def bit_case4(image): features = list() # Split channels R, G, B, and RGB r_g_b_rgb = split2(image) # Extract Biodiversity features bio_features = extract(bio, r_g_b_rgb) # Extract Biodiversity features (normalized) # bio_features_norm = normalized_extract(bio, r_g_b_rgb) features.append(bio_features) # Extract Taxonomic features (not normalized) taxo_features = extract(taxo, r_g_b_rgb) # Extract Taxonomic features (normalized) # taxo_features_norm = normalized_extract(taxo, r_g_b_rgb) features.append(taxo_features) feature_vector = [item for sublist in features for item in sublist] feature_vector = np.nan_to_num(feature_vector, nan=0.0) return feature_vector
def bit_case5(image): features = list() # Split channels R, G, B, and RGB r_g_b_rgb = split3(image) # Extract Biodiversity features bio_features = extract(bio, r_g_b_rgb) # Extract Biodiversity features (normalized) # bio_features_norm = normalized_extract(bio, r_g_b_rgb) features.append(bio_features) # Extract Taxonomic features (not normalized) taxo_features = extract(taxo, r_g_b_rgb) # Extract Taxonomic features (normalized) # taxo_features_norm = normalized_extract(taxo, r_g_b_rgb) features.append(taxo_features) feature_vector = [item for sublist in features for item in sublist] feature_vector = np.nan_to_num(feature_vector, nan=0.0) # normalize the array with linear algebra norm = np.linalg.norm(feature_vector) feature_vector = feature_vector / norm return feature_vector