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235 lines
9.6 KiB
Python
235 lines
9.6 KiB
Python
# Copyright (c) 2020, Baris Gecer. All rights reserved.
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#
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# This work is made available under the CC BY-NC-SA 4.0.
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# To view a copy of this license, see LICENSE
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import numpy as np
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import scipy.ndimage
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import PIL.Image
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def create_perspective_transform_matrix(src, dst):
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""" Creates a perspective transformation matrix which transforms points
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in quadrilateral ``src`` to the corresponding points on quadrilateral
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``dst``.
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Will raise a ``np.linalg.LinAlgError`` on invalid input.
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"""
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# See:
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# * http://xenia.media.mit.edu/~cwren/interpolator/
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# * http://stackoverflow.com/a/14178717/71522
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in_matrix = []
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for (x, y), (X, Y) in zip(src, dst):
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in_matrix.extend([
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[x, y, 1, 0, 0, 0, -X * x, -X * y],
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[0, 0, 0, x, y, 1, -Y * x, -Y * y],
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])
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A = np.matrix(in_matrix, dtype=np.float)
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B = np.array(dst).reshape(8)
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af = np.dot(np.linalg.inv(A.T * A) * A.T, B)
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return np.append(np.array(af).reshape(8), 1).reshape((3, 3))
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def create_perspective_transform(src, dst, round=False, splat_args=False):
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""" Returns a function which will transform points in quadrilateral
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``src`` to the corresponding points on quadrilateral ``dst``::
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... )
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>>> transform((5, 5))
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(74.99999999999639, 74.999999999999957)
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If ``round`` is ``True`` then points will be rounded to the nearest
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integer and integer values will be returned.
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... round=True,
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... )
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>>> transform((5, 5))
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(75, 75)
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If ``splat_args`` is ``True`` the function will accept two arguments
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instead of a tuple.
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... splat_args=True,
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... )
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>>> transform(5, 5)
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(74.99999999999639, 74.999999999999957)
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If the input values yield an invalid transformation matrix an identity
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function will be returned and the ``error`` attribute will be set to a
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description of the error::
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>>> tranform = create_perspective_transform(
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... np.zeros((4, 2)),
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... np.zeros((4, 2)),
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... )
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>>> transform((5, 5))
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(5.0, 5.0)
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>>> transform.error
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'invalid input quads (...): Singular matrix
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"""
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try:
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transform_matrix = create_perspective_transform_matrix(src, dst)
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error = None
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except np.linalg.LinAlgError as e:
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transform_matrix = np.identity(3, dtype=np.float)
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error = "invalid input quads (%s and %s): %s" %(src, dst, e)
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error = error.replace("\n", "")
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to_eval = "def perspective_transform(%s):\n" %(
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splat_args and "*pt" or "pt",
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)
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to_eval += " res = np.dot(transform_matrix, ((pt[0], ), (pt[1], ), (1, )))\n"
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to_eval += " res = res / res[2]\n"
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if round:
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to_eval += " return (int(round(res[0][0])), int(round(res[1][0])))\n"
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else:
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to_eval += " return (res[0][0], res[1][0])\n"
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locals = {
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"transform_matrix": transform_matrix,
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}
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locals.update(globals())
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exec(to_eval,locals,locals)
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res = locals["perspective_transform"]
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res.matrix = transform_matrix
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res.error = error
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return res
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def align_mesh2stylegan(temp_tcoords, transformation_params):
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temp_tcoords = temp_tcoords.copy()
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temp_tcoords[:, 0] = temp_tcoords[:, 0] - transformation_params['crop'][1]
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temp_tcoords[:, 1] = temp_tcoords[:, 1] - transformation_params['crop'][0]
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temp_tcoords[:, 0] = temp_tcoords[:, 0] + transformation_params['pad'][1]
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temp_tcoords[:, 1] = temp_tcoords[:, 1] + transformation_params['pad'][0]
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h, w = (4096, 4096) # transformation_params['new_size']
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transform = create_perspective_transform(
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transformation_params['quad'],
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[(0, 0), (0, h), (h, w), (w, 0)],
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splat_args=True,
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)
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for i in range(len(temp_tcoords)):
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temp_tcoords[i, 1], temp_tcoords[i, 0] = transform(temp_tcoords[i, 1], temp_tcoords[i, 0])
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new_tcoords = temp_tcoords[:, ::-1] / (h, w) # transformation_params['new_size']
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new_tcoords[:, 1] = 1 - new_tcoords[:, 1]
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return new_tcoords
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def align_im2stylegan(src_im, src_mask, face_landmarks, output_size=1024, transform_size=4096,
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enable_padding=True, x_scale=1, y_scale=1, em_scale=0.1, alpha=False):
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# Align function from FFHQ dataset pre-processing step
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# https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
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lm = np.array(face_landmarks)
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lm_chin = lm[0: 17] # left-right
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lm_eyebrow_left = lm[17: 22] # left-right
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lm_eyebrow_right = lm[22: 27] # left-right
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lm_nose = lm[27: 31] # top-down
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lm_nostrils = lm[31: 36] # top-down
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lm_eye_left = lm[36: 42] # left-clockwise
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lm_eye_right = lm[42: 48] # left-clockwise
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lm_mouth_outer = lm[48: 60] # left-clockwise
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lm_mouth_inner = lm[60: 68] # left-clockwise
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# Calculate auxiliary vectors.
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eye_left = np.mean(lm_eye_left, axis=0)
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eye_right = np.mean(lm_eye_right, axis=0)
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eye_avg = (eye_left + eye_right) * 0.5
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eye_to_eye = eye_right - eye_left
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mouth_left = lm_mouth_outer[0]
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mouth_right = lm_mouth_outer[6]
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mouth_avg = (mouth_left + mouth_right) * 0.5
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eye_to_mouth = mouth_avg - eye_avg
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# Choose oriented crop rectangle.
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x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
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x /= np.hypot(*x)
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x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
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x *= x_scale
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y = np.flipud(x) * [-y_scale, y_scale]
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c = eye_avg + eye_to_mouth * em_scale
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quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
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qsize = np.hypot(*x) * 2
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rsize = None
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img = src_im.convert('RGBA').convert('RGB')
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img_mask = src_mask.convert('L')
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img.putalpha(img_mask)
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# Shrink.
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shrink = int(np.floor(qsize / output_size * 0.5))
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if shrink > 1:
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rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
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img = img.resize(rsize, PIL.Image.ANTIALIAS)
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quad /= shrink
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qsize /= shrink
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# Crop.
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border = max(int(np.rint(qsize * 0.1)), 3)
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crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
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int(np.ceil(max(quad[:, 1]))))
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crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
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min(crop[3] + border, img.size[1]))
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if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
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img = img.crop(crop)
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quad -= crop[0:2]
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# Pad.
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pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
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int(np.ceil(max(quad[:, 1]))))
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pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
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max(pad[3] - img.size[1] + border, 0))
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if enable_padding and max(pad) > border - 4:
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pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
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img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'constant')
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h, w, _ = img.shape
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y, x, _ = np.ogrid[:h, :w, :1]
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mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
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1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
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blur = qsize * 0.02
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img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
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img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
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img = np.uint8(np.clip(np.rint(img), 0, 255))
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if alpha:
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mask = 1 - np.clip(3.0 * mask, 0.0, 1.0)
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mask = np.uint8(np.clip(np.rint(mask * 255), 0, 255))
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img = np.concatenate((img, mask), axis=2)
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img = PIL.Image.fromarray(img, 'RGBA')
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else:
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img = PIL.Image.fromarray(img, 'RGBA')
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quad += pad[:2]
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# Transform.
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aligned_mask = PIL.Image.fromarray(np.uint8(img)[:, :, 3])
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img = PIL.Image.fromarray(np.uint8(img)[:, :, :3])
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img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(),
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PIL.Image.BILINEAR)
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aligned_mask = aligned_mask.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(),
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PIL.Image.BILINEAR)
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if output_size < transform_size:
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img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
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aligned_mask = aligned_mask.resize((output_size, output_size), PIL.Image.ANTIALIAS)
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transformation_params = {
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'rsize': rsize,
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'crop': crop,
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'pad': pad,
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'quad': quad + 0.5,
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'new_size': (output_size, output_size)
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}
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# Save aligned image.
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return img, aligned_mask, transformation_params
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