dlib库标注特征点以及裁剪人脸组件
1、安装dlib
当在anaconda下使用命令 pip install dlib 安装dlib库时，提示错误安装不成功，可能是版本不匹配。在虚拟环境下查看Python版本，高版本不一定能和当前python版本兼容。可以将dlib wheel下载到本地，再手动安装。
dlib库：链接：https://pan.baidu.com/s/14FKO_wiMG85s2kgo-1aEZg 提取码：hg5m
2、标注人脸特征点
# _*_ coding:utf-8 _*_import numpy as npimport cv2import dlibfrom PIL import Imageimport osimport timedetector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')# dictory_name = "E:\code\celeba-1024"dictory_name = './imgs'if not os.path.exists('./eye'):os.mkdir('./eye')if not os.path.exists('./nose'):os.mkdir('./nose')if not os.path.exists('./mouse'):os.mkdir('./mouse')# 摄像头捕捉人脸图像def cap_face():cap = cv2.VideoCapture(0)while (cap.isOpened()):ret, frame = cap.read()frame = cv2.resize(frame, (800, 650))start = time.time()frame = cv2.flip(frame, 1)dets = detector(frame, 0)for k, point in enumerate(dets):shape = predictor(frame, point)landmarks = np.matrix([[p.x, p.y] for p in shape.parts()])for num in range(shape.num_parts):cv2.circle(frame, (shape.parts()[num].x, shape.parts()[num].y), 1, (0, 255, 0), -1)print("time ->", time.time() - start)cv2.imshow('frame', frame)if cv2.waitKey(1) & 0xFF == ord('q'):breakcap.release()cv2.destroyAllWindows()def landmark_face():img = cv2.imread(dictory_name + "\\" + "000085.jpg.jpg")# img = cv2.resize(img[:, :, ::-1],dsize=(256, 256))# 取灰度# img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)# 人脸数 rectsrects = detector(img, 0)for k, point in enumerate(rects):shape = predictor(img, point)landmarks = np.matrix([[p.x, p.y] for p in shape.parts()])for num in range(shape.num_parts):cv2.circle(img, (shape.parts()[num].x, shape.parts()[num].y), 3, (0, 255, 0), -1)cv2.imshow('image', img)cv2.imwrite("./imgs/landmark_face.jpg",img)cv2.waitKey(0)def crop_face():for filename in os.listdir(dictory_name):# cv2读取图像img = cv2.imread(dictory_name + "\\" + filename)img = cv2.resize(img[:, :, ::-1], dsize=(256, 256))# 取灰度# img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)# 人脸数 rectsrects = detector(img, 0)for k, point in enumerate(rects):shape = predictor(img, point)landmarks = np.matrix([[p.x, p.y] for p in shape.parts()])for num in range(shape.num_parts):cv2.circle(img, (shape.parts()[num].x, shape.parts()[num].y), 1, (0, 255, 0), -1)# cv2.imshow('image', img)# if len(rects) == 0:# 眼睛+眉毛# x_eye_left_up = 63 # 3 23# y_eye_left_up = 95 # 18 18# x_eye_right_down = 186 # 92 50# y_eye_right_down = 134 # 43 43## eye_box = (x_eye_left_up, y_eye_left_up, x_eye_right_down, y_eye_right_down)# eye_crop = Image.fromarray(img).crop(eye_box)# eye_crop.save("./eye3" + "/" + filename)# 鼻子# x_nose_left_up = 98# y_nose_left_up = 130# x_nose_right_down = 155# y_nose_right_down = 173## nose_box = (x_nose_left_up, y_nose_left_up, x_nose_right_down, y_nose_right_down)# nose_crop = Image.fromarray(img).crop(nose_box)# nose_crop.save("./nose" + "/" + filename)# 嘴巴# x_mouse_left_up = 90# y_mouse_left_up = 173# x_mouse_right_down = 160# y_mouse_right_down = 210## mouse_box = (x_mouse_left_up, y_mouse_left_up, x_mouse_right_down, y_mouse_right_down)# mouse_crop = Image.fromarray(img).crop(mouse_box)# mouse_crop.save("./mouse" + "/" + filename)# else:# for i in range(len(rects)): # 超过一个人脸时# landmarks = np.matrix([[p.x, p.y] for p in predictor(img, rects[i]).parts()])# # print(type(landmarks)) # numpy.matrix## # 眼睛+眉毛# x_left_up = landmarks[17, 0]# y_left_up = landmarks[18, 1] - (landmarks[29, 1] - landmarks[28, 1])*2# x_right_down = landmarks[26, 0]# y_right_down = landmarks[28, 1] + (landmarks[29, 1] - landmarks[28, 1])## # 嘴巴+鼻子# # x_left_up = landmarks[4, 0] + (landmarks[48, 0] - landmarks[4, 0]) / 2# # y_left_up = landmarks[29, 1]# # x_right_down = landmarks[54, 0] + (landmarks[12, 0] - landmarks[54, 0]) / 2# # y_right_down = landmarks[57, 1] + (landmarks[8, 1] - landmarks[57, 1]) / 2## # 眼睛+眉毛+鼻子# # x_left_up = landmarks[17, 0]# # y_left_up = landmarks[18, 1] - (landmarks[29, 1] - landmarks[28, 1])*2# # x_right_down = landmarks[26, 0]# # y_right_down = landmarks[33, 1] + (landmarks[51, 1] - landmarks[33, 1]) / 2## # 眼睛+眉毛+鼻子+嘴巴# # x_left_up = landmarks[17, 0]# # y_left_up = landmarks[18, 1] - (landmarks[29, 1] - landmarks[28, 1]) * 2# # x_right_down = landmarks[54, 0] + (landmarks[12, 0] - landmarks[54, 0])# # y_right_down = landmarks[57, 1] + (landmarks[8, 1] - landmarks[57, 1]) / 2## box = (x_left_up, y_left_up, x_right_down, y_right_down)# image_crop = Image.fromarray(img).crop(box)# image_crop.save("./eye3" + "/" + filename)if __name__ == '__main__':# cap_face()# crop_face()landmark_face()
调用landmark_face()，标注图像结果如下：
3、裁剪人脸组件
crop_face()方法：采用的固定大小的尺度去裁剪人脸组件（眼睛+眉毛，鼻子，嘴巴），使用PIL保存裁剪的结果，如下图所示：
眼睛：
鼻子：
嘴巴：
PIL库中的crop方法：采用两个像素点的坐标（x,y）去裁剪组件，两个点的位置分别为矩形框的左上角和右下角。注意x的方向为向右，y为向下，图像左上角的起始坐标为（0,0）。
4、后记
思考：借助dlib库检测人脸的特征点来辅助裁剪人脸组件，存在哪些问题？
- 当人脸有姿态和遮挡时，采用固定size的尺度裁剪，无法精准裁剪。优点是裁剪后大小一致，以便网络训练，以及进一步处理。
- 根据标注的特征点，利用其中的几个特征点来crop人脸组件，特征点中位置要进行加减一定的像素，但是裁剪后的图像大小不一致。当输入给神经网络时，需要resize处理，这样的裁剪好处是能将人脸组件精确裁剪下来。
由于作者水平有限，文中若有不正确的地方，欢迎大家指出，若有任何问题，请在下方讨论。
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