color = emotion_probability * np.asarray((255, 0, 0))
elif emotion == '厌恶':
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion == '恐惧':
color = emotion_probability * np.asarray((255, 255, 255))
elif emotion == '开心':
color = emotion_probability * np.asarray((255, 255, 0))
elif emotion == '难过':
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion == '惊喜':
color = emotion_probability * np.asarray((0, 255, 255))
else:
color = emotion_probability * np.asarray((0, 255, 0))
print(color)
color = [int(i) for i in color] # list元素转成int
color = tuple(color) # 传入参数需要为tuple
## color = color.tolist()
#cv2.rectangle(img, (x + 10, y + 10), (x + h - 10, y + w - 10),color, 2)
img = chineseText.cv2ImgAddText(img, emotion, 0, 410, color, 70)
cv2.imshow("Image", img)
if cv2.waitKey(100) & 0xff == ord('q'):
break
camera.release()
cv2.waitKey(0)
cv2.destroyAllWindows()
}
img = cv2.imread("D:/python/works/emotion.png")
face_classifier = cv2.CascadeClassifier(
"D:\pythoncode\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml"
)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(40, 40))
color = (255, 0, 0)
for (x, y, w, h) in faces:
gray_face = gray[(y):(y + h), (x):(x + w)]
gray_face = cv2.resize(gray_face, (48, 48))
gray_face = gray_face / 255.0
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
emotion = emotion_labels[emotion_label_arg]
cv2.rectangle(img, (x + 10, y + 10), (x + h - 10, y + w - 10),
(255, 255, 255), 2)
img = chineseText.cv2ImgAddText(img, emotion, x + h * 0.3, y, color, 20)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def discern(img):
#理论上来讲，python3的int类型支持无限大，如果一直加下去，内存就会爆炸，所以还是到了一定数值的时候重启归零好点
global num
num += 1
if (num == 1000):
num = 0
#清理系统终端内容
os.system("clear")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cap = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faceRects = cap.detectMultiScale(gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(50, 50))
if len(faceRects):
# #框出正方形人脸
#test
# for faceRect in faceRects:
# x, y, w, h = faceRect
# cv2.rectangle(img, (x, y), (x + h, y + w), (0, 255, 0), 3)
#视频的每一帧都框出每一张人脸，包括眼睛和鼻子
if (num % 2 == 0):
for faceRect in faceRects:
x, y, w, h = faceRect
# 框出人脸
cv2.rectangle(img, (x, y), (x + h, y + w), color, 2)
# 左眼
cv2.circle(img, (x + w // 4, y + h // 4 + 30),
min(w // 8, h // 8), color)
#右眼
cv2.circle(img, (x + 3 * w // 4, y + h // 4 + 30),
min(w // 8, h // 8), color)
#嘴巴
cv2.rectangle(img, (x + 3 * w // 8, y + 3 * h // 4),
(x + 5 * w // 8, y + 7 * h // 8), color)
#识别男女性别
if (num % 5 == 0):
try:
for (x, y, w, h) in faceRects:
face = img[(y - 60):(y + h + 60), (x - 30):(x + w + 30)]
face = cv2.resize(face, (48, 48))
face = np.expand_dims(face, 0)
face = face / 255.0
gender_label_arg = np.argmax(
gender_classifier.predict(face))
gender = gender_labels[gender_label_arg]
cv2.rectangle(img, (x, y), (x + h, y + w), color, 2)
img = chineseText.cv2ImgAddText(img, gender, x + h, y,
(224, 54, 54))
except:
print("错误代码：Oxf-001 已知错误！未检测到图像中的人脸或无法识别人脸性别")
if (num % 3 == 0):
#表情识别代码
for (x, y, w, h) in faceRects:
gray_face = gray[(y):(y + h), (x):(x + w)]
gray_face = cv2.resize(gray_face, (48, 48))
gray_face = gray_face / 255.0
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(
emotion_classifier.predict(gray_face))
emotion = emotion_labels[emotion_label_arg]
img = chineseText.cv2ImgAddText(img, emotion, x + h * 0.3, y,
(0, 0, 255))
#头像挂件代码
try:
for faceRect in faceRects:
# imgCompose = cv2.imread("img/maozi-1.png")
x, y, w, h = faceRect
sp = imgCompose.shape
imgComposeSizeH = int(sp[0] / sp[1] * w)
if imgComposeSizeH > (y - 20):
imgComposeSizeH = (y - 20)
imgComposeSize = cv2.resize(imgCompose, (w, imgComposeSizeH),
interpolation=cv2.INTER_NEAREST)
top = (y - imgComposeSizeH - 20)
if top <= 0:
top = 0
rows, cols, channels = imgComposeSize.shape
roi = img[top:top + rows, x:x + cols]
img2gray = cv2.cvtColor(imgComposeSize, cv2.COLOR_RGB2GRAY)
ret, mask = cv2.threshold(img2gray, 10, 255, cv2.THRESH_BINARY)
mask_inv = cv2.bitwise_not(mask)
img1_bg = cv2.bitwise_and(roi, roi, mask=mask_inv)
img2_fg = cv2.bitwise_and(imgComposeSize,
imgComposeSize,
mask=mask)
dst = cv2.add(img1_bg, img2_fg)
img[top:top + rows, x:x + cols] = dst
except:
print("错误代码：Oxf-002 已知错误！sRGB配置文件！此错误不可消除，除非更改图片文件")
#将识别效果创建窗口展示出来
cv2.namedWindow("Face recognition - q exit", 0)
# cv2.resizeWindow("Face recognition - q exit", 1280 , 720)
cv2.imshow("Face recognition - q exit", img)
import chineseText
img = cv2.imread("img/gather.png")
face_classifier = cv2.CascadeClassifier(
"C:\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml"
)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(140, 140))
gender_classifier = load_model(
"classifier/gender_models/simple_CNN.81-0.96.hdf5")
gender_labels = {0: '女', 1: '男'}
color = (255, 255, 255)
for (x, y, w, h) in faces:
face = img[(y - 60):(y + h + 60), (x - 30):(x + w + 30)]
face = cv2.resize(face, (48, 48))
face = np.expand_dims(face, 0)
face = face / 255.0
gender_label_arg = np.argmax(gender_classifier.predict(face))
gender = gender_labels[gender_label_arg]
cv2.rectangle(img, (x, y), (x + h, y + w), color, 2)
img = chineseText.cv2ImgAddText(img, gender, x + h, y, color, 30)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
from keras.models import load_model
import numpy as np
import chineseText
img = cv2.imread("img/gather.png")
face_classifier = cv2.CascadeClassifier(
"C:\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml"
)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(
gray, scaleFactor=1.2, minNeighbors=3, minSize=(140, 140))
gender_classifier = load_model(
"classifier/gender_models/simple_CNN.81-0.96.hdf5")
gender_labels = {0: '女', 1: '男'}
color = (255, 255, 255)
for (x, y, w, h) in faces:
face = img[(y - 60):(y + h + 60), (x - 30):(x + w + 30)]
face = cv2.resize(face, (48, 48))
face = np.expand_dims(face, 0)
face = face / 255.0
gender_label_arg = np.argmax(gender_classifier.predict(face))
gender = gender_labels[gender_label_arg]
cv2.rectangle(img, (x, y), (x + h, y + w), color, 2)
img = chineseText.cv2ImgAddText(img, gender, x + h, y, color, 30)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
Project_Name : raspberryPi-develop
File Name： chineseTest.py
Description :
Author : HuHongLin
date： 2018/8/23
-------------------------------------------------
Change Activity:
2018/8/23 15:36:
-------------------------------------------------
"""
import cv2
import sys
import os.path
# sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import chineseText as text
img = cv2.imread("bd_icon.png")
img = text.cv2ImgAddText(img, "北斗启航", 50, 320, (51, 51, 51), 48)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
3: '开心',
4: '难过',
5: '惊喜',
6: '平静'
}
img = cv2.imread("img/emotion/emotion.png")
face_classifier = cv2.CascadeClassifier(
"C:\Python36\Lib\site-packages\opencv-master\data\haarcascades\haarcascade_frontalface_default.xml"
)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(
gray, scaleFactor=1.2, minNeighbors=3, minSize=(40, 40))
color = (255, 0, 0)
for (x, y, w, h) in faces:
gray_face = gray[(y):(y + h), (x):(x + w)]
gray_face = cv2.resize(gray_face, (48, 48))
gray_face = gray_face / 255.0
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
emotion = emotion_labels[emotion_label_arg]
cv2.rectangle(img, (x + 10, y + 10), (x + h - 10, y + w - 10),
(255, 255, 255), 2)
img = chineseText.cv2ImgAddText(img, emotion, x + h * 0.3, y, color, 20)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()