10 examples of 'matplotlib resize image' in Python

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34def imresize(im, h, w):
35 if im.shape[:2] == (h, w):
36 return im
37
38 # This has problems re-reading a numpy array if it's not 8-bit anymore.
39 assert im.max() > 1, "PIL has problems resizing images after they've been changed to e.g. [0-1] range. Either install OpenCV or resize right after reading the image."
40 img = _Image.fromarray(im.astype(_np.uint8))
41 return _np.array(img.resize((w,h), _Image.BILINEAR), dtype=im.dtype)
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34def resize_image(image, size, mode=None):
35 return misc.imresize(image, size=size, mode=mode)
38def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA):
39 # ref: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv
40
41 # initialize the dimensions of the image to be resized and
42 # grab the image size
43 dim = None
44 (h, w) = image.shape[:2]
45
46 # if both the width and height are None, then return the
47 # original image
48 if width is None and height is None:
49 return image
50
51 # check to see if the width is None
52 if width is None:
53 # calculate the ratio of the height and construct the
54 # dimensions
55 r = height / float(h)
56 dim = (int(w * r), height)
57
58 # otherwise, the height is None
59 else:
60 # calculate the ratio of the width and construct the
61 # dimensions
62 r = width / float(w)
63 dim = (width, int(h * r))
64
65 # resize the image
66 resized = cv2.resize(image, dim, interpolation = inter)
67
68 # return the resized image
69 return resized
28@curry
29def resize(pil_img, size, interpolation=Image.BILINEAR):
30 return pil_img.resize(size, interpolation)
141def resize(self, width, height, resample_algorithm=nearest, resize_canvas=True):
142 pixels = resample_algorithm.resize(
143 self, width, height, resize_canvas=resize_canvas
144 )
145 return self._copy(pixels)
29def imresize(img, size, return_scale=False, interpolation='bilinear'):
30 """Resize image to a given size.
31
32 Args:
33 img (ndarray): The input image.
34 size (tuple): Target (w, h).
35 return_scale (bool): Whether to return `w_scale` and `h_scale`.
36 interpolation (str): Interpolation method, accepted values are
37 "nearest", "bilinear", "bicubic", "area", "lanczos".
38
39 Returns:
40 tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or
41 `resized_img`.
42 """
43 h, w = img.shape[:2]
44 resized_img = cv2.resize(
45 img, size, interpolation=interp_codes[interpolation])
46 if not return_scale:
47 return resized_img
48 else:
49 w_scale = size[0] / w
50 h_scale = size[1] / h
51 return resized_img, w_scale, h_scale
26def resize_image(image,w,h):
27 image=cv2.resize(image,(w,h))
28 cv2.imwrite(Folder_name+"/Resize-"+str(w)+"*"+str(h)+Extension, image)
33def _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
44def resize_img(img, size, interpolation=PIL.Image.BILINEAR):
45 """Resize image to match the given shape.
46
47 This method uses :mod:`cv2` or :mod:`PIL` for the backend.
48 If :mod:`cv2` is installed, this legacy uses the implementation in
49 :mod:`cv2`. This implementation is faster than the implementation in
50 :mod:`PIL`. Under Anaconda environment,
51 :mod:`cv2` can be installed by the following command.
52
53 .. code::
54
55 $ conda install -c menpo opencv3=3.2.0
56
57 Args:
58 img (~numpy.ndarray): An array to be transformed.
59 This is in CHW format and the type should be :obj:`numpy.float32`.
60 size (tuple): This is a tuple of length 2. Its elements are
61 ordered as (height, width).
62 interpolation (int): Determines sampling strategy. This is one of
63 :obj:`PIL.Image.NEAREST`, :obj:`PIL.Image.BILINEAR`,
64 :obj:`PIL.Image.BICUBIC`, :obj:`PIL.Image.LANCZOS`.
65 Bilinear interpolation is the default strategy.
66
67 Returns:
68 ~numpy.ndarray: A resize array in CHW format.
69
70 """
71 img = _resize(img, size, interpolation)
72 return img
676def apply_resize(image, resize_param):
677 assert isinstance(resize_param.interp_mode, (list, tuple))
678
679 interp_mode = cv2.INTER_LINEAR
680 num_interp_mode = len(resize_param.interp_mode)
681 if num_interp_mode > 0:
682 probs = [1. / num_interp_mode] * num_interp_mode
683 cumulative = np.cumsum(probs)
684 val = random.uniform(0, cumulative[-1])
685 prob_num = bisect.bisect_left(cumulative, val)
686 interp_mode = resize_param.interp_mode[prob_num]
687
688 if resize_param.resize_mode == ResizeParameter.WARP:
689 return cv2.resize(image, dsize=(resize_param.width, resize_param.height), interpolation=interp_mode)
690 elif resize_param.resize_mode == ResizeParameter.FIT_LARGE_SIZE_AND_PAD:
691 return aspect_keeping_resize_and_pad(image, resize_param.width, resize_param.height, resize_param.pad_mode,
692 resize_param.pad_val, interp_mode)
693 elif resize_param.resize_mode == ResizeParameter.FIT_SMALL_SIZE:
694 return aspect_keeping_resize_by_small(image, resize_param.width, resize_param.height, interp_mode)
695 raise Exception()

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