Update docs: fix API examples for merge, to_dict, iou, warp

Author: isarandi

docs/howto/coco-format.rst

@@ -46,8 +46,9 @@ For batch operations without creating objects::
     array = rlemasklib.decode(coco_dict)
 
     # Operations on dicts
-    union_dict = rlemasklib.merge([dict1, dict2], intersect=False)
-    intersection_dict = rlemasklib.merge([dict1, dict2], intersect=True)
+    from rlemasklib import BoolFunc
+    union_dict = rlemasklib.merge([dict1, dict2], BoolFunc.UNION)
+    intersection_dict = rlemasklib.merge([dict1, dict2], BoolFunc.INTERSECTION)
 
 Uncompressed counts
 -------------------
@@ -66,7 +67,7 @@ Extra compression
 For even smaller storage, gzip the compressed counts::
 
     # Returns dict with "zcounts" instead of "counts" (~40% smaller)
-    compressed_dict = mask.to_dict(compressed=True)
+    compressed_dict = mask.to_dict(zlevel=6)
 
     # Loading auto-detects the format
     mask = RLEMask.from_dict(compressed_dict)

docs/howto/compute-iou.rst

@@ -30,7 +30,7 @@ With COCO-format dicts::
 
     import rlemasklib
 
-    iou_matrix = rlemasklib.iou([mask1_dict, mask2_dict], [mask3_dict, mask4_dict])
+    iou = rlemasklib.iou([mask1_dict, mask2_dict])
 
 Manual computation
 ------------------

docs/howto/warp-mask.rst

@@ -16,7 +16,7 @@ For rotation, scaling, shearing, translation::
         [0.1,  0.9, 20]
     ], dtype=np.float32)
 
-    warped = mask.warp_affine(M, dst_shape=(480, 640))
+    warped = mask.warp_affine(M, output_imshape=(480, 640))
 
 Perspective transform
 ---------------------
@@ -30,7 +30,7 @@ For 3x3 homography matrices::
         [0.0001, 0.0002, 1]
     ], dtype=np.float32)
 
-    warped = mask.warp_perspective(H, dst_shape=(480, 640))
+    warped = mask.warp_perspective(H, output_imshape=(480, 640))
 
 From OpenCV
 -----------
@@ -44,11 +44,11 @@ If you have a transform matrix from OpenCV::
     dst_pts = np.array([[10, 10], [110, 20], [5, 115]], dtype=np.float32)
     M = cv2.getAffineTransform(src_pts, dst_pts)
 
-    warped = mask.warp_affine(M, dst_shape)
+    warped = mask.warp_affine(M, output_imshape)
 
     # Or perspective from 4 points
     H = cv2.getPerspectiveTransform(src_4pts, dst_4pts)
-    warped = mask.warp_perspective(H, dst_shape)
+    warped = mask.warp_perspective(H, output_imshape)
 
 Resize
 ------
@@ -61,7 +61,7 @@ Simple scaling is a special case::
     # Or use warp_affine with a scale matrix
     scale = 0.5
     M = np.array([[scale, 0, 0], [0, scale, 0]], dtype=np.float32)
-    resized = mask.warp_affine(M, dst_shape)
+    resized = mask.warp_affine(M, output_imshape)
 
 Decode-warp-encode fallback
 ---------------------------