性别识别代码添加

README.md

@@ -25,17 +25,16 @@
 
 	人脸检测：face_recognition
 
-	视频识别：OpenCV + face_recognition
+	性别识别：keras + tensorflow
 
 	文字识别：Tesseract OCR
 
 
+
 ## 教程文档 ##
 
 [OpenCV环境搭建](doc/settingup.md)
 
-[OpenCV添加中文](doc/chinese.md)
-
 [Tesseract OCR文字识别](doc/tesseractOCR.md)
 
 [图片人脸检测（OpenCV版）](doc/detectionOpenCV.md)
@@ -54,10 +53,14 @@
 
 [头像特效合成](doc/compose.md)
 
+性别识别
+
 ## 其他相关 ##
 
 [pip/pip3更换国内源](doc/pipChange.md)
 
+[OpenCV添加中文](doc/chinese.md)
+
 
 ## 功能预览 ##
 

faceai/ChineseText.py

@@ -5,7 +5,7 @@
 import numpy
 from PIL import Image, ImageDraw, ImageFont
 
-img = cv2.imread("img/xingye-1.png")
+# img = cv2.imread("img/xingye-1.png")
 
 
 def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20):
@@ -18,8 +18,8 @@ def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20):
     return cv2.cvtColor(numpy.asarray(img), cv2.COLOR_RGB2BGR)
 
 
-img = cv2ImgAddText(img, "大家好，我是星爷", 140, 60, (255, 255, 0), 20)
+# img = cv2ImgAddText(img, "大家好，我是星爷", 140, 60, (255, 255, 0), 20)
 
-cv2.imshow("Image", img)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
+# cv2.imshow("Image", img)
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()

faceai/gender.py

@@ -0,0 +1,34 @@
+#coding=utf-8
+#表情识别
+
+import cv2
+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()