mirror of https://github.com/vladmandic/automatic
192 lines
6.6 KiB
Python
192 lines
6.6 KiB
Python
"""
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brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
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author: lzhbrian (https://lzhbrian.me)
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link: https://gist.github.com/lzhbrian/bde87ab23b499dd02ba4f588258f57d5
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date: 2020.1.5
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note: code is heavily borrowed from
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https://github.com/NVlabs/ffhq-dataset
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http://dlib.net/face_landmark_detection.py.html
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requirements:
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conda install Pillow numpy scipy
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conda install -c conda-forge dlib
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# download face landmark model from:
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# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
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"""
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import cv2
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import dlib
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import glob
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import numpy as np
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import os
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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 sys
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import argparse
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# download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
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predictor = dlib.shape_predictor('weights/dlib/shape_predictor_68_face_landmarks-fbdc2cb8.dat')
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def get_landmark(filepath, only_keep_largest=True):
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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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img = dlib.load_rgb_image(filepath)
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dets = detector(img, 1)
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# Shangchen modified
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print("Number of faces detected: {}".format(len(dets)))
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if only_keep_largest:
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print('Detect several faces and only keep the largest.')
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face_areas = []
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for k, d in enumerate(dets):
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face_area = (d.right() - d.left()) * (d.bottom() - d.top())
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face_areas.append(face_area)
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largest_idx = face_areas.index(max(face_areas))
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d = dets[largest_idx]
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shape = predictor(img, d)
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print("Part 0: {}, Part 1: {} ...".format(
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shape.part(0), shape.part(1)))
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else:
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for k, d in enumerate(dets):
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print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
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k, d.left(), d.top(), d.right(), d.bottom()))
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# Get the landmarks/parts for the face in box d.
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shape = predictor(img, d)
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print("Part 0: {}, Part 1: {} ...".format(
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shape.part(0), shape.part(1)))
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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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# lm is a shape=(68,2) np.array
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return lm
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def align_face(filepath, out_path):
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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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try:
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lm = get_landmark(filepath)
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except:
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print('No landmark ...')
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return
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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.open(filepath)
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output_size = 512
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transform_size = 4096
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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)),
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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]))),
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int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
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crop = (max(crop[0] - border, 0), max(crop[1] - border, 0),
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min(crop[2] + border,
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img.size[0]), 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]))),
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int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
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pad = (max(-pad[0] + border,
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0), max(-pad[1] + border,
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0), max(pad[2] - img.size[0] + border,
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0), 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(
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np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)),
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'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(
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1.0 -
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np.minimum(np.float32(x) / pad[0],
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np.float32(w - 1 - x) / pad[2]), 1.0 -
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np.minimum(np.float32(y) / pad[1],
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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]) -
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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(
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np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
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quad += pad[:2]
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img = img.transform((transform_size, transform_size), PIL.Image.QUAD,
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(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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# Save aligned image.
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print('saveing: ', out_path)
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img.save(out_path)
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return img, np.max(quad[:, 0]) - np.min(quad[:, 0])
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--in_dir', type=str, default='./inputs/whole_imgs')
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parser.add_argument('--out_dir', type=str, default='./inputs/cropped_faces')
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args = parser.parse_args()
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img_list = sorted(glob.glob(f'{args.in_dir}/*.png'))
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img_list = sorted(img_list)
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for in_path in img_list:
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out_path = os.path.join(args.out_dir, in_path.split("/")[-1])
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out_path = out_path.replace('.jpg', '.png')
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size_ = align_face(in_path, out_path) |