if (len(commonName) == 0):
print("Warning: empty result for latin name {}".format(latinName))
commonName = latinName
return commonName
#%% Create the model(s)
assert os.path.isfile(CLASSIFICATION_MODEL_PATH)
if DETECTION_MODEL_PATH != None:
assert os.path.isfile(DETECTION_MODEL_PATH)
print("Loading model")
model = speciesapi.DetectionClassificationAPI(CLASSIFICATION_MODEL_PATH,
DETECTION_MODEL_PATH,
IMAGE_SIZES, USE_GPU)
print("Finished loading model")
#%% Prepare the list of images and query names
queries = None
if isinstance(IMAGES_TO_CLASSIFY, str) and os.path.isdir(IMAGES_TO_CLASSIFY):
images = glob.glob(os.path.join(IMAGES_TO_CLASSIFY, '**/*.*'),
recursive=True)
images = [fn for fn in images if os.path.isfile(fn)]
queries = [os.path.basename(os.path.dirname(fn)) for fn in images]
print('Loaded a folder of {} images'.format(len(images)))
if (len(common_name) == 0):
print("Warning: empty result for latin name {}".format(latin_name))
common_name = latin_name
return common_name
#%% Create the model(s)
assert os.path.isfile(classification_model_path)
if detection_model_path != None:
assert os.path.isfile(detection_model_path)
print("Loading model")
model = speciesapi.DetectionClassificationAPI(classification_model_path,
detection_model_path, image_sizes, use_gpu)
print("Finished loading model")
#%% Prepare the list of images and query names
queries = None
if isinstance(images_to_classify,str) and os.path.isdir(images_to_classify):
images = glob.glob(os.path.join(images_to_classify,'**/*.*'), recursive=True)
images = [fn for fn in images if os.path.isfile(fn)]
queries = [os.path.basename(os.path.dirname(fn)) for fn in images]
print('Loaded a folder of {} images'.format(len(images)))
elif isinstance(images_to_classify,str) and os.path.isfile(images_to_classify):