def stitch_with_stitched(result, imageB, info, thresh, vertical=False):
'''
stitch the prediction on a stitched image with a raw image.
'''
visual = Visualizer()
print(result[0].shape)
final_image = make_overlap(result[0], imageB, vertical=vertical)
prediction_B = get_prediction(imageB, info, display=False)
if not vertical:
masks_A, boxes_A = remove_predictions_on_edge(result[1],
result[2],
result[0].shape,
edge='right')
else:
masks_A, boxes_A = remove_predictions_on_edge(result[1],
result[2],
result[0].shape,
edge='bottom')
masks_kept, boxes_kept = remove_overlapped_masks(
prediction_B.get_field("mask"),
prediction_B.bbox,
thresh,
final_image.shape,
vertical=vertical)
stitched_masks, stitched_boxes = visual.display_stitched_instances(
final_image[:, :, ::-1],
boxes_A,
masks_A,
boxes_kept,
masks_kept,
show_mask=False)
plt.clf()
stitched_masks = convert_to_torch(stitched_masks)
stitched_boxes = convert_to_torch(stitched_boxes)
return [final_image, stitched_masks, stitched_boxes]
def stitch(imageA, imageB, info, thresh=100, vertical=False):
'''
get the prediction of imageA and imageB and stitch the prediction together
'''
result = make_overlap(imageA, imageB, vertical=vertical)
prediction_A = get_prediction(imageA, info, display=False)
prediction_B = get_prediction(imageB, info, display=False)
if not vertical:
masks_A, boxes_A = remove_predictions_on_edge(
prediction_A.get_field("mask"),
prediction_A.bbox,
imageA.shape,
edge='right')
else:
masks_A, boxes_A = remove_predictions_on_edge(
prediction_A.get_field("mask"),
prediction_A.bbox,
imageA.shape,
edge='bottom')
masks_kept, boxes_kept = remove_overlapped_masks(
prediction_B.get_field("mask"),
prediction_B.bbox,
thresh,
result.shape,
vertical=vertical)
visual = Visualizer()
stitched_masks, stitched_boxes = visual.display_stitched_instances(
result[:, :, ::-1],
boxes_A,
masks_A,
boxes_kept,
masks_kept,
show_mask=False)
plt.clf()
return result, stitched_masks, stitched_boxes
def stitch_two_stitched(imageA, imageB, thresh, vertical=True):
'''
stitch the stitched images together from the other direction.
'''
visual = Visualizer()
final_image = make_overlap(imageA[0], imageB[0], vertical=vertical)
if not vertical:
masks_A, boxes_A = remove_predictions_on_edge(imageA[1],
imageA[2],
imageA[0].shape,
edge='right')
else:
masks_A, boxes_A = remove_predictions_on_edge(imageA[1],
imageA[2],
imageA[0].shape,
edge='bottom')
masks_kept, boxes_kept = remove_overlapped_masks(imageB[1],
imageB[2],
thresh,
final_image.shape,
vertical=vertical)
#print(len(masks_kept))
stitched_masks, stitched_boxes = visual.display_stitched_instances(
final_image[:, :, ::-1],
boxes_A,
masks_A,
boxes_kept,
masks_kept,
show_mask=False)
plt.clf()
stitched_masks = convert_to_torch(stitched_masks)
stitched_boxes = convert_to_torch(stitched_boxes)
return [final_image, stitched_masks, stitched_boxes]
