upload lc100-s3olci dataset

lc100_s3olci/dataset_lc100_s3olci_cls.py

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+from torch.utils.data import DataLoader, Dataset
+import cv2
+import os
+import rasterio
+import torch
+import numpy as np
+from pyproj import Transformer
+from datetime import date
+
+S3_OLCI_SCALE = [0.0139465,0.0133873,0.0121481,0.0115198,0.0100953,0.0123538,0.00879161,0.00876539,
+                    0.0095103,0.00773378,0.00675523,0.0071996,0.00749684,0.0086512,0.00526779,0.00530267,
+                    0.00493004,0.00549962,0.00502847,0.00326378,0.00324118]
+
+LC100_CLSID = {
+    0: 0, # unknown
+    20: 1,
+    30: 2,
+    40: 3,
+    50: 4,
+    60: 5,
+    70: 6,
+    80: 7,
+    90: 8,
+    100: 9,
+    111: 10,
+    112: 11,
+    113: 12,
+    114: 13,
+    115: 14,
+    116: 15,
+    121: 16,
+    122: 17,
+    123: 18,
+    124: 19,
+    125: 20,
+    126: 21,
+    200: 22, # ocean
+}
+
+
+
+class S3OLCI_LC100ClsDataset(Dataset):
+    '''
+    6908/1727 train/test images 96x96x21
+    23 classes multilabel LULC
+    nodata: -inf
+    
+    '''
+    def __init__(self, root_dir, mode='static', split='train', meta=False):
+        self.root_dir = root_dir
+        self.mode = mode
+        self.meta = meta
+        self.img_dir = os.path.join(root_dir, split, 's3_olci')
+        self.lc100_cls = os.path.join(root_dir, split, 'lc100_multilabel.csv')
+        self.fnames = []
+        self.labels = []
+        with open(self.lc100_cls, 'r') as f:
+            lines = f.readlines()
+            for line in lines:
+                self.fnames.append(line.strip().split(',')[0])
+                self.labels.append(list(map(int, line.strip().split(',')[1:])))
+
+        if self.mode == 'static':
+            self.static_csv = os.path.join(root_dir, split, 'static_fnames.csv')
+            with open(self.static_csv, 'r') as f:
+                lines = f.readlines()
+                self.static_img = {}
+                for line in lines:
+                    dirname = line.strip().split(',')[0]
+                    img_fname = line.strip().split(',')[1]
+                    self.static_img[dirname] = img_fname
+                
+        if self.meta:
+            self.reference_date = date(1970, 1, 1)
+
+
+    def __len__(self):
+        return len(self.fnames)
+    
+    def __getitem__(self, idx):
+        fname = self.fnames[idx]
+
+        s3_path = os.path.join(self.img_dir, fname)
+        if self.mode == 'static':
+            img_fname = self.static_img[fname]
+            s3_paths = [os.path.join(s3_path, img_fname)]
+        else:
+            img_fnames = os.listdir(s3_path)
+            s3_paths = []
+            for img_fname in img_fnames:
+                s3_paths.append(os.path.join(s3_path, img_fname))
+        
+        imgs = []
+        img_paths = []
+        meta_infos = []
+        for img_path in s3_paths:
+            with rasterio.open(img_path) as src:
+                img = src.read()
+                chs = []
+                for b in range(21):
+                    ch = cv2.resize(img[b], (96,96), interpolation=cv2.INTER_CUBIC)
+                    chs.append(ch)
+                img = np.stack(chs)
+                img[np.isnan(img)] = 0
+                for b in range(21):
+                    img[b] = img[b]*S3_OLCI_SCALE[b]
+                img = torch.from_numpy(img).float()
+
+
+                if self.meta:
+                    # get lon, lat
+                    cx,cy = src.xy(src.height // 2, src.width // 2)
+                    # convert to lon, lat
+                    #crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326')
+                    #lon, lat = crs_transformer.transform(cx,cy)
+                    lon, lat = cx, cy
+                    # get time
+                    img_fname = os.path.basename(img_path)
+                    date_str = img_fname.split('_')[1][:8]
+                    date_obj = date(int(date_str[:4]), int(date_str[4:6]), int(date_str[6:8]))
+                    delta = (date_obj - self.reference_date).days
+                    meta_info = np.array([lon, lat, delta, 0]).astype(np.float32)
+                else:
+                    meta_info = np.array([np.nan,np.nan,np.nan,np.nan]).astype(np.float32)
+
+            imgs.append(img)
+            img_paths.append(img_path)
+            meta_infos.append(meta_info)
+
+        if self.mode == 'series':
+            # pad to 4 images if less than 4
+            while len(imgs) < 4:
+                imgs.append(img)
+                img_paths.append(img_path)
+                meta_infos.append(meta_info)
+
+        label = self.labels[idx]
+        labels = torch.zeros(23)
+        # turn into one-hot
+        for l in label:
+            cls_id = LC100_CLSID[l]
+            labels[cls_id] = 1
+
+        if self.mode == 'static':
+            return imgs[0], meta_infos[0], labels
+        elif self.mode == 'series':
+            return imgs[0], imgs[1], imgs[2], imgs[3], meta_infos[0], meta_infos[1], meta_infos[2], meta_infos[3], labels
+
+if __name__ == '__main__':
+    dataset = S3OLCI_LC100ClsDataset(root_dir='../data/downstream/cgls_lc100', mode='static', split=None, meta=True)
+    dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=4)
+    for i, data in enumerate(dataloader):
+        #print(data[0].shape)
+        #print(data[1].shape)
+        #print(data[1])
+        #print(data[2])
+        #print(data[0].max())
+        #break
+        pass

lc100_s3olci/dataset_lc100_s3olci_seg.py

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+from torch.utils.data import DataLoader, Dataset
+import cv2
+import os
+import rasterio
+import torch
+import numpy as np
+from pyproj import Transformer
+from datetime import date
+
+S3_OLCI_SCALE = [0.0139465,0.0133873,0.0121481,0.0115198,0.0100953,0.0123538,0.00879161,0.00876539,
+                    0.0095103,0.00773378,0.00675523,0.0071996,0.00749684,0.0086512,0.00526779,0.00530267,
+                    0.00493004,0.00549962,0.00502847,0.00326378,0.00324118]
+
+LC100_CLSID = {
+    0: 0, # unknown
+    20: 1,
+    30: 2,
+    40: 3,
+    50: 4,
+    60: 5,
+    70: 6,
+    80: 7,
+    90: 8,
+    100: 9,
+    111: 10,
+    112: 11,
+    113: 12,
+    114: 13,
+    115: 14,
+    116: 15,
+    121: 16,
+    122: 17,
+    123: 18,
+    124: 19,
+    125: 20,
+    126: 21,
+    200: 22, # ocean
+}
+
+
+
+class S3OLCI_LC100SegDataset(Dataset):
+    '''
+    6908/1727 train/test images 96x96x21
+    23 classes LULC segmentation
+    nodata: -inf
+    
+    '''
+    def __init__(self, root_dir, mode='static', split='train', meta=False):
+        self.root_dir = root_dir
+        self.mode = mode
+        self.meta = meta
+        self.img_dir = os.path.join(root_dir, split, 's3_olci')
+        self.label_dir = os.path.join(root_dir, split, 'lc100')
+        self.fnames = os.listdir(self.label_dir)
+
+        if self.mode == 'static':
+            self.static_csv = os.path.join(root_dir, split, 'static_fnames.csv')
+            with open(self.static_csv, 'r') as f:
+                lines = f.readlines()
+                self.static_img = {}
+                for line in lines:
+                    dirname = line.strip().split(',')[0]
+                    img_fname = line.strip().split(',')[1]
+                    self.static_img[dirname] = img_fname
+                
+        if self.meta:
+            self.reference_date = date(1970, 1, 1)
+
+
+    def __len__(self):
+        return len(self.fnames)
+    
+    def __getitem__(self, idx):
+        fname = self.fnames[idx]
+        label_path = os.path.join(self.label_dir, fname)
+        s3_path = os.path.join(self.img_dir, fname.replace('.tif', ''))
+        if self.mode == 'static':
+            img_fname = self.static_img[fname.replace('.tif', '')]
+            s3_paths = [os.path.join(s3_path, img_fname)]
+        else:
+            img_fnames = os.listdir(s3_path)
+            s3_paths = []
+            for img_fname in img_fnames:
+                s3_paths.append(os.path.join(s3_path, img_fname))
+        
+        imgs = []
+        img_paths = []
+        meta_infos = []
+        for img_path in s3_paths:
+            with rasterio.open(img_path) as src:
+                img = src.read()
+                chs = []
+                for b in range(21):
+                    #ch = cv2.resize(img[b], (96,96), interpolation=cv2.INTER_CUBIC)
+                    ch = cv2.resize(img[b], (282,282), interpolation=cv2.INTER_CUBIC)
+                    chs.append(ch)
+                img = np.stack(chs)
+                img[np.isnan(img)] = 0
+                for b in range(21):
+                    img[b] = img[b]*S3_OLCI_SCALE[b]
+                img = torch.from_numpy(img).float()
+
+
+                if self.meta:
+                    # get lon, lat
+                    cx,cy = src.xy(src.height // 2, src.width // 2)
+                    # convert to lon, lat
+                    #crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326')
+                    #lon, lat = crs_transformer.transform(cx,cy)
+                    lon, lat = cx, cy
+                    # get time
+                    img_fname = os.path.basename(img_path)
+                    date_str = img_fname.split('_')[1][:8]
+                    date_obj = date(int(date_str[:4]), int(date_str[4:6]), int(date_str[6:8]))
+                    delta = (date_obj - self.reference_date).days
+                    meta_info = np.array([lon, lat, delta, 0]).astype(np.float32)
+                else:
+                    meta_info = np.array([np.nan,np.nan,np.nan,np.nan]).astype(np.float32)
+
+            imgs.append(img)
+            img_paths.append(img_path)
+            meta_infos.append(meta_info)
+
+        if self.mode == 'series':
+            # pad to 4 images if less than 4
+            while len(imgs) < 4:
+                imgs.append(img)
+                img_paths.append(img_path)
+                meta_infos.append(meta_info)
+
+        with rasterio.open(label_path) as src:
+            label = src.read(1)
+            label = cv2.resize(label, (282,282), interpolation=cv2.INTER_NEAREST) # 0-650
+            label_new = np.zeros_like(label)
+            for k,v in LC100_CLSID.items():
+                label_new[label==k] = v
+            label = torch.from_numpy(label_new.astype('int64'))
+           
+
+
+
+
+        if self.mode == 'static':
+            return imgs[0], meta_infos[0], label
+        elif self.mode == 'series':
+            return imgs[0], imgs[1], imgs[2], imgs[3], meta_infos[0], meta_infos[1], meta_infos[2], meta_infos[3], label
+
+if __name__ == '__main__':
+    dataset = S3OLCI_LC100SegDataset(root_dir='../data/downstream/cgls_lc100', mode='static', split='train', meta=True)
+    dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=4)
+    for i, data in enumerate(dataloader):
+        #print(data[0].shape)
+        #print(data[1].shape)
+        #print(data[1])
+        #print(data[2])
+        #print(data[0].max())
+        #break
+        pass

lc100_s3olci/lc100_s3olci.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57c016e8a10c43d347e15f58a7f6ae58ce0d4a8c83d2512886b2ec81144ab04b
+size 1713726638