win10 tensorflow MTCNN Demo

doc mark here

win10安装的tensorflow是python3.5版本的，因此还要安装对应版本的opencv，python opencv下载地址：

http://www.lfd.uci.edu/~gohlke/pythonlibs/

选择32位还是64位的文件，比如：opencv_python-3.1.0-cp35-cp35m-win_amd64.whl

在python 安装地址下的 \Scripts有pip.exe  运行 

pip.exe install opencv_python-3.1.0-cp35-cp35m-win32.whl

MTCNN 入口代码

#coding = gbkimport tensorflow as tfimport numpy as npimport cv2import detect_faceimport time#face detection parametersminsize = 20 # minimum size of facethreshold = [ 0.6, 0.7, 0.7 ]  # three steps's thresholdfactor = 0.709 # scale factordef to_rgb(img):    w, h = img.shape    ret = np.empty((w, h, 3), dtype=np.uint8)    ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img    return ret# restore mtcnn modelprint('Creating networks and loading parameters')gpu_memory_fraction = 1.0with tf.Graph().as_default():    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)    sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))    with sess.as_default():        pnet, rnet, onet = detect_face.create_mtcnn(sess, './model_check_point/')video_capture = cv2.VideoCapture(0)while True:    # Capture frame-by-frame    ret, frame = video_capture.read()    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)    if gray.ndim == 2:        img = to_rgb(gray)    start = time.time()    bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)    end = time.time()    print("current frame processing time : %.2fms"%((end-start)*1000))    nrof_faces = bounding_boxes.shape[0]  # number of faces    # print('找到人脸数目为：{}'.format(nrof_faces))    for face_position in bounding_boxes:        face_position = face_position.astype(int)        cv2.rectangle(frame,                      (face_position[0], face_position[1]),                      (face_position[2], face_position[3]),                      (0, 255, 0), 2)    # print(faces)    cv2.imshow('MTCNN Demo', frame)    if cv2.waitKey(30) & 0xFF == ord('q'):        breakvideo_capture.release()cv2.destroyAllWindows()

MTCNN预先训练好的model去这个地址下载

https://github.com/yobibyte/yobiface/blob/master/model/model-20160506.ckpt-500000

下载之后放入代码同级目录model_check_point中

核心实现代码，代码中做了一些修改，主要是因为python版本的缘故，这里的代码可以在python3.5的环境先正常的run起来。

""" Tensorflow implementation of the face detection / alignment algorithm found athttps://github.com/kpzhang93/MTCNN_face_detection_alignment"""# MIT License# # Copyright (c) 2016 David Sandberg# # Permission is hereby granted, free of charge, to any person obtaining a copy# of this software and associated documentation files (the "Software"), to deal# in the Software without restriction, including without limitation the rights# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell# copies of the Software, and to permit persons to whom the Software is# furnished to do so, subject to the following conditions:# # The above copyright notice and this permission notice shall be included in all# copies or substantial portions of the Software.# # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE# SOFTWARE.from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport numpy as npimport tensorflow as tf#from math import floorimport cv2import osdef layer(op):    '''Decorator for composable network layers.'''    def layer_decorated(self, *args, **kwargs):        # Automatically set a name if not provided.        name = kwargs.setdefault('name', self.get_unique_name(op.__name__))        # Figure out the layer inputs.        if len(self.terminals) == 0:            raise RuntimeError('No input variables found for layer %s.' % name)        elif len(self.terminals) == 1:            layer_input = self.terminals[0]        else:            layer_input = list(self.terminals)        # Perform the operation and get the output.        layer_output = op(self, layer_input, *args, **kwargs)        # Add to layer LUT.        self.layers[name] = layer_output        # This output is now the input for the next layer.        self.feed(layer_output)        # Return self for chained calls.        return self    return layer_decoratedclass Network(object):    def __init__(self, inputs, trainable=True):        # The input nodes for this network        self.inputs = inputs        # The current list of terminal nodes        self.terminals = []        # Mapping from layer names to layers        self.layers = dict(inputs)        # If true, the resulting variables are set as trainable        self.trainable = trainable        self.setup()    def setup(self):        '''Construct the network. '''        raise NotImplementedError('Must be implemented by the subclass.')    def load(self, data_path, session, ignore_missing=False):        '''Load network weights.        data_path: The path to the numpy-serialized network weights        session: The current TensorFlow session        ignore_missing: If true, serialized weights for missing layers are ignored.        '''        #data_dict = np.load(data_path).item() #pylint: disable=no-member #change 2017-12-13        data_dict = np.load(data_path, encoding="latin1").item() #pylint: disable=no-member        for op_name in data_dict:            with tf.variable_scope(op_name, reuse=True):                #for param_name, data in data_dict[op_name].iteritems(): #python 2 coding style                for param_name, data in data_dict[op_name].items():                    try:                        var = tf.get_variable(param_name)                        session.run(var.assign(data))                    except ValueError:                        if not ignore_missing:                            raise    def feed(self, *args):        '''Set the input(s) for the next operation by replacing the terminal nodes.        The arguments can be either layer names or the actual layers.        '''        assert len(args) != 0        self.terminals = []        for fed_layer in args:            #if isinstance(fed_layer, basestring):            if isinstance(fed_layer, str):                try:                    fed_layer = self.layers[fed_layer]                except KeyError:                    raise KeyError('Unknown layer name fed: %s' % fed_layer)            self.terminals.append(fed_layer)        return self    def get_output(self):        '''Returns the current network output.'''        return self.terminals[-1]    def get_unique_name(self, prefix):        '''Returns an index-suffixed unique name for the given prefix.        This is used for auto-generating layer names based on the type-prefix.        '''        ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1        return '%s_%d' % (prefix, ident)    def make_var(self, name, shape):        '''Creates a new TensorFlow variable.'''        return tf.get_variable(name, shape, trainable=self.trainable)    def validate_padding(self, padding):        '''Verifies that the padding is one of the supported ones.'''        assert padding in ('SAME', 'VALID')    @layer    def conv(self,             inp,             k_h,             k_w,             c_o,             s_h,             s_w,             name,             relu=True,             padding='SAME',             group=1,             biased=True):        # Verify that the padding is acceptable        self.validate_padding(padding)        # Get the number of channels in the input        c_i = inp.get_shape()[-1]        # Verify that the grouping parameter is valid        assert c_i % group == 0        assert c_o % group == 0        # Convolution for a given input and kernel        convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)        with tf.variable_scope(name) as scope:            kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])            # This is the common-case. Convolve the input without any further complications.            output = convolve(inp, kernel)            # Add the biases            if biased:                biases = self.make_var('biases', [c_o])                output = tf.nn.bias_add(output, biases)            if relu:                # ReLU non-linearity                output = tf.nn.relu(output, name=scope.name)            return output    @layer    def prelu(self, inp, name):        with tf.variable_scope(name):            i = inp.get_shape().as_list()            alpha = self.make_var('alpha', shape=(i[-1]))            #output = tf.nn.relu(inp) + tf.mul(alpha, -tf.nn.relu(-inp)) //yafei change 2017-12-13            output = tf.nn.relu(inp) + tf.multiply(alpha, -tf.nn.relu(-inp))        return output    @layer    def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):        self.validate_padding(padding)        return tf.nn.max_pool(inp,                              ksize=[1, k_h, k_w, 1],                              strides=[1, s_h, s_w, 1],                              padding=padding,                              name=name)    @layer    def fc(self, inp, num_out, name, relu=True):        with tf.variable_scope(name):            input_shape = inp.get_shape()            if input_shape.ndims == 4:                # The input is spatial. Vectorize it first.                dim = 1                for d in input_shape[1:].as_list():                    dim *= d                feed_in = tf.reshape(inp, [-1, dim])            else:                feed_in, dim = (inp, input_shape[-1].value)            weights = self.make_var('weights', shape=[dim, num_out])            biases = self.make_var('biases', [num_out])            op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b            fc = op(feed_in, weights, biases, name=name)            return fc    """    Multi dimensional softmax,    refer to https://github.com/tensorflow/tensorflow/issues/210    compute softmax along the dimension of target    the native softmax only supports batch_size x dimension    """    @layer    def softmax(self, target, axis, name=None):        max_axis = tf.reduce_max(target, axis, keep_dims=True)        target_exp = tf.exp(target-max_axis)        normalize = tf.reduce_sum(target_exp, axis, keep_dims=True)        softmax = tf.div(target_exp, normalize, name)        return softmax    class PNet(Network):    def setup(self):        (self.feed('data') #pylint: disable=no-value-for-parameter, no-member             .conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')             .prelu(name='PReLU1')             .max_pool(2, 2, 2, 2, name='pool1')             .conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2')             .prelu(name='PReLU2')             .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3')             .prelu(name='PReLU3')             .conv(1, 1, 2, 1, 1, relu=False, name='conv4-1')             .softmax(3,name='prob1'))        (self.feed('PReLU3') #pylint: disable=no-value-for-parameter             .conv(1, 1, 4, 1, 1, relu=False, name='conv4-2'))        class RNet(Network):    def setup(self):        (self.feed('data') #pylint: disable=no-value-for-parameter, no-member             .conv(3, 3, 28, 1, 1, padding='VALID', relu=False, name='conv1')             .prelu(name='prelu1')             .max_pool(3, 3, 2, 2, name='pool1')             .conv(3, 3, 48, 1, 1, padding='VALID', relu=False, name='conv2')             .prelu(name='prelu2')             .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')             .conv(2, 2, 64, 1, 1, padding='VALID', relu=False, name='conv3')             .prelu(name='prelu3')             .fc(128, relu=False, name='conv4')             .prelu(name='prelu4')             .fc(2, relu=False, name='conv5-1')             .softmax(1,name='prob1'))        (self.feed('prelu4') #pylint: disable=no-value-for-parameter             .fc(4, relu=False, name='conv5-2'))class ONet(Network):    def setup(self):        (self.feed('data') #pylint: disable=no-value-for-parameter, no-member             .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1')             .prelu(name='prelu1')             .max_pool(3, 3, 2, 2, name='pool1')             .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2')             .prelu(name='prelu2')             .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')             .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3')             .prelu(name='prelu3')             .max_pool(2, 2, 2, 2, name='pool3')             .conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4')             .prelu(name='prelu4')             .fc(256, relu=False, name='conv5')             .prelu(name='prelu5')             .fc(2, relu=False, name='conv6-1')             .softmax(1, name='prob1'))        (self.feed('prelu5') #pylint: disable=no-value-for-parameter             .fc(4, relu=False, name='conv6-2'))        (self.feed('prelu5') #pylint: disable=no-value-for-parameter             .fc(10, relu=False, name='conv6-3'))def create_mtcnn(sess, model_path):    with tf.variable_scope('pnet'):        data = tf.placeholder(tf.float32, (None,None,None,3), 'input')        pnet = PNet({'data':data})        pnet.load(os.path.join(model_path, 'det1.npy'), sess)    with tf.variable_scope('rnet'):        data = tf.placeholder(tf.float32, (None,24,24,3), 'input')        rnet = RNet({'data':data})        rnet.load(os.path.join(model_path, 'det2.npy'), sess)    with tf.variable_scope('onet'):        data = tf.placeholder(tf.float32, (None,48,48,3), 'input')        onet = ONet({'data':data})        onet.load(os.path.join(model_path, 'det3.npy'), sess)            pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})    rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})    onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img})    return pnet_fun, rnet_fun, onet_fundef detect_face(img, minsize, pnet, rnet, onet, threshold, factor):    # im: input image    # minsize: minimum of faces' size    # pnet, rnet, onet: caffemodel    # threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold    # fastresize: resize img from last scale (using in high-resolution images) if fastresize==true    factor_count=0    total_boxes=np.empty((0,9))    points=[]    h=img.shape[0]    w=img.shape[1]    minl=np.amin([h, w])    m=12.0/minsize    minl=minl*m    # creat scale pyramid    scales=[]    while minl>=12:        scales += [m*np.power(factor, factor_count)]        minl = minl*factor        factor_count += 1    # first stage    for j in range(len(scales)):        scale=scales[j]        hs=int(np.ceil(h*scale))        ws=int(np.ceil(w*scale))        im_data = imresample(img, (hs, ws))        im_data = (im_data-127.5)*0.0078125        img_x = np.expand_dims(im_data, 0)        img_y = np.transpose(img_x, (0,2,1,3))        out = pnet(img_y)        out0 = np.transpose(out[0], (0,2,1,3))        out1 = np.transpose(out[1], (0,2,1,3))                boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0])                # inter-scale nms        pick = nms(boxes.copy(), 0.5, 'Union')        if boxes.size>0 and pick.size>0:            boxes = boxes[pick,:]            total_boxes = np.append(total_boxes, boxes, axis=0)    numbox = total_boxes.shape[0]    if numbox>0:        pick = nms(total_boxes.copy(), 0.7, 'Union')        total_boxes = total_boxes[pick,:]        regw = total_boxes[:,2]-total_boxes[:,0]        regh = total_boxes[:,3]-total_boxes[:,1]        qq1 = total_boxes[:,0]+total_boxes[:,5]*regw        qq2 = total_boxes[:,1]+total_boxes[:,6]*regh        qq3 = total_boxes[:,2]+total_boxes[:,7]*regw        qq4 = total_boxes[:,3]+total_boxes[:,8]*regh        total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]]))        total_boxes = rerec(total_boxes.copy())        total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32)        dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)    numbox = total_boxes.shape[0]    if numbox>0:        # second stage        tempimg = np.zeros((24,24,3,numbox))        for k in range(0,numbox):            tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))            tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]            if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:                tempimg[:,:,:,k] = imresample(tmp, (24, 24))            else:                return np.empty()        tempimg = (tempimg-127.5)*0.0078125        tempimg1 = np.transpose(tempimg, (3,1,0,2))        out = rnet(tempimg1)        out0 = np.transpose(out[0])        out1 = np.transpose(out[1])        score = out1[1,:]        ipass = np.where(score>threshold[1])        total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])        mv = out0[:,ipass[0]]        if total_boxes.shape[0]>0:            pick = nms(total_boxes, 0.7, 'Union')            total_boxes = total_boxes[pick,:]            total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))            total_boxes = rerec(total_boxes.copy())    numbox = total_boxes.shape[0]    if numbox>0:        # third stage        total_boxes = np.fix(total_boxes).astype(np.int32)        dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)        tempimg = np.zeros((48,48,3,numbox))        for k in range(0,numbox):            tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))            tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]            if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:                tempimg[:,:,:,k] = imresample(tmp, (48, 48))            else:                return np.empty()        tempimg = (tempimg-127.5)*0.0078125        tempimg1 = np.transpose(tempimg, (3,1,0,2))        out = onet(tempimg1)        out0 = np.transpose(out[0])        out1 = np.transpose(out[1])        out2 = np.transpose(out[2])        score = out2[1,:]        points = out1        ipass = np.where(score>threshold[2])        points = points[:,ipass[0]]        total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])        mv = out0[:,ipass[0]]        w = total_boxes[:,2]-total_boxes[:,0]+1        h = total_boxes[:,3]-total_boxes[:,1]+1        points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1        points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1        if total_boxes.shape[0]>0:            total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))            pick = nms(total_boxes.copy(), 0.7, 'Min')            total_boxes = total_boxes[pick,:]            points = points[:,pick]                    return total_boxes, points             # function [boundingbox] = bbreg(boundingbox,reg)def bbreg(boundingbox,reg):    # calibrate bounding boxes    if reg.shape[1]==1:        reg = np.reshape(reg, (reg.shape[2], reg.shape[3]))    w = boundingbox[:,2]-boundingbox[:,0]+1    h = boundingbox[:,3]-boundingbox[:,1]+1    b1 = boundingbox[:,0]+reg[:,0]*w    b2 = boundingbox[:,1]+reg[:,1]*h    b3 = boundingbox[:,2]+reg[:,2]*w    b4 = boundingbox[:,3]+reg[:,3]*h    boundingbox[:,0:4] = np.transpose(np.vstack([b1, b2, b3, b4 ]))    return boundingbox def generateBoundingBox(imap, reg, scale, t):    # use heatmap to generate bounding boxes    stride=2    cellsize=12    imap = np.transpose(imap)    dx1 = np.transpose(reg[:,:,0])    dy1 = np.transpose(reg[:,:,1])    dx2 = np.transpose(reg[:,:,2])    dy2 = np.transpose(reg[:,:,3])    y, x = np.where(imap >= t)    if y.shape[0]==1:        dx1 = np.flipud(dx1)        dy1 = np.flipud(dy1)        dx2 = np.flipud(dx2)        dy2 = np.flipud(dy2)    score = imap[(y,x)]    reg = np.transpose(np.vstack([ dx1[(y,x)], dy1[(y,x)], dx2[(y,x)], dy2[(y,x)] ]))    if reg.size==0:        reg = np.empty((0,3))    bb = np.transpose(np.vstack([y,x]))    q1 = np.fix((stride*bb+1)/scale)    q2 = np.fix((stride*bb+cellsize-1+1)/scale)    boundingbox = np.hstack([q1, q2, np.expand_dims(score,1), reg])    return boundingbox, reg # function pick = nms(boxes,threshold,type)def nms(boxes, threshold, method):    if boxes.size==0:        return np.empty((0,3))    x1 = boxes[:,0]    y1 = boxes[:,1]    x2 = boxes[:,2]    y2 = boxes[:,3]    s = boxes[:,4]    area = (x2-x1+1) * (y2-y1+1)    I = np.argsort(s)    pick = np.zeros_like(s, dtype=np.int16)    counter = 0    while I.size>0:        i = I[-1]        pick[counter] = i        counter += 1        idx = I[0:-1]        xx1 = np.maximum(x1[i], x1[idx])        yy1 = np.maximum(y1[i], y1[idx])        xx2 = np.minimum(x2[i], x2[idx])        yy2 = np.minimum(y2[i], y2[idx])        w = np.maximum(0.0, xx2-xx1+1)        h = np.maximum(0.0, yy2-yy1+1)        inter = w * h        if method is 'Min':            o = inter / np.minimum(area[i], area[idx])        else:            o = inter / (area[i] + area[idx] - inter)        I = I[np.where(o<=threshold)]    pick = pick[0:counter]    return pick# function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)def pad(total_boxes, w, h):    # compute the padding coordinates (pad the bounding boxes to square)    tmpw = (total_boxes[:,2]-total_boxes[:,0]+1).astype(np.int32)    tmph = (total_boxes[:,3]-total_boxes[:,1]+1).astype(np.int32)    numbox = total_boxes.shape[0]    dx = np.ones((numbox), dtype=np.int32)    dy = np.ones((numbox), dtype=np.int32)    edx = tmpw.copy().astype(np.int32)    edy = tmph.copy().astype(np.int32)    x = total_boxes[:,0].copy().astype(np.int32)    y = total_boxes[:,1].copy().astype(np.int32)    ex = total_boxes[:,2].copy().astype(np.int32)    ey = total_boxes[:,3].copy().astype(np.int32)    tmp = np.where(ex>w)    #edx[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],1)    edx[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],0)    ex[tmp] = w        tmp = np.where(ey>h)    #edy[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],1)    edy[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],0)    ey[tmp] = h    tmp = np.where(x<1)    #dx[tmp] = np.expand_dims(2-x[tmp],1)    dx[tmp] = np.expand_dims(2-x[tmp],0)    x[tmp] = 1    tmp = np.where(y<1)    #dy[tmp] = np.expand_dims(2-y[tmp],1)    dy[tmp] = np.expand_dims(2-y[tmp],0)    y[tmp] = 1        return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph# function [bboxA] = rerec(bboxA)def rerec(bboxA):    # convert bboxA to square    h = bboxA[:,3]-bboxA[:,1]    w = bboxA[:,2]-bboxA[:,0]    l = np.maximum(w, h)    bboxA[:,0] = bboxA[:,0]+w*0.5-l*0.5    bboxA[:,1] = bboxA[:,1]+h*0.5-l*0.5    bboxA[:,2:4] = bboxA[:,0:2] + np.transpose(np.tile(l,(2,1)))    return bboxAdef imresample(img, sz):    im_data = cv2.resize(img, (sz[1], sz[0]), interpolation=cv2.INTER_AREA) #pylint: disable=no-member    return im_data    # This method is kept for debugging purpose#     h=img.shape[0]#     w=img.shape[1]#     hs, ws = sz#     dx = float(w) / ws#     dy = float(h) / hs#     im_data = np.zeros((hs,ws,3))#     for a1 in range(0,hs):#         for a2 in range(0,ws):#             for a3 in range(0,3):#                 im_data[a1,a2,a3] = img[int(floor(a1*dy)),int(floor(a2*dx)),a3]#     return im_data