mirror of
https://github.com/wassname/IndicoIo-python.git
synced 2026-06-27 16:10:34 +08:00
dianavermilya
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
"""
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Image Utils
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Handles preprocessing images before they are sent to the server
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"""
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import os.path, base64, StringIO, re, warnings
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from PIL import Image
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from indicoio.utils.errors import IndicoError, DataStructureException
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B64_PATTERN = re.compile("^([A-Za-z0-9+/]{4})*([A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)")
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def image_preprocess(image, size=None, min_axis=None, batch=False):
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"""
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Takes an image and prepares it for sending to the api including
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resizing and image data/structure standardizing.
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"""
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if batch:
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return [image_preprocess(img, batch=False) for img in image]
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if isinstance(image, basestring):
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b64_str = re.sub('^data:image/.+;base64,', '', image)
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if os.path.isfile(image):
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# check type of element
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out_image = Image.open(image)
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elif B64_PATTERN.match(b64_str) is not None:
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return b64_str
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else:
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raise IndicoError("String provided must be a valid filepath or base64 encoded string")
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elif isinstance(image, Image.Image):
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out_image = image
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elif type(image).__name__ == "ndarray": # image is from numpy/scipy
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if "float" in str(image.dtype) and image.min() >= 0 and image.max() <= 1:
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image *= 255.
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try:
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out_image = Image.fromarray(image.astype("uint8"))
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except TypeError as e:
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raise IndicoError("Please ensure the numpy array is acceptable by PIL. Values must be between 0 and 1 or between 0 and 255 in greyscale, rgb, or rgba format.")
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else:
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raise IndicoError("Image must be a filepath, base64 encoded string, or a numpy array")
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if size or min_axis:
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out_image = resize_image(out_image, size, min_axis)
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# convert to base64
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temp_output = StringIO.StringIO()
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out_image.save(temp_output, format='PNG')
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temp_output.seek(0)
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output_s = temp_output.read()
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return base64.b64encode(output_s)
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def resize_image(image, size, min_axis):
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if min_axis:
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min_idx, other_idx = (0,1) if image.size[0] < image.size[1] else (1,0)
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aspect = image.size[other_idx]/float(image.size[min_idx])
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if aspect > 10:
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warnings.warn(
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"An aspect ratio greater than 10:1 is not recommended",
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Warning
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)
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size_arr = [0,0]
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size_arr[min_idx] = min_axis
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size_arr[other_idx] = int(min_axis * aspect)
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image = image.resize(tuple(size_arr))
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elif size:
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image = image.resize(size)
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return image
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def get_list_dimensions(_list):
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"""
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Takes a nested list and returns the size of each dimension followed
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by the element type in the list
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"""
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if isinstance(_list, list) or isinstance(_list, tuple):
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return [len(_list)] + get_list_dimensions(_list[0])
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return []
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def get_element_type(_list, dimens):
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"""
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Given the dimensions of a nested list and the list, returns the type of the
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elements in the inner list.
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"""
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elem = _list
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for _ in xrange(len(dimens)):
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elem = elem[0]
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return type(elem)
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