import os import h5py import numpy home = '/home/hackathon/output_64_Javier_labelled/' good_block_count = 0 total_block_count = 0 bad_block_count = 0 file_count = 0 for filename in os.listdir(home): file_path = os.path.join(home, filename) # Ignore directories if os.path.isdir(file_path): continue file_count += 1 # grab h5py file object try: hf_file = h5py.File(file_path, 'r') # 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: total_block_count += 1 Classification_Accuracy = hf_file[image_num + '/ClassificationAccuracy'][()] if Classification_Accuracy == 1: Feature_Labels = hf_file[image_num + '/FeatureLabels'][()] Image_Classification = hf_file[image_num + '/ImageClassification'][()] Image_Features = hf_file[image_num + '/ImageFeatures'][()] good_block_count += 1 elif Classification_Accuracy == 0: bad_block_count += 1 except Exception as ex: continue print("good_block_count:", good_block_count) print("total_block_count:", total_block_count) print("bad_block_count:", bad_block_count) print("file_count:", file_count)