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model/util/KP_dataset.py

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+# Written by Ukcheol Shin, Jan. 24, 2023 using the following two repositories.
+# MS-UDA: https://github.com/yeong5366/MS-UDA
+# Mask2Former: https://github.com/facebookresearch/Mask2Former
+
+import cv2
+import numpy as np
+import os, torch
+# from imageio import imread
+from torch.nn import functional as F
+from torch.utils.data.dataset import Dataset
+import PIL
+
+class KP_dataset(Dataset):
+
+    def __init__(self, data_dir, split):
+        super(KP_dataset, self).__init__()
+
+        assert (split in ['train', 'val', 'test', 'test_day', 'test_night']),\
+         'split must be train | val | test | test_day | test_night |'
+
+        if split == 'train':        
+            with open(os.path.join(data_dir, 'train_day.txt'), 'r') as file:
+                self.data_list = [name.strip() for idx, name in enumerate(file)]
+            with open(os.path.join(data_dir, 'train_night.txt'), 'r') as file:
+                self.data_list += [name.strip()for idx, name in enumerate(file)]
+        elif split == 'val':            
+            with open(os.path.join(data_dir, 'val_day.txt'), 'r') as file:
+                self.data_list = [name.strip() for idx, name in enumerate(file)]
+            with open(os.path.join(data_dir, 'val_night.txt'), 'r') as file:
+                self.data_list += [name.strip()for idx, name in enumerate(file)]
+        elif split == 'test':            
+            with open(os.path.join(data_dir, 'test_day.txt'), 'r') as file:
+                self.data_list = [name.strip() for idx, name in enumerate(file)]
+            with open(os.path.join(data_dir, 'test_night.txt'), 'r') as file:
+                self.data_list += [name.strip()for idx, name in enumerate(file)]
+        self.data_list.sort()
+
+        self.data_dir  = data_dir
+        self.split     = split
+        self.n_data    = len(self.data_list)
+        self.size_divisibility = -1
+        self.ignore_label = 19
+
+    def read_image(self, name, folder):
+        splited_name = name.split('_')
+        file_path = os.path.join(self.data_dir, 'images', splited_name[0], splited_name[1], folder, splited_name[2].replace('png','jpg',1))
+        image = imread(file_path).astype('float32') # HxWxC
+        return image
+
+    def read_label(self, name, folder):
+        file_path = os.path.join(self.data_dir, '%s/%s' % (folder, name))
+        image     = imread(file_path).astype('float32')
+        return image
+
+    def __getitem__(self, index):
+        name  = self.data_list[index]
+        image_rgb = self.read_image(name, 'visible')                        # 通过实验，我发现KP和PST数据集RGB和thr分开的,即RGB是3通道,thr是1通道
+        image_thr = np.expand_dims(self.read_image(name, 'lwir').mean(axis=2), axis=2)
+        image = np.concatenate((image_rgb,image_thr),axis=2)                # 这句话将RGB图和thr图从通道上连接了,难怪通道数是4
+        label = self.read_label(name, 'labels').astype("double")
+
+        # Pad image and segmentation label here!
+        #image      = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+        #label = torch.as_tensor(label.astype("long"))
+        #image = np.asarray(PIL.Image.fromarray(image).resize((self.input_w, self.input_h)))
+        #image = image.astype('float32')
+        image = np.transpose(image, (2,0,1))/255.0
+        #label = np.asarray(PIL.Image.fromarray(label).resize((self.input_w, self.input_h), resample=PIL.Image.NEAREST))
+        #label = label.astype('int64')
+
+        if self.size_divisibility > 0:
+            image_size = (image.shape[-2], image.shape[-1])
+            padding_size = [
+                0,
+                self.size_divisibility - image_size[1],
+                0,
+                self.size_divisibility - image_size[0],
+            ]
+            image      = F.pad(image, padding_size, value=128).contiguous()
+            label = F.pad(label, padding_size, value=self.ignore_label).contiguous()
+
+        image_shape = (image.shape[-2], image.shape[-1])  # h, w
+
+        # Packing data
+        result = {}
+        result["name"]  = name
+        result["image"] = image
+        #result["label"] = label.long()
+
+        return torch.tensor(image), torch.tensor(label), name
+
+    def __len__(self):
+        return self.n_data

model/util/MF_dataset.py

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+# By Yuxiang Sun, Jul. 3, 2021
+# Email: sun.yuxiang@outlook.com
+
+import os, torch
+from torch.utils.data.dataset import Dataset
+import numpy as np
+import PIL
+
+class MF_dataset(Dataset):
+
+    def __init__(self, data_dir, split, input_h=480, input_w=640 ,transform=[]):
+        super(MF_dataset, self).__init__()
+
+        assert split in ['train', 'val', 'test', 'test_day', 'test_night', 'val_test', 'most_wanted'], \
+            'split must be "train"|"val"|"test"|"test_day"|"test_night"|"val_test"|"most_wanted"'  # test_day, test_night
+
+        with open(os.path.join(data_dir, split+'.txt'), 'r') as f:
+            self.names = [name.strip() for name in f.readlines()]
+
+        self.data_dir  = data_dir
+        self.split     = split
+        self.input_h   = input_h
+        self.input_w   = input_w
+        self.transform = transform
+        self.n_data    = len(self.names)
+
+    def read_image(self, name, folder):
+        file_path = os.path.join(self.data_dir, '%s/%s.png' % (folder, name))
+        image     = np.asarray(PIL.Image.open(file_path))
+        return image
+
+    def __getitem__(self, index):
+        name  = self.names[index]
+        image = self.read_image(name, 'images')
+        label = self.read_image(name, 'labels')
+        for func in self.transform:
+            image, label = func(image, label)
+            
+        image = np.asarray(PIL.Image.fromarray(image).resize((self.input_w, self.input_h)))
+        image = image.astype('float32')
+        image = np.transpose(image, (2,0,1))/255.0
+        label = np.asarray(PIL.Image.fromarray(label).resize((self.input_w, self.input_h), resample=PIL.Image.NEAREST))
+        label = label.astype('int64')
+        
+        return torch.tensor(image), torch.tensor(label), name
+
+    def __len__(self):
+        return self.n_data

model/util/PST_dataset.py

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+# Written by Ukcheol Shin, Jan. 24, 2023 using the following two repositories.
+# PST900: https://github.com/ShreyasSkandanS/pst900_thermal_rgb
+# Mask2Former: https://github.com/facebookresearch/Mask2Former
+
+import cv2
+import numpy as np
+import os, torch
+# from imageio import imread
+from torch.nn import functional as F
+from torch.utils.data.dataset import Dataset
+
+
+class PST_dataset(Dataset):
+
+    def __init__(self, data_dir, split):
+        super(PST_dataset, self).__init__()
+
+        assert split in ['train', 'val', 'test'], \
+            'split must be "train"|"val"|"test"' 
+
+        # read dataset list, all files have the same name across 'rgb', 'label', 'thermal', 'depth' folders
+        self.data_dir = os.path.join(data_dir, split)   # 这行和下一行都是我修改之后,先把未被修改的data_dir与split结合
+        data_dir = data_dir + split                     # 不加这行data_dir为./dataset/PSTdataset/RGB导致找不到文件,加入之后路径变为./dataset/PSTdataset/split/RGB
+        self.data_list = os.listdir(os.path.join(data_dir, 'rgb'))
+        self.data_list.sort()
+
+        # self.data_dir  = os.path.join(data_dir, split)
+        self.split     = split
+        self.n_data    = len(self.data_list)
+        self.size_divisibility = -1
+        self.ignore_label = 19
+        
+    def read_image(self, name, folder):
+        file_path = os.path.join(self.data_dir, '%s/%s' % (folder, name))
+        image     = imread(file_path).astype('float32')
+        return image
+
+    def __getitem__(self, index):
+        name  = self.data_list[index]
+        image_rgb = self.read_image(name, 'rgb')                            # 通过实验，我发现KP和PST数据集RGB和thr分开的,即RGB是3通道,thr是1通道
+        image_thr = np.expand_dims(self.read_image(name, 'thermal'), axis=2)
+        image = np.concatenate((image_rgb,image_thr),axis=2)
+        # depth = self.read_image(name, 'depth')
+
+        label = self.read_image(name, 'labels').astype("double")
+
+        # Pad image and segmentation label here!
+        #image      = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+        #label = torch.as_tensor(label.astype("long"))
+        image = np.transpose(image, (2, 0, 1)) / 255.0
+        if self.size_divisibility > 0:
+            image_size = (image.shape[-2], image.shape[-1])
+            padding_size = [
+                0,
+                self.size_divisibility - image_size[1],
+                0,
+                self.size_divisibility - image_size[0],
+            ]
+            image      = F.pad(image, padding_size, value=128).contiguous()
+            label = F.pad(label, padding_size, value=self.ignore_label).contiguous()
+
+        image_shape = (image.shape[-2], image.shape[-1])  # h, w
+
+        # Packing data
+        #result = {}
+        #result["name"]  = name
+        #result["image"] = image
+        #result["sem_seg_gt"] = sem_seg_gt.long()
+
+        return torch.tensor(image), torch.tensor(label), name
+
+    def __len__(self):
+        return self.n_data

model/util/__init__.py

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+

model/util/augmentation.py

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+import numpy as np
+from PIL import Image
+#from ipdb import set_trace as st
+
+
+class RandomFlip():
+    def __init__(self, prob=0.5):
+        #super(RandomFlip, self).__init__()
+        self.prob = prob
+
+    def __call__(self, image, label):
+        if np.random.rand() < self.prob:
+            image = image[:,::-1]
+            label = label[:,::-1]
+        return image, label
+
+
+class RandomCrop():
+    def __init__(self, crop_rate=0.1, prob=1.0):
+        #super(RandomCrop, self).__init__()
+        self.crop_rate = crop_rate
+        self.prob      = prob
+
+    def __call__(self, image, label):
+        if np.random.rand() < self.prob:
+            w, h, c = image.shape
+
+            h1 = np.random.randint(0, h*self.crop_rate)
+            w1 = np.random.randint(0, w*self.crop_rate)
+            h2 = np.random.randint(h-h*self.crop_rate, h+1)
+            w2 = np.random.randint(w-w*self.crop_rate, w+1)
+
+            image = image[w1:w2, h1:h2]
+            label = label[w1:w2, h1:h2]
+
+        return image, label
+
+
+class RandomCropOut():
+    def __init__(self, crop_rate=0.2, prob=1.0):
+        #super(RandomCropOut, self).__init__()
+        self.crop_rate = crop_rate
+        self.prob      = prob
+
+    def __call__(self, image, label):
+        if np.random.rand() < self.prob:
+            w, h, c = image.shape
+
+            h1 = np.random.randint(0, h*self.crop_rate)
+            w1 = np.random.randint(0, w*self.crop_rate)
+            h2 = int(h1 + h*self.crop_rate)
+            w2 = int(w1 + w*self.crop_rate)
+
+            image[w1:w2, h1:h2] = 0
+            label[w1:w2, h1:h2] = 0
+
+        return image, label
+
+
+class RandomBrightness():
+    def __init__(self, bright_range=0.15, prob=0.9):
+        #super(RandomBrightness, self).__init__()
+        self.bright_range = bright_range
+        self.prob = prob
+
+    def __call__(self, image, label):
+        if np.random.rand() < self.prob:
+            bright_factor = np.random.uniform(1-self.bright_range, 1+self.bright_range)
+            image = (image * bright_factor).astype(image.dtype)
+
+        return image, label
+
+
+class RandomNoise():
+    def __init__(self, noise_range=5, prob=0.9):
+        #super(RandomNoise, self).__init__()
+        self.noise_range = noise_range
+        self.prob = prob
+
+    def __call__(self, image, label):
+        if np.random.rand() < self.prob:
+            w, h, c = image.shape
+
+            noise = np.random.randint(
+                -self.noise_range,
+                self.noise_range,
+                (w,h,c)
+            )
+
+            image = (image + noise).clip(0,255).astype(image.dtype)
+
+        return image, label
+        
+
+

model/util/util.py

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+# By Yuxiang Sun, Dec. 4, 2020
+# Email: sun.yuxiang@outlook.com
+
+import numpy as np 
+from PIL import Image 
+
+# 0:unlabeled, 1:car, 2:person, 3:bike, 4:curve, 5:car_stop, 6:guardrail, 7:color_cone, 8:bump 
+def get_palette():
+    unlabelled = [0,0,0]
+    car        = [64,0,128]
+    person     = [64,64,0]
+    bike       = [0,128,192]
+    curve      = [0,0,192]
+    car_stop   = [128,128,0]
+    guardrail  = [64,64,128]
+    color_cone = [192,128,128]
+    bump       = [192,64,0]
+    palette    = np.array([unlabelled,car, person, bike, curve, car_stop, guardrail, color_cone, bump])
+
+    #road = [128, 64, 128]
+    #sidewalk = [244, 35, 232]
+    #building = [70, 70, 70]
+    #wall = [102, 102, 156]
+    #fence = [190, 153, 153]
+    #pole = [153, 153, 153]
+    #traffic_light = [250, 170, 30]
+    #traffic_sign = [220, 220, 0]
+    #vegetation = [107, 142, 35]
+    #terrain = [152, 251, 152]
+    #sky = [70, 130, 180]
+    #person = [220, 20, 60]
+    #rider = [255, 0, 0]
+    #car = [0, 0, 142]
+    #truck = [0, 0, 70]
+    #bus = [0, 60, 100]
+    #train = [0, 80, 100]
+    #motorcycle = [0, 0, 230]
+    #bicycle = [119, 11, 32]
+
+    #void = [0, 0, 0]
+    # unlabelled = [0, 0, 0]
+    # fire_extinhuisher = [0, 0, 255]
+    # backpack = [0, 255, 0]
+    # hand_drill = [255, 0, 0]
+    # rescue_randy = [255, 255, 255]
+    # palette = np.array([unlabelled, fire_extinhuisher, backpack, hand_drill, rescue_randy]).astype(np.uint8)
+    return palette
+
+def visualize(image_name, predictions, weight_name):
+    palette = get_palette()
+    for (i, pred) in enumerate(predictions):
+        pred = predictions[i].cpu().numpy()
+        img = np.zeros((pred.shape[0], pred.shape[1], 3), dtype=np.uint8)
+        for cid in range(0, len(palette)): # fix the mistake from the MFNet code on Dec.27, 2019
+            img[pred == cid] = palette[cid]
+        img = Image.fromarray(np.uint8(img))
+        img.save('run/Pred_' + weight_name + '_' + image_name[i] + '.png')
+
+def compute_results(conf_total):
+    n_class =  conf_total.shape[0]
+    consider_unlabeled = True  # must consider the unlabeled, please set it to True
+    if consider_unlabeled is True:
+        start_index = 0
+    else:
+        start_index = 1
+    precision_per_class = np.zeros(n_class)
+    recall_per_class = np.zeros(n_class)
+    iou_per_class = np.zeros(n_class)
+    for cid in range(start_index, n_class): # cid: class id
+        if conf_total[start_index:, cid].sum() == 0:
+            precision_per_class[cid] =  np.nan
+        else:
+            precision_per_class[cid] = float(conf_total[cid, cid]) / float(conf_total[start_index:, cid].sum()) # precision = TP/TP+FP
+        if conf_total[cid, start_index:].sum() == 0:
+            recall_per_class[cid] = np.nan
+        else:
+            recall_per_class[cid] = float(conf_total[cid, cid]) / float(conf_total[cid, start_index:].sum()) # recall = TP/TP+FN
+        if (conf_total[cid, start_index:].sum() + conf_total[start_index:, cid].sum() - conf_total[cid, cid]) == 0:
+            iou_per_class[cid] = np.nan
+        else:
+            iou_per_class[cid] = float(conf_total[cid, cid]) / float((conf_total[cid, start_index:].sum() + conf_total[start_index:, cid].sum() - conf_total[cid, cid])) # IoU = TP/TP+FP+FN
+
+    return precision_per_class, recall_per_class, iou_per_class