本代码基于作者提供的python版本代码修改，参考：
https://github.com/DuinoDu/mtcnn/blob/master/demo.py （作者提供）
https://github.com/dlunion/mtcnn/blob/master/train/gen_48net_data2.py
1，生成positive,negative,part三种样本，用作者的net1->net2生成bbox， 根据预测的bbox和ground truth计算IOU：
positive: IOU >= 0.65;
negative: IOU < 0.3;
part: 0.4 <= IOU < 0.65
代码如下：
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import _init_paths
import caffe
import cv2
import numpy as np
#from python_wrapper import *
import os
def bbreg(boundingbox, reg):
reg = reg.T
# calibrate bouding boxes
if reg.shape[1] == 1:
print "reshape of reg"
pass # reshape of reg
w = boundingbox[:,2] - boundingbox[:,0] + 1
h = boundingbox[:,3] - boundingbox[:,1] + 1
bb0 = boundingbox[:,0] + reg[:,0]*w
bb1 = boundingbox[:,1] + reg[:,1]*h
bb2 = boundingbox[:,2] + reg[:,2]*w
bb3 = boundingbox[:,3] + reg[:,3]*h
boundingbox[:,0:4] = np.array([bb0, bb1, bb2, bb3]).T
#print "bb", boundingbox
return boundingbox
def pad(boxesA, w, h):
boxes = boxesA.copy() # shit, value parameter!!!
tmph = boxes[:,3] - boxes[:,1] + 1
tmpw = boxes[:,2] - boxes[:,0] + 1
numbox = boxes.shape[0]
dx = np.ones(numbox)
dy = np.ones(numbox)
edx = tmpw
edy = tmph
x = boxes[:,0:1][:,0]
y = boxes[:,1:2][:,0]
ex = boxes[:,2:3][:,0]
ey = boxes[:,3:4][:,0]
tmp = np.where(ex > w)[0]
if tmp.shape[0] != 0:
edx[tmp] = -ex[tmp] + w-1 + tmpw[tmp]
ex[tmp] = w-1
tmp = np.where(ey > h)[0]
if tmp.shape[0] != 0:
edy[tmp] = -ey[tmp] + h-1 + tmph[tmp]
ey[tmp] = h-1
tmp = np.where(x < 1)[0]
if tmp.shape[0] != 0:
dx[tmp] = 2 - x[tmp]
x[tmp] = np.ones_like(x[tmp])
tmp = np.where(y < 1)[0]
if tmp.shape[0] != 0:
dy[tmp] = 2 - y[tmp]
y[tmp] = np.ones_like(y[tmp])
# for python index from 0, while matlab from 1
dy = np.maximum(0, dy-1)
dx = np.maximum(0, dx-1)
y = np.maximum(0, y-1)
x = np.maximum(0, x-1)
edy = np.maximum(0, edy-1)
edx = np.maximum(0, edx-1)
ey = np.maximum(0, ey-1)
ex = np.maximum(0, ex-1)
return [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph]
def IoU(box, boxes):
"""Compute IoU between detect box and gt boxes
Parameters:
----------
box: numpy array , shape (5, ): x1, y1, x2, y2, score
input box
boxes: numpy array, shape (n, 4): x1, y1, x2, y2
input ground truth boxes
Returns:
-------
ovr: numpy.array, shape (n, )
IoU
"""
box_area = (box[2] - box[0] + 1) * (box[3] - box[1] + 1)
area = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)
xx1 = np.maximum(box[0], boxes[:, 0])
yy1 = np.maximum(box[1], boxes[:, 1])
xx2 = np.minimum(box[2], boxes[:, 2])
yy2 = np.minimum(box[3], boxes[:, 3])
# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (box_area + area - inter)
return ovr
def rerec(bboxA):
# convert bboxA to square
w = bboxA[:,2] - bboxA[:,0]
h = bboxA[:,3] - bboxA[:,1]
l = np.maximum(w,h).T
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.repeat([l], 2, axis = 0).T
return bboxA
def nms(boxes, threshold, type):
"""nms
:boxes: [:,0:5]
:threshold: 0.5 like
:type: 'Min' or others
:returns: TODO
"""
if boxes.shape[0] == 0:
return np.array([])
x1 = boxes[:,0]
y1 = boxes[:,1]
x2 = boxes[:,2]
y2 = boxes[:,3]
s = boxes[:,4]
area = np.multiply(x2-x1+1, y2-y1+1)
I = np.array(s.argsort()) # read s using I
pick = [];
while len(I) > 0:
xx1 = np.maximum(x1[I[-1]], x1[I[0:-1]])
yy1 = np.maximum(y1[I[-1]], y1[I[0:-1]])
xx2 = np.minimum(x2[I[-1]], x2[I[0:-1]])
yy2 = np.minimum(y2[I[-1]], y2[I[0:-1]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
if type == 'Min':
o = inter / np.minimum(area[I[-1]], area[I[0:-1]])
else:
o = inter / (area[I[-1]] + area[I[0:-1]] - inter)
pick.append(I[-1])
I = I[np.where( o <= threshold)[0]]
return pick
def generateBoundingBox(map, reg, scale, t):
stride = 2
cellsize = 12
map = map.T
dx1 = reg[0,:,:].T
dy1 = reg[1,:,:].T
dx2 = reg[2,:,:].T
dy2 = reg[3,:,:].T
(x, y) = np.where(map >= t)
yy = y
xx = x
score = map[x,y]
reg = np.array([dx1[x,y], dy1[x,y], dx2[x,y], dy2[x,y]])
if reg.shape[0] == 0:
pass
boundingbox = np.array([yy, xx]).T
bb1 = np.fix((stride * (boundingbox) + 1) / scale).T # matlab index from 1, so with "boundingbox-1"
bb2 = np.fix((stride * (boundingbox) + cellsize - 1 + 1) / scale).T # while python don't have to
score = np.array([score])
boundingbox_out = np.concatenate((bb1, bb2, score, reg), axis=0)
return boundingbox_out.T
def drawBoxes(im, boxes):
x1 = boxes[:,0]
y1 = boxes[:,1]
x2 = boxes[:,2]
y2 = boxes[:,3]
for i in range(x1.shape[0]):
cv2.rectangle(im, (int(x1[i]), int(y1[i])), (int(x2[i]), int(y2[i])), (0,255,0), 1)
return im
def drawlandmark(im, points):
for i in range(points.shape[0]):
for j in range(5):
cv2.circle(im, (int(points[i][j]), int(points[i][j+5])), 2, (255,0,0))
return im
from time import time
_tstart_stack = []
def tic():
_tstart_stack.append(time())
def toc(fmt="Elapsed: %s s"):
print fmt % (time()-_tstart_stack.pop())
def detect_face(img, minsize, PNet, RNet, threshold, fastresize, factor):
img2 = img.copy()
factor_count = 0
total_boxes = np.zeros((0,9), np.float)
points = []
h = img.shape[0]
w = img.shape[1]
minl = min(h, w)
img = img.astype(float)
m = 12.0/minsize
minl = minl*m
# create scale pyramid
scales = []
while minl >= 12:
scales.append(m * pow(factor, factor_count))
minl *= factor
factor_count += 1
# first stage
for scale in scales:
hs = int(np.ceil(h*scale))
ws = int(np.ceil(w*scale))
if fastresize:
im_data = (img-127.5)*0.0078125 # [0,255] -> [-1,1]
im_data = cv2.resize(im_data, (ws,hs)) # default is bilinear
else:
im_data = cv2.resize(img, (ws,hs)) # default is bilinear
im_data = (im_data-127.5)*0.0078125 # [0,255] -> [-1,1]
#im_data = imResample(img, hs, ws); print "scale:", scale
im_data = np.swapaxes(im_data, 0, 2)
im_data = np.array([im_data], dtype = np.float)
PNet.blobs['data'].reshape(1, 3, ws, hs)
PNet.blobs['data'].data[...] = im_data
out = PNet.forward()
boxes = generateBoundingBox(out['prob1'][0,1,:,:], out['conv4-2'][0], scale, threshold[0])
if boxes.shape[0] != 0:
pick = nms(boxes, 0.5, 'Union')
if len(pick) > 0 :
boxes = boxes[pick, :]
if boxes.shape[0] != 0:
total_boxes = np.concatenate((total_boxes, boxes), axis=0)
#np.save('total_boxes_101.npy', total_boxes)
#####
# 1 #
#####
# print "[1]:",total_boxes.shape[0]
#print total_boxes
#return total_boxes, []
numbox = total_boxes.shape[0]
if numbox > 0:
# nms
pick = nms(total_boxes, 0.7, 'Union')
total_boxes = total_boxes[pick, :]
# print "[2]:",total_boxes.shape[0]
# revise and convert to square
regh = total_boxes[:,3] - total_boxes[:,1]
regw = total_boxes[:,2] - total_boxes[:,0]
t1 = total_boxes[:,0] + total_boxes[:,5]*regw
t2 = total_boxes[:,1] + total_boxes[:,6]*regh
t3 = total_boxes[:,2] + total_boxes[:,7]*regw
t4 = total_boxes[:,3] + total_boxes[:,8]*regh
t5 = total_boxes[:,4]
total_boxes = np.array([t1,t2,t3,t4,t5]).T
total_boxes = rerec(total_boxes) # convert box to square
# print "[4]:",total_boxes.shape[0]
total_boxes[:,0:4] = np.fix(total_boxes[:,0:4])
# print "[4.5]:",total_boxes.shape[0]
#print total_boxes
[dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = pad(total_boxes, w, h)
numbox = total_boxes.shape[0]
if numbox > 0:
# second stage
# construct input for RNet
tempimg = np.zeros((numbox, 24, 24, 3)) # (24, 24, 3, numbox)
for k in range(numbox):
tmp = np.zeros((int(tmph[k]) +1, int(tmpw[k]) + 1,3))
tmp[int(dy[k]):int(edy[k])+1, int(dx[k]):int(edx[k])+1] = img[int(y[k]):int(ey[k])+1, int(x[k]):int(ex[k])+1]
#print "y,ey,x,ex", y[k], ey[k], x[k], ex[k]
#print "tmp", tmp.shape
tempimg[k,:,:,:] = cv2.resize(tmp, (24, 24))
#print tempimg.shape
#print tempimg[0,0,0,:]
tempimg = (tempimg-127.5)*0.0078125 # done in imResample function wrapped by python
# RNet
tempimg = np.swapaxes(tempimg, 1, 3)
#print tempimg[0,:,0,0]
RNet.blobs['data'].reshape(numbox, 3, 24, 24)
RNet.blobs['data'].data[...] = tempimg
out = RNet.forward()
score = out['prob1'][:,1]
#print 'score', score
pass_t = np.where(score>threshold[1])[0]
#print 'pass_t', pass_t
score = np.array([score[pass_t]]).T
total_boxes = np.concatenate( (total_boxes[pass_t, 0:4], score), axis = 1)
# print "[5]:",total_boxes.shape[0]
#print total_boxes
#print "1.5:",total_boxes.shape
mv = out['conv5-2'][pass_t, :].T
#print "mv", mv
if total_boxes.shape[0] > 0:
pick = nms(total_boxes, 0.7, 'Union')
# print 'pick', pick
if len(pick) > 0:
total_boxes = total_boxes[pick, :]
# print "[6]:", total_boxes.shape[0]
total_boxes = bbreg(total_boxes, mv[:, pick])
# print "[7]:", total_boxes.shape[0]
total_boxes = rerec(total_boxes)
# print "[8]:", total_boxes.shape[0]
return total_boxes
def main():
img_dir = "/home/xiao/code/mtcnn-caffe/prepare_data/WIDER_train/images/"
imglistfile = "wider_face_train.txt"
with open(imglistfile, 'r') as f:
annotations = f.readlines()
num = len(annotations)
print "%d pics in total" % num
neg_save_dir = "/media/xiao/软件/mtcnn/train/48/negative/"
pos_save_dir = "/media/xiao/软件/mtcnn/train/48/positive/"
part_save_dir = "/media/xiao/软件/mtcnn/train/48/part/"
image_size = 48
f1 = open('/media/xiao/软件/mtcnn/train/48/pos_48.txt', 'w')
f2 = open('/media/xiao/软件/mtcnn/train/48/neg_48.txt', 'w')
f3 = open('/media/xiao/软件/mtcnn/train/48/part_48.txt', 'w')
p_idx = 0 # positive
n_idx = 0 # negative
d_idx = 0 # dont care
image_idx = 0
minsize = 20
caffe_model_path = "./model"
threshold = [0.6, 0.7, 0.7]
factor = 0.709
caffe.set_mode_gpu()
PNet = caffe.Net(caffe_model_path+"/det1.prototxt", caffe_model_path+"/det1.caffemodel", caffe.TEST)
RNet = caffe.Net(caffe_model_path+"/det2.prototxt", caffe_model_path+"/det2.caffemodel", caffe.TEST)
for annotation in annotations:
# imgpath = imgpath.split('\n')[0]
annotation = annotation.strip().split(' ')
bbox = map(float, annotation[1:])
gts = np.array(bbox, dtype=np.float32).reshape(-1, 4)
img_path = img_dir + annotation[0] + '.jpg'
#print "######\n", img_path
print image_idx
image_idx += 1
img = cv2.imread(img_path)
img_matlab = img.copy()
tmp = img_matlab[:,:,2].copy()
img_matlab[:,:,2] = img_matlab[:,:,0]
img_matlab[:,:,0] = tmp
boundingboxes = detect_face(img_matlab, minsize, PNet, RNet, threshold, False, factor)
#img = drawBoxes(img, boundingboxes)
#cv2.imshow('img', img)
#cv2.waitKey(1000)
# generate positive,negative,part samples
for box in boundingboxes:
x_left, y_top, x_right, y_bottom, _ = box
crop_w = x_right - x_left + 1
crop_h = y_bottom - y_top + 1
# ignore box that is too small or beyond image border
if crop_w < image_size / 2 or crop_h < image_size / 2:
continue
if x_left < 0 or y_top < 0:
continue
# compute intersection over union(IoU) between current box and all gt boxes
Iou = IoU(box, gts)
cropped_im = img[int(y_top):int(y_bottom + 1) , int(x_left):int(x_right + 1) ]
resized_im = cv2.resize(cropped_im, (image_size, image_size), interpolation=cv2.INTER_LINEAR)
#try:
# resized_im = cv2.resize(cropped_im, (image_size, image_size), interpolation=cv2.INTER_LINEAR)
#except Exception as e:
# print " 1 "
# print e
# save negative images and write label， 负样本
if np.max(Iou) < 0.3:
# Iou with all gts must below 0.3
save_file = os.path.join(neg_save_dir, "%s.jpg" % n_idx)
f2.write("%s/negative/%s.jpg" % (image_size, n_idx) + ' 0')
f2.write(" -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n")
cv2.imwrite(save_file, resized_im)
n_idx += 1
else:
# find gt_box with the highest iou
idx = np.argmax(Iou)
assigned_gt = gts[idx]
x1, y1, x2, y2 = assigned_gt
# compute bbox reg label，offset_x1，offset_y1相对于左上角； offset_x2，offset_y2相对于右上角
offset_x1 = (x1 - x_left) / float(crop_w)
offset_y1 = (y1 - y_top) / float(crop_h)
# offset_x2 = (x2 - x_left) / float(crop_w)
# offset_y2 = (y2 - y_top) / float(crop_h)
offset_x2 = (x2 - x_right) / float(crop_w)
offset_y2 = (y2 - y_bottom )/ float(crop_h)
# save positive and part-face images and write labels， 正样本
if np.max(Iou) >= 0.65:
save_file = os.path.join(pos_save_dir, "%s.jpg" % p_idx)
f1.write("%s/positive/%s.jpg" % (image_size, p_idx) + ' 1 %.6f %.6f %.6f %.6f' % (offset_x1, offset_y1, offset_x2, offset_y2))
f1.write(" -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n")
cv2.imwrite(save_file, resized_im)
p_idx += 1
# part 样本
elif np.max(Iou) >= 0.4:
save_file = os.path.join(part_save_dir, "%s.jpg" % d_idx)
f3.write("%s/part/%s.jpg" % (image_size, d_idx) + ' -1 %.6f %.6f %.6f %.6f' % (offset_x1, offset_y1, offset_x2, offset_y2))
f3.write(" -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n")
cv2.imwrite(save_file, resized_im)
d_idx += 1
f.close()
f1.close()
f2.close()
f3.close()
if __name__ == "__main__":
main()
2，生成landmark样本，用作者的net1->net2生成bbox， 根据5个landmark是否都在bbox中作为判别条件：
代码如下：
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import _init_paths
import caffe
import cv2
import numpy as np
#from python_wrapper import *
import os
def bbreg(boundingbox, reg):
reg = reg.T
# calibrate bouding boxes
if reg.shape[1] == 1:
print "reshape of reg"
pass # reshape of reg
w = boundingbox[:,2] - boundingbox[:,0] + 1
h = boundingbox[:,3] - boundingbox[:,1] + 1
bb0 = boundingbox[:,0] + reg[:,0]*w
bb1 = boundingbox[:,1] + reg[:,1]*h
bb2 = boundingbox[:,2] + reg[:,2]*w
bb3 = boundingbox[:,3] + reg[:,3]*h
boundingbox[:,0:4] = np.array([bb0, bb1, bb2, bb3]).T
#print "bb", boundingbox
return boundingbox
def pad(boxesA, w, h):
boxes = boxesA.copy() # shit, value parameter!!!
tmph = boxes[:,3] - boxes[:,1] + 1
tmpw = boxes[:,2] - boxes[:,0] + 1
numbox = boxes.shape[0]
dx = np.ones(numbox)
dy = np.ones(numbox)
edx = tmpw
edy = tmph
x = boxes[:,0:1][:,0]
y = boxes[:,1:2][:,0]
ex = boxes[:,2:3][:,0]
ey = boxes[:,3:4][:,0]
tmp = np.where(ex > w)[0]
if tmp.shape[0] != 0:
edx[tmp] = -ex[tmp] + w-1 + tmpw[tmp]
ex[tmp] = w-1
tmp = np.where(ey > h)[0]
if tmp.shape[0] != 0:
edy[tmp] = -ey[tmp] + h-1 + tmph[tmp]
ey[tmp] = h-1
tmp = np.where(x < 1)[0]
if tmp.shape[0] != 0:
dx[tmp] = 2 - x[tmp]
x[tmp] = np.ones_like(x[tmp])
tmp = np.where(y < 1)[0]
if tmp.shape[0] != 0:
dy[tmp] = 2 - y[tmp]
y[tmp] = np.ones_like(y[tmp])
# for python index from 0, while matlab from 1
dy = np.maximum(0, dy-1)
dx = np.maximum(0, dx-1)
y = np.maximum(0, y-1)
x = np.maximum(0, x-1)
edy = np.maximum(0, edy-1)
edx = np.maximum(0, edx-1)
ey = np.maximum(0, ey-1)
ex = np.maximum(0, ex-1)
return [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph]
def IoU(box, boxes):
"""Compute IoU between detect box and gt boxes
Parameters:
----------
box: numpy array , shape (5, ): x1, y1, x2, y2, score
input box
boxes: numpy array, shape (n, 4): x1, y1, x2, y2
input ground truth boxes
Returns:
-------
ovr: numpy.array, shape (n, )
IoU
"""
box_area = (box[2] - box[0] + 1) * (box[3] - box[1] + 1)
area = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)
xx1 = np.maximum(box[0], boxes[:, 0])
yy1 = np.maximum(box[1], boxes[:, 1])
xx2 = np.minimum(box[2], boxes[:, 2])
yy2 = np.minimum(box[3], boxes[:, 3])
# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (box_area + area - inter)
return ovr
def rerec(bboxA):
# convert bboxA to square
w = bboxA[:,2] - bboxA[:,0]
h = bboxA[:,3] - bboxA[:,1]
l = np.maximum(w,h).T
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.repeat([l], 2, axis = 0).T
return bboxA
def nms(boxes, threshold, type):
"""nms
:boxes: [:,0:5]
:threshold: 0.5 like
:type: 'Min' or others
:returns: TODO
"""
if boxes.shape[0] == 0:
return np.array([])
x1 = boxes[:,0]
y1 = boxes[:,1]
x2 = boxes[:,2]
y2 = boxes[:,3]
s = boxes[:,4]
area = np.multiply(x2-x1+1, y2-y1+1)
I = np.array(s.argsort()) # read s using I
pick = [];
while len(I) > 0:
xx1 = np.maximum(x1[I[-1]], x1[I[0:-1]])
yy1 = np.maximum(y1[I[-1]], y1[I[0:-1]])
xx2 = np.minimum(x2[I[-1]], x2[I[0:-1]])
yy2 = np.minimum(y2[I[-1]], y2[I[0:-1]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
if type == 'Min':
o = inter / np.minimum(area[I[-1]], area[I[0:-1]])
else:
o = inter / (area[I[-1]] + area[I[0:-1]] - inter)
pick.append(I[-1])
I = I[np.where( o <= threshold)[0]]
return pick
def generateBoundingBox(map, reg, scale, t):
stride = 2
cellsize = 12
map = map.T
dx1 = reg[0,:,:].T
dy1 = reg[1,:,:].T
dx2 = reg[2,:,:].T
dy2 = reg[3,:,:].T
(x, y) = np.where(map >= t)
yy = y
xx = x
score = map[x,y]
reg = np.array([dx1[x,y], dy1[x,y], dx2[x,y], dy2[x,y]])
if reg.shape[0] == 0:
pass
boundingbox = np.array([yy, xx]).T
bb1 = np.fix((stride * (boundingbox) + 1) / scale).T # matlab index from 1, so with "boundingbox-1"
bb2 = np.fix((stride * (boundingbox) + cellsize - 1 + 1) / scale).T # while python don't have to
score = np.array([score])
boundingbox_out = np.concatenate((bb1, bb2, score, reg), axis=0)
return boundingbox_out.T
def drawBoxes(im, boxes):
x1 = boxes[:,0]
y1 = boxes[:,1]
x2 = boxes[:,2]
y2 = boxes[:,3]
for i in range(x1.shape[0]):
cv2.rectangle(im, (int(x1[i]), int(y1[i])), (int(x2[i]), int(y2[i])), (0,255,0), 1)
return im
def drawBoxes_align(im, boxe):
x1 = boxe[0]
y1 = boxe[1]
x2 = boxe[2]
y2 = boxe[3]
cv2.rectangle(im, (int(x1), int(y1)), (int(x2), int(y2)), (0,255,0), 1)
return im
def drawlandmark(im, points):
for i in range(points.shape[0]):
for j in range(5):
cv2.circle(im, (int(points[i][j]), int(points[i][j+5])), 2, (255,0,0))
return im
def drawlandmark_align(im, point):
for j in range(5):
cv2.circle(im, (int(point[j*2]), int(point[j*2+1])), 2, (255,0,0))
return im
from time import time
_tstart_stack = []
def tic():
_tstart_stack.append(time())
def toc(fmt="Elapsed: %s s"):
print fmt % (time()-_tstart_stack.pop())
def detect_face(img, minsize, PNet, RNet, threshold, fastresize, factor):
img2 = img.copy()
factor_count = 0
total_boxes = np.zeros((0,9), np.float)
points = []
h = img.shape[0]
w = img.shape[1]
minl = min(h, w)
img = img.astype(float)
m = 12.0/minsize
minl = minl*m
# create scale pyramid
scales = []
while minl >= 12:
scales.append(m * pow(factor, factor_count))
minl *= factor
factor_count += 1
# first stage
for scale in scales:
hs = int(np.ceil(h*scale))
ws = int(np.ceil(w*scale))
if fastresize:
im_data = (img-127.5)*0.0078125 # [0,255] -> [-1,1]
im_data = cv2.resize(im_data, (ws,hs)) # default is bilinear
else:
im_data = cv2.resize(img, (ws,hs)) # default is bilinear
im_data = (im_data-127.5)*0.0078125 # [0,255] -> [-1,1]
#im_data = imResample(img, hs, ws); print "scale:", scale
im_data = np.swapaxes(im_data, 0, 2)
im_data = np.array([im_data], dtype = np.float)
PNet.blobs['data'].reshape(1, 3, ws, hs)
PNet.blobs['data'].data[...] = im_data
out = PNet.forward()
boxes = generateBoundingBox(out['prob1'][0,1,:,:], out['conv4-2'][0], scale, threshold[0])
if boxes.shape[0] != 0:
pick = nms(boxes, 0.5, 'Union')
if len(pick) > 0 :
boxes = boxes[pick, :]
if boxes.shape[0] != 0:
total_boxes = np.concatenate((total_boxes, boxes), axis=0)
#np.save('total_boxes_101.npy', total_boxes)
#####
# 1 #
#####
# print "[1]:",total_boxes.shape[0]
#print total_boxes
#return total_boxes, []
numbox = total_boxes.shape[0]
if numbox > 0:
# nms
pick = nms(total_boxes, 0.7, 'Union')
total_boxes = total_boxes[pick, :]
# print "[2]:",total_boxes.shape[0]
# revise and convert to square
regh = total_boxes[:,3] - total_boxes[:,1]
regw = total_boxes[:,2] - total_boxes[:,0]
t1 = total_boxes[:,0] + total_boxes[:,5]*regw
t2 = total_boxes[:,1] + total_boxes[:,6]*regh
t3 = total_boxes[:,2] + total_boxes[:,7]*regw
t4 = total_boxes[:,3] + total_boxes[:,8]*regh
t5 = total_boxes[:,4]
total_boxes = np.array([t1,t2,t3,t4,t5]).T
total_boxes = rerec(total_boxes) # convert box to square
# print "[4]:",total_boxes.shape[0]
total_boxes[:,0:4] = np.fix(total_boxes[:,0:4])
# print "[4.5]:",total_boxes.shape[0]
#print total_boxes
[dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = pad(total_boxes, w, h)
numbox = total_boxes.shape[0]
if numbox > 0:
# second stage
# construct input for RNet
tempimg = np.zeros((numbox, 24, 24, 3)) # (24, 24, 3, numbox)
for k in range(numbox):
tmp = np.zeros((int(tmph[k]) +1, int(tmpw[k]) + 1,3))
tmp[int(dy[k]):int(edy[k])+1, int(dx[k]):int(edx[k])+1] = img[int(y[k]):int(ey[k])+1, int(x[k]):int(ex[k])+1]
#print "y,ey,x,ex", y[k], ey[k], x[k], ex[k]
#print "tmp", tmp.shape
tempimg[k,:,:,:] = cv2.resize(tmp, (24, 24))
#print tempimg.shape
#print tempimg[0,0,0,:]
tempimg = (tempimg-127.5)*0.0078125 # done in imResample function wrapped by python
# RNet
tempimg = np.swapaxes(tempimg, 1, 3)
#print tempimg[0,:,0,0]
RNet.blobs['data'].reshape(numbox, 3, 24, 24)
RNet.blobs['data'].data[...] = tempimg
out = RNet.forward()
score = out['prob1'][:,1]
#print 'score', score
pass_t = np.where(score>threshold[1])[0]
#print 'pass_t', pass_t
score = np.array([score[pass_t]]).T
total_boxes = np.concatenate( (total_boxes[pass_t, 0:4], score), axis = 1)
# print "[5]:",total_boxes.shape[0]
#print total_boxes
#print "1.5:",total_boxes.shape
mv = out['conv5-2'][pass_t, :].T
#print "mv", mv
if total_boxes.shape[0] > 0:
pick = nms(total_boxes, 0.7, 'Union')
# print 'pick', pick
if len(pick) > 0:
total_boxes = total_boxes[pick, :]
# print "[6]:", total_boxes.shape[0]
total_boxes = bbreg(total_boxes, mv[:, pick])
# print "[7]:", total_boxes.shape[0]
total_boxes = rerec(total_boxes)
# print "[8]:", total_boxes.shape[0]
return total_boxes
def main():
img_dir = "/media/xiao/学习/face_alignment/data/CelebA/Img/img_celeba.7z/img_celeba/"
anno_file = "celebA_bbox_landmark.txt"
with open(anno_file, 'r') as f:
annotations = f.readlines()
num = len(annotations)
print "%d pics in total" % num
# 图片大小为48*48
image_size = 48
# landmark_save_dir = "48/landmark/"
landmark_save_dir = "/media/xiao/软件/mtcnn/train/48/landmark/"
# save_dir = "./" + str(image_size)
f1 = open('/media/xiao/软件/mtcnn/train/48/landmark_48.txt', 'w')
l_idx = 0 # landmark
image_idx = 0
minsize = 40
caffe_model_path = "./model"
threshold = [0.6, 0.7, 0.7]
factor = 0.709
caffe.set_mode_gpu()
PNet = caffe.Net(caffe_model_path+"/det1.prototxt", caffe_model_path+"/det1.caffemodel", caffe.TEST)
RNet = caffe.Net(caffe_model_path+"/det2.prototxt", caffe_model_path+"/det2.caffemodel", caffe.TEST)
for annotation in annotations:
# imgpath = imgpath.split('\n')[0]
annotation = annotation.strip().split(' ')
im_path = annotation[0]
# bbox = map(float, annotation[1:-10])
pts = map(float, annotation[-10:])
# boxes = np.array(bbox, dtype=np.float32).reshape(-1, 4)
im_path = img_dir + im_path
backupPts = pts[:]
#print "######\n", img_path
print image_idx
image_idx += 1
img = cv2.imread(im_path)
img_matlab = img.copy()
tmp = img_matlab[:,:,2].copy()
img_matlab[:,:,2] = img_matlab[:,:,0]
img_matlab[:,:,0] = tmp
# 用作者提供的net1->net2生成人脸框
boundingboxes = detect_face(img_matlab, minsize, PNet, RNet, threshold, False, factor)
#img = drawBoxes(img, boundingboxes)
#cv2.imshow('img', img)
#cv2.waitKey(1000)
# generate landmark samples
for box in boundingboxes:
x_left, y_top, x_right, y_bottom, _ = box # 得到人脸bbox坐标
crop_w = x_right - x_left + 1
crop_h = y_bottom - y_top + 1
# ignore box that is too small or beyond image border
if crop_w < image_size / 2 or crop_h < image_size / 2:
continue
if x_left < 0 or y_top < 0:
continue
# 当五个landmark都在bbox中时，这样的样本作为我们的landmark训练样本
if pts[0] < x_left or pts[0] > x_right:
continue
if pts[2] < x_left or pts[2] > x_right:
continue
if pts[4] < x_left or pts[4] > x_right:
continue
if pts[6] < x_left or pts[6] > x_right:
continue
if pts[8] < x_left or pts[8] > x_right:
continue
if pts[1] < y_top or pts[1] > y_bottom:
continue
if pts[3] < y_top or pts[3] > y_bottom:
continue
if pts[5] < y_top or pts[5] > y_bottom:
continue
if pts[7] < y_top or pts[7] > y_bottom:
continue
if pts[9] < y_top or pts[9] > y_bottom:
continue
# show image
#img1 = drawBoxes_align(img, box)
#img1 = drawlandmark_align(img1, pts)
#cv2.imshow('img', img1)
#cv2.waitKey(1000)
# our method, x0,y0,x1,y1,x2,y2,x3,y3,x4,y4
#for k in range(len(pts) / 2):
# pts[k * 2] = (pts[k * 2] - x_left) / float(crop_w);
# pts[k * 2 + 1] = (pts[k * 2 + 1] - y_top) / float(crop_h);
#author method, x0,x1,x2,x3,x4,y0,y1,y2,y3,y4
ptsss = pts[:]
# landmark标注为其相对于crop区域左上角的归一化值
for k in range(len(ptsss) / 2):
pts[k] = (ptsss[k * 2] - x_left) / float(crop_w);
pts[5+k] = (ptsss[k * 2 + 1] - y_top) / float(crop_h);
# 将crop区域进行resize
cropped_im = img[int(y_top):int(y_bottom + 1), int(x_left):int(x_right + 1)]
resized_im = cv2.resize(cropped_im, (image_size, image_size), interpolation=cv2.INTER_LINEAR)
# box_ = box.reshape(1, -1)
# 写图片名，class坐标，bbox坐标。
save_file = os.path.join(landmark_save_dir, "%s.jpg" % l_idx)
f1.write(str(image_size) + "/landmark/%s.jpg" % l_idx + ' -1 -1 -1 -1 -1')
# 写landmark坐标并保存图片
for k in range(len(pts)):
f1.write(" %f" % pts[k])
f1.write("\n")
cv2.imwrite(save_file, resized_im)
l_idx += 1
f.close()
f1.close()
if __name__ == "__main__":
main()