Upload dataset_biomass_s3olci.py

biomass_s3olci/dataset_biomass_s3olci.py

@@ -4,6 +4,8 @@ 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,
@@ -16,7 +18,7 @@ BIOMASS_STD = 110.5369
 class S3OLCI_BiomassDataset(Dataset):
     '''
     4000/1000 train/test images 94x94x21 (full dataset is 25K)
-    CCI biomass 282x282
+    CCI biomass regression 282x282
     nodata: -inf
     time series: 1-4 images / location
     
@@ -32,6 +34,18 @@ class S3OLCI_BiomassDataset(Dataset):
 
         self.fnames = os.listdir(self.biomass_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
+                
+
+
     def __len__(self):
         return len(self.fnames)
     
@@ -39,13 +53,19 @@ class S3OLCI_BiomassDataset(Dataset):
         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))
         
+        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()
@@ -59,12 +79,30 @@ class S3OLCI_BiomassDataset(Dataset):
                 for b in range(21):
                     img[b] = img[b]*S3_OLCI_SCALE[b]
                 img = torch.from_numpy(img).float()
+
+                if self.meta:
+                    cx,cy = src.xy(src.height // 2, src.width // 2)
+                    #crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326')
+                    #lon, lat = crs_transformer.transform(cx,cy)
+                    lon, lat = cx, cy
+                    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, np.nan]).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)
-        # pad to 4 images if less than 4
-        while len(imgs) < 4:
-            imgs.append(img)
-            img_paths.append(img_path)
+        
+        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(biomass_path) as src:
            biomass = src.read(1)
@@ -72,6 +110,6 @@ class S3OLCI_BiomassDataset(Dataset):
            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
+            return imgs[0], meta_infos[0], biomass # 94x94x21, 282x282
         elif self.mode == 'series':
-            return imgs[0], imgs[1], imgs[2], imgs[3], biomass # 94x94x21, 94x94x21, 94x94x21, 94x94x21, 282x282
+            return imgs[0], imgs[1], imgs[2], imgs[3], meta_infos[0], meta_infos[1], meta_infos[2], meta_infos[3], biomass # 94x94x21, 94x94x21, 94x94x21, 94x94x21, 282x282