基于Opencv和MTCNN检测人脸五个关键点进行仿射变换人脸对齐 |
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基于Opencv和MTCNN检测人脸五个关键点进行仿射变换人脸对齐
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最近需要做人脸对齐的算法，通俗理解就是将图片人人脸姿态不太正确的给矫正过来，所以写了python版本的人脸对齐算法。也有对应的C++版本，点击这里。基本原理是先通过MTCNN检测到人脸的五个关键点，再把原图中人脸区域外扩100%（这样做的目的是保证对齐后图片中没有黑色区域，当然这个外扩的比例是看对齐效果自己可以调节的，我这里设置的100%）。最后的人脸对齐尺寸分为两种：112X96尺寸和112X112尺寸，其中首先需要定死仿射变换后人脸在目标图上的坐标，然后直接变换。废话不多说，直接手撕代码。 # 该代码实现利用人脸的五点仿射变换实现人脸对齐 # 具体就是首先使用mtcnn检测算法检测出人脸区域，并得到lanmarks关键点坐标和检测框坐标 # 之后对人脸区域外扩60%，然后对该外扩后的区域重新得到关键点，进行五点仿射变换得到即可。 # 参考链接：https://blog.csdn.net/oTengYue/article/details/79278572 # _*_ coding:utf-8 _*_ import os import cv2 import numpy import logging import tensorflow as tf from detection.mtcnn import detect_face logging.basicConfig( level=logging.DEBUG, format='%(asctime)s %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S' ) logger = logging.getLogger(__name__) # 最终的人脸对齐图像尺寸分为两种：112x96和112x112，并分别对应结果图像中的两组仿射变换目标点,如下所示 imgSize1 = [112,96] imgSize2 = [112,112] coord5point1 = [[30.2946, 51.6963], # 112x96的目标点 [65.5318, 51.6963], [48.0252, 71.7366], [33.5493, 92.3655], [62.7299, 92.3655]] coord5point2 = [[30.2946+8.0000, 51.6963], # 112x112的目标点 [65.5318+8.0000, 51.6963], [48.0252+8.0000, 71.7366], [33.5493+8.0000, 92.3655], [62.7299+8.0000, 92.3655]] def transformation_from_points(points1, points2): points1 = points1.astype(numpy.float64) points2 = points2.astype(numpy.float64) c1 = numpy.mean(points1, axis=0) c2 = numpy.mean(points2, axis=0) points1 -= c1 points2 -= c2 s1 = numpy.std(points1) s2 = numpy.std(points2) points1 /= s1 points2 /= s2 U, S, Vt = numpy.linalg.svd(points1.T * points2) R = (U * Vt).T return numpy.vstack([numpy.hstack(((s2 / s1) * R,c2.T - (s2 / s1) * R * c1.T)),numpy.matrix([0., 0., 1.])]) def warp_im(img_im, orgi_landmarks,tar_landmarks): pts1 = numpy.float64(numpy.matrix([[point[0], point[1]] for point in orgi_landmarks])) pts2 = numpy.float64(numpy.matrix([[point[0], point[1]] for point in tar_landmarks])) M = transformation_from_points(pts1, pts2) dst = cv2.warpAffine(img_im, M[:2], (img_im.shape[1], img_im.shape[0])) return dst def main(): # 对一个路径下的所有图片进行两种方式对齐，并保存 pic_path = 'C:\\Users\\pfm\\Desktop\\test_image\\' #加载mtcnn参数 with tf.Graph().as_default(): sess = tf.Session() pnet, rnet, onet = detect_face.create_mtcnn(sess, None) minsize = 50 # minimum size of face threshold = [0.6, 0.7, 0.7] # three steps's threshold factor = 0.709 # scale factor # Size Parameter lower_threshold = 100 upper_threshold = 200 num = 0 pic_name_list = os.listdir(pic_path) for every_pic_name in pic_name_list: img_im = cv2.imread(pic_path + every_pic_name) #关键点检测 if img_im is None: continue else: shape = img_im.shape height = shape[0] width = shape[1] bounding_boxes, points = detect_face.detect_face(img_im, minsize, pnet, rnet, onet, threshold, factor) # 处理该张图片中的每个框 if bounding_boxes.shape[0] > 0: for i in range(bounding_boxes.shape[0]): # 根据行号得到每张图片有多少个回归框 x1, y1, x2, y2 = int(min(bounding_boxes[i][0], min(points[:, i][:5]))), \ int(min(bounding_boxes[i][1], min(points[:, i][5:]))), \ int(max(bounding_boxes[i][2], max(points[:, i][:5]))), \ int(max(bounding_boxes[i][3], max(points[:, i][5:]))) # 外扩大100%，防止对齐后人脸出现黑边 new_x1 = max(int(1.50 * x1 - 0.50 * x2),0) new_x2 = min(int(1.50 * x2 - 0.50 * x1),width-1) new_y1 = max(int(1.50 * y1 - 0.50 * y2),0) new_y2 = min(int(1.50 * y2 - 0.50 * y1),height-1) # new_x1 = max(int(1.30 * x1 - 0.30 * x2),0) # new_x2 = min(int(1.30 * x2 - 0.30 * x1),width-1) # new_y1 = max(int(1.30 * y1 - 0.30 * y2),0) # new_y2 = min(int(1.30 * y2 - 0.30 * y1),height-1) # 得到原始图中关键点坐标 left_eye_x = points[:, i][:5][0] right_eye_x = points[:, i][:5][1] nose_x = points[:, i][:5][2] left_mouth_x = points[:, i][:5][3] right_mouth_x = points[:, i][:5][4] left_eye_y = points[:, i][5:][0] right_eye_y = points[:, i][5:][1] nose_y = points[:, i][5:][2] left_mouth_y = points[:, i][5:][3] right_mouth_y = points[:, i][5:][4] # 得到外扩100%后图中关键点坐标 new_left_eye_x = left_eye_x - new_x1 new_right_eye_x = right_eye_x - new_x1 new_nose_x = nose_x - new_x1 new_left_mouth_x = left_mouth_x - new_x1 new_right_mouth_x = right_mouth_x - new_x1 new_left_eye_y = left_eye_y - new_y1 new_right_eye_y = right_eye_y - new_y1 new_nose_y = nose_y - new_y1 new_left_mouth_y = left_mouth_y - new_y1 new_right_mouth_y = right_mouth_y - new_y1 face_landmarks = [[new_left_eye_x,new_left_eye_y], # 在扩大100%人脸图中关键点坐标 [new_right_eye_x,new_right_eye_y], [new_nose_x,new_nose_y], [new_left_mouth_x,new_left_mouth_y], [new_right_mouth_x,new_right_mouth_y]] face = img_im[new_y1: new_y2, new_x1: new_x2] # 扩大100%的人脸区域 dst1 = warp_im(face,face_landmarks,coord5point1) # 112x96对齐后尺寸 dst2 = warp_im(face,face_landmarks,coord5point2) # 112x112对齐后尺寸 crop_im1 = dst1[0:imgSize1[0],0:imgSize1[1]] crop_im2 = dst2[0:imgSize2[0],0:imgSize2[1]] cv2.imwrite(pic_path + every_pic_name[:-4] + '_' + str(num) + '_align_112x96.jpg',crop_im1) cv2.imwrite(pic_path + every_pic_name[:-4] + '_' + str(num) + '_align_112x112.jpg',crop_im2) num = num + 1 if __name__ == '__main__': main() cv2.waitKey() pass接下来是运行的效果：
如果想运行这份代码，可以点击这里（code: djcb）下载完整的工程即可。 |
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