IntBox and FloatBox

tensorpack/dataflow/imgaug/crop.py

@@ -3,7 +3,7 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 from .base import ImageAugmentor
-from ...utils.rect import Rect
+from ...utils.rect import IntBox
 from ...utils.argtools import shape2d
 
 from six.moves import range
@@ -81,7 +81,7 @@ def perturb_BB(image_shape, bb, max_perturb_pixel,
 
     Args:
         image_shape: [h, w]
-        bb (Rect): original bounding box
+        bb (IntBox): original bounding box
         max_perturb_pixel: perturbation on each coordinate
         max_aspect_ratio_diff: result can't have an aspect ratio too different from the original
         max_try: if cannot find a valid bounding box, return the original
@@ -94,13 +94,11 @@ def perturb_BB(image_shape, bb, max_perturb_pixel,
     for _ in range(max_try):
         p = rng.randint(-max_perturb_pixel, max_perturb_pixel, [4])
         newbb = bb.copy()
-        newbb.x += p[0]
-        newbb.y += p[1]
-        newx1 = bb.x1 + p[2]
-        newy1 = bb.y1 + p[3]
-        newbb.w = newx1 - newbb.x
-        newbb.h = newy1 - newbb.y
-        if not newbb.validate(image_shape):
+        newbb.x1 += p[0]
+        newbb.y1 += p[1]
+        newbb.x2 = bb.x2 + p[2]
+        newbb.y2 = bb.y2 + p[3]
+        if not newbb.is_valid_box(image_shape):
             continue
         new_ratio = newbb.h * 1.0 / newbb.w
         diff = abs(new_ratio - orig_ratio)
@@ -128,7 +126,7 @@ class RandomCropAroundBox(ImageAugmentor):
 
     def _get_augment_params(self, img):
         shape = img.shape[:2]
-        box = Rect(0, 0, shape[1] - 1, shape[0] - 1)
+        box = IntBox(0, 0, shape[1] - 1, shape[0] - 1)
         dist = self.perturb_ratio * np.sqrt(shape[0] * shape[1])
         newbox = perturb_BB(shape, box, dist,
                             self.rng, self.max_aspect_ratio_diff)
@@ -138,8 +136,8 @@ class RandomCropAroundBox(ImageAugmentor):
         return newbox.roi(img)
 
     def _augment_coords(self, coords, newbox):
-        coords[:, 0] = coords[:, 0] - newbox.x0
-        coords[:, 1] = coords[:, 1] - newbox.y0
+        coords[:, 0] = coords[:, 0] - newbox.x1
+        coords[:, 1] = coords[:, 1] - newbox.y1
         return coords
 
 
@@ -185,4 +183,4 @@ class RandomCropRandomShape(ImageAugmentor):
 
 
 if __name__ == '__main__':
-    print(perturb_BB([100, 100], Rect(3, 3, 50, 50), 50))
+    print(perturb_BB([100, 100], IntBox(3, 3, 50, 50), 50))

tensorpack/utils/rect.py

@@ -5,38 +5,17 @@
 
 import numpy as np
 
+__all__ = ['IntBox', 'FloatBox']
 
-class Rect(object):
-    """
-    A rectangle class.
-
-    Note that x1 = x + w, not x+w-1 or something else.
-    """
-    __slots__ = ['x', 'y', 'w', 'h']
 
-    def __init__(self, x=0, y=0, w=0, h=0, allow_neg=False):
-        self.x = x
-        self.y = y
-        self.w = w
-        self.h = h
-        if not allow_neg:
-            assert min(self.x, self.y, self.w, self.h) >= 0
+class BoxBase(object):
+    __slots__ = ['x1', 'y1', 'x2', 'y2']
 
-    @property
-    def x0(self):
-        return self.x
-
-    @property
-    def y0(self):
-        return self.y
-
-    @property
-    def x1(self):
-        return self.x + self.w
-
-    @property
-    def y1(self):
-        return self.y + self.h
+    def __init__(self, x1, y1, x2, y2):
+        self.x1 = x1
+        self.y1 = y1
+        self.x2 = x2
+        self.y2 = y2
 
     def copy(self):
         new = type(self)()
@@ -45,66 +24,103 @@ class Rect(object):
         return new
 
     def __str__(self):
-        return 'Rect(x={}, y={}, w={}, h={})'.format(self.x, self.y, self.w, self.h)
+        return '{}(x1={}, y1={}, x2={}, y2={})'.format(
+            type(self).__name__, self.x1, self.y1, self.x2, self.y2)
+
+    __repr__ = __str__
 
     def area(self):
         return self.w * self.h
 
-    def validate(self, shape=None):
+    def is_box(self):
+        return self.area() > 0
+
+
+class IntBox(BoxBase):
+    def __init__(self, x1, y1, x2, y2):
+        for k in [x1, y1, x2, y2]:
+            assert isinstance(k, int)
+        super(IntBox, self).__init__(x1, y1, x2, y2)
+
+    @property
+    def w(self):
+        return self.x2 - self.x1 + 1
+
+    @property
+    def h(self):
+        return self.y2 - self.y1 + 1
+
+    def is_valid_box(self, shape):
         """
         Check that this rect is a valid bounding box within this shape.
+
         Args:
-            shape: [h, w]
+            shape: int [h, w] or None.
         Returns:
             bool
         """
-        if min(self.x, self.y) < 0:
+        if min(self.x1, self.y1) < 0:
             return False
         if min(self.w, self.h) <= 0:
             return False
-        if shape is None:
-            return True
-        if self.x1 > shape[1] - 1:
+        if self.x2 >= shape[1]:
             return False
-        if self.y1 > shape[0] - 1:
+        if self.y2 >= shape[0]:
             return False
         return True
 
     def roi(self, img):
         assert self.validate(img.shape[:2]), "{} vs {}".format(self, img.shape[:2])
-        return img[self.y0:self.y1 + 1, self.x0:self.x1 + 1]
-
-    def expand(self, frac):
-        assert frac > 1.0, frac
-        neww = self.w * frac
-        newh = self.h * frac
-        newx = self.x - (neww - self.w) * 0.5
-        newy = self.y - (newh - self.h) * 0.5
-        return Rect(*(map(int, [newx, newy, neww, newh])), allow_neg=True)
-
-    def roi_zeropad(self, img):
-        shp = list(img.shape)
-        shp[0] = self.h
-        shp[1] = self.w
-        ret = np.zeros(tuple(shp), dtype=img.dtype)
-
-        xstart = 0 if self.x >= 0 else -self.x
-        ystart = 0 if self.y >= 0 else -self.y
-
-        xmin = max(self.x0, 0)
-        ymin = max(self.y0, 0)
-        xmax = min(self.x1, img.shape[1])
-        ymax = min(self.y1, img.shape[0])
-        patch = img[ymin:ymax, xmin:xmax]
-        ret[ystart:ystart + patch.shape[0], xstart:xstart + patch.shape[1]] = patch
-        return ret
+        return img[self.y1:self.y2 + 1, self.x1:self.x2 + 1]
+
+    # def expand(self, frac):
+    #     assert frac > 1.0, frac
+    #     neww = self.w * frac
+    #     newh = self.h * frac
+    #     newx = self.x - (neww - self.w) * 0.5
+    #     newy = self.y - (newh - self.h) * 0.5
+    #     return Rect(*(map(int, [newx, newy, neww, newh])), allow_neg=True)
+
+    # def roi_zeropad(self, img):
+    #     shp = list(img.shape)
+    #     shp[0] = self.h
+    #     shp[1] = self.w
+    #     ret = np.zeros(tuple(shp), dtype=img.dtype)
+
+    #     xstart = 0 if self.x >= 0 else -self.x
+    #     ystart = 0 if self.y >= 0 else -self.y
+
+    #     xmin = max(self.x0, 0)
+    #     ymin = max(self.y0, 0)
+    #     xmax = min(self.x1, img.shape[1])
+    #     ymax = min(self.y1, img.shape[0])
+    #     patch = img[ymin:ymax, xmin:xmax]
+    #     ret[ystart:ystart + patch.shape[0], xstart:xstart + patch.shape[1]] = patch
+    #     return ret
+
+
+class FloatBox(BoxBase):
+    def __init__(self, x1, y1, x2, y2):
+        for k in [x1, y1, x2, y2]:
+            assert isinstance(k, float)
+        super(FloatBox, self).__init__(x1, y1, x2, y2)
 
-    __repr__ = __str__
+    @property
+    def w(self):
+        return self.x2 - self.x1
+
+    @property
+    def h(self):
+        return self.y2 - self.y1
+
+    @staticmethod
+    def from_intbox(intbox):
+        return FloatBox(intbox.x1, intbox.y1,
+                        intbox.x2 + 1, intbox.y2 + 1)
 
 
 if __name__ == '__main__':
-    x = Rect(2, 1, 3, 3, allow_neg=True)
+    x = IntBox(2, 1, 3, 3)
 
     img = np.random.rand(3, 3)
     print(img)
-    print(x.roi_zeropad(img))