import os

model = {
    'name': 'ssd_resnet50',
    'graph_name': 'frozen_inference_graph.pb',
    'download_base_url': 'http://download.tensorflow.org/models/object_detection/',
    'label': os.path.join('labels', 'label_map.pbtxt')
}

model['file'] = model['name'] + '.tar.gz'
model['path'] = os.path.join('models', model['name'], model['graph_name'])

to_download = False

maximum_classes_to_detect = 90

path = {
    'image_dir': os.path.join('input', 'images', 'random_traffic'),
    'video_dir': os.path.join('input', 'videos'),
    'video_name': 'final_final.mp4',
    'output_image_name': 'my_test',
    'output_video_name': 'final_django.avi',
    'output_image_dir': os.path.join('output', 'images', 'random_traffic'),
    'output_video_dir': os.path.join('output', 'videos', 'final_final'),
}

is_image = False
generate_video = True
# Size, in inches, of the output images.
image_size = (12, 8)

to_show = False

to_save = True
save_by_class = True

minimum_detection = 20

detect_all_categories = False
show_labels = True

categories = ['car', 'motorcycle', 'airplane', 'bus', 'truck', 'traffic light']
# categories = ['motorcycle']

# 100%=doubling the image
motor_person_offset_percent = 70

min_score_thresh = 0.5
apply_threshold = True
