def test_evaluate(self):
evaluator = Evaluator(model_dir=self.model_dir,
input_shape=self.input_shape)
BAR, EAR = evaluator.evaluate(basic_model=self.basic_model,
evaluate_model=self.evaluate_model,
valid_stocks=self.valid_stocks,
rounds=3)
print BAR, EAR
self.assertNotEqual(BAR, EAR)
def prec_recall(data, gt):
search_engine = SearchEngine(data)
print('\n> Running Evaluation...\n', end='')
evaluator = Evaluator(search_engine, gt)
prec, avg_prec_recall = evaluator.evaluate()
mkdir(EVALUATION_PATH)
save_to_csv(prec, os.path.join(EVALUATION_PATH, 'precision.csv'))
save_to_csv(avg_prec_recall, os.path.join(EVALUATION_PATH, 'avg_prec_recall.csv'), index=True)
print('\n Results of evaluation saved to directory "%s"' % os.path.relpath(EVALUATION_PATH, PROJ_ROOT))
def eval_run_func(params):
from evaluation.evaluator import Evaluator
# get input parameters
model_dir = params['model_dir']
basic_model = params['basic_model']
evaluate_model = params['evaluate_model']
input_shape = params['input_shape']
rounds = params['rounds']
valid_stocks = params['valid_stocks']
_evaluator = Evaluator(model_dir=model_dir, input_shape=input_shape)
BAR, EAR = _evaluator.evaluate(basic_model, evaluate_model, valid_stocks,
rounds)
return BAR, EAR
for x, y in buffered_points_ref:
img_ref[y][x] = (0, 0.7, 0)
mapping_tool = start_mapping(img, img_ref, mapping_style)
statistics = mapping_tool.get_statistics()
extractions = mapping_tool.extractions
points = []
for extraction in extractions:
points.extend(extraction.get_pixels())
if len(points) > 0:
evaluation_count += 1
mpp = meter_per_pixel(center.lat, zoom_level)
evaluation, _ = evaluator.evaluate(points, points_ref, points_ref,
buffer_width, mpp)
total_len_extracted = evaluator.total_len_ext - total_len_extracted_previous
total_len_reference = evaluator.total_len_ref - total_len_reference_previous
extra = 'Total distance extracted: {} km\n'.format(
round(total_len_extracted / 1000, 2))
extra += 'Total distance reference: {} km\n'.format(
round(total_len_reference / 1000, 2))
total_len_extracted_previous = evaluator.total_len_ext
total_len_reference_previous = evaluator.total_len_ref
save_results(road_name, statistics, evaluation, extractions,
mapping_style, extra)
for k, v in statistics.items():
if k in total_statistics.keys():
fig, ax = plt.subplots()
ax.plot(ex, ey, 'r-')
ax.plot(sx, sy, 'b-')
ax.plot(rx, ry, 'g-')
plt.show()
all_references = set()
for road_ref in roads:
input = road_ref.pixels(size, zoom_level, center=center)
for x, y in input:
if 0 <= x < len(img[0]) and 0 <= y < len(img):
all_references.add((x, y))
evaluation, matched_ext = evaluator.evaluate(smoothed_extraction,
reference, all_references,
buffer_width, mpp)
print('------------')
print('Completeness: {} %'.format(
round(100 * evaluation.completeness, 2)))
print('Correctness: {} %'.format(round(100 * evaluation.correctness,
2)))
print('Correctness*: {} %'.format(
round(100 * evaluation.correctness_all, 2)))
print('Quality: {} %'.format(round(100 * evaluation.quality, 2)))
print('Quality*: {} %'.format(round(100 * evaluation.quality_all, 2)))
print('Redundancy: {} %'.format(round(100 * evaluation.redundancy, 2)))
print('Mean distance: {} m'.format(round(evaluation.mean_distance, 2)))
print('RMSE: {} m'.format(round(evaluation.rmse, 2)))
print('------------')
