<Github - zeusees/FaceBoxes>

主页： 智云视图

<Github - AIHGF/FaceBoxes>

FaceBoxes 完整复现.

论文： FaceBoxes: A CPU Real-time Face Detector with High Accuracy - 2018

论文源码：<Github - sfzhang15/FaceBoxes>

1. FaceBoxes 测试

Caffemodel - FaceBoxes_1024x1024.caffemodel (3.6M)

Prototxt - faceboxes_deploy.prototxt

Caffe - [caffe-ssd]()

# -*- coding: utf-8 -*
import numpy as np
import matplotlib.pyplot as plt
import sys,os  
import cv2
caffe_root = '/path/to/caffe-ssd/'
sys.path.insert(0, caffe_root + 'python')  
import caffe  
import time


net_file= 'faceboxes_deploy.prototxt'  
caffe_model='FaceBoxes_1024x1024.caffemodel'  

caffe.set_mode_cpu() # cpu
# caffe.set_mode_gpu() # gpu
net = caffe.Net(net_file,caffe_model,caffe.TEST)  

CLASSES = ('background', 'face')

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2, 0, 1))
transformer.set_mean('data', np.array([104, 117, 123]))  # mean pixel


def preprocess(src):
    img = cv2.resize(src, (1024,1024))
    img = img - 127.5
    img = img * 0.007843
    return img


def postprocess(img, out):   
    h = img.shape[0]
    w = img.shape[1]
    box = out['detection_out'][0,0,:,3:7] * np.array([w, h, w, h])

    cls = out['detection_out'][0,0,:,1]
    conf = out['detection_out'][0,0,:,2]
    return (box.astype(np.int32), conf, cls)


def detect(imgfile):
    origimg = cv2.imread(imgfile)

    transformed_image = transformer.preprocess('data', origimg)
    net.blobs['data'].data[...] = transformed_image

    time_start = time.time()
    out = net.forward()
    print("[INFO]timecost: ", time.time() - time_start)

    box, conf, cls = postprocess(origimg, out)

    for i in range(len(box)):
        p1 = (box[i][0], box[i][1])
        p2 = (box[i][2], box[i][3])
        cv2.rectangle(origimg, p1, p2, (0, 255, 0))
        p3 = (max(p1[0], 15), max(p1[1], 15))
        title = "%s:%.2f" % (CLASSES[int(cls[i])], conf[i])
        cv2.putText(origimg, title, p3, cv2.FONT_ITALIC, 0.6, (0, 255, 0), 1)
    plt.imshow(origimg[:,:,::-1])
    plt.axis("off")
    plt.show()

    return True

if __name__ == '__main__':
    test_dir = "/path/to/images"

    for img_name in os.listdir(test_dir):
        img_file = os.path.join(test_dir, img_name)
        result = detect(img_file)

    print("[INFO]Done.")

如(会出现漏检)：

('[INFO]cpu timecost: ', 0.1766371726989746)
('[INFO]cpu timecost: ', 0.13269805908203125)

('[INFO]gpu timecost: ', 0.024658203125)
('[INFO]gpu timecost: ', 0.01454019546508789)