質問編集履歴
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補足しました。
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@@ -3,22 +3,193 @@
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### 発生している問題・エラーメッセージ
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```
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[*] Reading checkpoint...
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[!] Load failed...
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Processing image: ./datasets/voice/testA\at.jpg
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Traceback (most recent call last):
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File "main.py", line 53, in <module>
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tf.app.run()
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File "C:\Users\mounf\Anaconda3\envs\cycleganvoicechangerproject\lib\site-packages\tensorflow\python\platform\app.py", line 126, in run
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_sys.exit(main(argv))
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File "main.py", line 50, in main
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else model.test(args)
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File "C:\Users\mounf\PycharmProjects\nets\model.py", line 252, in test
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sample_image = [load_test_data(sample_file, args.fine_size)]
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File "C:\Users\mounf\PycharmProjects\nets\utils.py", line 46, in load_test_data
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img = imread(image_path)
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File "C:\Users\mounf\PycharmProjects\nets\utils.py", line 88, in imread
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return _imread(path, mode='RGB').astype(np.float)
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File "C:\Users\mounf\Anaconda3\envs\cycleganvoicechangerproject\lib\site-packages\imageio\core\functions.py", line 260, in imread
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'Invalid keyword argument "mode", ' 'perhaps you mean "pilmode"?'
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TypeError: Invalid keyword argument "mode", perhaps you mean "pilmode"?
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```
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### 該当のソースコード
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```python
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♯utils.py
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from __future__ import division
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import math
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import pprint
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import scipy.misc
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import numpy as np
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import copy
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try:
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_imread = scipy.misc.imread
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except AttributeError:
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from imageio import imread as _imread
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pp = pprint.PrettyPrinter()
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get_stddev = lambda x, k_h, k_w: 1/math.sqrt(k_w*k_h*x.get_shape()[-1])
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♯new added functions for cyclegan
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class ImagePool(object):
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def __init__(self, maxsize=50):
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self.maxsize = maxsize
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self.num_img = 0
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self.images = []
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def __call__(self, image):
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if self.maxsize <= 0:
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return image
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if self.num_img < self.maxsize:
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self.images.append(image)
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self.num_img += 1
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return image
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if np.random.rand() > 0.5:
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idx = int(np.random.rand()*self.maxsize)
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tmp1 = copy.copy(self.images[idx])[0]
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self.images[idx][0] = image[0]
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idx = int(np.random.rand()*self.maxsize)
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tmp2 = copy.copy(self.images[idx])[1]
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self.images[idx][1] = image[1]
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return [tmp1, tmp2]
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else:
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return image
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def load_test_data(image_path, fine_size=256):
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img = imread(image_path)
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img = scipy.misc.imresize(img, [fine_size, fine_size])
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img = img/127.5 - 1
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return img
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def load_train_data(image_path, load_size=286, fine_size=256, is_testing=False):
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img_A = imread(image_path[0])
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img_B = imread(image_path[1])
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if not is_testing:
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img_A = scipy.misc.imresize(img_A, [load_size, load_size])
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img_B = scipy.misc.imresize(img_B, [load_size, load_size])
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h1 = int(np.ceil(np.random.uniform(1e-2, load_size-fine_size)))
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w1 = int(np.ceil(np.random.uniform(1e-2, load_size-fine_size)))
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img_A = img_A[h1:h1+fine_size, w1:w1+fine_size]
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img_B = img_B[h1:h1+fine_size, w1:w1+fine_size]
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if np.random.random() > 0.5:
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img_A = np.fliplr(img_A)
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img_B = np.fliplr(img_B)
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else:
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img_A = scipy.misc.imresize(img_A, [fine_size, fine_size])
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img_B = scipy.misc.imresize(img_B, [fine_size, fine_size])
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img_A = img_A/127.5 - 1.
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img_B = img_B/127.5 - 1.
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img_AB = np.concatenate((img_A, img_B), axis=2)
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# img_AB shape: (fine_size, fine_size, input_c_dim + output_c_dim)
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return img_AB
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-----------------------------
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def get_image(image_path, image_size, is_crop=True, resize_w=64, is_grayscale = False):
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return transform(imread(image_path, is_grayscale), image_size, is_crop, resize_w)
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def save_images(images, size, image_path):
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return imsave(inverse_transform(images), size, image_path)
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def imread(path, is_grayscale = False):
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if (is_grayscale):
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return _imread(path, flatten=True).astype(np.float)
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else:
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return _imread(path, mode='RGB').astype(np.float)
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def merge_images(images, size):
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return inverse_transform(images)
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def merge(images, size):
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h, w = images.shape[1], images.shape[2]
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img = np.zeros((h * size[0], w * size[1], 3))
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for idx, image in enumerate(images):
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i = idx % size[1]
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j = idx // size[1]
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img[j*h:j*h+h, i*w:i*w+w, :] = image
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return img
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def imsave(images, size, path):
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return scipy.misc.imsave(path, merge(images, size))
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def center_crop(x, crop_h, crop_w,
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resize_h=64, resize_w=64):
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if crop_w is None:
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crop_w = crop_h
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h, w = x.shape[:2]
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j = int(round((h - crop_h)/2.))
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i = int(round((w - crop_w)/2.))
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return scipy.misc.imresize(
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x[j:j+crop_h, i:i+crop_w], [resize_h, resize_w])
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def transform(image, npx=64, is_crop=True, resize_w=64):
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# npx : # of pixels width/height of image
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if is_crop:
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cropped_image = center_crop(image, npx, resize_w=resize_w)
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else:
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cropped_image = image
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return np.array(cropped_image)/127.5 - 1.
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def inverse_transform(images):
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return (images+1.)/2.
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♯---ここからfunction.py
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♯Images
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def imread(uri, format=None, **kwargs):
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""" imread(uri, format=None, **kwargs)
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Reads an image from the specified file. Returns a numpy array, which
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comes with a dict of meta data at its 'meta' attribute.
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Note that the image data is returned as-is, and may not always have
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a dtype of uint8 (and thus may differ from what e.g. PIL returns).
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Parameters
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♯----------
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uri : {str, pathlib.Path, bytes, file}
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The resource to load the image from, e.g. a filename, pathlib.Path,
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http address or file object, see the docs for more info.
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format : str
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The format to use to read the file. By default imageio selects
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the appropriate for you based on the filename and its contents.
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kwargs : ...
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Further keyword arguments are passed to the reader. See :func:`.help`
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to see what arguments are available for a particular format.
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"""
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if "mode" in kwargs:
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raise TypeError(
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'Invalid keyword argument "mode", ' 'perhaps you mean "pilmode"?'
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)
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```
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♯Get reader and read first
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reader = read(uri, format, "i", **kwargs)
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with reader:
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return reader.get_data(0)
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### 試したこと
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ネットで調べましたが、情報が少なく、有効な解決手段が見つかりませんでした。
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