mirror of https://github.com/XingangPan/DragGAN
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268 lines
9.7 KiB
Python
268 lines
9.7 KiB
Python
# Copyright (c) SenseTime Research. All rights reserved.
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import numpy as np
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import PIL
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import PIL.Image
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import scipy
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import scipy.ndimage
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import dlib
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import copy
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from PIL import Image
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def get_landmark(img, detector, predictor):
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"""get landmark with dlib
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:return: np.array shape=(68, 2)
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"""
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# detector = dlib.get_frontal_face_detector()
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# dets, _, _ = detector.run(img, 1, -1)
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dets = detector(img, 1)
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for k, d in enumerate(dets):
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shape = predictor(img, d.rect)
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t = list(shape.parts())
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a = []
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for tt in t:
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a.append([tt.x, tt.y])
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lm = np.array(a)
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# face rect
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face_rect = [dets[0].rect.left(), dets[0].rect.top(), dets[0].rect.right(), dets[0].rect.bottom()]
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return lm, face_rect
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def align_face_for_insetgan(img, detector, predictor, output_size=256):
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"""
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:param img: numpy array rgb
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:return: PIL Image
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"""
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img_cp = copy.deepcopy(img)
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lm, face_rect = get_landmark(img, detector, predictor)
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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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y = np.flipud(x) * [-1, 1]
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c = eye_avg + eye_to_mouth * 0.1
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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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# read image
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# opencv to PIL
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img = PIL.Image.fromarray(img_cp)
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# img = PIL.Image.open(filepath)
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transform_size = output_size
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enable_padding = False
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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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# img.save("debug/raw.jpg")
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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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# img.save("debug/crop.jpg")
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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)), 'reflect')
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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 = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
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# quad += pad[:2]
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# Transform.
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# crop shape to transform shape
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# nw =
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# print(img.size, quad+0.5, np.bound((quad+0.5).flatten()))
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# assert False
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# img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
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# img.save("debug/transform.jpg")
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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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# img.save("debug/resize.jpg")
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# print((quad+crop[0:2]).flatten())
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# assert False
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# Return aligned image.
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return img, crop, face_rect
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def align_face_for_projector(img, detector, predictor, output_size):
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"""
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:param filepath: str
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:return: PIL Image
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"""
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img_cp = copy.deepcopy(img)
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lm, face_rect = get_landmark(img, detector, predictor)
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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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y = np.flipud(x) * [-1, 1]
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c = eye_avg + eye_to_mouth * 0.1
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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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# read image
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img = PIL.Image.fromarray(img_cp)
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transform_size = output_size
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enable_padding = True
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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)), 'reflect')
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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 = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
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quad += pad[:2]
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# Transform.
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img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), 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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# Return aligned image.
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return img
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def reverse_quad_transform(image, quad_to_map_to, alpha):
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# forward mapping, for simplicity
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result = Image.new("RGBA",image.size)
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result_pixels = result.load()
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width, height = result.size
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for y in range(height):
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for x in range(width):
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result_pixels[x,y] = (0,0,0,0)
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p1 = (quad_to_map_to[0],quad_to_map_to[1])
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p2 = (quad_to_map_to[2],quad_to_map_to[3])
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p3 = (quad_to_map_to[4],quad_to_map_to[5])
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p4 = (quad_to_map_to[6],quad_to_map_to[7])
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p1_p2_vec = (p2[0] - p1[0],p2[1] - p1[1])
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p4_p3_vec = (p3[0] - p4[0],p3[1] - p4[1])
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for y in range(height):
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for x in range(width):
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pixel = image.getpixel((x,y))
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y_percentage = y / float(height)
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x_percentage = x / float(width)
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# interpolate vertically
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pa = (p1[0] + p1_p2_vec[0] * y_percentage, p1[1] + p1_p2_vec[1] * y_percentage)
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pb = (p4[0] + p4_p3_vec[0] * y_percentage, p4[1] + p4_p3_vec[1] * y_percentage)
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pa_to_pb_vec = (pb[0] - pa[0],pb[1] - pa[1])
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# interpolate horizontally
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p = (pa[0] + pa_to_pb_vec[0] * x_percentage, pa[1] + pa_to_pb_vec[1] * x_percentage)
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try:
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result_pixels[p[0],p[1]] = (pixel[0],pixel[1],pixel[2],min(int(alpha * 255),pixel[3]))
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except Exception:
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pass
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return result |