python使用dlib进行人脸检测和关键点的示例

#!/usr/bin/env python# -*- coding:utf-8-*-# file: {NAME}.py# @author: jory.d# @contact: dangxusheng163@163.com# @time: 2020/04/10 19:42# @desc: 使用dlib进行人脸检测和人脸关键点import cv2import numpy as npimport globimport dlibFACE_DETECT_PATH = \'/home/build/dlib-v19.18/data/mmod_human_face_detector.dat\'FACE_LANDMAKR_5_PATH = \'/home/build/dlib-v19.18/data/shape_predictor_5_face_landmarks.dat\'FACE_LANDMAKR_68_PATH = \'/home/build/dlib-v19.18/data/shape_predictor_68_face_landmarks.dat\'def face_detect():root = \'/media/dangxs/E/Project/DataSet/VGG Face Dataset/vgg_face_dataset/vgg_face_dataset/vgg_face_dataset\'imgs = glob.glob(root + \'/**/*.jpg\', recursive=True)assert len(imgs) > 0detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor(FACE_LANDMAKR_68_PATH)for f in imgs:img = cv2.imread(f)# The 1 in the second argument indicates that we should upsample the image# 1 time. This will make everything bigger and allow us to detect more# faces.dets = detector(img, 1)print(\"Number of faces detected: {}\".format(len(dets)))for i, d in enumerate(dets):x1, y1, x2, y2 = d.left(), d.top(), d.right(), d.bottom()print(\"Detection {}: Left: {} Top: {} Right: {} Bottom: {}\".format(i, x1, y1, x2, y2))cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 1)# Get the landmarks/parts for the face in box d.shape = predictor(img, d)print(\"Part 0: {}, Part 1: {} ...\".format(shape.part(0), shape.part(1)))# # Draw the face landmarks on the screen.\'\'\'# landmark 顺序： 外轮廓 - 左眉毛 - 右眉毛 - 鼻子 - 左眼 - 右眼 - 嘴巴\'\'\'for i in range(shape.num_parts):x, y = shape.part(i).x, shape.part(i).ycv2.circle(img, (x, y), 2, (0, 0, 255), 1)cv2.putText(img, str(i), (x, y), cv2.FONT_HERSHEY_COMPLEX, 0.3, (0, 0, 255), 1)cv2.resize(img, dsize=None, dst=img, fx=2, fy=2)cv2.imshow(\'w\', img)cv2.waitKey(0)def face_detect_mask():root = \'/media/dangxs/E/Project/DataSet/VGG Face Dataset/vgg_face_dataset/vgg_face_dataset/vgg_face_dataset\'imgs = glob.glob(root + \'/**/*.jpg\', recursive=True)assert len(imgs) > 0detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor(FACE_LANDMAKR_68_PATH)for f in imgs:img = cv2.imread(f)# The 1 in the second argument indicates that we should upsample the image# 1 time. This will make everything bigger and allow us to detect more# faces.dets = detector(img, 1)print(\"Number of faces detected: {}\".format(len(dets)))for i, d in enumerate(dets):x1, y1, x2, y2 = d.left(), d.top(), d.right(), d.bottom()print(\"Detection {}: Left: {} Top: {} Right: {} Bottom: {}\".format(i, x1, y1, x2, y2))cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 1)# Get the landmarks/parts for the face in box d.shape = predictor(img, d)print(\"Part 0: {}, Part 1: {} ...\".format(shape.part(0), shape.part(1)))# # Draw the face landmarks on the screen.\'\'\'# landmark 顺序： 外轮廓 - 左眉毛 - 右眉毛 - 鼻子 - 左眼 - 右眼 - 嘴巴\'\'\'points = []for i in range(shape.num_parts):x, y = shape.part(i).x, shape.part(i).yif i < 26:points.append([x, y])# cv2.circle(img, (x, y), 2, (0, 0, 255), 1)# cv2.putText(img, str(i), (x,y),cv2.FONT_HERSHEY_COMPLEX, 0.3 ,(0,0,255),1)# 只把脸切出来points[17:] = points[17:][::-1]points = np.asarray(points, np.int32).reshape(-1, 1, 2)img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)black_img = np.zeros_like(img)cv2.polylines(black_img, [points], 1, 255)cv2.fillPoly(black_img, [points], (1, 1, 1))mask = black_imgmasked_bgr = img * mask# 位运算时需要转化成灰度图像mask_gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)masked_gray = cv2.bitwise_and(img_gray, img_gray, mask=mask_gray)cv2.resize(img, dsize=None, dst=img, fx=2, fy=2)cv2.imshow(\'w\', img)cv2.imshow(\'mask\', mask)cv2.imshow(\'mask2\', masked_gray)cv2.imshow(\'mask3\', masked_bgr)cv2.waitKey(0)if __name__ == \'__main__\':face_detect()