人工智能之头像识别

图像识别是人工智能的一个重要方面,下面通过一个简单列子进行练习:

随着圣诞的到来,大家纷纷@官方微信给自己的头像加上一顶圣诞帽。当然这种事情用很多P图软件都可以做到。但是作为一个学习图像处理的技术人,还是觉得我们有必要写一个程序来做这件事情。而且这完全可以作为一个练手的小项目,工作量不大,而且很有意思。

我们用下面这张图作为我们的测试图片。

用dlib的正脸检测器进行人脸检测,用dlib提供的模型提取人脸的五个关键点。代码如下:

#!/usr/bin/python

# -*- coding: utf-8 -*-

import numpy as np

import cv2

import dlib

predictor_path = "shape_predictor_5_face_landmarks.dat"

predictor = dlib.shape_predictor(predictor_path)

# dlib正脸检测器

detector = dlib.get_frontal_face_detector()

img = cv2.imread("abm.jpg")

# 正脸检测

dets = detector(img, 1)

# 如果检测到人脸

if len(dets)>0:

for d in dets:

x,y,w,h = d.left(),d.top(), d.right()-d.left(), d.bottom()-d.top()

# x,y,w,h = faceRect

cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2,8,0)

# 关键点检测,5个关键点

shape = predictor(img, d)

for point in shape.parts():

cv2.circle(img,(point.x,point.y),3,color=(0,255,0))

cv2.imshow("image",img)

cv2.waitKey()

这部分效果如下图:

2.下面给出全部代码:

#!/usr/bin/python

# -*- coding: utf-8 -*-

import numpy as np

import cv2

import dlib

# 给img中的人头像加上圣诞帽,人脸最好为正脸

def add_hat(img,hat_img):

# 分离rgba通道,合成rgb三通道帽子图,a通道后面做mask用

r,g,b,a = cv2.split(hat_img)

rgb_hat = cv2.merge((r,g,b))

cv2.imwrite("hat_alpha.jpg",a)

# ------------------------- 用dlib的人脸检测代替OpenCV的人脸检测-----------------------

# # 灰度变换

# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# # 用opencv自带的人脸检测器检测人脸

# face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")

# faces = face_cascade.detectMultiScale(gray,1.05,3,cv2.CASCADE_SCALE_IMAGE,(50,50))

# ------------------------- 用dlib的人脸检测代替OpenCV的人脸检测-----------------------

# dlib人脸关键点检测器

predictor_path = "shape_predictor_5_face_landmarks.dat"

predictor = dlib.shape_predictor(predictor_path)

# dlib正脸检测器

detector = dlib.get_frontal_face_detector()

# 正脸检测

dets = detector(img, 1)

# 如果检测到人脸

if len(dets)>0:

for d in dets:

x,y,w,h = d.left(),d.top(), d.right()-d.left(), d.bottom()-d.top()

# x,y,w,h = faceRect

# cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2,8,0)

# 关键点检测,5个关键点

shape = predictor(img, d)

# for point in shape.parts():

# cv2.circle(img,(point.x,point.y),3,color=(0,255,0))

# cv2.imshow("image",img)

# cv2.waitKey()

# 选取左右眼眼角的点

point1 = shape.part(0)

point2 = shape.part(2)

# 求两点中心

eyes_center = ((point1.x+point2.x)//2,(point1.y+point2.y)//2)

# cv2.circle(img,eyes_center,3,color=(0,255,0))

# cv2.imshow("image",img)

# cv2.waitKey()

# 根据人脸大小调整帽子大小

factor = 1.5

resized_hat_h = int(round(rgb_hat.shape[0]*w/rgb_hat.shape[1]*factor))

resized_hat_w = int(round(rgb_hat.shape[1]*w/rgb_hat.shape[1]*factor))

if resized_hat_h > y:

resized_hat_h = y-1

# 根据人脸大小调整帽子大小

resized_hat = cv2.resize(rgb_hat,(resized_hat_w,resized_hat_h))

# 用alpha通道作为mask

mask = cv2.resize(a,(resized_hat_w,resized_hat_h))

mask_inv = cv2.bitwise_not(mask)

# 帽子相对与人脸框上线的偏移量

dh = 0

dw = 0

# 原图ROI

# bg_roi = img[y+dh-resized_hat_h:y+dh, x+dw:x+dw+resized_hat_w]

bg_roi = img[y+dh-resized_hat_h:y+dh,(eyes_center[0]-resized_hat_w//3):(eyes_center[0]+resized_hat_w//3*2)]

# 原图ROI中提取放帽子的区域

bg_roi = bg_roi.astype(float)

mask_inv = cv2.merge((mask_inv,mask_inv,mask_inv))

alpha = mask_inv.astype(float)/255

# 相乘之前保证两者大小一致(可能会由于四舍五入原因不一致)

alpha = cv2.resize(alpha,(bg_roi.shape[1],bg_roi.shape[0]))

# print("alpha size: ",alpha.shape)

# print("bg_roi size: ",bg_roi.shape)

bg = cv2.multiply(alpha, bg_roi)

bg = bg.astype('uint8')

cv2.imwrite("bg.jpg",bg)

# cv2.imshow("image",img)

# cv2.waitKey()

# 提取帽子区域

hat = cv2.bitwise_and(resized_hat,resized_hat,mask = mask)

cv2.imwrite("hat.jpg",hat)

# cv2.imshow("hat",hat)

# cv2.imshow("bg",bg)

# print("bg size: ",bg.shape)

# print("hat size: ",hat.shape)

# 相加之前保证两者大小一致(可能会由于四舍五入原因不一致)

hat = cv2.resize(hat,(bg_roi.shape[1],bg_roi.shape[0]))

# 两个ROI区域相加

add_hat = cv2.add(bg,hat)

# cv2.imshow("add_hat",add_hat)

# 把添加好帽子的区域放回原图

img[y+dh-resized_hat_h:y+dh,(eyes_center[0]-resized_hat_w//3):(eyes_center[0]+resized_hat_w//3*2)] = add_hat

# 展示效果

# cv2.imshow("img",img )

# cv2.waitKey(0)

return img

# 读取帽子图,第二个参数-1表示读取为rgba通道,否则为rgb通道

hat_img = cv2.imread("hat2.png",-1)

# 读取头像图

img = cv2.imread("abm.jpg")

output = add_hat(img,hat_img)

# 展示效果

cv2.imshow("output_yt",output )

cv2.waitKey(0)

cv2.imwrite("output_yt.jpg",output)

# import glob as gb

# img_path = gb.glob("./images/*.jpg")

# for path in img_path:

# img = cv2.imread(path)

# # 添加帽子

# output = add_hat(img,hat_img)

# # 展示效果

# cv2.imshow("output",output )

# cv2.waitKey(0)

cv2.destroyAllWindows()

这部分效果如下图:

源代码地址:https://github.com/yuntiandaxia/Add-Christmas-Hat

  • 发表于:
  • 原文链接:http://kuaibao.qq.com/s/20171228G0GULP00?refer=cp_1026

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