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made much smaller
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Abubakar Abid
parent
bae3bb1095
commit
6b6f456913
@ -14,7 +14,6 @@ from gradio.component import Component
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import base64
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import numpy as np
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import PIL
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from skimage.segmentation import slic
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import scipy.io.wavfile
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from gradio import processing_utils, test_data
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import pandas as pd
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@ -678,6 +677,11 @@ class Image(InputComponent):
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if self.shape is not None:
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x = processing_utils.resize_and_crop(x, self.shape)
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image = np.array(x)
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try:
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from skimage.segmentation import slic
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except ImportError:
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print("Running default interpretation for images requires scikit-image, please install it first.")
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return
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segments_slic = slic(image, self.interpretation_segments, compactness=10, sigma=1)
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leave_one_out_tokens, masks = [], []
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replace_color = np.mean(image, axis=(0, 1))
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@ -37,7 +37,7 @@ def encode_plot_to_base64(plt):
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def encode_array_to_base64(image_array):
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with BytesIO() as output_bytes:
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PIL_image = Image.fromarray(skimage.img_as_ubyte(image_array))
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PIL_image = Image.fromarray(_convert(image_array, np.uint8, force_copy=False))
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PIL_image.save(output_bytes, 'PNG')
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bytes_data = output_bytes.getvalue()
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base64_str = str(base64.b64encode(bytes_data), 'utf-8')
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@ -92,6 +92,290 @@ def decode_base64_to_file(encoding, encryption_key=None):
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file_obj.flush()
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return file_obj
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def _convert(image, dtype, force_copy=False, uniform=False):
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"""
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Adapted from: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/dtype.py#L510-L531
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Convert an image to the requested data-type.
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Warnings are issued in case of precision loss, or when negative values
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are clipped during conversion to unsigned integer types (sign loss).
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Floating point values are expected to be normalized and will be clipped
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to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
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signed integers respectively.
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Numbers are not shifted to the negative side when converting from
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unsigned to signed integer types. Negative values will be clipped when
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converting to unsigned integers.
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Parameters
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----------
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image : ndarray
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Input image.
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dtype : dtype
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Target data-type.
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force_copy : bool, optional
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Force a copy of the data, irrespective of its current dtype.
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uniform : bool, optional
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Uniformly quantize the floating point range to the integer range.
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By default (uniform=False) floating point values are scaled and
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rounded to the nearest integers, which minimizes back and forth
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conversion errors.
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.. versionchanged :: 0.15
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``_convert`` no longer warns about possible precision or sign
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information loss. See discussions on these warnings at:
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https://github.com/scikit-image/scikit-image/issues/2602
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https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
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https://github.com/scikit-image/scikit-image/pull/3575
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References
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----------
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.. [1] DirectX data conversion rules.
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https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
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.. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
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pp 7-8. Khronos Group, 2010.
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.. [3] Proper treatment of pixels as integers. A.W. Paeth.
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In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
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.. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
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pp 47-57. Morgan Kaufmann, 1998.
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"""
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dtype_range = {bool: (False, True),
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np.bool_: (False, True),
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np.bool8: (False, True),
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float: (-1, 1),
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np.float_: (-1, 1),
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np.float16: (-1, 1),
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np.float32: (-1, 1),
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np.float64: (-1, 1)}
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def _dtype_itemsize(itemsize, *dtypes):
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"""Return first of `dtypes` with itemsize greater than `itemsize`
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Parameters
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----------
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itemsize: int
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The data type object element size.
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Other Parameters
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----------------
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*dtypes:
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Any Object accepted by `np.dtype` to be converted to a data
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type object
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Returns
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-------
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dtype: data type object
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First of `dtypes` with itemsize greater than `itemsize`.
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"""
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return next(dt for dt in dtypes if np.dtype(dt).itemsize >= itemsize)
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def _dtype_bits(kind, bits, itemsize=1):
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"""Return dtype of `kind` that can store a `bits` wide unsigned int
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Parameters:
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kind: str
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Data type kind.
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bits: int
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Desired number of bits.
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itemsize: int
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The data type object element size.
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Returns
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-------
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dtype: data type object
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Data type of `kind` that can store a `bits` wide unsigned int
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"""
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s = next(i for i in (itemsize, ) + (2, 4, 8) if
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bits < (i * 8) or (bits == (i * 8) and kind == 'u'))
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return np.dtype(kind + str(s))
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def _scale(a, n, m, copy=True):
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"""Scale an array of unsigned/positive integers from `n` to `m` bits.
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Numbers can be represented exactly only if `m` is a multiple of `n`.
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Parameters
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----------
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a : ndarray
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Input image array.
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n : int
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Number of bits currently used to encode the values in `a`.
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m : int
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Desired number of bits to encode the values in `out`.
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copy : bool, optional
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If True, allocates and returns new array. Otherwise, modifies
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`a` in place.
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Returns
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-------
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out : array
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Output image array. Has the same kind as `a`.
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"""
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kind = a.dtype.kind
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if n > m and a.max() < 2 ** m:
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mnew = int(np.ceil(m / 2) * 2)
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if mnew > m:
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dtype = "int{}".format(mnew)
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else:
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dtype = "uint{}".format(mnew)
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n = int(np.ceil(n / 2) * 2)
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return a.astype(_dtype_bits(kind, m))
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elif n == m:
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return a.copy() if copy else a
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elif n > m:
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# downscale with precision loss
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if copy:
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b = np.empty(a.shape, _dtype_bits(kind, m))
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np.floor_divide(a, 2**(n - m), out=b, dtype=a.dtype,
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casting='unsafe')
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return b
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else:
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a //= 2**(n - m)
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return a
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elif m % n == 0:
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# exact upscale to a multiple of `n` bits
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if copy:
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b = np.empty(a.shape, _dtype_bits(kind, m))
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np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
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return b
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else:
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a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False)
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a *= (2**m - 1) // (2**n - 1)
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return a
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else:
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# upscale to a multiple of `n` bits,
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# then downscale with precision loss
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o = (m // n + 1) * n
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if copy:
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b = np.empty(a.shape, _dtype_bits(kind, o))
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np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
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b //= 2**(o - m)
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return b
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else:
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a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False)
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a *= (2**o - 1) // (2**n - 1)
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a //= 2**(o - m)
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return a
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image = np.asarray(image)
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dtypeobj_in = image.dtype
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if dtype is np.floating:
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dtypeobj_out = np.dtype('float64')
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else:
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dtypeobj_out = np.dtype(dtype)
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dtype_in = dtypeobj_in.type
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dtype_out = dtypeobj_out.type
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kind_in = dtypeobj_in.kind
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kind_out = dtypeobj_out.kind
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itemsize_in = dtypeobj_in.itemsize
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itemsize_out = dtypeobj_out.itemsize
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# Below, we do an `issubdtype` check. Its purpose is to find out
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# whether we can get away without doing any image conversion. This happens
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# when:
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#
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# - the output and input dtypes are the same or
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# - when the output is specified as a type, and the input dtype
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# is a subclass of that type (e.g. `np.floating` will allow
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# `float32` and `float64` arrays through)
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if np.issubdtype(dtype_in, np.obj2sctype(dtype)):
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if force_copy:
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image = image.copy()
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return image
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if kind_in in 'ui':
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imin_in = np.iinfo(dtype_in).min
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imax_in = np.iinfo(dtype_in).max
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if kind_out in 'ui':
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imin_out = np.iinfo(dtype_out).min
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imax_out = np.iinfo(dtype_out).max
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# any -> binary
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if kind_out == 'b':
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return image > dtype_in(dtype_range[dtype_in][1] / 2)
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# binary -> any
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if kind_in == 'b':
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result = image.astype(dtype_out)
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if kind_out != 'f':
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result *= dtype_out(dtype_range[dtype_out][1])
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return result
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# float -> any
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if kind_in == 'f':
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if kind_out == 'f':
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# float -> float
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return image.astype(dtype_out)
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if np.min(image) < -1.0 or np.max(image) > 1.0:
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raise ValueError("Images of type float must be between -1 and 1.")
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# floating point -> integer
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# use float type that can represent output integer type
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computation_type = _dtype_itemsize(itemsize_out, dtype_in,
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np.float32, np.float64)
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if not uniform:
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if kind_out == 'u':
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image_out = np.multiply(image, imax_out,
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dtype=computation_type)
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else:
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image_out = np.multiply(image, (imax_out - imin_out) / 2,
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dtype=computation_type)
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image_out -= 1.0 / 2.
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np.rint(image_out, out=image_out)
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np.clip(image_out, imin_out, imax_out, out=image_out)
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elif kind_out == 'u':
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image_out = np.multiply(image, imax_out + 1,
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dtype=computation_type)
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np.clip(image_out, 0, imax_out, out=image_out)
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else:
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image_out = np.multiply(image, (imax_out - imin_out + 1.0) / 2.0,
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dtype=computation_type)
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np.floor(image_out, out=image_out)
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np.clip(image_out, imin_out, imax_out, out=image_out)
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return image_out.astype(dtype_out)
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# signed/unsigned int -> float
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if kind_out == 'f':
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# use float type that can exactly represent input integers
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computation_type = _dtype_itemsize(itemsize_in, dtype_out,
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np.float32, np.float64)
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if kind_in == 'u':
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# using np.divide or np.multiply doesn't copy the data
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# until the computation time
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image = np.multiply(image, 1. / imax_in,
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dtype=computation_type)
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# DirectX uses this conversion also for signed ints
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# if imin_in:
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# np.maximum(image, -1.0, out=image)
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else:
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image = np.add(image, 0.5, dtype=computation_type)
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image *= 2 / (imax_in - imin_in)
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return np.asarray(image, dtype_out)
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# unsigned int -> signed/unsigned int
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if kind_in == 'u':
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if kind_out == 'i':
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# unsigned int -> signed int
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image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
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return image.view(dtype_out)
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else:
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# unsigned int -> unsigned int
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return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
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# signed int -> unsigned int
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if kind_out == 'u':
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image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
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result = np.empty(image.shape, dtype_out)
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np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
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return result
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# signed int -> signed int
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if itemsize_in > itemsize_out:
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return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
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image = image.astype(_dtype_bits('i', itemsize_out * 8))
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image -= imin_in
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image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
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image += imin_out
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return image.astype(dtype_out)
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##################
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# AUDIO FILES
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##################
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