for c in classes:
im_dir = os.path.join(data_path, c)
exists = os.path.join(pad_path, c)
if os.path.isdir(exists) is False:
os.mkdir(exists)
images = os.listdir(im_dir)
for im_name in images:
im_path = os.path.join(im_dir, im_name)
im_pad_path = os.path.join(exists, im_name)
plankton = Segmentation(im_path, target_shape=(75, 75, 3))
plankton.segment()
padded = plankton.get_padded()
padded = cv2.cvtColor(padded, cv2.COLOR_RGB2BGR)
cv2.imwrite(im_pad_path, padded)
feats[im_name] = plankton.get_features()
feat_list.append(plankton.get_features())
with open('features.p', 'wb') as handle:
pickle.dump(feats, handle, protocol=pickle.HIGHEST_PROTOCOL)
df_feats = pd.DataFrame(data=feat_list, columns=plankton.get_columns())
df_feats.to_csv('extracted_features.csv', index=False)
mms = MinMaxScaler()
mms.fit(df_feats.values)
with open('normalizer.p', 'wb') as handle:
pickle.dump(mms, handle, protocol=pickle.HIGHEST_PROTOCOL)