def show_failed_samples(grading_data_dir_name,
subset_name,
num_of_samples=100,
run_num='run_1'):
"""Predicted Samples Viewer"""
# Count all iamges
path = os.path.join('..', 'data', grading_data_dir_name)
ims = np.array(plotting_tools.get_im_files(path, subset_name))
print('All images: ', len(ims))
# Show samples
im_files, n_preds, n_false_pos, n_false_neg = get_failed_im_file_sample(
grading_data_dir_name,
subset_name,
run_num,
n_file_names=num_of_samples)
print(
'number of validation samples intersection over the union evaulated on {}'
.format(n_preds))
print(
'number false positives: {}(P={:.6}), number false negatives: {}(P={:.6})'
.format(n_false_pos, n_false_pos / n_preds, n_false_neg,
n_false_neg / n_preds))
print('number failed: {}(P={:.6})'.format(
n_false_pos + n_false_neg, (n_false_pos + n_false_neg) / n_preds))
print()
print('Sample images: ', len(im_files))
for i in range(len(im_files[:num_of_samples])):
print(i)
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
val_no_targ, pred_no_targ = model_tools.write_predictions_grade_set(
model, run_num, 'patrol_non_targ', 'sample_evaluation_data')
val_following, pred_following = model_tools.write_predictions_grade_set(
model, run_num, 'following_images', 'sample_evaluation_data')
# Now lets look at your predictions, and compare them to the ground truth labels and original images.
# Run each of the following cells to visualize some sample images from the predictions in the validation set.
# In[15]:
# images while following the target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data',
'following_images', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# In[16]:
# images while at patrol without target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data',
'patrol_non_targ', run_num)
for i in range(3):
im_tuple = plotting_tools.load_images(im_files[i])
plotting_tools.show_images(im_tuple)
# In[17]:
# images while at patrol with target
im_files = plotting_tools.get_im_file_sample('sample_evaluation_data',
random.shuffle(x)
return x
im_files0 = get_files(pred_folder)
im_files = shuffle(im_files0)
ipdb.set_trace()
# I don't need to go with any of these things.
validation_path
output_path = pred_folder
scoring_utils.score_run(gt_folder, output_path)
for i in range(30):
pred_name = im_files[i]
base_pred_name = os.path.basename(pred_name)
im_name, mask_name = get_img_mask(base_pred_name, gt_folder)
if not os.path.exists(im_name):
print('{} does not exist'.format(im_name))
if not os.path.exists(mask_name):
print('{} does not exist'.format(mask_name))
new_im_files = (im_name, mask_name, pred_name)
im_tuple = plotting_tools.load_images(new_im_files)
plotting_tools.show_images(im_tuple, fig_id=3)
ipdb.set_trace()
