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| import os | |
| import cv2 | |
| import time | |
| import glob | |
| import argparse | |
| import scipy | |
| import numpy as np | |
| from PIL import Image | |
| from tqdm import tqdm | |
| from itertools import cycle | |
| from torch.multiprocessing import Pool, Process, set_start_method | |
| """ | |
| brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset) | |
| author: lzhbrian (https://lzhbrian.me) | |
| date: 2020.1.5 | |
| note: code is heavily borrowed from | |
| https://github.com/NVlabs/ffhq-dataset | |
| http://dlib.net/face_landmark_detection.py.html | |
| requirements: | |
| apt install cmake | |
| conda install Pillow numpy scipy | |
| pip install dlib | |
| # download face landmark model from: | |
| # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 | |
| """ | |
| import numpy as np | |
| from PIL import Image | |
| import dlib | |
| class Croper: | |
| def __init__(self, path_of_lm): | |
| # download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 | |
| self.predictor = dlib.shape_predictor(path_of_lm) | |
| def get_landmark(self, img_np): | |
| """get landmark with dlib | |
| :return: np.array shape=(68, 2) | |
| """ | |
| detector = dlib.get_frontal_face_detector() | |
| dets = detector(img_np, 1) | |
| if len(dets) == 0: | |
| return None | |
| d = dets[0] | |
| # Get the landmarks/parts for the face in box d. | |
| shape = self.predictor(img_np, d) | |
| t = list(shape.parts()) | |
| a = [] | |
| for tt in t: | |
| a.append([tt.x, tt.y]) | |
| lm = np.array(a) | |
| return lm | |
| def align_face(self, img, lm, output_size=1024): | |
| """ | |
| :param filepath: str | |
| :return: PIL Image | |
| """ | |
| lm_chin = lm[0: 17] # left-right | |
| lm_eyebrow_left = lm[17: 22] # left-right | |
| lm_eyebrow_right = lm[22: 27] # left-right | |
| lm_nose = lm[27: 31] # top-down | |
| lm_nostrils = lm[31: 36] # top-down | |
| lm_eye_left = lm[36: 42] # left-clockwise | |
| lm_eye_right = lm[42: 48] # left-clockwise | |
| lm_mouth_outer = lm[48: 60] # left-clockwise | |
| lm_mouth_inner = lm[60: 68] # left-clockwise | |
| # Calculate auxiliary vectors. | |
| eye_left = np.mean(lm_eye_left, axis=0) | |
| eye_right = np.mean(lm_eye_right, axis=0) | |
| eye_avg = (eye_left + eye_right) * 0.5 | |
| eye_to_eye = eye_right - eye_left | |
| mouth_left = lm_mouth_outer[0] | |
| mouth_right = lm_mouth_outer[6] | |
| mouth_avg = (mouth_left + mouth_right) * 0.5 | |
| eye_to_mouth = mouth_avg - eye_avg | |
| # Choose oriented crop rectangle. | |
| x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] | |
| x /= np.hypot(*x) | |
| x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) | |
| y = np.flipud(x) * [-1, 1] | |
| c = eye_avg + eye_to_mouth * 0.1 | |
| quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) | |
| qsize = np.hypot(*x) * 2 | |
| # Shrink. | |
| shrink = int(np.floor(qsize / output_size * 0.5)) | |
| if shrink > 1: | |
| rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) | |
| img = img.resize(rsize, Image.ANTIALIAS) | |
| quad /= shrink | |
| qsize /= shrink | |
| # Crop. | |
| border = max(int(np.rint(qsize * 0.1)), 3) | |
| crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), | |
| int(np.ceil(max(quad[:, 1])))) | |
| crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), | |
| min(crop[3] + border, img.size[1])) | |
| if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: | |
| quad -= crop[0:2] | |
| # Transform. | |
| quad = (quad + 0.5).flatten() | |
| lx = max(min(quad[0], quad[2]), 0) | |
| ly = max(min(quad[1], quad[7]), 0) | |
| rx = min(max(quad[4], quad[6]), img.size[0]) | |
| ry = min(max(quad[3], quad[5]), img.size[0]) | |
| # Save aligned image. | |
| return crop, [lx, ly, rx, ry] | |
| def crop(self, img_np_list, xsize=512): # first frame for all video | |
| idx = 0 | |
| while idx < len(img_np_list)//2 : # TODO | |
| img_np = img_np_list[idx] | |
| lm = self.get_landmark(img_np) | |
| if lm is not None: | |
| break # can detect face | |
| idx += 1 | |
| if lm is None: | |
| return None | |
| crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=xsize) | |
| clx, cly, crx, cry = crop | |
| lx, ly, rx, ry = quad | |
| lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry) | |
| for _i in range(len(img_np_list)): | |
| _inp = img_np_list[_i] | |
| _inp = _inp[cly:cry, clx:crx] | |
| _inp = _inp[ly:ry, lx:rx] | |
| img_np_list[_i] = _inp | |
| return img_np_list, crop, quad | |