk/citations?user=T9MTcK0AAAAJ&hl=en)
## for his helps in implementing cache machanism of our DIS dataloader.
from __future__ import print_function, division

import numpy as np
import random
from copy import deepcopy
import json
from tqdm import tqdm
from skimage import io
import os
from glob import glob
import matplotlib.pyplot as plt
from PIL import Image, ImageOps
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from torchvision.transforms.functional import normalize
import torch.nn.functional as F
import cv2
from scipy.ndimage import label

def show_gray_images(images, m=4):
    """

    展示一组灰度图像



    参数:

    images: 一个形状为(n, h, w)的数组，其中n是图像的数量，h和w分别是图像的高度和宽度。

    m: 每行展示的图像数量，默认为4。



    返回值:

    无

    """
    n, h, w = images.shape  # 获取输入图像的数量、高度和宽度
    num_rows = (n + m - 1) // m  # 计算需要的行数
    fig, axes = plt.subplots(num_rows, m, figsize=(m*2, num_rows*2))  # 创建画布和子图
    plt.subplots_adjust(wspace=0.05, hspace=0.05)  # 调整子图间的间距
    for i in range(num_rows):
        for j in range(m):
            idx = i*m + j  # 计算当前图像的索引
            if idx < n:
                axes[i, j].imshow(images[idx], cmap='gray')  # 展示图像
                axes[i, j].axis('off')  # 关闭坐标轴显示
    plt.show()  # 显示图像
#### --------------------- DIS dataloader cache ---------------------####

def segment_connected_components(mask):
    # 将mask转换为PyTorch张量
    mask_tensor = torch.tensor(mask)

    # 使用Scipy的label函数找到连通组件
    labeled_array, num_features = label(mask_tensor.numpy())

    # 创建一个字典来存储每个连通组件的像素值
    components = {}
    for label_idx in range(1, num_features + 1):
        component_mask = (labeled_array == label_idx)
        components[label_idx] = component_mask.astype(int)

    return components

def FillHole(im_in):
    img = np.array(im_in,dtype=np.uint8)[0]
    mask = np.zeros_like(img)
    contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    for contour in contours:
        cv2.drawContours(mask, [contour], -1, 255, thickness=cv2.FILLED)
    im_out = torch.from_numpy(mask)[None,...].float()
    return im_out

def get_im_gt_name_dict(datasets, flag='valid'):
    print("------------------------------", flag, "--------------------------------")
    name_im_gt_mid_list = []
    for i in range(len(datasets)):
        print("--->>>", flag, " dataset ",i,"/",len(datasets)," ",datasets[i]["name"],"<<<---")
        tmp_im_list, tmp_gt_list, tmp_mid_list = [], [], []
        tmp_im_list = glob(datasets[i]["im_dir"]+os.sep+'*'+datasets[i]["im_ext"])

        # img_name_dict[im_dirs[i][0]] = tmp_im_list
        # print('-im-',datasets[i]["name"],datasets[i]["im_dir"], ': ',len(tmp_im_list))

        if(datasets[i]["gt_dir"]==""):
            print('-gt-', datasets[i]["name"], datasets[i]["gt_dir"], ': ', 'No Ground Truth Found')
            tmp_gt_list = []
        else:
            tmp_gt_list = [datasets[i]["gt_dir"]+os.sep+x.split(os.sep)[-1].split(datasets[i]["im_ext"])[0]+datasets[i]["gt_ext"] for x in tmp_im_list]

            # lbl_name_dict[im_dirs[i][0]] = tmp_gt_list
            # print('-gt-', datasets[i]["name"],datasets[i]["gt_dir"], ': ',len(tmp_gt_list))

        if(datasets[i]["mid_dir"]==""):
            print('-mid-', datasets[i]["name"], datasets[i]["mid_dir"], ': ', 'No mid Found')
            tmp_mid_list = []
        else:
            tmp_mid_list = [datasets[i]["mid_dir"]+os.sep+x.split(os.sep)[-1].split(datasets[i]["im_ext"])[0]+datasets[i]["mid_ext"] for x in tmp_im_list]

            # lbl_name_dict[im_dirs[i][0]] = tmp_gt_list
            # print('-mid-', datasets[i]["name"],datasets[i]["mid_dir"], ': ',len(tmp_gt_list))



        if flag=="train": ## combine multiple training sets into one dataset
            if len(name_im_gt_mid_list)==0:
                name_im_gt_mid_list.append({"dataset_name":datasets[i]["name"],
                                        "im_path":tmp_im_list,
                                        "gt_path":tmp_gt_list,
                                        "mid_path":tmp_mid_list,
                                        "im_ext":datasets[i]["im_ext"],
                                        "gt_ext":datasets[i]["gt_ext"],
                                        "mid_ext":datasets[i]["mid_ext"],
                                        "cache_dir":datasets[i]["cache_dir"]})
            else:
                name_im_gt_mid_list[0]["dataset_name"] = name_im_gt_mid_list[0]["dataset_name"] + "_" + datasets[i]["name"]
                name_im_gt_mid_list[0]["im_path"] = name_im_gt_mid_list[0]["im_path"] + tmp_im_list
                name_im_gt_mid_list[0]["gt_path"] = name_im_gt_mid_list[0]["gt_path"] + tmp_gt_list
                name_im_gt_mid_list[0]["mid_path"] = name_im_gt_mid_list[0]["mid_path"] + tmp_mid_list
                if datasets[i]["im_ext"]!=".jpg" or datasets[i]["gt_ext"]!=".png":
                    print("Error: Please make sure all you images and ground truth masks are in jpg and png format respectively !!!")
                    exit()
                name_im_gt_mid_list[0]["im_ext"] = ".jpg"
                name_im_gt_mid_list[0]["gt_ext"] = ".png"
                name_im_gt_mid_list[0]["mid_ext"] = ".png"
                name_im_gt_mid_list[0]["cache_dir"] = os.sep.join(datasets[i]["cache_dir"].split(os.sep)[0:-1])+os.sep+name_im_gt_mid_list[0]["dataset_name"]
        else: ## keep different validation or inference datasets as separate ones
            name_im_gt_mid_list.append({"dataset_name":datasets[i]["name"],
                                    "im_path":tmp_im_list,
                                    "gt_path":tmp_gt_list,
                                    "mid_path":tmp_mid_list,
                                    "im_ext":datasets[i]["im_ext"],
                                    "gt_ext":datasets[i]["gt_ext"],
                                    "mid_ext":datasets[i]["mid_ext"],
                                    "cache_dir":datasets[i]["cache_dir"]})

    return name_im_gt_mid_list

def create_dataloaders(name_im_gt_mid_list, cache_size=[], cache_boost=True, my_transforms=[], batch_size=1, shuffle=False,is_train=True):
    ## model="train": return one dataloader for training
    ## model="valid": return a list of dataloaders for validation or testing

    gos_dataloaders = []
    gos_datasets = []

    if(len(name_im_gt_mid_list)==0):
        return gos_dataloaders, gos_datasets

    num_workers_ = 0
    # if(batch_size>1):
    #     num_workers_ = 2
    # if(batch_size>4):
    #     num_workers_ = 4
    # if(batch_size>8):
    #     num_workers_ = 8

    for i in range(0,len(name_im_gt_mid_list)):
        gos_dataset = GOSDatasetCache([name_im_gt_mid_list[i]],
                                      cache_size = cache_size,
                                      cache_path = name_im_gt_mid_list[i]["cache_dir"],
                                      cache_boost = cache_boost,
                                      transform = transforms.Compose(my_transforms),
                                      is_train=is_train)
        gos_dataloaders.append(DataLoader(gos_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers_))
        gos_datasets.append(gos_dataset)

    return gos_dataloaders, gos_datasets

def im_reader(im_path):
    image = Image.open(im_path).convert('RGB')
    corrected_image = ImageOps.exif_transpose(image)
    # return plt.imread(im_path)
    return np.array(corrected_image)

def im_preprocess(im,size):
    if len(im.shape) > 3:
        im = im[:,:,:3]
    if len(im.shape) < 3:
        im = im[:, :, np.newaxis]
    if im.shape[2] == 1:
        im = np.repeat(im, 3, axis=2)
    im_tensor = torch.tensor(im.copy(), dtype=torch.float32)
    im_tensor = torch.transpose(torch.transpose(im_tensor,1,2),0,1)
    if(len(size)<2):
        return im_tensor, im.shape[0:2]
    else:
        im_tensor = torch.unsqueeze(im_tensor,0)
        im_tensor = F.upsample(im_tensor, size, mode="bilinear")
        im_tensor = torch.squeeze(im_tensor,0)

    return im_tensor.type(torch.uint8), im.shape[0:2]

def gt_preprocess(gt,size):
    if len(gt.shape) > 2:
        gt = gt[:, :, 0]

    gt_tensor = torch.unsqueeze(torch.tensor(gt, dtype=torch.uint8),0)

    if(len(size)<2):
        return gt_tensor.type(torch.uint8), gt.shape[0:2]
    else:
        gt_tensor = torch.unsqueeze(torch.tensor(gt_tensor, dtype=torch.float32),0)
        gt_tensor = F.upsample(gt_tensor, size, mode="bilinear")
        gt_tensor = torch.squeeze(gt_tensor,0)

    return gt_tensor.type(torch.uint8), gt.shape[0:2]
    # return gt_tensor, gt.shape[0:2]

class GOSRandomHFlip(object):
    def __init__(self,prob=0.25):
        self.prob = prob
    def __call__(self,sample):
        imidx, image, label, shape, box, mask =  sample['imidx'], sample['image'], sample['label'], sample['shape'], sample['box'], sample['mask']

        # random horizontal flip
        randomnum = random.random()
        if randomnum <= self.prob:
            image = torch.flip(image,dims=[2])
            label = torch.flip(label,dims=[2])
            box = torch.flip(box,dims=[2])
            mask = torch.flip(mask,dims=[2])
        elif randomnum <= self.prob*2:
            image = torch.flip(image,dims=[1])
            label = torch.flip(label,dims=[1])
            box = torch.flip(box,dims=[1])
            mask = torch.flip(mask,dims=[1])
        elif randomnum <= self.prob*3:
            image = torch.flip(image,dims=[2])
            label = torch.flip(label,dims=[2])
            box = torch.flip(box,dims=[2])
            mask = torch.flip(mask,dims=[2])
            image = torch.flip(image,dims=[1])
            label = torch.flip(label,dims=[1])
            box = torch.flip(box,dims=[1])
            mask = torch.flip(mask,dims=[1])

        return {'imidx':imidx,'image':image, 'label':label, 'shape':shape, 'mask':mask, 'box':box}

class GOSResize(object):
    def __init__(self,size=[320,320]):
        self.size = size
    def __call__(self,sample):
        imidx, image, label, shape, box, mask =  sample['imidx'], sample['image'], sample['label'], sample['shape'], sample['box'], sample['mask']

        # import time
        # start = time.time()

        image = torch.squeeze(F.upsample(torch.unsqueeze(image,0),self.size,mode='bilinear'),dim=0)
        label = torch.squeeze(F.upsample(torch.unsqueeze(label,0),self.size,mode='bilinear'),dim=0)

        # print("time for resize: ", time.time()-start)

        return {'imidx':imidx,'image':image, 'label':label, 'shape':shape, 'mask':mask, 'box':box}

class GOSRandomCrop(object):
    def __init__(self,size=[288,288]):
        self.size = size
    def __call__(self,sample):
        imidx, image, label, shape, box, mask =  sample['imidx'], sample['image'], sample['label'], sample['shape'], sample['box'], sample['mask']

        h, w = image.shape[1:]
        new_h, new_w = self.size

        top = np.random.randint(0, h - new_h)
        left = np.random.randint(0, w - new_w)

        image = image[:,top:top+new_h,left:left+new_w]
        label = label[:,top:top+new_h,left:left+new_w]

        return {'imidx':imidx,'image':image, 'label':label, 'shape':shape, 'mask':mask, 'box':box}


class GOSNormalize(object):
    def __init__(self, mean=[0.485,0.456,0.406,0], std=[0.229,0.224,0.225,1.0]):
        self.mean = mean
        self.std = std

    def __call__(self,sample):

        imidx, image, label, shape, box, mask =  sample['imidx'], sample['image'], sample['label'], sample['shape'], sample['box'], sample['mask']
        # print(image.shape)
        image = normalize(image,self.mean,self.std)
        mask = normalize(mask,0,1)
        box = normalize(box,0,1)

        return {'imidx':imidx,'image':image, 'label':label, 'shape':shape, 'mask':mask, 'box':box}

class GOSRandomthorw(object):
    def __init__(self,ratio=0.25):
        self.ratio = ratio
    def __call__(self,sample):
        imidx, image, label, shape, box, mask =  sample['imidx'], sample['image'], sample['label'], sample['shape'], sample['box'], sample['mask']
        randomnum = random.random()
        if randomnum < self.ratio:
            mask = torch.zeros_like(mask)
        elif randomnum < self.ratio*2:
            box = torch.zeros_like(box)
        elif randomnum < self.ratio*3:
            mask = torch.zeros_like(mask)
            box = torch.zeros_like(box)

        return {'imidx':imidx,'image':image, 'label':label, 'shape':shape, 'mask':mask, 'box':box}

class GOSDatasetCache(Dataset):

    def __init__(self, name_im_gt_mid_list, cache_size=[], cache_path='./cache', cache_file_name='dataset.json', cache_boost=False, transform=None, is_train=True):

        self.is_train = is_train
        self.cache_size = cache_size
        self.cache_path = cache_path
        self.cache_file_name = cache_file_name
        self.cache_boost_name = ""

        self.cache_boost = cache_boost
        # self.ims_npy = None
        # self.gts_npy = None

        ## cache all the images and ground truth into a single pytorch tensor
        self.ims_pt = None
        self.gts_pt = None
        self.mid_pt = None

        ## we will cache the npy as well regardless of the cache_boost
        # if(self.cache_boost):
        self.cache_boost_name = cache_file_name.split('.json')[0]

        self.transform = transform

        self.dataset = {}

        ## combine different datasets into one
        dataset_names = []
        dt_name_list = [] # dataset name per image
        im_name_list = [] # image name
        im_path_list = [] # im path
        gt_path_list = [] # gt path
        mid_path_list = []
        im_ext_list = [] # im ext
        gt_ext_list = [] # gt ext
        mid_ext_list = []
        for i in range(0,len(name_im_gt_mid_list)):
            dataset_names.append(name_im_gt_mid_list[i]["dataset_name"])
            # dataset name repeated based on the number of images in this dataset
            dt_name_list.extend([name_im_gt_mid_list[i]["dataset_name"] for x in name_im_gt_mid_list[i]["im_path"]])
            im_name_list.extend([x.split(os.sep)[-1].split(name_im_gt_mid_list[i]["im_ext"])[0] for x in name_im_gt_mid_list[i]["im_path"]])
            im_path_list.extend(name_im_gt_mid_list[i]["im_path"])
            gt_path_list.extend(name_im_gt_mid_list[i]["gt_path"])
            mid_path_list.extend(name_im_gt_mid_list[i]["mid_path"])
            im_ext_list.extend([name_im_gt_mid_list[i]["im_ext"] for x in name_im_gt_mid_list[i]["im_path"]])
            gt_ext_list.extend([name_im_gt_mid_list[i]["gt_ext"] for x in name_im_gt_mid_list[i]["gt_path"]])
            mid_ext_list.extend([name_im_gt_mid_list[i]["mid_ext"] for x in name_im_gt_mid_list[i]["mid_path"]])


        self.dataset["data_name"] = dt_name_list
        self.dataset["im_name"] = im_name_list
        self.dataset["im_path"] = im_path_list
        self.dataset["ori_im_path"] = deepcopy(im_path_list)
        self.dataset["gt_path"] = gt_path_list
        self.dataset["ori_gt_path"] = deepcopy(gt_path_list)
        self.dataset["mid_path"] = mid_path_list
        self.dataset["ori_mid_path"] = deepcopy(mid_path_list)
        self.dataset["im_shp"] = []
        self.dataset["gt_shp"] = []
        self.dataset["mid_shp"] = []
        self.dataset["im_ext"] = im_ext_list
        self.dataset["gt_ext"] = gt_ext_list
        self.dataset["mid_ext"] = mid_ext_list


        self.dataset["ims_pt_dir"] = ""
        self.dataset["gts_pt_dir"] = ""
        self.dataset["mid_pt_dir"] = ""

        self.dataset = self.manage_cache(dataset_names)

    def manage_cache(self,dataset_names):
        if not os.path.exists(self.cache_path): # create the folder for cache
            os.makedirs(self.cache_path)
        cache_folder = os.path.join(self.cache_path, "_".join(dataset_names)+"_"+"x".join([str(x) for x in self.cache_size]))
        # if cache_folder.__len__() > 100: cache_folder = cache_folder[:100]
        if not os.path.exists(cache_folder): # check if the cache files are there, if not then cache
            return self.cache(cache_folder)
        return self.load_cache(cache_folder)

    def cache(self,cache_folder):
        os.mkdir(cache_folder)
        cached_dataset = deepcopy(self.dataset)

        # ims_list = []
        # gts_list = []
        ims_pt_list = []
        gts_pt_list = []
        mid_pt_list = []
        for i, im_path in tqdm(enumerate(self.dataset["im_path"]), total=len(self.dataset["im_path"])):

            im_id = cached_dataset["im_name"][i]
            # print("im_path: ", im_path)
            im = im_reader(im_path)
            im, im_shp = im_preprocess(im,self.cache_size)
            im_cache_file = os.path.join(cache_folder,self.dataset["data_name"][i]+"_"+im_id + "_im.pt")
            torch.save(im,im_cache_file)

            cached_dataset["im_path"][i] = im_cache_file
            if(self.cache_boost):
                ims_pt_list.append(torch.unsqueeze(im,0))
            # ims_list.append(im.cpu().data.numpy().astype(np.uint8))

            gt = np.zeros(im.shape[0:2])
            if len(self.dataset["gt_path"])!=0:
                gt = im_reader(self.dataset["gt_path"][i])
            gt, gt_shp = gt_preprocess(gt,self.cache_size)
            gt_cache_file = os.path.join(cache_folder,self.dataset["data_name"][i]+"_"+im_id + "_gt.pt")
            torch.save(gt,gt_cache_file)
            if len(self.dataset["gt_path"])>0:
                cached_dataset["gt_path"][i] = gt_cache_file
            else:
                cached_dataset["gt_path"].append(gt_cache_file)
            if(self.cache_boost):
                gts_pt_list.append(torch.unsqueeze(gt,0))

            mid = np.zeros(im.shape[0:2])
            if len(self.dataset["mid_path"])!=0:
                mid = im_reader(self.dataset["mid_path"][i])
            mid, mid_shp = gt_preprocess(mid,self.cache_size)
            mid_cache_file = os.path.join(cache_folder,self.dataset["data_name"][i]+"_"+im_id + "_mid.pt")
            torch.save(mid,mid_cache_file)
            if len(self.dataset["mid_path"])>0:
                cached_dataset["mid_path"][i] = mid_cache_file
            else:
                cached_dataset["mid_path"].append(mid_cache_file)
            if(self.cache_boost):
                mid_pt_list.append(torch.unsqueeze(mid,0))
            
            # gts_list.append(gt.cpu().data.numpy().astype(np.uint8))

            # im_shp_cache_file = os.path.join(cache_folder,im_id + "_im_shp.pt")
            # torch.save(gt_shp, shp_cache_file)
            cached_dataset["im_shp"].append(im_shp)
            # self.dataset["im_shp"].append(im_shp)

            # shp_cache_file = os.path.join(cache_folder,im_id + "_gt_shp.pt")
            # torch.save(gt_shp, shp_cache_file)
            cached_dataset["gt_shp"].append(gt_shp)
            # self.dataset["gt_shp"].append(gt_shp)

            cached_dataset["mid_shp"].append(mid_shp)

        if(self.cache_boost):
            cached_dataset["ims_pt_dir"] = os.path.join(cache_folder, self.cache_boost_name+'_ims.pt')
            cached_dataset["gts_pt_dir"] = os.path.join(cache_folder, self.cache_boost_name+'_gts.pt')
            cached_dataset["mid_pt_dir"] = os.path.join(cache_folder, self.cache_boost_name+'_mids.pt')
            self.ims_pt = torch.cat(ims_pt_list,dim=0)
            self.gts_pt = torch.cat(gts_pt_list,dim=0)
            self.mid_pt = torch.cat(mid_pt_list,dim=0)
            torch.save(torch.cat(ims_pt_list,dim=0),cached_dataset["ims_pt_dir"])
            torch.save(torch.cat(gts_pt_list,dim=0),cached_dataset["gts_pt_dir"])
            torch.save(torch.cat(mid_pt_list,dim=0),cached_dataset["mid_pt_dir"])

        try:
            json_file = open(os.path.join(cache_folder, self.cache_file_name),"w")
            json.dump(cached_dataset, json_file)
            json_file.close()
        except Exception:
            raise FileNotFoundError("Cannot create JSON")
        return cached_dataset

    def load_cache(self, cache_folder):
        print(os.path.join(cache_folder,self.cache_file_name))
        json_file = open(os.path.join(cache_folder,self.cache_file_name),"r")
        dataset = json.load(json_file)
        json_file.close()
        ## if cache_boost is true, we will load the image npy and ground truth npy into the RAM
        ## otherwise the pytorch tensor will be loaded
        if(self.cache_boost):
            # self.ims_npy = np.load(dataset["ims_npy_dir"])
            # self.gts_npy = np.load(dataset["gts_npy_dir"])
            self.ims_pt = torch.load(dataset["ims_pt_dir"], map_location='cpu')
            self.gts_pt = torch.load(dataset["gts_pt_dir"], map_location='cpu')
            self.mid_pt = torch.load(dataset["mid_pt_dir"], map_location='cpu')
        return dataset

    def __len__(self):
        return len(self.dataset["im_path"])

    def __getitem__(self, idx):

        im = None
        gt = None
        mid = None
        if(self.cache_boost and self.ims_pt is not None):

            # start = time.time()
            im = self.ims_pt[idx]#.type(torch.float32)
            gt = self.gts_pt[idx]#.type(torch.float32)
            mid = self.mid_pt[idx]#.type(torch.float32)
            # print(idx, 'time for pt loading: ', time.time()-start)

        else:
            # import time
            # start = time.time()
            # print("tensor***")
            im_pt_path = os.path.join(self.cache_path,os.sep.join(self.dataset["im_path"][idx].split(os.sep)[-2:]))
            im = torch.load(im_pt_path)#(self.dataset["im_path"][idx])
            gt_pt_path = os.path.join(self.cache_path,os.sep.join(self.dataset["gt_path"][idx].split(os.sep)[-2:]))
            gt = torch.load(gt_pt_path)#(self.dataset["gt_path"][idx])
            mid_pt_path = os.path.join(self.cache_path,os.sep.join(self.dataset["mid_path"][idx].split(os.sep)[-2:]))
            mid = torch.load(mid_pt_path)#(self.dataset["gt_path"][idx])
            # print(idx,'time for tensor loading: ', time.time()-start)


        im_shp = self.dataset["im_shp"][idx]
        # print("time for loading im and gt: ", time.time()-start)

        box = torch.zeros_like(gt[0])+gt[0]
        rows, cols = torch.where(box>0)
        left = torch.min(cols)
        top = torch.min(rows)
        right = torch.max(cols)
        bottom = torch.max(rows)
        box[top:bottom,left:right] = 255
        box[box!=255] = 0
        box = box[None,...]
        gim = torch.cat([im,mid,box],dim=0)

        # start_time = time.time()
        im = torch.divide(gim,255.0)
        gt = torch.divide(gt,255.0)
        mask = torch.divide(mid,255.0)
        box = torch.divide(box,255.0)

        
        sample = {
        "imidx": torch.from_numpy(np.array(idx)),
        "image": im,
        "label": gt,
        "mask": mask,
        'box': box,
        "shape": torch.from_numpy(np.array(im_shp)),
        }

        if self.transform:
            sample = self.transform(sample)
        return sample