module hackathon.image_helper
#
Short summary#
module ensae_projects.hackathon.image_helper
Helpers for the hackathon 2017 (Label Emmaüs).
Functions#
function |
truncated documentation |
---|---|
Enumerates all batches saved in a folder. |
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Lists all images in one folder assuming subfolders indicates the class of each image belongs to. |
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Splits images from a folder into train and test. The function saves images into two separate folders. |
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Computes the distribution of images size. |
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Resizes an image (from PIL). |
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Converts an image (PIL) to gray scale. |
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Returns the last element of sequence assuming they were generated by an iterator or a generator. |
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Loads a batch file saved by |
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Plots a gallery of images using matplotlib. Extracts a random sample from a folder which contains many images. … |
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Reads an image. |
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Resizes an image until one of its dimension becomes smaller than maxdim after dividing the dimensions by two many … |
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Applies a transform on every image in a folder, saves it in another one. It keeps the same subfolders. |
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Copies all images from src_folder to dest_folder if valid(name) is True. |
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Downloads images based on their urls. |
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Considers all images in a folder, transform them into features (function transform) and saves them with pickle … |
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Extracts a random sample from a folder which contains many images. Relies on fonction |
Documentation#
Helpers for the hackathon 2017 (Label Emmaüs).
- ensae_projects.hackathon.image_helper.enumerate_batch_features(folder, batch_or_image=False)#
Enumerates all batches saved in a folder.
- Parameters:
folder – folder where to find the batches.
batch_or_image – False to enumerate filenames, True for couple (features, class)
- Returns:
enumerator
- ensae_projects.hackathon.image_helper.enumerate_image_class(folder, abspath=True, ext={'.jpg', '.png'})#
Lists all images in one folder assuming subfolders indicates the class of each image belongs to.
- Parameters:
folder – folder
abspath – use absolute paths
ext – allowed extensions
- Returns:
list of (filename, class)
- ensae_projects.hackathon.image_helper.folder_split_train_test(src_folder, dest_train, dest_test, seed=None, ext={'.jpg', '.png'}, test_size=0.25)#
Splits images from a folder into train and test. The function saves images into two separate folders.
- Parameters:
src_folder – source folder
dest_train – destination folder for the train set
dest_test – destination folder for the test set
ext – desired extensions
seed – random seed
test_size – test ratio
- Returns:
list of copied files in a 2-uple
The function relies on
enumerate_image_class
to extract the image from folder src_folder. The subfolder is used to perform a stratitied split.
- ensae_projects.hackathon.image_helper.histogram_image_size(folder, ext={'.jpg', '.png'})#
Computes the distribution of images size.
- Parameters:
folder – folder
ext – allowed extensions
- Returns:
histogram
- ensae_projects.hackathon.image_helper.image_zoom(img, new_size, **kwargs)#
Resizes an image (from PIL).
- Parameters:
img – PIL.Image
new_size – size after zoom
kwargs – additional arguments
- Returns:
new image
- ensae_projects.hackathon.image_helper.img2gray(img, mode='L')#
Converts an image (PIL) to gray scale.
- ensae_projects.hackathon.image_helper.last_element(iter)#
Returns the last element of sequence assuming they were generated by an iterator or a generator.
- Parameters:
iter – iterator or generator
- Returns:
element
<<<
from ensae_projects.hackathon.image_helper import last_element def gen(): for i in range(10): yield "A%d" % i print(last_element(gen()))
>>>
A9
- ensae_projects.hackathon.image_helper.load_batch_features(batch_file)#
Loads a batch file saved by
stream_image2features
.- Parameters:
batch_file – batch file
- Returns:
features, classes
- ensae_projects.hackathon.image_helper.plot_gallery_random_images(folder, n=12, seed=None, ext={'.jpg', '.png'}, **kwargs)#
Plots a gallery of images using matplotlib. Extracts a random sample from a folder which contains many images. Relies on fonction
enumerate_image_class
. Calls plot_gallery_images to build the gallery.- Parameters:
folder – folder
n – number of requested images
seed – seed
ext – allowed extensions
kwargs – argument to send to matplotlib
- Returns:
tuple (ax, random sample)
The function is a streaming function, it yields the current state of a sample drawn with the reservoir sampling algorithm. It also works with
- ensae_projects.hackathon.image_helper.read_image(filename_or_bytes)#
Reads an image.
- Parameters:
filename_or_bytes – filename or bytes
- Returns:
Image from PIL
- ensae_projects.hackathon.image_helper.resize_image(filename_or_bytes, maxdim=512, dest=None, format=None)#
Resizes an image until one of its dimension becomes smaller than maxdim after dividing the dimensions by two many times.
- Parameters:
filename_or_bytes – filename or bytes
maxdim – maximum dimension
dest – if filename is a str
format – saved image format (if filename_or_bytes is bytes)
- Returns:
same type
- ensae_projects.hackathon.image_helper.stream_apply_image_transform(src_folder, dest_folder, transform, ext={'.jpg', '.png'}, fLOG=None)#
Applies a transform on every image in a folder, saves it in another one. It keeps the same subfolders.
- Parameters:
src_folder – source folder
dest_folder – destination folder
transform – function,
trans(img) -> img
ext – image extension to consider
logging – function
- Returns:
number of processed image
The function yields every created filename and returns an iterator.
Example:
list(stream_apply_image_transform(src, fest, lambda im: img2gray(im)))
- ensae_projects.hackathon.image_helper.stream_copy_images(src_folder, dest_folder, valid, ext={'.jpg', '.png'}, fLOG=None)#
Copies all images from src_folder to dest_folder if valid(name) is True.
- Parameters:
src_folder – source folder
dest_folder – destination folder
valid – function
valid(name) -> bool
ext – allowed extensions
fLOG – loggung function
- Returns:
iterator on copied files
- ensae_projects.hackathon.image_helper.stream_download_images(urls, dest_folder, fLOG=None, use_request=None, skipif_done=True, dummys=None, skip=0)#
Downloads images based on their urls.
- Parameters:
urls – filename or list of urls
dest_folder – destination folder
fLOG – logging function
use_request – None to let the function choose, True to use urllib3, False to use urllib.request.
skipif_done – skip if the image was already downloaded
dummys – some website returns a dummy image to tell there is no image at this specific address, if an image is part of this set of images, it is ignored, if the value is None, it is replaced by a default set of images
skip – skip the first images
- Returns:
enumerator on created files
The function continue if an error occurs. Use
fLOG=print
to see which url failed. Parameter dummys can be set to avoid images like the following:The function does not download an image already downloaded but still yields it.
- ensae_projects.hackathon.image_helper.stream_image2features(src_folder, dest_folder, transform, batch_size=1000, prefix='batch', ext={'.jpg', '.png'}, fLOG=None)#
Considers all images in a folder, transform them into features (function transform) and saves them with pickle into numpy arrays by batch.
- Parameters:
src_folder – folder which contains images
dest_folder – destination of the batches
transform – from image to features, function,
trans(img) -> numpy.array
batch_size – number of images to save together
prefix – prefix name for the batch files
ext – list of extensions to process
fLOG – logging function
- Returns:
list of written files (iterator)
The function yields a batch file when one is ready. It does not wait the end before returning all of them. The saved files contains two arrays, first one for the features, second one for the classes.
Example:
list(stream_image2features(this, temp, lambda im: numpy.array(image_zoom(img2gray(im), (10, 12)))))
- ensae_projects.hackathon.image_helper.stream_random_sample(folder, n=1000, seed=None, abspath=True, ext={'.jpg', '.png'})#
Extracts a random sample from a folder which contains many images. Relies on fonction
enumerate_image_class
.- Parameters:
folder – folder
n – number of requested images
seed – seed
abspath – use absolute paths
ext – allowed extensions
- Returns:
list of (filename, class)
The function is a streaming function, it yields the current state of a sample drawn with the reservoir sampling algorithm. It also works with
<<<
import os from ensae_projects.hackathon.image_helper import stream_random_sample, last_element this = os.path.join(os.path.dirname(__file__), '..') res = last_element(stream_random_sample( this, abspath=False, ext={'.py', '.rst', '.pyc'})) print(res)
>>>
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