from __future__ import annotations
import base64
import json
import logging
import os
import shutil
import subprocess
import tempfile
import warnings
from io import BytesIO
from pathlib import Path
import numpy as np
from gradio_client import utils as client_utils
from PIL import Image, ImageOps, PngImagePlugin
from gradio import wasm_utils
if not wasm_utils.IS_WASM:
# TODO: Support ffmpeg on Wasm
from ffmpy import FFmpeg, FFprobe, FFRuntimeError
with warnings.catch_warnings():
warnings.simplefilter("ignore") # Ignore pydub warning if ffmpeg is not installed
from pydub import AudioSegment
log = logging.getLogger(__name__)
#########################
# GENERAL
#########################
def to_binary(x: str | dict) -> bytes:
"""Converts a base64 string or dictionary to a binary string that can be sent in a POST."""
if isinstance(x, dict):
if x.get("data"):
base64str = x["data"]
else:
base64str = client_utils.encode_url_or_file_to_base64(x["name"])
else:
base64str = x
return base64.b64decode(extract_base64_data(base64str))
def extract_base64_data(x: str) -> str:
"""Just extracts the base64 data from a general base64 string."""
return x.rsplit(",", 1)[-1]
#########################
# IMAGE PRE-PROCESSING
#########################
def decode_base64_to_image(encoding: str) -> Image.Image:
image_encoded = extract_base64_data(encoding)
img = Image.open(BytesIO(base64.b64decode(image_encoded)))
try:
if hasattr(ImageOps, "exif_transpose"):
img = ImageOps.exif_transpose(img)
except Exception:
log.warning(
"Failed to transpose image %s based on EXIF data.",
img,
exc_info=True,
)
return img
def encode_plot_to_base64(plt):
with BytesIO() as output_bytes:
plt.savefig(output_bytes, format="png")
bytes_data = output_bytes.getvalue()
base64_str = str(base64.b64encode(bytes_data), "utf-8")
return "data:image/png;base64," + base64_str
def get_pil_metadata(pil_image):
# Copy any text-only metadata
metadata = PngImagePlugin.PngInfo()
for key, value in pil_image.info.items():
if isinstance(key, str) and isinstance(value, str):
metadata.add_text(key, value)
return metadata
def encode_pil_to_bytes(pil_image, format="png"):
with BytesIO() as output_bytes:
pil_image.save(output_bytes, format, pnginfo=get_pil_metadata(pil_image))
return output_bytes.getvalue()
def encode_pil_to_base64(pil_image):
bytes_data = encode_pil_to_bytes(pil_image)
base64_str = str(base64.b64encode(bytes_data), "utf-8")
return "data:image/png;base64," + base64_str
def encode_array_to_base64(image_array):
with BytesIO() as output_bytes:
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")
return "data:image/png;base64," + base64_str
def resize_and_crop(img, size, crop_type="center"):
"""
Resize and crop an image to fit the specified size.
args:
size: `(width, height)` tuple. Pass `None` for either width or height
to only crop and resize the other.
crop_type: can be 'top', 'middle' or 'bottom', depending on this
value, the image will cropped getting the 'top/left', 'middle' or
'bottom/right' of the image to fit the size.
raises:
ValueError: if an invalid `crop_type` is provided.
"""
if crop_type == "top":
center = (0, 0)
elif crop_type == "center":
center = (0.5, 0.5)
else:
raise ValueError
resize = list(size)
if size[0] is None:
resize[0] = img.size[0]
if size[1] is None:
resize[1] = img.size[1]
return ImageOps.fit(img, resize, centering=center) # type: ignore
##################
# Audio
##################
def audio_from_file(filename, crop_min=0, crop_max=100):
try:
audio = AudioSegment.from_file(filename)
except FileNotFoundError as e:
isfile = Path(filename).is_file()
msg = (
f"Cannot load audio from file: `{'ffprobe' if isfile else filename}` not found."
+ " Please install `ffmpeg` in your system to use non-WAV audio file formats"
" and make sure `ffprobe` is in your PATH."
if isfile
else ""
)
raise RuntimeError(msg) from e
if crop_min != 0 or crop_max != 100:
audio_start = len(audio) * crop_min / 100
audio_end = len(audio) * crop_max / 100
audio = audio[audio_start:audio_end]
data = np.array(audio.get_array_of_samples())
if audio.channels > 1:
data = data.reshape(-1, audio.channels)
return audio.frame_rate, data
def audio_to_file(sample_rate, data, filename, format="wav"):
if format == "wav":
data = convert_to_16_bit_wav(data)
audio = AudioSegment(
data.tobytes(),
frame_rate=sample_rate,
sample_width=data.dtype.itemsize,
channels=(1 if len(data.shape) == 1 else data.shape[1]),
)
file = audio.export(filename, format=format)
file.close() # type: ignore
def convert_to_16_bit_wav(data):
# Based on: https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.write.html
warning = "Trying to convert audio automatically from {} to 16-bit int format."
if data.dtype in [np.float64, np.float32, np.float16]:
warnings.warn(warning.format(data.dtype))
data = data / np.abs(data).max()
data = data * 32767
data = data.astype(np.int16)
elif data.dtype == np.int32:
warnings.warn(warning.format(data.dtype))
data = data / 65538
data = data.astype(np.int16)
elif data.dtype == np.int16:
pass
elif data.dtype == np.uint16:
warnings.warn(warning.format(data.dtype))
data = data - 32768
data = data.astype(np.int16)
elif data.dtype == np.uint8:
warnings.warn(warning.format(data.dtype))
data = data * 257 - 32768
data = data.astype(np.int16)
else:
raise ValueError(
"Audio data cannot be converted automatically from "
f"{data.dtype} to 16-bit int format."
)
return data
##################
# OUTPUT
##################
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), # type: ignore
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:
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
dtypeobj_out = np.dtype("float64") if dtype is np.floating else 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 # type: ignore
imax_out = np.iinfo(dtype_out).max # type: ignore
# 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) # type: ignore
else:
image_out = np.multiply(
image, (imax_out - imin_out) / 2, dtype=computation_type # type: ignore
)
image_out -= 1.0 / 2.0
np.rint(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out) # type: ignore
elif kind_out == "u":
image_out = np.multiply(image, imax_out + 1, dtype=computation_type) # type: ignore
np.clip(image_out, 0, imax_out, out=image_out) # type: ignore
else:
image_out = np.multiply(
image, (imax_out - imin_out + 1.0) / 2.0, dtype=computation_type # type: ignore
)
np.floor(image_out, out=image_out)
np.clip(image_out, imin_out, imax_out, out=image_out) # type: ignore
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, 1.0 / imax_in, dtype=computation_type) # type: ignore
# 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) # type: ignore
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 # type: ignore
image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
image += imin_out # type: ignore
return image.astype(dtype_out)
def ffmpeg_installed() -> bool:
if wasm_utils.IS_WASM:
# TODO: Support ffmpeg in WASM
return False
return shutil.which("ffmpeg") is not None
def video_is_playable(video_filepath: str) -> bool:
"""Determines if a video is playable in the browser.
A video is playable if it has a playable container and codec.
.mp4 -> h264
.webm -> vp9
.ogg -> theora
"""
try:
container = Path(video_filepath).suffix.lower()
probe = FFprobe(
global_options="-show_format -show_streams -select_streams v -print_format json",
inputs={video_filepath: None},
)
output = probe.run(stderr=subprocess.PIPE, stdout=subprocess.PIPE)
output = json.loads(output[0])
video_codec = output["streams"][0]["codec_name"]
return (container, video_codec) in [
(".mp4", "h264"),
(".ogg", "theora"),
(".webm", "vp9"),
]
# If anything goes wrong, assume the video can be played to not convert downstream
except (FFRuntimeError, IndexError, KeyError):
return True
def convert_video_to_playable_mp4(video_path: str) -> str:
"""Convert the video to mp4. If something goes wrong return the original video."""
try:
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
output_path = Path(video_path).with_suffix(".mp4")
shutil.copy2(video_path, tmp_file.name)
# ffmpeg will automatically use h264 codec (playable in browser) when converting to mp4
ff = FFmpeg(
inputs={str(tmp_file.name): None},
outputs={str(output_path): None},
global_options="-y -loglevel quiet",
)
ff.run()
except FFRuntimeError as e:
print(f"Error converting video to browser-playable format {str(e)}")
output_path = video_path
finally:
# Remove temp file
os.remove(tmp_file.name) # type: ignore
return str(output_path)