detect_face_2.py

#!/usr/bin/env python

'''
face detection using haar cascades

USAGE:
    facedetect.py [--cascade <cascade_fn>] [--nested-cascade <cascade_fn>] [<video_source>]
'''

# Python 2/3 compatibility
from __future__ import print_function

import numpy as np
import cv2


def clock():
    return cv2.getTickCount() / cv2.getTickFrequency()

def draw_str(dst, target, s):
    x, y = target
    cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv2.LINE_AA)
    cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.LINE_AA)

presets = dict(
    empty = 'synth:',
    lena = 'synth:bg=../data/lena.jpg:noise=0.1',
    chess = 'synth:class=chess:bg=../data/lena.jpg:noise=0.1:size=640x480',
    book = 'synth:class=book:bg=../data/graf1.png:noise=0.1:size=640x480',
    cube = 'synth:class=cube:bg=../data/pca_test1.jpg:noise=0.0:size=640x480'
)

def create_capture(source = 0, fallback = presets['chess']):
    '''source: <int> or '<int>|<filename>|synth [:<param_name>=<value> [:...]]'
    '''
    source = str(source).strip()
    chunks = source.split(':')
    # handle drive letter ('c:', ...)
    if len(chunks) > 1 and len(chunks[0]) == 1 and chunks[0].isalpha():
        chunks[1] = chunks[0] + ':' + chunks[1]
        del chunks[0]

    source = chunks[0]
    try: source = int(source)
    except ValueError: pass
    params = dict( s.split('=') for s in chunks[1:] )

    cap = None
    if source == 'synth':
        Class = classes.get(params.get('class', None), VideoSynthBase)
        try: cap = Class(**params)
        except: pass
    else:
        cap = cv2.VideoCapture(source)
        if 'size' in params:
            w, h = map(int, params['size'].split('x'))
            cap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
            cap.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
    if cap is None or not cap.isOpened():
        print('Warning: unable to open video source: ', source)
        if fallback is not None:
            return create_capture(fallback, None)
    return cap


def detect(img, cascade):
    rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
                                     flags=cv2.CASCADE_SCALE_IMAGE)
    if len(rects) == 0:
        return []
    rects[:,2:] += rects[:,:2]
    return rects

def draw_rects(img, rects, color):
    for x1, y1, x2, y2 in rects:
        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)

if __name__ == '__main__':
    import sys, getopt
    print(__doc__)

    args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
    try:
        video_src = video_src[0]
    except:
        video_src = 0
    args = dict(args)
    cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
    nested_fn  = args.get('--nested-cascade', "../../data/haarcascades/haarcascade_eye.xml")

    cascade = cv2.CascadeClassifier(cascade_fn)
    nested = cv2.CascadeClassifier(nested_fn)

    cam = create_capture(video_src, fallback='synth:bg=../data/lena.jpg:noise=0.05')

    while True:
        ret, img = cam.read()
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        gray = cv2.equalizeHist(gray)

        t = clock()
        rects = detect(gray, cascade)
        vis = img.copy()
        draw_rects(vis, rects, (0, 255, 0))
        if not nested.empty():
            for x1, y1, x2, y2 in rects:
                roi = gray[y1:y2, x1:x2]
                vis_roi = vis[y1:y2, x1:x2]
                subrects = detect(roi.copy(), nested)
                draw_rects(vis_roi, subrects, (255, 0, 0))
        dt = clock() - t

        draw_str(vis, (20, 20), 'time: %.1f ms' % (dt*1000))
        cv2.imshow('facedetect', vis)

        if cv2.waitKey(5) == 27:
            break
    cv2.destroyAllWindows()