feat: add numpy array extraction script

extract.py

+import h5py
+import os
+import numpy as np
+from tqdm import tqdm
+
+giant_array = []
+giant_label_array = []
+
+home = '/home/hackathon/output_64_Javier_labelled'
+
+for path in tqdm(os.scandir(home)):
+    if path.path.endswith(".hdf"):
+       file_path = str(path.path)
+    else:
+       continue
+
+    try:
+       #grab h5py file object
+       hf_file = h5py.File(file_path, 'r')
+    except:
+       continue
+    #list the main groups; image number in this case
+    hf_keys = list(hf_file.keys())
+
+    #access all data within images; save into an array if you like
+    #automatically extracted as numpy arrays
+    for image_num in hf_keys:
+        Classification_Accuracy = hf_file[image_num + '/ClassificationAccuracy'][()]
+        Feature_Labels          = hf_file[image_num + '/FeatureLabels'][()]
+        Image_Classification    = hf_file[image_num + '/ImageClassification'][()]
+        Image_Features          = hf_file[image_num + '/ImageFeatures'][()]
+        
+        giant_array.append(Image_Features)
+        giant_label_array.append(Image_Classification)
+
+giant_numpy_array = np.array(giant_array)
+giant_numpy_label_array = np.array(giant_label_array)
+print(giant_numpy_array.shape)
+print(giant_numpy_label_array.shape)
+np.save('/home/user/modis_full', giant_numpy_array)
+np.save('/home/user/modis_full_labels', giant_numpy_label_array)
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