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
