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，加深一些模型，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：
20170920084446842.jpg