PIL报错:TypeError: Cannot handle this data type: (1, 1, 3), <f4及解决Image.fromarray保存后的结果是纯黑的图片

PIL报错:TypeError: Cannot handle this data type: 1, 1, 3 <f4及解决Image.fromarray保存后的结果是纯黑的图片

  • 1.问题背景
  • 2.解决办法
  • 2.1.解决`Image.fromarray()`保存图片报错
  • 2.2.解决保存后的结果是纯黑的图片
  • 1.问题背景


    from PIL import Image
    import os
    import uuid
    img_file = Image.fromarray(images_array_list[_index])
    img_file.save(os.path.join(images_save_path, "{}-{}.jpg".format(TIME_STAMP, uuid.uuid4())))

    TypeError: Cannot handle this data type: (1, 1, 3), <f4


    Traceback (most recent call last):
      File "C:\Users\Anaconda3\envs\tf1.7\lib\site-packages\PIL\Image.py", line 2828, in fromarray
        mode, rawmode = _fromarray_typemap[typekey]
    KeyError: ((1, 1, 3), '<f4')
    The above exception was the direct cause of the following exception:
    Traceback (most recent call last):
      File "C:\Users\Anaconda3\envs\tf1.7\lib\site-packages\IPython\core\interactiveshell.py", line 3343, in run_code
        exec(code_obj, self.user_global_ns, self.user_ns)
      File "<ipython-input-2-13650c2b0b93>", line 1, in <module>
        runfile('E:/Code/Python/keras不使用generator批量预测图像.py', wdir='E:/Code/Python')
      File "C:\Program Files\JetBrains\PyCharm 2020.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
        pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
      File "C:\Program Files\JetBrains\PyCharm 2020.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
        exec(compile(contents+"\n", file, 'exec'), glob, loc)
      File "E:/Code/Python/keras不使用generator批量预测图像.py", line 44, in <module>
        img_file = Image.fromarray(images_list[_index])
      File "C:\Users\Anaconda3\envs\tf1.7\lib\site-packages\PIL\Image.py", line 2830, in fromarray
        raise TypeError("Cannot handle this data type: %s, %s" % typekey) from e
    TypeError: Cannot handle this data type: (1, 1, 3), <f4



    原因是Image.fromarray() 要求输入的numpy数据类型不支持float32类型的数据,但我这边输入的images_array_list数据类型是 float32,便造成了上述报错!此时,只需要将类型转换为Image.fromarray() 支持的类型即可(本文转为uint8类型),如下所示:

    from PIL import Image
    import os
    import uuid
    import numpy as np
    img_file = Image.fromarray(np.uint8(images_list[_index]))
    img_file.save(os.path.join(images_save_path, "{}-{}.jpg".format(TIME_STAMP, uuid.uuid4())))






    def array_to_img(x, data_format=None, scale=True):
        """Converts a 3D Numpy array to a PIL Image instance.
        # Arguments
            x: Input Numpy array.
            data_format: Image data format.
            scale: Whether to rescale image values
                to be within [0, 255].
        # Returns
            A PIL Image instance.
        # Raises
            ImportError: if PIL is not available.
            ValueError: if invalid `x` or `data_format` is passed.
        if pil_image is None:
            raise ImportError('Could not import PIL.Image. '
                              'The use of `array_to_img` requires PIL.')
        x = np.asarray(x, dtype=K.floatx())
        if x.ndim != 3:
            raise ValueError('Expected image array to have rank 3 (single image). '
                             'Got array with shape:', x.shape)
        if data_format is None:
            data_format = K.image_data_format()
        if data_format not in {'channels_first', 'channels_last'}:
            raise ValueError('Invalid data_format:', data_format)
        # Original Numpy array x has format (height, width, channel)
        # or (channel, height, width)
        # but target PIL image has format (width, height, channel)
        if data_format == 'channels_first':
            x = x.transpose(1, 2, 0)
        if scale:
            x = x + max(-np.min(x), 0)
            x_max = np.max(x)
            if x_max != 0:
                x /= x_max
            x *= 255
        if x.shape[2] == 3:
            # RGB
            return pil_image.fromarray(x.astype('uint8'), 'RGB')
        elif x.shape[2] == 1:
            # grayscale
            return pil_image.fromarray(x[:, :, 0].astype('uint8'), 'L')
            raise ValueError('Unsupported channel number: ', x.shape[2])


    img_file = Image.fromarray(np.uint8(_images_list[_index]))


    img_file = array_to_img(images_list[_index])



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