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tools_matrix.py
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tools_matrix.py
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import sys
from operator import itemgetter
import numpy as np
import cv2
'''
Function:
change rectangles into squares (matrix version)
Input:
rectangles: rectangles[i][0:3] is the position, rectangles[i][4] is score
Output:
squares: same as input
'''
def rect2square(rectangles):
w = rectangles[:,2] - rectangles[:,0]
h = rectangles[:,3] - rectangles[:,1]
l = np.maximum(w,h).T
rectangles[:,0] = rectangles[:,0] + w*0.5 - l*0.5
rectangles[:,1] = rectangles[:,1] + h*0.5 - l*0.5
rectangles[:,2:4] = rectangles[:,0:2] + np.repeat([l], 2, axis = 0).T
return rectangles
'''
Function:
apply NMS(non-maximum suppression) on ROIs in same scale(matrix version)
Input:
rectangles: rectangles[i][0:3] is the position, rectangles[i][4] is score
Output:
rectangles: same as input
'''
def NMS(rectangles,threshold,type):
if len(rectangles)==0:
return rectangles
boxes = np.array(rectangles)
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())
pick = []
while len(I)>0:
xx1 = np.maximum(x1[I[-1]], x1[I[0:-1]]) #I[-1] have hightest prob score, I[0:-1]->others
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 == 'iom':
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]]
result_rectangle = boxes[pick].tolist()
return result_rectangle
'''
Function:
Detect face position and calibrate bounding box on 12net feature map(matrix version)
Input:
cls_prob : softmax feature map for face classify
roi : feature map for regression
out_side : feature map's largest size
scale : current input image scale in multi-scales
width : image's origin width
height : image's origin height
threshold: 0.6 can have 99% recall rate
'''
def detect_face_12net(cls_prob,roi,out_side,scale,width,height,threshold):
in_side = 2*out_side+11
stride = 0
if out_side != 1:
stride = float(in_side-12)/(out_side-1)
(x,y) = np.where(cls_prob>=threshold)
boundingbox = np.array([x,y]).T
bb1 = np.fix((stride * (boundingbox) + 0 ) * scale)
bb2 = np.fix((stride * (boundingbox) + 11) * scale)
boundingbox = np.concatenate((bb1,bb2),axis = 1)
dx1 = roi[0][x,y]
dx2 = roi[1][x,y]
dx3 = roi[2][x,y]
dx4 = roi[3][x,y]
score = np.array([cls_prob[x,y]]).T
offset = np.array([dx1,dx2,dx3,dx4]).T
boundingbox = boundingbox + offset*12.0*scale
rectangles = np.concatenate((boundingbox,score),axis=1)
rectangles = rect2square(rectangles)
pick = []
for i in range(len(rectangles)):
x1 = int(max(0 ,rectangles[i][0]))
y1 = int(max(0 ,rectangles[i][1]))
x2 = int(min(width ,rectangles[i][2]))
y2 = int(min(height,rectangles[i][3]))
sc = rectangles[i][4]
if x2>x1 and y2>y1:
pick.append([x1,y1,x2,y2,sc])
return NMS(pick,0.5,'iou')
'''
Function:
Filter face position and calibrate bounding box on 12net's output
Input:
cls_prob : softmax feature map for face classify
roi_prob : feature map for regression
rectangles: 12net's predict
width : image's origin width
height : image's origin height
threshold : 0.6 can have 97% recall rate
Output:
rectangles: possible face positions
'''
def filter_face_24net(cls_prob,roi,rectangles,width,height,threshold):
prob = cls_prob[:,1]
pick = np.where(prob>=threshold)
rectangles = np.array(rectangles)
x1 = rectangles[pick,0]
y1 = rectangles[pick,1]
x2 = rectangles[pick,2]
y2 = rectangles[pick,3]
sc = np.array([prob[pick]]).T
dx1 = roi[pick,0]
dx2 = roi[pick,1]
dx3 = roi[pick,2]
dx4 = roi[pick,3]
w = x2-x1
h = y2-y1
x1 = np.array([(x1+dx1*w)[0]]).T
y1 = np.array([(y1+dx2*h)[0]]).T
x2 = np.array([(x2+dx3*w)[0]]).T
y2 = np.array([(y2+dx4*h)[0]]).T
rectangles = np.concatenate((x1,y1,x2,y2,sc),axis=1)
rectangles = rect2square(rectangles)
pick = []
for i in range(len(rectangles)):
x1 = int(max(0 ,rectangles[i][0]))
y1 = int(max(0 ,rectangles[i][1]))
x2 = int(min(width ,rectangles[i][2]))
y2 = int(min(height,rectangles[i][3]))
sc = rectangles[i][4]
if x2>x1 and y2>y1:
pick.append([x1,y1,x2,y2,sc])
return NMS(pick,0.7,'iou')
'''
Function:
Filter face position and calibrate bounding box on 12net's output
Input:
cls_prob : cls_prob[1] is face possibility
roi : roi offset
pts : 5 landmark
rectangles: 12net's predict, rectangles[i][0:3] is the position, rectangles[i][4] is score
width : image's origin width
height : image's origin height
threshold : 0.7 can have 94% recall rate on CelebA-database
Output:
rectangles: face positions and landmarks
'''
def filter_face_48net(cls_prob,roi,pts,rectangles,width,height,threshold):
prob = cls_prob[:,1]
pick = np.where(prob>=threshold)
rectangles = np.array(rectangles)
x1 = rectangles[pick,0]
y1 = rectangles[pick,1]
x2 = rectangles[pick,2]
y2 = rectangles[pick,3]
sc = np.array([prob[pick]]).T
dx1 = roi[pick,0]
dx2 = roi[pick,1]
dx3 = roi[pick,2]
dx4 = roi[pick,3]
w = x2-x1
h = y2-y1
pts0= np.array([(w*pts[pick,0]+x1)[0]]).T
pts1= np.array([(h*pts[pick,5]+y1)[0]]).T
pts2= np.array([(w*pts[pick,1]+x1)[0]]).T
pts3= np.array([(h*pts[pick,6]+y1)[0]]).T
pts4= np.array([(w*pts[pick,2]+x1)[0]]).T
pts5= np.array([(h*pts[pick,7]+y1)[0]]).T
pts6= np.array([(w*pts[pick,3]+x1)[0]]).T
pts7= np.array([(h*pts[pick,8]+y1)[0]]).T
pts8= np.array([(w*pts[pick,4]+x1)[0]]).T
pts9= np.array([(h*pts[pick,9]+y1)[0]]).T
x1 = np.array([(x1+dx1*w)[0]]).T
y1 = np.array([(y1+dx2*h)[0]]).T
x2 = np.array([(x2+dx3*w)[0]]).T
y2 = np.array([(y2+dx4*h)[0]]).T
rectangles=np.concatenate((x1,y1,x2,y2,sc,pts0,pts1,pts2,pts3,pts4,pts5,pts6,pts7,pts8,pts9),axis=1)
pick = []
for i in range(len(rectangles)):
x1 = int(max(0 ,rectangles[i][0]))
y1 = int(max(0 ,rectangles[i][1]))
x2 = int(min(width ,rectangles[i][2]))
y2 = int(min(height,rectangles[i][3]))
if x2>x1 and y2>y1:
pick.append([x1,y1,x2,y2,rectangles[i][4],
rectangles[i][5],rectangles[i][6],rectangles[i][7],rectangles[i][8],rectangles[i][9],rectangles[i][10],rectangles[i][11],rectangles[i][12],rectangles[i][13],rectangles[i][14]])
return NMS(pick,0.7,'iom')
'''
Function:
calculate multi-scale and limit the maxinum side to 1000
Input:
img: original image
Output:
pr_scale: limit the maxinum side to 1000, < 1.0
scales : Multi-scale
'''
def calculateScales(img):
caffe_img = img.copy()
pr_scale = 1.0
h,w,ch = caffe_img.shape
if min(w,h)>1000:
pr_scale = 1000.0/min(h,w)
w = int(w*pr_scale)
h = int(h*pr_scale)
elif max(w,h)<1000:
pr_scale = 1000.0/max(h,w)
w = int(w*pr_scale)
h = int(h*pr_scale)
#multi-scale
scales = []
factor = 0.709
factor_count = 0
minl = min(h,w)
while minl >= 12:
scales.append(pr_scale*pow(factor, factor_count))
minl *= factor
factor_count += 1
return scales