def segm_results(cls_boxes, masks, ref_boxes, im_h, im_w):
if 'deeplab' in cfg.MRCNN.ROI_MASK_HEAD:
return segm_results_deeplab(cls_boxes, masks, ref_boxes, im_h, im_w)
num_classes = cfg.MODEL.NUM_CLASSES
cls_segms = [[] for _ in range(num_classes)]
mask_ind = 0
# To work around an issue with cv2.resize (it seems to automatically pad
# with repeated border values), we manually zero-pad the masks by 1 pixel
# prior to resizing back to the original image resolution. This prevents
# "top hat" artifacts. We therefore need to expand the reference boxes by an
# appropriate factor.
M = cfg.MRCNN.RESOLUTION
scale = (M + 2.0) / M
ref_boxes = box_utils.expand_boxes(ref_boxes, scale)
ref_boxes = ref_boxes.astype(np.int32)
padded_mask = np.zeros((M + 2, M + 2), dtype=np.float32)
# skip j = 0, because it's the background class
for j in range(1, num_classes):
segms = []
for _ in range(cls_boxes[j].shape[0]):
if cfg.MRCNN.CLS_SPECIFIC_MASK:
padded_mask[1:-1, 1:-1] = masks[mask_ind, j, :, :]
else:
padded_mask[1:-1, 1:-1] = masks[mask_ind, 0, :, :]
ref_box = ref_boxes[mask_ind, :]
w = ref_box[2] - ref_box[0] + 1
h = ref_box[3] - ref_box[1] + 1
w = np.maximum(w, 1)
h = np.maximum(h, 1)
mask = cv2.resize(padded_mask, (w, h))
mask = np.array(mask > cfg.MRCNN.THRESH_BINARIZE, dtype=np.uint8)
im_mask = np.zeros((im_h, im_w), dtype=np.uint8)
x_0 = max(ref_box[0], 0)
x_1 = min(ref_box[2] + 1, im_w)
y_0 = max(ref_box[1], 0)
y_1 = min(ref_box[3] + 1, im_h)
im_mask[y_0:y_1, x_0:x_1] = mask[
(y_0 - ref_box[1]):(y_1 - ref_box[1]),
(x_0 - ref_box[0]):(x_1 - ref_box[0])
]
# Get RLE encoding used by the COCO evaluation API
rle = mask_util.encode(
np.array(im_mask[:, :, np.newaxis], order='F')
)[0]
segms.append(rle)
mask_ind += 1
cls_segms[j] = segms
assert mask_ind == masks.shape[0]
return cls_segms
def segm_results(cls_boxes, masks, ref_boxes, im_h, im_w):
num_classes = cfg.MODEL.NUM_CLASSES
cls_segms = [[] for _ in range(num_classes)]
mask_ind = 0
# To work around an issue with cv2.resize (it seems to automatically pad
# with repeated border values), we manually zero-pad the masks by 1 pixel
# prior to resizing back to the original image resolution. This prevents
# "top hat" artifacts. We therefore need to expand the reference boxes by an
# appropriate factor.
M = cfg.MRCNN.RESOLUTION
scale = (M + 2.0) / M
ref_boxes = box_utils.expand_boxes(ref_boxes, scale)
ref_boxes = ref_boxes.astype(np.int32)
padded_mask = np.zeros((M + 2, M + 2), dtype=np.float32)
# skip j = 0, because it's the background class
for j in range(1, num_classes):
segms = []
for _ in range(cls_boxes[j].shape[0]):
if cfg.MRCNN.CLS_SPECIFIC_MASK:
padded_mask[1:-1, 1:-1] = masks[mask_ind, j, :, :]
else:
padded_mask[1:-1, 1:-1] = masks[mask_ind, 0, :, :]
ref_box = ref_boxes[mask_ind, :]
w = ref_box[2] - ref_box[0] + 1
h = ref_box[3] - ref_box[1] + 1
w = np.maximum(w, 1)
h = np.maximum(h, 1)
mask = cv2.resize(padded_mask, (w, h))
mask = np.array(mask > cfg.MRCNN.THRESH_BINARIZE, dtype=np.uint8)
im_mask = np.zeros((im_h, im_w), dtype=np.uint8)
x_0 = max(ref_box[0], 0)
x_1 = min(ref_box[2] + 1, im_w)
y_0 = max(ref_box[1], 0)
y_1 = min(ref_box[3] + 1, im_h)
im_mask[y_0:y_1, x_0:x_1] = mask[
(y_0 - ref_box[1]):(y_1 - ref_box[1]),
(x_0 - ref_box[0]):(x_1 - ref_box[0])
]
# Get RLE encoding used by the COCO evaluation API
rle = mask_util.encode(
np.array(im_mask[:, :, np.newaxis], order='F')
)[0]
segms.append(rle)
mask_ind += 1
cls_segms[j] = segms
assert mask_ind == masks.shape[0]
return cls_segms
def segm_results(cls_boxes, masks, ref_boxes, im_h, im_w, timers):
num_classes = cfg.MODEL.NUM_CLASSES
cls_segms = [[] for _ in range(num_classes)]
ind = []
mask_ind = 0
# To work around an issue with cv2.resize (it seems to automatically pad
# with repeated border values), we manually zero-pad the masks by 1 pixel
# prior to resizing back to the original image resolution. This prevents
# "top hat" artifacts. We therefore need to expand the reference boxes by an
# appropriate factor.
M = cfg.MRCNN.RESOLUTION
scale = (M + 2.0) / M
ref_boxes = box_utils.expand_boxes(ref_boxes, scale)
ref_boxes = ref_boxes.astype(np.int32)
padded_mask = np.zeros((M + 2, M + 2), dtype=np.float32)
# skip j = 0, because it's the background class
for j in range(1, num_classes):
segms = []
num_threads = multiprocessing.cpu_count() - 1
for cls_segm, indicator in Parallel(n_jobs=num_threads)(delayed(test)(k, im_w, im_h, ref_boxes, padded_mask, masks, j) for k in range(cls_boxes[j].shape[0])):
cls_segms[j].append(cls_segm)
ind.append(indicator)
mask_ind = mask_ind + max(ind) + 1
# for _ in range(cls_boxes[j].shape[0]):
# if cfg.MRCNN.CLS_SPECIFIC_MASK:
# padded_mask[1:-1, 1:-1] = masks[mask_ind, j, :, :]
# else:
# padded_mask[1:-1, 1:-1] = masks[mask_ind, 0, :, :]
#
# ref_box = ref_boxes[mask_ind, :]
# w = ref_box[2] - ref_box[0] + 1
# h = ref_box[3] - ref_box[1] + 1
# w = np.maximum(w, 1)
# h = np.maximum(h, 1)
#
# mask = cv2.resize(padded_mask, (w, h))
# mask = np.array(mask > cfg.MRCNN.THRESH_BINARIZE, dtype=np.uint8)
# im_mask = np.zeros((im_h, im_w), dtype=np.uint8)
#
# x_0 = max(ref_box[0], 0)
# x_1 = min(ref_box[2] + 1, im_w)
# y_0 = max(ref_box[1], 0)
# y_1 = min(ref_box[3] + 1, im_h)
#
# im_mask[y_0:y_1, x_0:x_1] = mask[
# (y_0 - ref_box[1]):(y_1 - ref_box[1]),
# (x_0 - ref_box[0]):(x_1 - ref_box[0])
# ]
#
# # Get RLE encoding used by the COCO evaluation API
# rle = mask_util.encode(
# np.array(im_mask[:, :, np.newaxis], order='F')
# )[0]
# segms.append(rle)
#
# mask_ind += 1
#
# cls_segms[j] = segms
assert mask_ind == masks.shape[0]
return cls_segms
