人脸检测——fcn

在上一篇的基础上修改即可：人脸检测——滑动窗口篇（训练和实现）

！！！注意：这些是我的调试版本，最优版本不方便公开，但是自己可以查看论文，自行在此基础上修改，一定要加上回归框，要不fcn容易出现较大偏差。

fcn：

import tensorflow as tf  import numpy as np  import sys  # from models import *  from PIL import Image  from PIL import ImageDraw  from PIL import ImageFile  from skimage.transform import pyramid_gaussian  from skimage.transform import resize  from matplotlib import pyplot  ImageFile.LOAD_TRUNCATED_IMAGES = True  import utils  import cv2  import pylab    def fcn_12_detect(threshold, dropout=False, activation=tf.nn.relu):            imgs = tf.placeholder(tf.float32, [None, None, None, 3])      labels = tf.placeholder(tf.float32, [None, 2])      keep_prob = tf.placeholder(tf.float32, name='keep_prob')      with tf.variable_scope('net_12'):          conv1,_ = utils.conv2d(x=imgs, n_output=16, k_w=3, k_h=3, d_w=1, d_h=1, name="conv1")          conv1 = activation(conv1)          pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME", name="pool1")          ip1,W1 = utils.conv2d(x=pool1, n_output=16, k_w=6, k_h=6, d_w=1, d_h=1, padding="VALID", name="ip1")          ip1 = activation(ip1)          if dropout:              ip1 = tf.nn.dropout(ip1, keep_prob)          ip2,W2 = utils.conv2d(x=ip1, n_output=2, k_w=1, k_h=1, d_w=1, d_h=1, name="ip2")            #pred = tf.nn.sigmoid(utils.flatten(ip2))          pred = tf.nn.sigmoid(ip2)                    return {'imgs': imgs, 'keep_prob': keep_prob,'pred': pred, 'features': ip1}    def fcn_24_detect(threshold, dropout=False, activation=tf.nn.relu):        imgs = tf.placeholder(tf.float32, [None, 24, 24, 3])      labels = tf.placeholder(tf.float32, [None, 2])      keep_prob = tf.placeholder(tf.float32, name='keep_prob')            with tf.variable_scope('net_24'):          conv1, _ = utils.conv2d(x=imgs, n_output=64, k_w=5, k_h=5, d_w=1, d_h=1, name="conv1")          conv1 = activation(conv1)          pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding="SAME", name="pool1")          ip1, W1 = utils.conv2d(x=pool1, n_output=128, k_w=12, k_h=12, d_w=1, d_h=1, padding="VALID", name="ip1")          ip1 = activation(ip1)          concat = ip1          if dropout:              concat = tf.nn.dropout(concat, keep_prob)          ip2, W2 = utils.conv2d(x=concat, n_output=2, k_w=1, k_h=1, d_w=1, d_h=1, name="ip2")            pred = tf.nn.sigmoid(utils.flatten(ip2))          target = utils.flatten(labels)            regularizer = 8e-3 * (tf.nn.l2_loss(W1)+100*tf.nn.l2_loss(W2))            loss = tf.reduce_mean(tf.div(tf.add(-tf.reduce_sum(target * tf.log(pred + 1e-9),1), -tf.reduce_sum((1-target) * tf.log(1-pred + 1e-9),1)),2)) + regularizer          cost = tf.reduce_mean(loss)                    predict = pred          max_idx_p = tf.argmax(predict, 1)            max_idx_l = tf.argmax(target, 1)            correct_pred = tf.equal(max_idx_p, max_idx_l)            acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))              thresholding_24 = tf.cast(tf.greater(pred, threshold), "float")          recall_24 = tf.reduce_sum(tf.cast(tf.logical_and(tf.equal(thresholding_24, tf.constant([1.0])), tf.equal(target, tf.constant([1.0]))), "float")) / tf.reduce_sum(target)              return { 'imgs': imgs, 'labels': labels,               'keep_prob': keep_prob, 'cost': cost, 'pred': pred, 'accuracy': acc, 'features': concat,              'recall': recall_24, 'thresholding': thresholding_24}    def py_nms(dets, thresh, mode="Union"):      """         greedily select boxes with high confidence         keep boxes overlap <= thresh         rule out overlap > thresh         :param dets: [[x1, y1, x2, y2 score]]         :param thresh: retain overlap <= thresh         :return: indexes to keep         """      if len(dets) == 0:          return []      x1 = dets[:, 0]      y1 = dets[:, 1]      x2 = dets[:, 2]      y2 = dets[:, 3]      scores = dets[:, 4]            areas = (x2 - x1 + 1) * (y2 - y1 + 1)      order = scores.argsort()[::-1]            keep = []      while order.size > 0:          i = order[0]          keep.append(i)          xx1 = np.maximum(x1[i], x1[order[1:]])          yy1 = np.maximum(y1[i], y1[order[1:]])          xx2 = np.minimum(x2[i], x2[order[1:]])          yy2 = np.minimum(y2[i], y2[order[1:]])                    w = np.maximum(0.0, xx2 - xx1 + 1)          h = np.maximum(0.0, yy2 - yy1 + 1)          inter = w * h          if mode == "Union":              ovr = inter / (areas[i] + areas[order[1:]] - inter)          elif mode == "Minimum":              ovr = inter / np.minimum(areas[i], areas[order[1:]])                    inds = np.where(ovr <= thresh)[0]          order = order[inds + 1]            return dets[keep]    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    # 预处理变了要重新训练哦。def image_preprocess(img):    img = (img - 127.5)*0.0078125    '''m = img.mean()    s = img.std()    min_s = 1.0/(np.sqrt(img.shape[0]*img.shape[1]*img.shape[2]))    std = max(min_s, s)      img = (img-m)/std'''    return img  def min_face(img, F, window_size, stride):      # img：输入图像，F：最小人脸大小， window_size：滑动窗，stride：滑动窗的步长。      h, w, _ = img.shape      w_re = int(float(w)*window_size/F)      h_re = int(float(h)*window_size/F)      if w_re<=window_size+stride or h_re<=window_size+stride:          print (None)      # 调整图片大小的时候注意参数，千万不要写反了      # 根据最小人脸缩放图片      img = cv2.resize(img, (w_re, h_re))      return img    # 构建图像的金字塔，以便进行多尺度滑动窗口  # image是输入图像，f为缩放的尺度， window_size最小尺度  def pyramid(image, f, window_size):      w = image.shape[1]      h = image.shape[0]      img_ls = []      while( w > window_size and h > window_size):          img_ls.append(image)          w = int(w * f)          h = int(h * f)          image = cv2.resize(image, (w, h))      return img_ls    # 选取map中大于人脸阀值的点，映射到原图片的窗口大小，默认map中的一个点对应输入图中的12*12的窗口，最后要根据缩放比例映射到原图。  def generateBoundingBox(imap, scale, t):      # use heatmap to generate bounding boxes      stride=2      cellsize=12        imap = np.transpose(imap)      y, x = np.where(imap >= t)            score = imap[(y,x)]      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)])      return boundingbox    def imresample(img, sz):      im_data = cv2.resize(img, (sz[1], sz[0]), interpolation=cv2.INTER_AREA) #pylint: disable=no-member      return im_data    if __name__ == '__main__':        image = cv2.imread('images/11.jpg')      h,w,_ = image.shape      # 调参的参数      IMAGE_SIZE = 12      # 步长      stride = 2      # 最小人脸大小      F = 24      # 构建金字塔的比例      ff = 0.8      # 概率多大时判定为人脸？      p_12 = 0.8      p_24 = 0.8      # nms      overlapThresh_12 = 0.7    # 是否启用net-24      net_24 = True      overlapThresh_24 = 0.3    '''''--------------------------------------'''            net_12 = fcn_12_detect(0.0)      net_12_vars = [v for v in tf.trainable_variables() if v.name.startswith('net_12')]      saver_net_12 = tf.train.Saver(net_12_vars)            net_24 = fcn_24_detect(0.0)      net_24_vars = [v for v in tf.trainable_variables() if v.name.startswith('net_24')]      saver_net_24 = tf.train.Saver(net_24_vars)              sess = tf.Session()      sess.run(tf.initialize_all_variables())        saver_net_12.restore(sess, 'model/12-net/model_net_12-123246')      saver_net_24.restore(sess, 'model/24-net/model_net_24-161800')      # saver_cal_48.restore(sess, 'model/model_cal_48-10000')                  # 需要检测的最小人脸      img =image      factor_count=0      total_boxes=np.empty((0,5))      points=[]      h=img.shape[0]      w=img.shape[1]      minl=np.amin([h, w])      m=12.0/F      minl=minl*m      # creat scale pyramid      scales=[]      factor=ff      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 = image_preprocess(im_data)          pred_cal_12 = sess.run(net_12['pred'], feed_dict={net_12['imgs']: [im_data]})          out = np.transpose(pred_cal_12, (0,2,1,3))          threshold_12 = p_12          boxes = generateBoundingBox(out[0,:,:,1].copy(), scale, threshold_12)          boxes = py_nms(boxes, overlapThresh_12, 'Union')          if boxes != []:              total_boxes = np.append(total_boxes, boxes, axis=0)                      window_net = total_boxes      # 后面24-net，48-net        if window_net == []:          print "windows is None!"      if window_net != []:          print(window_net.shape)          for box in window_net:              #ImageDraw.Draw(image).rectangle((box[1], box[0], box[3], box[2]), outline = "red")              cv2.rectangle(image, (int(box[1]),int(box[0])), (int(box[3]),int(box[2])), (0, 255, 0), 2)      cv2.imwrite("images/face_img.jpg", image)      cv2.imshow("face detection", image)      cv2.waitKey(10000)      cv2.destroyAllWindows()              sess.close()  

检测结果：