重磅干货,第一时间送达 目 录 content 一.项目介绍前言 二.识别检测方法
三.完整代码及效果展示 (1)基于点云数据的人脸识别 (2)基于面部特征的3D人脸识别 (1)基于深度图的人脸识别 (2)基于RGB-3DMM的人脸识别 (3)基于RGB-D的人脸识别 一般人脸中有5个关键点,其中包括眼睛两个,鼻子一个,嘴角两个。还可以细致的分为68个关键点,这样的话会概括的比较全面,我们本次研究就是68个关键点定位。 from collections import OrderedDict import numpy as np import argparse import dlib import cv2
FACIAL_LANDMARKS_68_IDXS = OrderedDict([ ('mouth', (48, 68)), ('right_eyebrow', (17, 22)), ('left_eyebrow', (22, 27)), ('right_eye', (36, 42)), ('left_eye', (42, 48)), ('nose', (27, 36)), ('jaw', (0, 17)) ])
detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(args['shape_predictor'])
rects = detector(gray, 1)
def shape_to_np(shape, dtype='int'): # 创建68*2 coords = np.zeros((shape.num_parts, 2), dtype=dtype) # 遍历每一个关键点 # 得到坐标 for i in range(0, shape.num_parts): coords[i] = (shape.part(i).x, shape.part(i).y) return coords
output = visualize_facial_landmarks(image, shape) cv2.imshow('Image', output) cv2.waitKey(0) 其中visualize_facial_landmarks函数就是:
from collections import OrderedDictimport numpy as npimport argparseimport dlibimport cv2ap = argparse.ArgumentParser()ap.add_argument('-p', '--shape-predictor', required=True, help='path to facial landmark predictor')ap.add_argument('-i', '--image', required=True, help='path to input image')args = vars(ap.parse_args())FACIAL_LANDMARKS_68_IDXS = OrderedDict([ ('mouth', (48, 68)), ('right_eyebrow', (17, 22)), ('left_eyebrow', (22, 27)), ('right_eye', (36, 42)), ('left_eye', (42, 48)), ('nose', (27, 36)), ('jaw', (0, 17))])FACIAL_LANDMARKS_5_IDXS = OrderedDict([ ('right_eye', (2, 3)), ('left_eye', (0, 1)), ('nose', (4))])def shape_to_np(shape, dtype='int'): # 创建68*2 coords = np.zeros((shape.num_parts, 2), dtype=dtype) # 遍历每一个关键点 # 得到坐标 for i in range(0, shape.num_parts): coords[i] = (shape.part(i).x, shape.part(i).y) return coordsdef visualize_facial_landmarks(image, shape, colors=None, alpha=0.75): # 创建两个copy # overlay and one for the final output image overlay = image.copy() output = image.copy() # 设置一些颜色区域 if colors is None: colors = [(19, 199, 109), (79, 76, 240), (230, 159, 23), (168, 100, 168), (158, 163, 32), (163, 38, 32), (180, 42, 220)] # 遍历每一个区域 for (i, name) in enumerate(FACIAL_LANDMARKS_68_IDXS.keys()): # 得到每一个点的坐标 (j, k) = FACIAL_LANDMARKS_68_IDXS[name] pts = shape[j:k] # 检查位置 if name == 'jaw': # 用线条连起来 for l in range(1, len(pts)): ptA = tuple(pts[l - 1]) ptB = tuple(pts[l]) cv2.line(overlay, ptA, ptB, colors[i], 2) # 计算凸包 else: hull = cv2.convexHull(pts) cv2.drawContours(overlay, [hull], -1, colors[i], -1) # 叠加在原图上,可以指定比例 cv2.addWeighted(overlay, alpha, output, 1 - alpha, 0, output) return output# 加载人脸检测与关键点定位detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor(args['shape_predictor'])# 读取输入数据,预处理image = cv2.imread(args['image'])(h, w) = image.shape[:2]width=500r = width / float(w)dim = (width, int(h * r))image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# 人脸检测rects = detector(gray, 1)# 遍历检测到的框for (i, rect) in enumerate(rects): # 对人脸框进行关键点定位 # 转换成ndarray shape = predictor(gray, rect) shape = shape_to_np(shape) # 遍历每一个部分 for (name, (i, j)) in FACIAL_LANDMARKS_68_IDXS.items(): clone = image.copy() cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # 根据位置画点 for (x, y) in shape[i:j]: cv2.circle(clone, (x, y), 3, (0, 0, 255), -1) # 提取ROI区域 (x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]])) roi = image[y:y + h, x:x + w] (h, w) = roi.shape[:2] width=250 r = width / float(w) dim = (width, int(h * r)) roi = cv2.resize(roi, dim, interpolation=cv2.INTER_AREA) # 显示每一部分 cv2.imshow('ROI', roi) cv2.imshow('Image', clone) cv2.waitKey(0) # 展示所有区域 output = visualize_facial_landmarks(image, shape) cv2.imshow('Image', output) cv2.waitKey(0) 本文仅做学术分享,如有侵权,请联系删文。 |
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