Object detection based on OpenCV haarcascades

OpenCV：https://github.com/opencv/opencv

haarcascades - This folder contains a specific type of classifier trained to detect objects , for example Face （ positive 、 The side ）、 Pedestrians, etc . Some classifiers have a special license. Please check the file for details .
OpenCV Already contains many for the face , eyes , Pre classifier of smiling face, etc . these XML Files are stored in opencv/data/haarcascades/ In the folder

OpenCV The trainer and detector are attached . If you want a car , Aircraft and any other objects train your own classifier , You can use OpenCV Create a . Its full details are given here ： Cascade classifier training .
https://docs.opencv.org/2.4/doc/user_guide/ug_traincascade.html

# openCV  Common usage 

#  Import opencv library 
import os
import cv2


#  Recognize faces in pictures ： Use  haarcascade_frontalface_default.xml
def face_detect(img_path):
    #  Initialize model 
    detector = cv2.CascadeClassifier('../haarcascades/haarcascade_frontalface_default.xml')
    #  Initialize window 
    cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)
    #  Read the picture 
    img = cv2.imread(img_path)
    #  display picture 
    # cv2.imshow('image', img)
    #  Convert to grayscale 
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #  Face detection 
    dets = detector.detectMultiScale(img_gray, 1.1, 5)
    #  Get face frame 
    faces = []
    for i, d in enumerate(dets):
        x1 = d[0] if d[0] > 0 else 0
        y1 = d[1] if d[1] > 0 else 0
        x2 = d[0] + d[2] if d[0] + d[2] > 0 else 0
        y2 = d[1] + d[3] if d[1] + d[3] > 0 else 0
    #  Show face box 
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
    #  Save the picture 
    cv2.imwrite('../result/face_detect_1.jpg', img)
    #  Display picture tag face box 
    # cv2.imshow('image', img)
    #  Wait for the window to close 
    # cv2.waitKey(0)


#  Eye detection ： Use  haarcascade_eye_tree_eyeglasses.xml
def eye_detect(img_path):
    #  Initialize model 
    detector = cv2.CascadeClassifier('../haarcascades/haarcascade_eye_tree_eyeglasses.xml')
    #  Initialize window 
    cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)
    #  Read the picture 
    img = cv2.imread(img_path)
    #  display picture 
    # cv2.imshow('image', img)
    #  Convert to grayscale 
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #  Eye detection 
    dets = detector.detectMultiScale(img_gray, 1.1, 5)
    #  Get eye frame 
    faces = []
    for i, d in enumerate(dets):
        x1 = d[0] if d[0] > 0 else 0
        y1 = d[1] if d[1] > 0 else 0
        x2 = d[0] + d[2] if d[0] + d[2] > 0 else 0
        y2 = d[1] + d[3] if d[1] + d[3] > 0 else 0
    #  Show face box 
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
    #  Save the picture 
    cv2.imwrite('../result/eye_detect_1.jpg', img)
    #  Display picture tag face box 
    cv2.imshow('image', img)
    #  Wait for the window to close 
    cv2.waitKey(0)


def eye_detect_save(img_path, filename):
    #  Initialize model 
    detector = cv2.CascadeClassifier('../haarcascades/haarcascade_eye_tree_eyeglasses.xml')
    #  Read the picture 
    img = cv2.imread(img_path)
    #  Convert to grayscale 
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #  Eye detection 
    dets = detector.detectMultiScale(img_gray, 1.1, 5)
    #  Get eye frame 
    faces = []
    for i, d in enumerate(dets):
        x1 = d[0] if d[0] > 0 else 0
        y1 = d[1] if d[1] > 0 else 0
        x2 = d[0] + d[2] if d[0] + d[2] > 0 else 0
        y2 = d[1] + d[3] if d[1] + d[3] > 0 else 0
    #  Show face box 
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
    #  Save the picture 
    save_path = '../result/' + filename
    cv2.imwrite(save_path, img)


#  Eye detection ： Use  haarcascade_eye.xml
def eye_detect_2(img_path):
    #  Initialize model 
    detector = cv2.CascadeClassifier('../haarcascades/haarcascade_eye.xml')
    #  Initialize window 
    cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)
    #  Read the picture 
    img = cv2.imread(img_path)
    #  display picture 
    # cv2.imshow('image', img)
    #  Convert to grayscale 
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #  Eye detection 
    dets = detector.detectMultiScale(img_gray, 1.1, 5)
    #  Get eye frame 
    faces = []
    for i, d in enumerate(dets):
        x1 = d[0] if d[0] > 0 else 0
        y1 = d[1] if d[1] > 0 else 0
        x2 = d[0] + d[2] if d[0] + d[2] > 0 else 0
        y2 = d[1] + d[3] if d[1] + d[3] > 0 else 0
    #  Show face box 
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
    #  Save the picture 
    cv2.imwrite('../result/eye_detect2_1.jpg', img)
    #  Display picture tag face box 
    cv2.imshow('image', img)
    #  Wait for the window to close 
    cv2.waitKey(0)


#  Portrait detection ：haarcascade_profileface.xml
def profile_face(img_path):
    #  Initialize model 
    detector = cv2.CascadeClassifier('../haarcascades/haarcascade_profileface.xml')
    #  Initialize window 
    cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)
    #  Read the picture 
    img = cv2.imread(img_path)
    #  display picture 
    # cv2.imshow('image', img)
    #  Convert to grayscale 
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #  Eye detection 
    dets = detector.detectMultiScale(img_gray, 1.1, 5)
    #  Get eye frame 
    faces = []
    for i, d in enumerate(dets):
        x1 = d[0] if d[0] > 0 else 0
        y1 = d[1] if d[1] > 0 else 0
        x2 = d[0] + d[2] if d[0] + d[2] > 0 else 0
        y2 = d[1] + d[3] if d[1] + d[3] > 0 else 0
    #  Show face box 
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
    #  Save the picture 
    cv2.imwrite('../result/profile_face_6.jpg', img)
    #  Display picture tag face box 
    cv2.imshow('image', img)
    #  Wait for the window to close 
    cv2.waitKey(0)


#  Smile detection ：haarcascade_smile.xml
def smile_detect(img_path):
    #  Initialize model 
    detector = cv2.CascadeClassifier('../haarcascades/haarcascade_smile.xml')
    #  Initialize window 
    cv2.namedWindow('image', cv2.WINDOW_AUTOSIZE)
    #  Read the picture 
    img = cv2.imread(img_path)
    #  display picture 
    # cv2.imshow('image', img)
    #  Convert to grayscale 
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    #  Eye detection 
    dets = detector.detectMultiScale(img_gray, 1.1, 5)
    #  Get eye frame 
    faces = []
    for i, d in enumerate(dets):
        x1 = d[0] if d[0] > 0 else 0
        y1 = d[1] if d[1] > 0 else 0
        x2 = d[0] + d[2] if d[0] + d[2] > 0 else 0
        y2 = d[1] + d[3] if d[1] + d[3] > 0 else 0
    #  Show face box 
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
    #  Save the picture 
    cv2.imwrite('../result/smile_detect_1.jpg', img)
    #  Display picture tag face box 
    cv2.imshow('image', img)
    #  Wait for the window to close 
    cv2.waitKey(0)


if __name__ == '__main__':
    print('PyCharm')
    # face_detect('../face/face01.jpg')
    # eye_detect('../face/face01.jpg')
    # eye_detect_2('../face/face01.jpg')
    # profile_face('../face/face06.jpg')
    # smile_detect('../face/face08.jpg')
    #  Traverse face Folder   Output   Eye detection   Output   Mark to  result Folder 
    for dirpath, dirnames, filenames in os.walk('../face'):
        for filename in filenames:
            print(os.path.join(dirpath, filename))
            eye_detect_save(os.path.join(dirpath, filename), filename)