image_utils.py

# From https://github.com/ageitgey/image_to_numpy/blob/master/image_to_numpy/src.py
# Altered only slightly
import PIL.Image
from PIL import Image
import PIL.ImageOps
import numpy as np

# Utility function from https://medium.com/@ageitgey/the-dumb-reason-your-fancy-computer-vision-app-isnt-working-exif-orientation-73166c7d39da
def exif_transpose(img):
    """
    If an image has an Exif Orientation tag, transpose the image
    accordingly.
    Note: Very recent versions of Pillow have an internal version
    of this function. So this is only needed if Pillow isn't at the
    latest version.
    :param image: The image to transpose.
    :return: An image.
    """
    if not img:
        return img

    exif_orientation_tag = 274

    # Check for EXIF data (only present on some files)
    if hasattr(img, "_getexif") and isinstance(img._getexif(), dict) and exif_orientation_tag in img._getexif():
        exif_data = img._getexif()
        orientation = exif_data[exif_orientation_tag]

        # Handle EXIF Orientation
        if orientation == 1:
            # Normal image - nothing to do!
            pass
        elif orientation == 2:
            # Mirrored left to right
            img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)
        elif orientation == 3:
            # Rotated 180 degrees
            img = img.rotate(180)
        elif orientation == 4:
            # Mirrored top to bottom
            img = img.rotate(180).transpose(PIL.Image.FLIP_LEFT_RIGHT)
        elif orientation == 5:
            # Mirrored along top-left diagonal
            img = img.rotate(-90, expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT)
        elif orientation == 6:
            # Rotated 90 degrees
            img = img.rotate(-90, expand=True)
        elif orientation == 7:
            # Mirrored along top-right diagonal
            img = img.rotate(90, expand=True).transpose(PIL.Image.FLIP_LEFT_RIGHT)
        elif orientation == 8:
            # Rotated 270 degrees
            img = img.rotate(90, expand=True)

    return img


def load_image_file(file, mode='RGB'):
    """
    Loads an image file (.jpg, .png, etc) into a numpy array
    Defaults to returning the image data as a 3-channel array of 8-bit data. That is
    controlled by the mode parameter.
    Supported modes:
        1 (1-bit pixels, black and white, stored with one pixel per byte)
        L (8-bit pixels, black and white)
        RGB (3x8-bit pixels, true color)
        RGBA (4x8-bit pixels, true color with transparency mask)
        CMYK (4x8-bit pixels, color separation)
        YCbCr (3x8-bit pixels, color video format)
        I (32-bit signed integer pixels)
        F (32-bit floating point pixels)
    :param file: image file name or file object to load
    :param mode: format to convert the image to - 'RGB' (8-bit RGB, 3 channels), 'L' (black and white)
    :return: image contents as numpy array
    """

    # Load the image with PIL
    img = PIL.Image.open(file)

    if False: #hasattr(PIL.ImageOps, 'exif_transpose'):
        # Very recent versions of PIL can do exit transpose internally
        img = PIL.ImageOps.exif_transpose(img)
    else:
        # Otherwise, do the exif transpose ourselves
        img = exif_transpose(img)

    #img = img.convert(mode)

    return img

# From https://gist.github.com/sigilioso/2957026
def resize_and_crop_and_save(img, modified_path, size, crop_type='middle'):
    """
    Resize and crop an image to fit the specified size.

    args:
        img: img
        modified_path: path to store the modified image.
        size: `(width, height)` tuple.
        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:
        Exception: if can not open the file in img_path of there is problems
            to save the image.
        ValueError: if an invalid `crop_type` is provided.
    """
    # If height is higher we resize vertically, if not we resize horizontally
    #img = Image.open(img_path)
    # Get current and desired ratio for the images
    img_ratio = img.size[0] / float(img.size[1])
    ratio = size[0] / float(size[1])
    #The image is scaled/cropped vertically or horizontally depending on the ratio
    if ratio > img_ratio:
        img = img.resize((size[0], round(size[0] * img.size[1] / img.size[0])),
                Image.ANTIALIAS)
        # Crop in the top, middle or bottom
        if crop_type == 'top':
            box = (0, 0, img.size[0], size[1])
        elif crop_type == 'middle':
            box = (0, round((img.size[1] - size[1]) / 2), img.size[0],
                   round((img.size[1] + size[1]) / 2))
        elif crop_type == 'bottom':
            box = (0, img.size[1] - size[1], img.size[0], img.size[1])
        else :
            raise ValueError('ERROR: invalid value for crop_type')
        img = img.crop(box)
    elif ratio < img_ratio:
        img = img.resize((round(size[1] * img.size[0] / img.size[1]), size[1]),
                Image.ANTIALIAS)
        # Crop in the top, middle or bottom
        if crop_type == 'top':
            box = (0, 0, size[0], img.size[1])
        elif crop_type == 'middle':
            box = (round((img.size[0] - size[0]) / 2), 0,
                   round((img.size[0] + size[0]) / 2), img.size[1])
        elif crop_type == 'bottom':
            box = (img.size[0] - size[0], 0, img.size[0], img.size[1])
        else :
            raise ValueError('ERROR: invalid value for crop_type')
        img = img.crop(box)
    else :
        img = img.resize((size[0], size[1]),
                Image.ANTIALIAS)
        # If the scale is the same, we do not need to crop
    img.save(modified_path)