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10 def _resize_pillow(src, height, width, interpolation): 11 if interpolation == "linear": 12 interpolation = PIL.Image.LINEAR 13 elif interpolation == "nearest": 14 interpolation = PIL.Image.NEAREST 15 else: 16 raise ValueError("unsupported interpolation: {}".format(interpolation)) 17 18 if np.issubdtype(src.dtype, np.integer): 19 dst = PIL.Image.fromarray(src) 20 dst = dst.resize((width, height), resample=interpolation) 21 dst = np.array(dst) 22 else: 23 assert np.issubdtype(src.dtype, np.floating) 24 ndim = src.ndim 25 if ndim == 2: 26 src = src[:, :, None] 27 28 C = src.shape[2] 29 dst = np.zeros((height, width, C), dtype=src.dtype) 30 for c in range(C): 31 src_c = src[:, :, c] 32 src_c = PIL.Image.fromarray(src_c) 33 dst[:, :, c] = src_c.resize( 34 (width, height), resample=interpolation 35 ) 36 37 if ndim == 2: 38 dst = dst[:, :, 0] 39 return dst
28 def pillow(): 29 IN = '../images/bird.jpg' 30 PIXL_OUT = '../bin/bird_scl_pixl.jpg' 31 PILLOW_OUT = '../bin/bird_scl_pillow.jpg' 32 33 TARGET_WIDTH = 500 34 TARGET_HEIGHT = 333 35 36 # pixl 37 start = time.time() 38 image = pixl.Image(IN) 39 #image.resize(TARGET_WIDTH, TARGET_HEIGHT, pixl.ResizeMethod.BILINEAR) 40 image.convolution([0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], 1.0).invert().grayscale() 41 42 image.save(PIXL_OUT) 43 print('pixl:', time.time()-start, "s") 44 45 # pillow 46 start = time.time() 47 image = Image.open(IN) 48 image = image.resize((TARGET_WIDTH, TARGET_HEIGHT), PIL.Image.BILINEAR) 49 image.save(PILLOW_OUT) 50 print('pillow:', time.time()-start, "s")
33 def _resize_pil(img, size, interpolation): 34 C = img.shape[0] 35 H, W = size 36 out = np.empty((C, H, W), dtype=img.dtype) 37 for ch, out_ch in zip(img, out): 38 ch = PIL.Image.fromarray(ch, mode='F') 39 out_ch[:] = ch.resize((W, H), resample=interpolation) 40 return out
1001 def _pil_convert_(self, image, mode="L"): 1002 """ Convert image. Actually calls ``image.convert(mode)``. 1003 1004 Parameters 1005 ---------- 1006 mode : str 1007 Pass 'L' to convert to grayscale 1008 src : str 1009 Component to get images from. Default is 'images'. 1010 dst : str 1011 Component to write images to. Default is 'images'. 1012 p : float 1013 Probability of applying the transform. Default is 1. 1014 """ 1015 return image.convert(mode)
29 def pilresize(img, size, interpolation=Image.BILINEAR): 30 31 """Resize the input PIL Image to the given size. 32 Args: 33 img (PIL Image): Image to be resized. 34 size (sequence or int): Desired output size. If size is a sequence like 35 (h, w), the output size will be matched to this. If size is an int, 36 the smaller edge of the image will be matched to this number maintaing 37 the aspect ratio. i.e, if height > width, then image will be rescaled to 38 (size * height / width, size) 39 interpolation (int, optional): Desired interpolation. Default is 40 ``PIL.Image.BILINEAR`` 41 Returns: 42 PIL Image: Resized image. 43 """ 44 45 if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)): 46 raise TypeError('Got inappropriate size arg: {}'.format(size)) 47 48 if isinstance(size, int): 49 w, h = img.size 50 if (w <= h and w == size) or (h <= w and h == size): 51 return img 52 if w < h: 53 ow = size 54 oh = int(size * float(h) / w) 55 return img.resize((ow, oh), interpolation) 56 else: 57 oh = size 58 ow = int(size * float(w) / h) 59 return img.resize((ow, oh), interpolation) 60 else: 61 return img.resize(size[::-1], interpolation)
28 @curry 29 def resize(pil_img, size, interpolation=Image.BILINEAR): 30 return pil_img.resize(size, interpolation)
103 def __resize_image(self, image): 104 self.input_height = image.shape[0] 105 if self.height is not None: 106 zoom = self.height / float(image.shape[0]) 107 width = int(float(image.shape[1]) * zoom) + 1 108 image = cv2.resize(image, (width, self.height), interpolation=cv2.INTER_CUBIC) 109 return image