0531

07 视频检测.py

@@ -5,8 +5,8 @@ def face_detect_demo(img):
     #将图片转换为灰度图片
     gary = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
     #自带的分类器
-    face_detect = cv.CascadeClassifier('D:/PyCharm 2024.1.1/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
+    face_detect = cv.CascadeClassifier('D:/PyCharm 2024.1.1/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
-    face = face_detect.detectMultiScale(gary)
+    face = face_detect.detectMultiScale(gary,1.1,5,0,(50,50),(300,300))
     for (x,y,w,h) in face:
         cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
     # 显示图像
@@ -14,7 +14,7 @@ def face_detect_demo(img):
 
 #读取摄像头
 #cap = cv.VideoCapture(0)
-cap = cv.VideoCapture("output/1.mp4")
+cap = cv.VideoCapture("pic/1.mp4")
 
 #循环
 #等待
@@ -23,7 +23,7 @@ while True:
     if not flag:
         break
     face_detect_demo(frame)
-    if ord('q') == cv.waitKey(0):
+    if ord('q') == cv.waitKey(10):
         break
 #>0 等待delay毫秒 =0 无限等待 <0 等待键盘单击
 #释放内存

09数据训练.py

@@ -0,0 +1,44 @@
+import os
+import cv2
+from PIL import Image
+import numpy as np
+
+def getImageAndLabels(path):
+    #存储人脸数据
+    facesSamples=[]
+    #储存姓名数据
+    ids=[]
+    #存储图片信息
+    imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
+    #加载分类器
+    face_detector = cv2.CascadeClassifier('D:/PyCharm 2024.1.1/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
+    #遍历列表的图片
+    for imagePath in imagePaths:
+        #打开图片，灰度化PIL有九种不同模式：1,L,P,PGB,RGBA,CMYK,YcbCr,I,F.
+        PIL_img = Image.open(imagePath).convert('L') #1代表黑白打开，L代表灰度图像
+        #将图像转换为数组，以黑白深浅
+        img_numpy = np.array(PIL_img, 'uint8')
+        #获取图片人脸特征
+        faces = face_detector.detectMultiScale(img_numpy)
+        #获取每张图片的id和姓名
+        id = int(os.path.split(imagePath)[1].split('.')[0])
+        #预防无面容图片
+        for (x,y,w,h) in faces:
+            ids.append(id)
+            facesSamples.append(img_numpy[y:y+h,x:x+w])
+    #打印脸部特征和id
+    print('id:',id)
+    print('fs:',facesSamples)
+    return facesSamples,ids
+
+if __name__ == '__main__':
+    #图片路径
+    path='./output/'
+    #获取图像数组和id标签数组和姓名
+    face,ids=getImageAndLabels(path)
+    #加载识别器
+    recognizer=cv2.face.LBPHFaceRecognizer_create()
+    #训练
+    recognizer.train(face,np.array(ids))
+    #保存文件
+    recognizer.write('trainer/trainer.yml')