aboutsummaryrefslogtreecommitdiffstats
path: root/contrib/python/Pillow/py3/PIL/ImageOps.py
blob: a9e626b2b2a9d100d81e6e38a5ad7a8d107f2d52 (plain) (blame)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
#
# The Python Imaging Library.
# $Id$
#
# standard image operations
#
# History:
# 2001-10-20 fl   Created
# 2001-10-23 fl   Added autocontrast operator
# 2001-12-18 fl   Added Kevin's fit operator
# 2004-03-14 fl   Fixed potential division by zero in equalize
# 2005-05-05 fl   Fixed equalize for low number of values
#
# Copyright (c) 2001-2004 by Secret Labs AB
# Copyright (c) 2001-2004 by Fredrik Lundh
#
# See the README file for information on usage and redistribution.
#
from __future__ import annotations

import functools
import operator
import re

from . import ExifTags, Image, ImagePalette

#
# helpers


def _border(border):
    if isinstance(border, tuple):
        if len(border) == 2:
            left, top = right, bottom = border
        elif len(border) == 4:
            left, top, right, bottom = border
    else:
        left = top = right = bottom = border
    return left, top, right, bottom


def _color(color, mode):
    if isinstance(color, str):
        from . import ImageColor

        color = ImageColor.getcolor(color, mode)
    return color


def _lut(image, lut):
    if image.mode == "P":
        # FIXME: apply to lookup table, not image data
        msg = "mode P support coming soon"
        raise NotImplementedError(msg)
    elif image.mode in ("L", "RGB"):
        if image.mode == "RGB" and len(lut) == 256:
            lut = lut + lut + lut
        return image.point(lut)
    else:
        msg = f"not supported for mode {image.mode}"
        raise OSError(msg)


#
# actions


def autocontrast(image, cutoff=0, ignore=None, mask=None, preserve_tone=False):
    """
    Maximize (normalize) image contrast. This function calculates a
    histogram of the input image (or mask region), removes ``cutoff`` percent of the
    lightest and darkest pixels from the histogram, and remaps the image
    so that the darkest pixel becomes black (0), and the lightest
    becomes white (255).

    :param image: The image to process.
    :param cutoff: The percent to cut off from the histogram on the low and
                   high ends. Either a tuple of (low, high), or a single
                   number for both.
    :param ignore: The background pixel value (use None for no background).
    :param mask: Histogram used in contrast operation is computed using pixels
                 within the mask. If no mask is given the entire image is used
                 for histogram computation.
    :param preserve_tone: Preserve image tone in Photoshop-like style autocontrast.

                          .. versionadded:: 8.2.0

    :return: An image.
    """
    if preserve_tone:
        histogram = image.convert("L").histogram(mask)
    else:
        histogram = image.histogram(mask)

    lut = []
    for layer in range(0, len(histogram), 256):
        h = histogram[layer : layer + 256]
        if ignore is not None:
            # get rid of outliers
            try:
                h[ignore] = 0
            except TypeError:
                # assume sequence
                for ix in ignore:
                    h[ix] = 0
        if cutoff:
            # cut off pixels from both ends of the histogram
            if not isinstance(cutoff, tuple):
                cutoff = (cutoff, cutoff)
            # get number of pixels
            n = 0
            for ix in range(256):
                n = n + h[ix]
            # remove cutoff% pixels from the low end
            cut = n * cutoff[0] // 100
            for lo in range(256):
                if cut > h[lo]:
                    cut = cut - h[lo]
                    h[lo] = 0
                else:
                    h[lo] -= cut
                    cut = 0
                if cut <= 0:
                    break
            # remove cutoff% samples from the high end
            cut = n * cutoff[1] // 100
            for hi in range(255, -1, -1):
                if cut > h[hi]:
                    cut = cut - h[hi]
                    h[hi] = 0
                else:
                    h[hi] -= cut
                    cut = 0
                if cut <= 0:
                    break
        # find lowest/highest samples after preprocessing
        for lo in range(256):
            if h[lo]:
                break
        for hi in range(255, -1, -1):
            if h[hi]:
                break
        if hi <= lo:
            # don't bother
            lut.extend(list(range(256)))
        else:
            scale = 255.0 / (hi - lo)
            offset = -lo * scale
            for ix in range(256):
                ix = int(ix * scale + offset)
                if ix < 0:
                    ix = 0
                elif ix > 255:
                    ix = 255
                lut.append(ix)
    return _lut(image, lut)


def colorize(image, black, white, mid=None, blackpoint=0, whitepoint=255, midpoint=127):
    """
    Colorize grayscale image.
    This function calculates a color wedge which maps all black pixels in
    the source image to the first color and all white pixels to the
    second color. If ``mid`` is specified, it uses three-color mapping.
    The ``black`` and ``white`` arguments should be RGB tuples or color names;
    optionally you can use three-color mapping by also specifying ``mid``.
    Mapping positions for any of the colors can be specified
    (e.g. ``blackpoint``), where these parameters are the integer
    value corresponding to where the corresponding color should be mapped.
    These parameters must have logical order, such that
    ``blackpoint <= midpoint <= whitepoint`` (if ``mid`` is specified).

    :param image: The image to colorize.
    :param black: The color to use for black input pixels.
    :param white: The color to use for white input pixels.
    :param mid: The color to use for midtone input pixels.
    :param blackpoint: an int value [0, 255] for the black mapping.
    :param whitepoint: an int value [0, 255] for the white mapping.
    :param midpoint: an int value [0, 255] for the midtone mapping.
    :return: An image.
    """

    # Initial asserts
    assert image.mode == "L"
    if mid is None:
        assert 0 <= blackpoint <= whitepoint <= 255
    else:
        assert 0 <= blackpoint <= midpoint <= whitepoint <= 255

    # Define colors from arguments
    black = _color(black, "RGB")
    white = _color(white, "RGB")
    if mid is not None:
        mid = _color(mid, "RGB")

    # Empty lists for the mapping
    red = []
    green = []
    blue = []

    # Create the low-end values
    for i in range(0, blackpoint):
        red.append(black[0])
        green.append(black[1])
        blue.append(black[2])

    # Create the mapping (2-color)
    if mid is None:
        range_map = range(0, whitepoint - blackpoint)

        for i in range_map:
            red.append(black[0] + i * (white[0] - black[0]) // len(range_map))
            green.append(black[1] + i * (white[1] - black[1]) // len(range_map))
            blue.append(black[2] + i * (white[2] - black[2]) // len(range_map))

    # Create the mapping (3-color)
    else:
        range_map1 = range(0, midpoint - blackpoint)
        range_map2 = range(0, whitepoint - midpoint)

        for i in range_map1:
            red.append(black[0] + i * (mid[0] - black[0]) // len(range_map1))
            green.append(black[1] + i * (mid[1] - black[1]) // len(range_map1))
            blue.append(black[2] + i * (mid[2] - black[2]) // len(range_map1))
        for i in range_map2:
            red.append(mid[0] + i * (white[0] - mid[0]) // len(range_map2))
            green.append(mid[1] + i * (white[1] - mid[1]) // len(range_map2))
            blue.append(mid[2] + i * (white[2] - mid[2]) // len(range_map2))

    # Create the high-end values
    for i in range(0, 256 - whitepoint):
        red.append(white[0])
        green.append(white[1])
        blue.append(white[2])

    # Return converted image
    image = image.convert("RGB")
    return _lut(image, red + green + blue)


def contain(image, size, method=Image.Resampling.BICUBIC):
    """
    Returns a resized version of the image, set to the maximum width and height
    within the requested size, while maintaining the original aspect ratio.

    :param image: The image to resize.
    :param size: The requested output size in pixels, given as a
                 (width, height) tuple.
    :param method: Resampling method to use. Default is
                   :py:attr:`~PIL.Image.Resampling.BICUBIC`.
                   See :ref:`concept-filters`.
    :return: An image.
    """

    im_ratio = image.width / image.height
    dest_ratio = size[0] / size[1]

    if im_ratio != dest_ratio:
        if im_ratio > dest_ratio:
            new_height = round(image.height / image.width * size[0])
            if new_height != size[1]:
                size = (size[0], new_height)
        else:
            new_width = round(image.width / image.height * size[1])
            if new_width != size[0]:
                size = (new_width, size[1])
    return image.resize(size, resample=method)


def cover(image, size, method=Image.Resampling.BICUBIC):
    """
    Returns a resized version of the image, so that the requested size is
    covered, while maintaining the original aspect ratio.

    :param image: The image to resize.
    :param size: The requested output size in pixels, given as a
                 (width, height) tuple.
    :param method: Resampling method to use. Default is
                   :py:attr:`~PIL.Image.Resampling.BICUBIC`.
                   See :ref:`concept-filters`.
    :return: An image.
    """

    im_ratio = image.width / image.height
    dest_ratio = size[0] / size[1]

    if im_ratio != dest_ratio:
        if im_ratio < dest_ratio:
            new_height = round(image.height / image.width * size[0])
            if new_height != size[1]:
                size = (size[0], new_height)
        else:
            new_width = round(image.width / image.height * size[1])
            if new_width != size[0]:
                size = (new_width, size[1])
    return image.resize(size, resample=method)


def pad(image, size, method=Image.Resampling.BICUBIC, color=None, centering=(0.5, 0.5)):
    """
    Returns a resized and padded version of the image, expanded to fill the
    requested aspect ratio and size.

    :param image: The image to resize and crop.
    :param size: The requested output size in pixels, given as a
                 (width, height) tuple.
    :param method: Resampling method to use. Default is
                   :py:attr:`~PIL.Image.Resampling.BICUBIC`.
                   See :ref:`concept-filters`.
    :param color: The background color of the padded image.
    :param centering: Control the position of the original image within the
                      padded version.

                          (0.5, 0.5) will keep the image centered
                          (0, 0) will keep the image aligned to the top left
                          (1, 1) will keep the image aligned to the bottom
                          right
    :return: An image.
    """

    resized = contain(image, size, method)
    if resized.size == size:
        out = resized
    else:
        out = Image.new(image.mode, size, color)
        if resized.palette:
            out.putpalette(resized.getpalette())
        if resized.width != size[0]:
            x = round((size[0] - resized.width) * max(0, min(centering[0], 1)))
            out.paste(resized, (x, 0))
        else:
            y = round((size[1] - resized.height) * max(0, min(centering[1], 1)))
            out.paste(resized, (0, y))
    return out


def crop(image, border=0):
    """
    Remove border from image.  The same amount of pixels are removed
    from all four sides.  This function works on all image modes.

    .. seealso:: :py:meth:`~PIL.Image.Image.crop`

    :param image: The image to crop.
    :param border: The number of pixels to remove.
    :return: An image.
    """
    left, top, right, bottom = _border(border)
    return image.crop((left, top, image.size[0] - right, image.size[1] - bottom))


def scale(image, factor, resample=Image.Resampling.BICUBIC):
    """
    Returns a rescaled image by a specific factor given in parameter.
    A factor greater than 1 expands the image, between 0 and 1 contracts the
    image.

    :param image: The image to rescale.
    :param factor: The expansion factor, as a float.
    :param resample: Resampling method to use. Default is
                     :py:attr:`~PIL.Image.Resampling.BICUBIC`.
                     See :ref:`concept-filters`.
    :returns: An :py:class:`~PIL.Image.Image` object.
    """
    if factor == 1:
        return image.copy()
    elif factor <= 0:
        msg = "the factor must be greater than 0"
        raise ValueError(msg)
    else:
        size = (round(factor * image.width), round(factor * image.height))
        return image.resize(size, resample)


def deform(image, deformer, resample=Image.Resampling.BILINEAR):
    """
    Deform the image.

    :param image: The image to deform.
    :param deformer: A deformer object.  Any object that implements a
                    ``getmesh`` method can be used.
    :param resample: An optional resampling filter. Same values possible as
       in the PIL.Image.transform function.
    :return: An image.
    """
    return image.transform(
        image.size, Image.Transform.MESH, deformer.getmesh(image), resample
    )


def equalize(image, mask=None):
    """
    Equalize the image histogram. This function applies a non-linear
    mapping to the input image, in order to create a uniform
    distribution of grayscale values in the output image.

    :param image: The image to equalize.
    :param mask: An optional mask.  If given, only the pixels selected by
                 the mask are included in the analysis.
    :return: An image.
    """
    if image.mode == "P":
        image = image.convert("RGB")
    h = image.histogram(mask)
    lut = []
    for b in range(0, len(h), 256):
        histo = [_f for _f in h[b : b + 256] if _f]
        if len(histo) <= 1:
            lut.extend(list(range(256)))
        else:
            step = (functools.reduce(operator.add, histo) - histo[-1]) // 255
            if not step:
                lut.extend(list(range(256)))
            else:
                n = step // 2
                for i in range(256):
                    lut.append(n // step)
                    n = n + h[i + b]
    return _lut(image, lut)


def expand(image, border=0, fill=0):
    """
    Add border to the image

    :param image: The image to expand.
    :param border: Border width, in pixels.
    :param fill: Pixel fill value (a color value).  Default is 0 (black).
    :return: An image.
    """
    left, top, right, bottom = _border(border)
    width = left + image.size[0] + right
    height = top + image.size[1] + bottom
    color = _color(fill, image.mode)
    if image.palette:
        palette = ImagePalette.ImagePalette(palette=image.getpalette())
        if isinstance(color, tuple):
            color = palette.getcolor(color)
    else:
        palette = None
    out = Image.new(image.mode, (width, height), color)
    if palette:
        out.putpalette(palette.palette)
    out.paste(image, (left, top))
    return out


def fit(image, size, method=Image.Resampling.BICUBIC, bleed=0.0, centering=(0.5, 0.5)):
    """
    Returns a resized and cropped version of the image, cropped to the
    requested aspect ratio and size.

    This function was contributed by Kevin Cazabon.

    :param image: The image to resize and crop.
    :param size: The requested output size in pixels, given as a
                 (width, height) tuple.
    :param method: Resampling method to use. Default is
                   :py:attr:`~PIL.Image.Resampling.BICUBIC`.
                   See :ref:`concept-filters`.
    :param bleed: Remove a border around the outside of the image from all
                  four edges. The value is a decimal percentage (use 0.01 for
                  one percent). The default value is 0 (no border).
                  Cannot be greater than or equal to 0.5.
    :param centering: Control the cropping position.  Use (0.5, 0.5) for
                      center cropping (e.g. if cropping the width, take 50% off
                      of the left side, and therefore 50% off the right side).
                      (0.0, 0.0) will crop from the top left corner (i.e. if
                      cropping the width, take all of the crop off of the right
                      side, and if cropping the height, take all of it off the
                      bottom).  (1.0, 0.0) will crop from the bottom left
                      corner, etc. (i.e. if cropping the width, take all of the
                      crop off the left side, and if cropping the height take
                      none from the top, and therefore all off the bottom).
    :return: An image.
    """

    # by Kevin Cazabon, Feb 17/2000
    # kevin@cazabon.com
    # https://www.cazabon.com

    # ensure centering is mutable
    centering = list(centering)

    if not 0.0 <= centering[0] <= 1.0:
        centering[0] = 0.5
    if not 0.0 <= centering[1] <= 1.0:
        centering[1] = 0.5

    if not 0.0 <= bleed < 0.5:
        bleed = 0.0

    # calculate the area to use for resizing and cropping, subtracting
    # the 'bleed' around the edges

    # number of pixels to trim off on Top and Bottom, Left and Right
    bleed_pixels = (bleed * image.size[0], bleed * image.size[1])

    live_size = (
        image.size[0] - bleed_pixels[0] * 2,
        image.size[1] - bleed_pixels[1] * 2,
    )

    # calculate the aspect ratio of the live_size
    live_size_ratio = live_size[0] / live_size[1]

    # calculate the aspect ratio of the output image
    output_ratio = size[0] / size[1]

    # figure out if the sides or top/bottom will be cropped off
    if live_size_ratio == output_ratio:
        # live_size is already the needed ratio
        crop_width = live_size[0]
        crop_height = live_size[1]
    elif live_size_ratio >= output_ratio:
        # live_size is wider than what's needed, crop the sides
        crop_width = output_ratio * live_size[1]
        crop_height = live_size[1]
    else:
        # live_size is taller than what's needed, crop the top and bottom
        crop_width = live_size[0]
        crop_height = live_size[0] / output_ratio

    # make the crop
    crop_left = bleed_pixels[0] + (live_size[0] - crop_width) * centering[0]
    crop_top = bleed_pixels[1] + (live_size[1] - crop_height) * centering[1]

    crop = (crop_left, crop_top, crop_left + crop_width, crop_top + crop_height)

    # resize the image and return it
    return image.resize(size, method, box=crop)


def flip(image):
    """
    Flip the image vertically (top to bottom).

    :param image: The image to flip.
    :return: An image.
    """
    return image.transpose(Image.Transpose.FLIP_TOP_BOTTOM)


def grayscale(image):
    """
    Convert the image to grayscale.

    :param image: The image to convert.
    :return: An image.
    """
    return image.convert("L")


def invert(image):
    """
    Invert (negate) the image.

    :param image: The image to invert.
    :return: An image.
    """
    lut = list(range(255, -1, -1))
    return image.point(lut) if image.mode == "1" else _lut(image, lut)


def mirror(image):
    """
    Flip image horizontally (left to right).

    :param image: The image to mirror.
    :return: An image.
    """
    return image.transpose(Image.Transpose.FLIP_LEFT_RIGHT)


def posterize(image, bits):
    """
    Reduce the number of bits for each color channel.

    :param image: The image to posterize.
    :param bits: The number of bits to keep for each channel (1-8).
    :return: An image.
    """
    mask = ~(2 ** (8 - bits) - 1)
    lut = [i & mask for i in range(256)]
    return _lut(image, lut)


def solarize(image, threshold=128):
    """
    Invert all pixel values above a threshold.

    :param image: The image to solarize.
    :param threshold: All pixels above this grayscale level are inverted.
    :return: An image.
    """
    lut = []
    for i in range(256):
        if i < threshold:
            lut.append(i)
        else:
            lut.append(255 - i)
    return _lut(image, lut)


def exif_transpose(image, *, in_place=False):
    """
    If an image has an EXIF Orientation tag, other than 1, transpose the image
    accordingly, and remove the orientation data.

    :param image: The image to transpose.
    :param in_place: Boolean. Keyword-only argument.
        If ``True``, the original image is modified in-place, and ``None`` is returned.
        If ``False`` (default), a new :py:class:`~PIL.Image.Image` object is returned
        with the transposition applied. If there is no transposition, a copy of the
        image will be returned.
    """
    image.load()
    image_exif = image.getexif()
    orientation = image_exif.get(ExifTags.Base.Orientation)
    method = {
        2: Image.Transpose.FLIP_LEFT_RIGHT,
        3: Image.Transpose.ROTATE_180,
        4: Image.Transpose.FLIP_TOP_BOTTOM,
        5: Image.Transpose.TRANSPOSE,
        6: Image.Transpose.ROTATE_270,
        7: Image.Transpose.TRANSVERSE,
        8: Image.Transpose.ROTATE_90,
    }.get(orientation)
    if method is not None:
        transposed_image = image.transpose(method)
        if in_place:
            image.im = transposed_image.im
            image.pyaccess = None
            image._size = transposed_image._size
        exif_image = image if in_place else transposed_image

        exif = exif_image.getexif()
        if ExifTags.Base.Orientation in exif:
            del exif[ExifTags.Base.Orientation]
            if "exif" in exif_image.info:
                exif_image.info["exif"] = exif.tobytes()
            elif "Raw profile type exif" in exif_image.info:
                exif_image.info["Raw profile type exif"] = exif.tobytes().hex()
            elif "XML:com.adobe.xmp" in exif_image.info:
                for pattern in (
                    r'tiff:Orientation="([0-9])"',
                    r"<tiff:Orientation>([0-9])</tiff:Orientation>",
                ):
                    exif_image.info["XML:com.adobe.xmp"] = re.sub(
                        pattern, "", exif_image.info["XML:com.adobe.xmp"]
                    )
        if not in_place:
            return transposed_image
    elif not in_place:
        return image.copy()