aws-samples / amazon-sagemaker-tensorflow-object-detection-api
Conditional Complexity

The distribution of complexity of units (measured with McCabe index).

Intro
  • Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
  • Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
  • Conditional complexity is measured at the unit level (methods, functions...).
  • Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).
Learn more...
Conditional Complexity Overall
  • There are 34 units with 856 lines of code in units (91.1% of code).
    • 0 very complex units (0 lines of code)
    • 1 complex units (118 lines of code)
    • 3 medium complex units (242 lines of code)
    • 6 simple units (173 lines of code)
    • 24 very simple units (323 lines of code)
0% | 13% | 28% | 20% | 37%
Legend:
51+
26-50
11-25
6-10
1-5
Alternative Visuals
Conditional Complexity per Extension
51+
26-50
11-25
6-10
1-5
py0% | 13% | 28% | 20% | 37%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
3_predict0% | 15% | 30% | 22% | 31%
1_prepare_data/docker/code/utils0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def visualize_boxes_and_labels_on_image_array()
in 3_predict/visualization_utils.py
 118 29 21
def draw_keypoints_on_image()
in 3_predict/visualization_utils.py
 44 18 10
def draw_side_by_side_evaluation_image()
in 3_predict/visualization_utils.py
 134 15 6
def draw_bounding_boxes_on_image_tensors()
in 3_predict/visualization_utils.py
 64 11 15
def draw_bounding_box_on_image()
in 3_predict/visualization_utils.py
 44 9 8
def draw_densepose_visualizations()
in 3_predict/visualization_utils.py
 45 8 5
def _get_multiplier_for_color_randomness()
in 3_predict/visualization_utils.py
 10 6 0
def draw_bounding_boxes_on_image()
in 3_predict/visualization_utils.py
 16 6 5
def create_visualization_fn()
in 3_predict/visualization_utils.py
 36 6 6
def draw_float_channel_on_image_array()
in 3_predict/visualization_utils.py
 22 6 5
def draw_part_mask_on_image_array()
in 3_predict/visualization_utils.py
 18 5 4
def add_images()
in 3_predict/visualization_utils.py
 6 5 2
def get_estimator_eval_metric_ops()
in 3_predict/visualization_utils.py
 28 5 2
def draw_heatmaps_on_image_tensors()
in 3_predict/visualization_utils.py
 20 4 3
def draw_mask_on_image_array()
in 3_predict/visualization_utils.py
 16 4 4
def generate_tf_records()
in 1_prepare_data/docker/code/utils/tf_record_util.py
 13 3 1
def _create_tf_example()
in 1_prepare_data/docker/code/utils/tf_record_util.py
 47 3 3
def draw_heatmaps_on_image_array()
in 3_predict/visualization_utils.py
 8 3 2
def draw_heatmaps_on_image()
in 3_predict/visualization_utils.py
 12 2 2
def __init__()
in 1_prepare_data/docker/code/utils/tf_record_util.py
 5 1 5