demo.py

import numpy as np
import mxnet as mx
import argparse
import cv2
import time
from core.symbol import P_Net, R_Net, O_Net
from core.imdb import IMDB
from config import config
from core.loader import TestLoader
from core.detector import Detector
from core.fcn_detector import FcnDetector
from tools.load_model import load_param
from core.MtcnnDetector import MtcnnDetector

def test_net(prefix, epoch, batch_size, ctx,
             thresh=[0.6, 0.6, 0.7], min_face_size=24,
             stride=2, slide_window=False):

    detectors = [None, None, None]

    # load pnet model
    args, auxs = load_param(prefix[0], epoch[0], convert=True, ctx=ctx)
    if slide_window:
        PNet = Detector(P_Net("test"), 12, batch_size[0], ctx, args, auxs)
    else:
        PNet = FcnDetector(P_Net("test"), ctx, args, auxs)
    detectors[0] = PNet

    # load rnet model
    args, auxs = load_param(prefix[1], epoch[0], convert=True, ctx=ctx)
    RNet = Detector(R_Net("test"), 24, batch_size[1], ctx, args, auxs)
    detectors[1] = RNet

    # load onet model
    args, auxs = load_param(prefix[2], epoch[2], convert=True, ctx=ctx)
    ONet = Detector(O_Net("test"), 48, batch_size[2], ctx, args, auxs)
    detectors[2] = ONet

    mtcnn_detector = MtcnnDetector(detectors=detectors, ctx=ctx, min_face_size=min_face_size,
                                   stride=stride, threshold=thresh, slide_window=slide_window)

    video_capture = cv2.VideoCapture(0)
    boxes = []
    boxes_c = []
    while True :
    #img = cv2.imread('/home/zzg/Opensource/mtcnn-master/data/custom/02.jpg')
        _, img = video_capture.read()
        t1 = time.time()

        boxes, boxes_c = mtcnn_detector.detect_pnet(img)
        if boxes_c is None :
            continue
        boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
        if boxes_c is None:
            continue
        boxes, boxes_c = mtcnn_detector.detect_onet(img, boxes_c)

        print 'time: ',time.time() - t1

        if boxes_c is not None:
            draw = img.copy()
            font = cv2.FONT_HERSHEY_SIMPLEX
            for b in boxes_c:
                cv2.rectangle(draw, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])), (0, 255, 255), 1)
                #cv2.putText(draw, '%.3f'%b[4], (int(b[0]), int(b[1])), font, 0.4, (255, 255, 255), 1)

            cv2.imshow("detection result", draw)
            #cv2.imwrite("o12.jpg",draw)
            cv2.waitKey(10)

#    img = cv2.imread('/home/zzg/Opensource/mtcnn-master/data/custom/03.jpg')
#    t1 = time.time()

#    boxes, boxes_c = mtcnn_detector.detect_pnet(img)
#    boxes, boxes_c = mtcnn_detector.detect_rnet(img, boxes_c)
#    boxes, boxes_c = mtcnn_detector.detect_onet(img, boxes_c)

#    print 'time: ',time.time() - t1

#    if boxes_c is not None:
#        draw = img.copy()
#        font = cv2.FONT_HERSHEY_SIMPLEX
#        for b in boxes_c:
#            cv2.rectangle(draw, (int(b[0]), int(b[1])), (int(b[2]), int(b[3])), (0, 255, 255), 1)
#            #cv2.putText(draw, '%.3f'%b[4], (int(b[0]), int(b[1])), font, 0.4, (255, 255, 255), 1)

#        cv2.imshow("detection result", draw)
#        cv2.imwrite("o12.jpg",draw)
#        cv2.waitKey(10)

def parse_args():
    parser = argparse.ArgumentParser(description='Test mtcnn',
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--prefix', dest='prefix', help='prefix of model name', nargs="+",
                        default=['model/pnet', 'model/rnet', 'model/onet'], type=str)
    parser.add_argument('--epoch', dest='epoch', help='epoch number of model to load', nargs="+",
                        default=[16, 16, 16], type=int)
    parser.add_argument('--batch_size', dest='batch_size', help='list of batch size used in prediction', nargs="+",
                        default=[2048, 256, 16], type=int)
    parser.add_argument('--thresh', dest='thresh', help='list of thresh for pnet, rnet, onet', nargs="+",
                        default=[0.8, 0.9, 0.9], type=float)
    parser.add_argument('--min_face', dest='min_face', help='minimum face size for detection',
                        default=40, type=int)
    parser.add_argument('--stride', dest='stride', help='stride of sliding window',
                        default=2, type=int)
    parser.add_argument('--sw', dest='slide_window', help='use sliding window in pnet', action='store_true')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU device to train with',
                        default=-1, type=int)
    args = parser.parse_args()
    return args

if __name__ == '__main__':
    args = parse_args()
    print 'Called with argument:'
    print args
    ctx = mx.gpu(args.gpu_id)
    if args.gpu_id == -1:
        ctx = mx.cpu(0)
    test_net(args.prefix, args.epoch, args.batch_size,
             ctx, args.thresh, args.min_face,
             args.stride, args.slide_window)