在opencv python中使用Cascade Classifier进行人脸检测

Question

我正在使用内置的级联分类器进行人脸检测.这是代码的方式(OpenCV Python Tutorials):

import numpy as np
import cv2

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

img = cv2.imread('ammma.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.3, 5)

for (x,y,w,h) in faces:
    cv2.Rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = img[y:y+h, x:x+w]
    eyes = eye_cascade.detectMultiScale(roi_gray)
    for(ex,ey,ew,eh) in eyes:
        cv2.Rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

但是当我运行代码时,我收到以下错误:

C:\ Python27\python.exe C:/Users/DELL/Downloads/Amma/code/fd.py

OpenCV错误:断言失败(!empty())在cv :: CascadeClassifier :: detectMultiScale中,文件C:\ builds\master_PackSlaveAddon-win32-vc12-static\opencv\modules\objdetect\src\cascadedetect.cpp,第1634行Traceback(最近一次调用最后一次):文件"C:/Users/DELL/Downloads/Amma/code/fd.py",第10行,在faces = face_cascade.detectMultiScale(img,1.3,5)cv2.error:C:\ builds\master_PackSlaveAddon-win32-vc12-static\opencv\modules\objdetect\src\cascadedetect.cpp:1634:错误:(-215)!empty()in function cv :: CascadeClassifier :: detectMultiScale

Answer 1

请参阅此行代码,检查级联非空时失败.请检查具有经过培训的级联的XML文件的路径.您可能需要指定XML的完整路径,如下所示:

face_cascade = cv2.CascadeClassifier('D:\opencv\data\haarcascades\haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('D:\opencv\data\haarcascades\haarcascade_eye.xml')

或者只是将这些文件放到包含脚本的目录中.

Answer 2

您不需要下载或复制.xml文件。根据OpenCV-Python PyPi页面，您可以简单地使用打包路径到已安装的层叠- cv2.data.haarcascades：

import cv2

# Globals
FACE_CLASSIFIER = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
EYE_CLASSIFIER = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
SCALE_FACTOR = 1.3
BLUE_COLOR = (255, 0, 0)
MIN_NEIGHBORS = 5

# Then use it however you'd like
try:
    faces = FACE_CLASSIFIER.detectMultiScale(gray, SCALE_FACTOR, MIN_NEIGHBORS)
    for (x, y, w, h) in faces:
        cv2.rectangle(self.roi_frame, (x, y), (x+w, y+h), BLUE_COLOR, HAAR_LINE_THICKNESS)
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = self.roi_frame[y:y+h, x:x+w]
        eyes = EYE_CLASSIFIER.detectMultiScale(roi_gray)
        for (ex, ey, ew, eh) in eyes:
            cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), GREEN_COLOR, HAAR_LINE_THICKNESS)
except Exception as e:
    warnings.warn('{}.show_haar_features: got exception {}'.format(__name__, e))