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Merge pull request #164 from gradio-app/abidlabs/smaller

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Abidlabs/smaller
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Abubakar Abid 2021-04-29 23:25:46 -04:00 committed by GitHub
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7 changed files with 308 additions and 25 deletions
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2
gradio.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 1.0
Name: gradio
Version: 1.7.0
Version: 1.7.1
Summary: Python library for easily interacting with trained machine learning models
Home-page: https://github.com/gradio-app/gradio-UI
Author: Abubakar Abid

4
gradio.egg-info/requires.txt
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@ -6,12 +6,8 @@ flask-cachebuster
Flask-Login
paramiko
scipy
IPython
scikit-image
analytics-python
pandas
ffmpy
librosa
colorama>=0.3.9
markdown2
pycryptodome

6
gradio/inputs.py
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@ -14,7 +14,6 @@ from gradio.component import Component
import base64
import numpy as np
import PIL
from skimage.segmentation import slic
import scipy.io.wavfile
from gradio import processing_utils, test_data
import pandas as pd
@ -678,6 +677,11 @@ class Image(InputComponent):
if self.shape is not None:
x = processing_utils.resize_and_crop(x, self.shape)
image = np.array(x)
try:
from skimage.segmentation import slic
except ImportError:
print("Running default interpretation for images requires scikit-image, please install it first.")
return
segments_slic = slic(image, self.interpretation_segments, compactness=10, sigma=1)
leave_one_out_tokens, masks = [], []
replace_color = np.mean(image, axis=(0, 1))

21
gradio/interface.py
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@ -456,15 +456,18 @@ class Interface:
if inline is None:
inline = utils.ipython_check()
if inline:
from IPython.display import IFrame, display
# Embed the remote interface page if on google colab; otherwise, embed the local page.
print(strings.en["INLINE_DISPLAY_BELOW"])
if share:
while not networking.url_ok(share_url):
time.sleep(1)
display(IFrame(share_url, width=1000, height=500))
else:
display(IFrame(path_to_local_server, width=1000, height=500))
try:
from IPython.display import IFrame, display
# Embed the remote interface page if on google colab; otherwise, embed the local page.
print(strings.en["INLINE_DISPLAY_BELOW"])
if share:
while not networking.url_ok(share_url):
time.sleep(1)
display(IFrame(share_url, width=1000, height=500))
else:
display(IFrame(path_to_local_server, width=1000, height=500))
except ImportError:
pass # IPython is not available so does not print inline.
send_launch_analytics(analytics_enabled=self.analytics_enabled, inbrowser=inbrowser, is_colab=is_colab,
share=share, share_url=share_url)

287
gradio/processing_utils.py
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@ -5,7 +5,6 @@ import tempfile
import scipy.io.wavfile
from scipy.fftpack import dct
import numpy as np
import skimage
from gradio import encryptor
#########################
@ -37,7 +36,7 @@ def encode_plot_to_base64(plt):
def encode_array_to_base64(image_array):
with BytesIO() as output_bytes:
PIL_image = Image.fromarray(skimage.img_as_ubyte(image_array))
PIL_image = Image.fromarray(_convert(image_array, np.uint8, force_copy=False))
PIL_image.save(output_bytes, 'PNG')
bytes_data = output_bytes.getvalue()
base64_str = str(base64.b64encode(bytes_data), 'utf-8')
@ -92,6 +91,290 @@ def decode_base64_to_file(encoding, encryption_key=None):
file_obj.flush()
return file_obj
def _convert(image, dtype, force_copy=False, uniform=False):
"""
Adapted from: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/dtype.py#L510-L531
Convert an image to the requested data-type.
Warnings are issued in case of precision loss, or when negative values
are clipped during conversion to unsigned integer types (sign loss).
Floating point values are expected to be normalized and will be clipped
to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
signed integers respectively.
Numbers are not shifted to the negative side when converting from
unsigned to signed integer types. Negative values will be clipped when
converting to unsigned integers.
Parameters
----------
image : ndarray
Input image.
dtype : dtype
Target data-type.
force_copy : bool, optional
Force a copy of the data, irrespective of its current dtype.
uniform : bool, optional
Uniformly quantize the floating point range to the integer range.
By default (uniform=False) floating point values are scaled and
rounded to the nearest integers, which minimizes back and forth
conversion errors.
.. versionchanged :: 0.15
``_convert`` no longer warns about possible precision or sign
information loss. See discussions on these warnings at:
https://github.com/scikit-image/scikit-image/issues/2602
https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
https://github.com/scikit-image/scikit-image/pull/3575
References
----------
.. [1] DirectX data conversion rules.
https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
.. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
pp 7-8. Khronos Group, 2010.
.. [3] Proper treatment of pixels as integers. A.W. Paeth.
In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
.. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
pp 47-57. Morgan Kaufmann, 1998.
"""
dtype_range = {bool: (False, True),
np.bool_: (False, True),
np.bool8: (False, True),
float: (-1, 1),
np.float_: (-1, 1),
np.float16: (-1, 1),
np.float32: (-1, 1),
np.float64: (-1, 1)}
def _dtype_itemsize(itemsize, *dtypes):
"""Return first of `dtypes` with itemsize greater than `itemsize`
Parameters
----------
itemsize: int
The data type object element size.
Other Parameters
----------------
*dtypes:
Any Object accepted by `np.dtype` to be converted to a data
type object
Returns
-------
dtype: data type object
First of `dtypes` with itemsize greater than `itemsize`.
"""
return next(dt for dt in dtypes if np.dtype(dt).itemsize >= itemsize)
def _dtype_bits(kind, bits, itemsize=1):
"""Return dtype of `kind` that can store a `bits` wide unsigned int
Parameters:
kind: str
Data type kind.
bits: int
Desired number of bits.
itemsize: int
The data type object element size.
Returns
-------
dtype: data type object
Data type of `kind` that can store a `bits` wide unsigned int
"""
s = next(i for i in (itemsize, ) + (2, 4, 8) if
bits < (i * 8) or (bits == (i * 8) and kind == 'u'))
return np.dtype(kind + str(s))
def _scale(a, n, m, copy=True):
"""Scale an array of unsigned/positive integers from `n` to `m` bits.
Numbers can be represented exactly only if `m` is a multiple of `n`.
Parameters
----------
a : ndarray
Input image array.
n : int
Number of bits currently used to encode the values in `a`.
m : int
Desired number of bits to encode the values in `out`.
copy : bool, optional
If True, allocates and returns new array. Otherwise, modifies
`a` in place.
Returns
-------
out : array
Output image array. Has the same kind as `a`.
"""
kind = a.dtype.kind
if n > m and a.max() < 2 ** m:
mnew = int(np.ceil(m / 2) * 2)
if mnew > m:
dtype = "int{}".format(mnew)
else:
dtype = "uint{}".format(mnew)
n = int(np.ceil(n / 2) * 2)
return a.astype(_dtype_bits(kind, m))
elif n == m:
return a.copy() if copy else a
elif n > m:
# downscale with precision loss
if copy:
b = np.empty(a.shape, _dtype_bits(kind, m))
np.floor_divide(a, 2**(n - m), out=b, dtype=a.dtype,
casting='unsafe')
return b
else:
a //= 2**(n - m)
return a
elif m % n == 0:
# exact upscale to a multiple of `n` bits
if copy:
b = np.empty(a.shape, _dtype_bits(kind, m))
np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
return b
else:
a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False)
a *= (2**m - 1) // (2**n - 1)
return a
else:
# upscale to a multiple of `n` bits,
# then downscale with precision loss
o = (m // n + 1) * n
if copy:
b = np.empty(a.shape, _dtype_bits(kind, o))
np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
b //= 2**(o - m)
return b
else:
a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False)
a *= (2**o - 1) // (2**n - 1)
a //= 2**(o - m)
return a
image = np.asarray(image)
dtypeobj_in = image.dtype
if dtype is np.floating:
dtypeobj_out = np.dtype('float64')
else:
dtypeobj_out = np.dtype(dtype)
dtype_in = dtypeobj_in.type
dtype_out = dtypeobj_out.type
kind_in = dtypeobj_in.kind
kind_out = dtypeobj_out.kind
itemsize_in = dtypeobj_in.itemsize
itemsize_out = dtypeobj_out.itemsize
# Below, we do an `issubdtype` check. Its purpose is to find out
# whether we can get away without doing any image conversion. This happens
# when:
#
# - the output and input dtypes are the same or
# - when the output is specified as a type, and the input dtype
# is a subclass of that type (e.g. `np.floating` will allow
# `float32` and `float64` arrays through)
if np.issubdtype(dtype_in, np.obj2sctype(dtype)):
if force_copy:
image = image.copy()
return image
if kind_in in 'ui':
imin_in = np.iinfo(dtype_in).min
imax_in = np.iinfo(dtype_in).max
if kind_out in 'ui':
imin_out = np.iinfo(dtype_out).min
imax_out = np.iinfo(dtype_out).max
# any -> binary
if kind_out == 'b':
return image > dtype_in(dtype_range[dtype_in][1] / 2)
# binary -> any
if kind_in == 'b':
result = image.astype(dtype_out)
if kind_out != 'f':
result *= dtype_out(dtype_range[dtype_out][1])
return result
# float -> any
if kind_in == 'f':
if kind_out == 'f':
# float -> float
return image.astype(dtype_out)
if np.min(image) < -1.0 or np.max(image) > 1.0:
raise ValueError("Images of type float must be between -1 and 1.")
# floating point -> integer
# use float type that can represent output integer type
computation_type = _dtype_itemsize(itemsize_out, dtype_in,
np.float32, np.float64)
if not uniform:
if kind_out == 'u':
image_out = np.multiply(image, imax_out,
dtype=computation_type)
else:
image_out = np.multiply(image, (imax_out - imin_out) / 2,
dtype=computation_type)
image_out -= 1.0 / 2.
np.rint(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
elif kind_out == 'u':
image_out = np.multiply(image, imax_out + 1,
dtype=computation_type)
np.clip(image_out, 0, imax_out, out=image_out)
else:
image_out = np.multiply(image, (imax_out - imin_out + 1.0) / 2.0,
dtype=computation_type)
np.floor(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out)
return image_out.astype(dtype_out)
# signed/unsigned int -> float
if kind_out == 'f':
# use float type that can exactly represent input integers
computation_type = _dtype_itemsize(itemsize_in, dtype_out,
np.float32, np.float64)
if kind_in == 'u':
# using np.divide or np.multiply doesn't copy the data
# until the computation time
image = np.multiply(image, 1. / imax_in,
dtype=computation_type)
# DirectX uses this conversion also for signed ints
# if imin_in:
# np.maximum(image, -1.0, out=image)
else:
image = np.add(image, 0.5, dtype=computation_type)
image *= 2 / (imax_in - imin_in)
return np.asarray(image, dtype_out)
# unsigned int -> signed/unsigned int
if kind_in == 'u':
if kind_out == 'i':
# unsigned int -> signed int
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
return image.view(dtype_out)
else:
# unsigned int -> unsigned int
return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
# signed int -> unsigned int
if kind_out == 'u':
image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
result = np.empty(image.shape, dtype_out)
np.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
return result
# signed int -> signed int
if itemsize_in > itemsize_out:
return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
image = image.astype(_dtype_bits('i', itemsize_out * 8))
image -= imin_in
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
image += imin_out
return image.astype(dtype_out)
##################
# AUDIO FILES
##################

7
gradio/utils.py
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@ -1,7 +1,6 @@
import requests
import pkg_resources
from distutils.version import StrictVersion
from IPython import get_ipython
analytics_url = 'https://api.gradio.app/'
PKG_VERSION_URL = "https://api.gradio.app/pkg-version"
@ -40,10 +39,11 @@ def colab_check():
"""
is_colab = False
try: # Check if running interactively using ipython.
from IPython import get_ipython
from_ipynb = get_ipython()
if "google.colab" in str(from_ipynb):
is_colab = True
except NameError:
except (ImportError, NameError):
error_analytics("NameError")
return is_colab
@ -54,9 +54,10 @@ def ipython_check():
:return is_ipython (bool): True or False
"""
try: # Check if running interactively using ipython.
from IPython import get_ipython
get_ipython()
is_ipython = True
except NameError:
except (ImportError, NameError):
is_ipython = False
return is_ipython

6
setup.py
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@ -5,7 +5,7 @@ except ImportError:
setup(
name='gradio',
version='1.7.0',
version='1.7.1',
include_package_data=True,
description='Python library for easily interacting with trained machine learning models',
author='Abubakar Abid',
@ -22,13 +22,9 @@ setup(
'Flask-Login',
'paramiko',
'scipy',
'IPython',
'scikit-image',
'analytics-python',
'pandas',
'ffmpy',
'librosa',
'colorama >= 0.3.9',
'markdown2',
'pycryptodome'
],