feat: support pil resize

Support PIL resizse with PIL interpolation to train transformer.
Almost all vision transformer models need using PIL.Image.BICUBIC
as interpolation in resize.

deploy/python/preprocess.py

@@ -19,12 +19,14 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
+from functools import partial
 import six
 import math
 import random
 import cv2
 import numpy as np
 import importlib
+from PIL import Image
 
 from python.det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize
 
@@ -50,6 +52,47 @@ def create_operators(params):
     return ops
 
 
+class UnifiedResize(object):
+    def __init__(self, interpolation=None, backend="cv2"):
+        _cv2_interp_from_str = {
+            'nearest': cv2.INTER_NEAREST,
+            'bilinear': cv2.INTER_LINEAR,
+            'area': cv2.INTER_AREA,
+            'bicubic': cv2.INTER_CUBIC,
+            'lanczos': cv2.INTER_LANCZOS4
+        }
+        _pil_interp_from_str = {
+            'nearest': Image.NEAREST,
+            'bilinear': Image.BILINEAR,
+            'bicubic': Image.BICUBIC,
+            'box': Image.BOX,
+            'lanczos': Image.LANCZOS,
+            'hamming': Image.HAMMING
+        }
+
+        def _pil_resize(src, size, resample):
+            pil_img = Image.fromarray(src)
+            pil_img = pil_img.resize(size, resample)
+            return np.asarray(pil_img)
+
+        if backend.lower() == "cv2":
+            if isinstance(interpolation, str):
+                interpolation = _cv2_interp_from_str[interpolation.lower()]
+            self.resize_func = partial(cv2.resize, interpolation=interpolation)
+        elif backend.lower() == "pil":
+            if isinstance(interpolation, str):
+                interpolation = _pil_interp_from_str[interpolation.lower()]
+            self.resize_func = partial(_pil_resize, resample=interpolation)
+        else:
+            logger.warning(
+                f"The backend of Resize only support \"cv2\" or \"PIL\". \"f{backend}\" is unavailable. Use \"cv2\" instead."
+            )
+            self.resize_func = cv2.resize
+
+    def __call__(self, src, size):
+        return self.resize_func(src, size)
+
+
 class OperatorParamError(ValueError):
     """ OperatorParamError
     """
@@ -87,8 +130,11 @@ class DecodeImage(object):
 class ResizeImage(object):
     """ resize image """
 
-    def __init__(self, size=None, resize_short=None, interpolation=-1):
-        self.interpolation = interpolation if interpolation >= 0 else None
+    def __init__(self,
+                 size=None,
+                 resize_short=None,
+                 interpolation=None,
+                 backend="cv2"):
         if resize_short is not None and resize_short > 0:
             self.resize_short = resize_short
             self.w = None
@@ -101,6 +147,9 @@ class ResizeImage(object):
             raise OperatorParamError("invalid params for ReisizeImage for '\
                 'both 'size' and 'resize_short' are None")
 
+        self._resize_func = UnifiedResize(
+            interpolation=interpolation, backend=backend)
+
     def __call__(self, img):
         img_h, img_w = img.shape[:2]
         if self.resize_short is not None:
@@ -110,10 +159,7 @@ class ResizeImage(object):
         else:
             w = self.w
             h = self.h
-        if self.interpolation is None:
-            return cv2.resize(img, (w, h))
-        else:
-            return cv2.resize(img, (w, h), interpolation=self.interpolation)
+        return self._resize_func(img, (w, h))
 
 
 class CropImage(object):
@@ -145,9 +191,12 @@ class CropImage(object):
 class RandCropImage(object):
     """ random crop image """
 
-    def __init__(self, size, scale=None, ratio=None, interpolation=-1):
-
-        self.interpolation = interpolation if interpolation >= 0 else None
+    def __init__(self,
+                 size,
+                 scale=None,
+                 ratio=None,
+                 interpolation=None,
+                 backend="cv2"):
         if type(size) is int:
             self.size = (size, size)  # (h, w)
         else:
@@ -156,6 +205,9 @@ class RandCropImage(object):
         self.scale = [0.08, 1.0] if scale is None else scale
         self.ratio = [3. / 4., 4. / 3.] if ratio is None else ratio
 
+        self._resize_func = UnifiedResize(
+            interpolation=interpolation, backend=backend)
+
     def __call__(self, img):
         size = self.size
         scale = self.scale
@@ -181,10 +233,8 @@ class RandCropImage(object):
         j = random.randint(0, img_h - h)
 
         img = img[j:j + h, i:i + w, :]
-        if self.interpolation is None:
-            return cv2.resize(img, size)
-        else:
-            return cv2.resize(img, size, interpolation=self.interpolation)
+
+        return self._resize_func(img, size)
 
 
 class RandFlipImage(object):

ppcls/data/preprocess/ops/operators.py

@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
+from functools import partial
 import six
 import math
 import random
@@ -28,6 +29,48 @@ from PIL import Image
 
 from .autoaugment import ImageNetPolicy
 from .functional import augmentations
+from ppcls.utils import logger
+
+
+class UnifiedResize(object):
+    def __init__(self, interpolation=None, backend="cv2"):
+        _cv2_interp_from_str = {
+            'nearest': cv2.INTER_NEAREST,
+            'bilinear': cv2.INTER_LINEAR,
+            'area': cv2.INTER_AREA,
+            'bicubic': cv2.INTER_CUBIC,
+            'lanczos': cv2.INTER_LANCZOS4
+        }
+        _pil_interp_from_str = {
+            'nearest': Image.NEAREST,
+            'bilinear': Image.BILINEAR,
+            'bicubic': Image.BICUBIC,
+            'box': Image.BOX,
+            'lanczos': Image.LANCZOS,
+            'hamming': Image.HAMMING
+        }
+
+        def _pil_resize(src, size, resample):
+            pil_img = Image.fromarray(src)
+            pil_img = pil_img.resize(size, resample)
+            return np.asarray(pil_img)
+
+        if backend.lower() == "cv2":
+            if isinstance(interpolation, str):
+                interpolation = _cv2_interp_from_str[interpolation.lower()]
+            self.resize_func = partial(cv2.resize, interpolation=interpolation)
+        elif backend.lower() == "pil":
+            if isinstance(interpolation, str):
+                interpolation = _pil_interp_from_str[interpolation.lower()]
+            self.resize_func = partial(_pil_resize, resample=interpolation)
+        else:
+            logger.warning(
+                f"The backend of Resize only support \"cv2\" or \"PIL\". \"f{backend}\" is unavailable. Use \"cv2\" instead."
+            )
+            self.resize_func = cv2.resize
+
+    def __call__(self, src, size):
+        return self.resize_func(src, size)
 
 
 class OperatorParamError(ValueError):
@@ -67,8 +110,11 @@ class DecodeImage(object):
 class ResizeImage(object):
     """ resize image """
 
-    def __init__(self, size=None, resize_short=None, interpolation=-1):
-        self.interpolation = interpolation if interpolation >= 0 else None
+    def __init__(self,
+                 size=None,
+                 resize_short=None,
+                 interpolation=None,
+                 backend="cv2"):
         if resize_short is not None and resize_short > 0:
             self.resize_short = resize_short
             self.w = None
@@ -81,6 +127,9 @@ class ResizeImage(object):
             raise OperatorParamError("invalid params for ReisizeImage for '\
                 'both 'size' and 'resize_short' are None")
 
+        self._resize_func = UnifiedResize(
+            interpolation=interpolation, backend=backend)
+
     def __call__(self, img):
         img_h, img_w = img.shape[:2]
         if self.resize_short is not None:
@@ -90,10 +139,7 @@ class ResizeImage(object):
         else:
             w = self.w
             h = self.h
-        if self.interpolation is None:
-            return cv2.resize(img, (w, h))
-        else:
-            return cv2.resize(img, (w, h), interpolation=self.interpolation)
+        return self._resize_func(img, (w, h))
 
 
 class CropImage(object):
@@ -119,9 +165,12 @@ class CropImage(object):
 class RandCropImage(object):
     """ random crop image """
 
-    def __init__(self, size, scale=None, ratio=None, interpolation=-1):
-
-        self.interpolation = interpolation if interpolation >= 0 else None
+    def __init__(self,
+                 size,
+                 scale=None,
+                 ratio=None,
+                 interpolation=None,
+                 backend="cv2"):
         if type(size) is int:
             self.size = (size, size)  # (h, w)
         else:
@@ -130,6 +179,9 @@ class RandCropImage(object):
         self.scale = [0.08, 1.0] if scale is None else scale
         self.ratio = [3. / 4., 4. / 3.] if ratio is None else ratio
 
+        self._resize_func = UnifiedResize(
+            interpolation=interpolation, backend=backend)
+
     def __call__(self, img):
         size = self.size
         scale = self.scale
@@ -155,10 +207,8 @@ class RandCropImage(object):
         j = random.randint(0, img_h - h)
 
         img = img[j:j + h, i:i + w, :]
-        if self.interpolation is None:
-            return cv2.resize(img, size)
-        else:
-            return cv2.resize(img, size, interpolation=self.interpolation)
+
+        return self._resize_func(img, size)
 
 
 class RandFlipImage(object):