upload biomass-s3olci dataset

biomass_s3olci/biomass_dataset_5k.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5103c9e4caa2b9ca8dec0170abcbbfee8e6a0bb89e0b188447b285ee1309cc20
+size 1158553387

biomass_s3olci/dataset_biomass_s3olci.py

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+from torch.utils.data import DataLoader, Dataset
+import cv2
+import os
+import rasterio
+import torch
+import numpy as np
+
+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]
+
+BIOMASS_MEAN = 93.8317
+BIOMASS_STD = 110.5369
+
+
+class S3OLCI_BiomassDataset(Dataset):
+    '''
+    4000/1000 train/test images 94x94x21 (full dataset is 25K)
+    CCI biomass 282x282
+    nodata: -inf
+    time series: 1-4 images / location
+    
+    '''
+
+
+    def __init__(self, root_dir, split='train', mode='static'):
+        self.root_dir = root_dir
+        self.split = split
+        self.mode = mode
+        self.img_dir = os.path.join(root_dir, split, 's3_olci')
+        self.biomass_dir = os.path.join(root_dir, split, 'biomass')
+
+        self.fnames = os.listdir(self.biomass_dir)
+
+    def __len__(self):
+        return len(self.fnames)
+    
+    def __getitem__(self, idx):
+        fname = self.fnames[idx]
+        biomass_path = os.path.join(self.biomass_dir, fname)
+        s3_path = os.path.join(self.img_dir, fname.replace('.tif',''))
+        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 = []
+        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], (94,94), 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()
+            imgs.append(img)
+            img_paths.append(img_path)
+        # pad to 4 images if less than 4
+        while len(imgs) < 4:
+            imgs.append(img)
+            img_paths.append(img_path)
+
+        with rasterio.open(biomass_path) as src:
+           biomass = src.read(1)
+           biomass = cv2.resize(biomass, (282,282), interpolation=cv2.INTER_CUBIC) # 0-650
+           biomass = torch.from_numpy(biomass.astype('float32'))
+           biomass = (biomass - BIOMASS_MEAN) / BIOMASS_STD # 0-center normalized
+        if self.mode == 'static':
+            return imgs[0], biomass # 94x94x21, 282x282
+        elif self.mode == 'series':
+            return imgs[0], imgs[1], imgs[2], imgs[3], biomass # 94x94x21, 94x94x21, 94x94x21, 94x94x21, 282x282