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utils.py
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utils.py
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import os
import sys
import numpy as np
from PIL import Image, ImageDraw
import cv2
import scipy.misc
import cfgconst
import math
import genregiontruth_bnum
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
def iou(boxlist, x0_list, y0_list, x1_list, y1_list, w, h):
ioulist = []
for box in boxlist:
maxv =0
for x0,y0,x1,y1 in zip(x0_list, y0_list, x1_list, y1_list):
box_x0 = (box.x - box.w/2)*w
box_x1 = (box.x + box.w/2)*w
box_y0 = (box.y - box.h/2)*h
box_y1 = (box.y + box.h/2)*h
ow = min(x1,box_x1) - max(x0,box_x0)
oh = min(y1,box_y1) - max(y0,box_y0)
intersec = max(0,ow) * max(0,oh)
union = box.w*box.h*(w*h) + (x1-x0)*(y1-y0) - intersec
v = intersec / union
if v > maxv:
maxv =v
ioulist.append(maxv)
return ioulist
def limit(x):
if x > 100:
y= 100
elif x <-100:
y=-100
else:
y=x
return y
def sigmoid(x):
#print 'x='+str(x)
if x<-100:
y = -100
else:
y = x
return 1 / (1 + math.exp(-y))
def predict(X_test, testmodel, confid_thresh,w,h,c):
#print 'predict, confid_thresh='+str(confid_thresh)
pred = testmodel.predict(X_test)
#(s,w,h,c) = testmodel.layers[0].input_shape
# find confidence value > 0.5
confid_index_list =[]
confid_value_list =[]
x_value_list = []
y_value_list =[]
w_value_list =[]
h_value_list =[]
class_id_list =[]
classprob_list =[]
x0_list = []
x1_list = []
y0_list = []
y1_list = []
#
bnum = cfgconst.bnum
side = cfgconst.side
classes = cfgconst.classes
xtext_index =0
foundindex = False
max_confid =0
#
for p in pred:
#foundindex = False
for k in range(bnum): #5+classes):
#print 'L'+str(k)
for i in range(side):
for j in range(side):
#if k==0:
max_confid = max(max_confid,p[k*(side**2)+i*side+j])
#sys.stdout.write( str(sigmoid(p[k*(side**2)+i*side+j]))+', ' )
#if k==0 and sigmoid(p[k*(side**2)+i*side+j])>confid_thresh:
if sigmoid(p[k*(side**2)+i*side+j])>confid_thresh:
confid_index_list.append(k*(side**2)+i*side+j)
foundindex = True
#print '-'
#print 'max_confid='+str(max_confid)
#
for confid_index in confid_index_list:
confid_value = max(0,sigmoid(limit(p[0*(side**2)*bnum+confid_index])))
confid_value_list.append(confid_value)
x_value = max(0,sigmoid(limit(p[1*(side**2)*bnum+confid_index])))
y_value = max(0,sigmoid(limit(p[2*(side**2)*bnum+confid_index])))
w_value = max(0,sigmoid(limit(p[3*(side**2)*bnum+confid_index])))
h_value = max(0,sigmoid(limit(p[4*(side**2)*bnum+confid_index])))
#print 'x_value='+str(x_value)+',y_value='+str(y_value)+',w_value='+str(w_value)+',h_value='+str(h_value)
maxclassprob = 0
maxclassprob_i =-1
for i in range(classes):
if p[(5+i)*(side**2)*bnum+confid_index] > maxclassprob and foundindex:
maxclassprob = p[(5+i)*(side**2)*bnum+confid_index]
maxclassprob_i = i
classprob_list.append( sigmoid(maxclassprob))
class_id_list.append( maxclassprob_i)
#print 'max_confid='+str(max_confid)+',c='+str(confid_value)+',x='+str(x_value)+',y='+str(y_value)+',w='+str(w_value)+',h='+str(h_value)+',cid='+str(maxclassprob_i)+',prob='+str(maxclassprob)
#
# in case confid_index at 2nd,3rd,4th... bbox
row = (confid_index % (side**2)) / side
col = (confid_index % (side**2)) % side
x = (w / side) * (col + x_value)
y = (h / side) * (row + y_value)
#print 'max_confid='+str(max_confid)+',c='+str(confid_value)+',x='+str(x_value)+',y='+str(y_value)+',w='+str(w_value)+',h='+str(h_value)+',cid='+str(maxclassprob_i)+',prob='+str(maxclassprob)+',row='+str(row)+',col='+str(col)
#print 'confid_index='+str(confid_index)+',x='+str(x)+',y='+str(y)+',row='+str(row)+',col='+str(col)
#draw = ImageDraw.Draw(nim)
#draw.rectangle([x-(w_value/2)*w,y-(h_value/2)*h,x+(w_value/2)*w,y+(h_value/2)*h])
#del draw
#nim.save('predbox.png')
#sourceimage = X_test[xtext_index].copy()
x0_list.append( max(0, int(x-(w_value/2)*w)) )
y0_list.append( max(0, int(y-(h_value/2)*h)) )
x1_list.append( min(w, int(x+(w_value/2)*w)) )
y1_list.append( min(h, int(y+(h_value/2)*h)) )
break
#xtext_index = xtext_index + 1
#print pred
sourceimage = X_test[0].copy()
return sourceimage, x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list
def testfeature(testmodel, img,w,h,c, onefeature, confid_thresh=0.2, waittime=1000):
# predict
tttimg, x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list = predict(np.asarray([onefeature]), testmodel, confid_thresh,w,h,c)
#
for x0,y0,x1,y1,classprob,class_id,confid_value in zip(x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list):
#
if x0 >w or x1>w or y0>h or y1>h :
continue
#print 'box='+str(x0)+','+str(y0)+','+str(x1)+','+str(y1)
# draw bounding box
cv2.rectangle(img, (x0, y0), (x1, y1), (255,255,255), 2)
# draw classimg
classimg = cv2.imread(cfgconst.label_names[class_id])
if y0-classimg.shape[0] <= 0:
yst =0
yend =classimg.shape[0]
elif y0 >= img.shape[0]:
yst = img.shape[0]-classimg.shape[0]-1
yend = img.shape[0]-1
else:
yst = y0 - classimg.shape[0]
yend = y0
#
if x0+classimg.shape[1] >= img.shape[1]:
xst = img.shape[1]-classimg.shape[1]-1
xend = img.shape[1]-1
elif x0 <=0:
xst = 0
xend = classimg.shape[1]
else:
xst = x0
xend = x0+classimg.shape[1]
#
img[yst:yend, xst:xend] = classimg
# draw text
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, str(classprob), (x0,y0+classimg.shape[0]-1), font, 0.5,(255,255,255),1,cv2.LINE_AA)
cv2.putText(img, str(confid_value), (x0,y1), font, 0.5,(255,255,255),1,cv2.LINE_AA)
#
cv2.imshow('prediction',img)
if cv2.waitKey(waittime):
if 0xFF == ord('q'):
break
else:
continue
def testonefile(testmodel, img_path, confid_thresh=0.3, fordebug=False ):
(s,w,h,c) = testmodel.layers[0].input_shape
fimg, sx, sy, dx, dy, flip,ssx,ssy = genregiontruth_bnum.crop_image(img_path.strip(), w, h, randomize=False)
xx = fimg.copy()
img = fimg.astype(float)
if fordebug: # read label
fn=img_path.replace("/JPEGImages/","/labels/")
fn=fn.replace(".jpg",".txt") #VOC
boxlist = genregiontruth_bnum.readlabel(fn.strip(), sx, sy, dx, dy, flip, ssx, ssy)
for box in boxlist:
draw.rectangle([(box.x-box.w/2)*w,(box.y-box.h/2)*h,(box.x+box.w/2)*w,(box.y+box.h/2)*h])
#
ttimg, x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list = predict(preprocess_input(np.asarray([xx])), testmodel, confid_thresh,w,h,c)
iimg = Image.fromarray(img.astype(np.uint8))
draw = ImageDraw.Draw(iimg, 'RGBA')
sortedindexlist = np.argsort(confid_value_list)
colors=[]
for i in range(3):
for j in range(7):
if i==0:
rcolor = (j+1)*32
gcolor = 0
bcolor = 0
elif i==1:
rcolor = 0
gcolor = (j+1)*32
bcolor = 0
else:
rcolor = 0
gcolor = 0
bcolor = (j+1)*32
colors.append( (rcolor, gcolor, bcolor, 127) )
#print colors
for i in range(len(confid_value_list)):
index = sortedindexlist[len(confid_value_list)-i-1]
for k in range(5): # thick line
draw.rectangle([x0_list[index]+k,y0_list[index]+k,x1_list[index]-k,y1_list[index]-k], outline=colors[class_id_list[index]])
labelim = Image.open(cfgconst.label_names[class_id_list[index]])
draw.bitmap((x0_list[index],y0_list[index]),labelim)
x = (x0_list[index]+x1_list[index])/2.
y = (y0_list[index]+y1_list[index])/2.
x0 = int(x/w*cfgconst.side)*w/cfgconst.side
y0 = int(y/h*cfgconst.side)*h/cfgconst.side
x1 = x0 + float(w)/cfgconst.side
y1 = y0 + float(h)/cfgconst.side
draw.rectangle([x0,y0,x1,y1], fill=colors[class_id_list[index]] )
print cfgconst.label_names[class_id_list[index]].split('/')[1].split('.')[0]+': '+str(confid_value_list[index])
del draw
iimg.save('predicted.png')
def testfile(testmodel, imglist_path, confid_thresh=0.2, waittime=50000, fordebug=False ):
#print 'testfile: '+imglist_path
# custom objective function
#print (s,w,h,c)
#exit()
randomize= (cfgconst.randomize==1)
if os.path.isfile(imglist_path):
#testmodel = load_model(model_weights_path, custom_objects={'yololoss': ddd.yololoss})
(s,w,h,c) = testmodel.layers[0].input_shape
f = open(imglist_path)
for img_path in f:
#
#if fordebug: # read label
# fn=img_path.replace("/JPEGImages/","/labels/")
# fn=fn.replace(".jpg",".txt") #VOC
# boxlist = genregiontruth_bnum.readlabel(fn.strip())
#X_test = []
if os.path.isfile(img_path.strip()):
#frame = Image.open(img_path.strip())
#(orgw,orgh) = img.size
#nim = scipy.misc.imresize(frame, (w, h, c))
#if nim.shape != (w, h, c):
# continue
#img = nim
#timg = image.load_img( img_path.strip(), target_size=(w, h))
#xx = image.img_to_array(timg)
#
fimg, sx, sy, dx, dy, flip,ssx,ssy = genregiontruth_bnum.crop_image(img_path.strip(), w, h, randomize=randomize)
if flip == -1: # not rgb color image
continue
xx = fimg.copy()
img = fimg.astype(float)
if fordebug: # read label
fn=img_path.replace("/JPEGImages/","/labels/")
fn=fn.replace(".jpg",".txt") #VOC
boxlist = genregiontruth_bnum.readlabel(fn.strip(), sx, sy, dx, dy, flip, ssx, ssy)
#print 'img='+str(img.shape)+str(img[0][0])
#nim = img.resize( (w, h), Image.BILINEAR )
ttimg, x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list = predict(preprocess_input(np.asarray([xx])), testmodel, confid_thresh,w,h,c)
#X_test.append(np.asarray(nim))
#predict(np.asarray(X_test), testmodel, confid_thresh)
# found confid box
# iou
font = cv2.FONT_HERSHEY_SIMPLEX
if fordebug:
ioulist = iou(boxlist, x0_list, y0_list, x1_list, y1_list, w, h)
ii = 0
for iouvalue in ioulist:
#print 'iouvalue='+str(iouvalue)+',ii='+str(ii)
cv2.putText(img, "%0.4f" %(iouvalue), (10+100*ii,20), font, 0.4,(128,255,128),1,cv2.LINE_AA)
ii = ii+ 1
for box in boxlist:
x0 = int((box.x - box.w/2)*w)
x1 = int((box.x + box.w/2)*w)
y0 = int((box.y - box.h/2)*h)
y1 = int((box.y + box.h/2)*h)
cv2.rectangle(img, (x0, y0), (x1, y1), (128,255,128), 2)
cv2.putText(img, str(box.id), (x0,y0), font, 1,(128,255,128),1,cv2.LINE_AA)
#
for x0,y0,x1,y1,classprob,class_id,confid_value in zip(x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list):
#
# draw bounding box
cv2.rectangle(img, (x0, y0), (x1, y1), (255,255,255), 2)
# draw classimg
classimg = cv2.imread(cfgconst.label_names[class_id])
if y0-classimg.shape[0] <= 0:
yst =0
yend =classimg.shape[0]
elif y0 >= img.shape[0]:
yst = img.shape[0]-classimg.shape[0]-1
yend = img.shape[0]-1
else:
yst = y0 - classimg.shape[0]
yend = y0
if x0+classimg.shape[1] >= img.shape[1]:
xst = img.shape[1]-classimg.shape[1]-1
xend = img.shape[1]-1
elif x0 <=0:
xst = 0
xend = classimg.shape[1]
else:
xst = x0
xend = x0+classimg.shape[1]
#
#print 'box='+str(x0)+','+str(y0)+','+str(x1)+','+str(y1)+', '+str(img.shape)+', '+str(classimg.shape)+', '+str(yst)+', '+str(yend)+', '+str(xst)+', '+str(xend)
#
img[yst:yend, xst:xend] = classimg
# draw text
#font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, str(classprob), (x0,y0+classimg.shape[0]-1), font, 0.5,(255,255,255),1,cv2.LINE_AA)
cv2.putText(img, str(confid_value), (x0,y1), font, 0.5,(128,255,255),1,cv2.LINE_AA)
#
cv2.imshow('prediction',cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2BGR))
if cv2.waitKey(waittime):
if 0xFF == ord('q'):
break
else:
continue
else:
print img_path+' predict fail'
# only show first image
if fordebug:
break
#cv2.destroyAllWindows()
else:
print imglist_path+' does not exist'
def testvideo(testmodel, videofile, confid_thresh=0.2):
print 'testdemo '+videofile
#testmodel = load_model(model_weights_path, custom_objects={'yololoss': ddd.yololoss})
(s,w,h,c) = testmodel.layers[0].input_shape
cap = cv2.VideoCapture(videofile)
while (cap.isOpened()):
ret, frame = cap.read()
if not ret:
break
#print frame
nim = scipy.misc.imresize(frame, (w, h, c))
img = nim
xx = image.img_to_array(cv2.cvtColor(nim, cv2.COLOR_RGB2BGR))
ttimg, x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list = predict(preprocess_input(np.asarray([xx])), testmodel, confid_thresh,w,h,c)
# found confid box
for x0,y0,x1,y1,classprob,class_id,confid_value in zip(x0_list, y0_list, x1_list, y1_list, classprob_list, class_id_list, confid_value_list):
#
# draw bounding box
cv2.rectangle(img, (x0, y0), (x1, y1), (255,255,255), 2)
# draw classimg
classimg = cv2.imread(cfgconst.label_names[class_id])
if y0-classimg.shape[0] <= 0:
yst =0
yend =classimg.shape[0]
elif y0 >= img.shape[0]:
yst = img.shape[0]-classimg.shape[0]-1
yend = img.shape[0]-1
else:
yst = y0 - classimg.shape[0]
yend = y0
if x0+classimg.shape[1] >= img.shape[1]:
xst = img.shape[1]-classimg.shape[1]-1
xend = img.shape[1]-1
elif x0 <=0:
xst = 0
xend = classimg.shape[1]
else:
xst = x0
xend = x0+classimg.shape[1]
#
img[yst:yend, xst:xend] = classimg
# draw text
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, str(classprob), (x0,y0+classimg.shape[0]-1), font, 0.5,(255,255,255),2,cv2.LINE_AA)
cv2.putText(img, str(confid_value), (x0,y1), font, 0.5,(128,255,255),1,cv2.LINE_AA)
#
cv2.imshow('frame',img)
if cv2.waitKey(100) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()