Datasets:
jblitzar
/
github-python

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global loss_weights
if loss_weights is None:
loss_weights = np.ones((len(PRED_DOWNSCALE_FACTORS), NUM_BOXES_PER_SCALE+1))
def test_function(img_batch, gt_batch, roi_batch):
global test_loss
global counter
gt_batch = (gt_batch > 0).astype(np.float32)
loss, pred_batch, gt_batch = test_funcs(img_batch, gt_batch, loss_weights, network)
test_loss += loss
counter += 1
return (*pred_batch), (*gt_batch)
if isinstance(print_output, str):
print_path = print_output
elif isinstance(print_output, bool) and print_output:
print_path = './models/dump'
else:
print_path = None
e = dataset.iterate_over_test_data(test_function, set_name)
for e_idx, e_iter in enumerate(e):
image_split = e_iter[1].split('/')
image_name = image_split[len(image_split)-1]
image = cv2.imread(e_iter[1])
maps = [(image, {}),
(e_iter[2], {'cmap': 'jet', 'vmin': 0., 'vmax': 1.})]
pred_dot_map, pred_box_map = get_box_and_dot_maps(e_iter[0][0:4], thresh=thresh) # prediction_downscale
# -- Plotting boxes
boxed_image_pred = get_boxed_img(image, pred_box_map, pred_box_map, \
pred_dot_map, prediction_downscale=2, \
thickness=2, multi_colours=False)
boxed_image_pred_path = os.path.join(print_path, image_name + '_boxed_image.png')
cv2.imwrite(boxed_image_pred_path, boxed_image_pred.astype(np.uint8).transpose((1, 2, 0)))
print_graph(maps, "", os.path.join(print_path, image_name))
# -- Calculate metrics
metrics_test = calculate_metrics(pred_dot_map, e_iter[2], metrics_test)
for m in metrics_:
metrics_test[m] /= float(e_idx+1)
metrics_test['mse'] = np.sqrt(metrics_test['mse'])
metrics_test['loss1'] = test_loss / float(counter)
txt = ''
for metric in metrics_test.keys():
if metric == "mle" and (args.mle == False):
continue
txt += '%s: %s ' % (metric, metrics_test[metric])
return metrics_test, txt
'''
This function calculates the various counting and localization metrics
Parameters
----------
pred: dot map prediction of LSC-CNN (HxW)
true: ground truth map (HxW)
metrics_test: dictionary of metrics
Returns
----------
metrics_test: updated dictionary of metrics
'''
def calculate_metrics(pred, true, metrics_test):
pred_count = np.sum(pred)
true_count = np.sum(true)
head_x_true, head_y_true = np.where(pred > 0)[-2:]
head_x_pred, head_y_pred = np.where(true > 0)[-2:]
if args.mle:
if len(head_x_pred) == 0:
off = 16*len(head_y_pred)
else:
off, _, _ = get_offset_error(head_x_pred, head_y_pred, head_x_true, head_y_true, output_downscale)
metrics_test['mle'] += off
metrics_test['new_mae'] += np.abs(true_count - pred_count)
metrics_test['mse'] += (true_count - pred_count) ** 2
return metrics_test
'''
This function finds the optimal threshold on the validation set.
Parameters
----------
f: (file object) file writer
iters: Number of iterations to run the binary search
test_funcs: lsccnn test function
splits: number of splits to the range of thresholds
beg: beginning threshold
end: ending threshold
Returns
----------
optimal_threshold: optimal threshold where the mae is
lowest on validation set.
'''