def mask_voc2coco(voc_masks, voc_boxes, im_height, im_width, binary_thresh = 0.4):
num_pred = len(voc_masks)
assert(num_pred==voc_boxes.shape[0])
mask_img = np.zeros((im_height, im_width, num_pred), dtype=np.uint8, order='F')
for i in xrange(num_pred):
pred_box = np.round(voc_boxes[i, :4]).astype(int)
pred_mask = voc_masks[i]
pred_mask = cv2.resize(pred_mask.astype(np.float32), (pred_box[2] - pred_box[0] + 1, pred_box[3] - pred_box[1] + 1))
mask_img[pred_box[1]:pred_box[3]+1, pred_box[0]:pred_box[2]+1, i] = pred_mask >= binary_thresh
coco_mask = encodeMask_c(mask_img)
return coco_mask
def mask_voc2coco(voc_masks, voc_boxes, im_height, im_width, binary_thresh = 0.4):
num_pred = len(voc_masks)
assert(num_pred==voc_boxes.shape[0])
mask_img = np.zeros((im_height, im_width, num_pred), dtype=np.uint8, order='F')
for i in xrange(num_pred):
pred_box = np.round(voc_boxes[i, :4]).astype(int)
pred_mask = voc_masks[i]
pred_mask = cv2.resize(pred_mask.astype(np.float32), (pred_box[2] - pred_box[0] + 1, pred_box[3] - pred_box[1] + 1))
mask_img[pred_box[1]:pred_box[3]+1, pred_box[0]:pred_box[2]+1, i] = pred_mask >= binary_thresh
coco_mask = encodeMask_c(mask_img)
return coco_mask