def loadimg(self, args=()):
i = args
self.selected = i
self.img = cv2.imread(self.imagelistFiles[i])
self.match.reset()
#load the labels if the xml file exists
xmlfilename = self.imagelistFiles[self.selected][:-3] + "xml"
if os.path.exists(xmlfilename):
root = ET.parse(xmlfilename).getroot()
for l in root.findall('object'):
name = l.find('name')
#print(name.text)
bndbox = l.find('bndbox')
xmin = bndbox.find("xmin")
ymin = bndbox.find("ymin")
xmax = bndbox.find("xmax")
ymax = bndbox.find("ymax")
x = float(xmin.text)
y = float(ymin.text)
w = float(xmax.text) - float(xmin.text)
h = float(ymax.text) - float(ymin.text)
try:
i = self.actionbar.classes.index(name.text)
c = hsv_to_rgb(self.actionbar.colors[i].slideValue / 360,
1, 1)
except:
c = (0, 0, 0)
loadedlabel = label(self.world, self.match, x, y, w, h,
name.text, c)
self.match.labels.append(loadedlabel)
print("loaded xml")
def match(self, img, template=None, templatename=""):
if not template is None:
self.template=template
else:
if self.template is None:
return
#cv2.imwrite("test.png",template)
#self.tempautolabels=[]
templabel=[]
self.templateheight,self.templatewidth,_=self.template.shape
#run template matching, get minimum val
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray_template = cv2.cvtColor(self.template,cv2.COLOR_BGR2GRAY)
res = cv2.matchTemplate(gray, gray_template, cv2.TM_CCOEFF_NORMED)#TM_SQDIFF_NORMED
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# create threshold from min val, find where sqdiff is less than thresh
min_thresh = self.accuracyslider.slideValue#.9# (min_val + 1e-6) * 1.5
match_locations = np.where(res >= min_thresh)
match_locations = (np.array(match_locations).T).tolist()
match_locations = group_points(match_locations,10)
#get the defaults from the actionbar
try:
labeltext = self.controller.actionbar.classes[self.controller.actionbar.defaultmatch]
labelcolor=hsv_to_rgb(self.controller.actionbar.colors[self.controller.actionbar.defaultmatch].slideValue/360,1,1)
except:
labeltext = "NONE"
labelcolor=(0,0,0)
for m in match_locations:
l=label(self.world,self.controller,m[1],m[0],self.templatewidth,self.templateheight,labeltext,(0,255,0),accuracy=res[m[0]][m[1]]*100,template=templatename)
#self.labels.append(l)
#self.tempautolabels.append(l)
templabel.append(l)
#print(self.match_locations)
self.predict_missing_boxes(labeltext,templabel,templatename=templatename)
def update(self):
try:
self.height, self.width, channels = self.img.shape
except:
return
#update the labels selection
tempselect = False
for l in self.labels:
l.update()
#deselecting
if self.world.mouse_left_down:
if (self.x < self.world.mouse_x < self.x + self.width and
self.y < self.world.mouse_y < self.y + self.height):
t = l.checkselect()
if t:
tempselect = True
if self.world.mouse_left_down:
if (self.x < self.world.mouse_x < self.x + self.width
and self.y < self.world.mouse_y < self.y + self.height):
if not tempselect:
self.updatelabelselect(None)
#start single select
if self.world.mouse_left_down:
if self.selectsinglebox:
if (self.x < self.world.mouse_x < self.x + self.width and
self.y < self.world.mouse_y < self.y + self.height):
self.selectbox[0] = self.world.mouse_x
self.selectbox[1] = self.world.mouse_y
self.selecting = True
self.selected = False
#start multi select
if self.world.mouse_left_down:
if self.selectmultibox:
if (self.x < self.world.mouse_x < self.x + self.width and
self.y < self.world.mouse_y < self.y + self.height):
self.selectbox[0] = self.world.mouse_x
self.selectbox[1] = self.world.mouse_y
self.selecting = True
self.selected = False
self.lines = []
self.intersections = []
#self.labels=[]
self.tempautolabels = []
if self.selecting:
self.selectbox[2] = clamp(self.world.mouse_x - self.selectbox[0],
self.x - self.selectbox[0],
self.x + self.width - self.selectbox[0])
self.selectbox[3] = clamp(self.world.mouse_y - self.selectbox[1],
self.y - self.selectbox[1],
self.y + self.height - self.selectbox[1])
#single
if self.selectsinglebox:
if self.world.mouse_left_up:
self.selecting = False
self.selectmultibox = False
if abs(self.selectbox[2]) > 0 and abs(
self.selectbox[3]) > 0:
self.selected = True
#create the label
#if not self.controller.actionbar.classes==None:
if not self.controller.actionbar.selected == None:
labeltext = self.controller.actionbar.classes[
self.controller.actionbar.selected]
labelcolor = hsv_to_rgb(
self.controller.actionbar.colors[
self.controller.actionbar.selected].
slideValue / 360, 1, 1)
else:
labeltext = "None"
labelcolor = (0, 0, 0)
l = label(self.world, self, self.selectbox[0] - self.x,
self.selectbox[1] - self.y,
self.selectbox[2], self.selectbox[3],
labeltext, labelcolor)
self.labels.append(l)
self.selectsinglebox = False
#select the label that was just created
self.updatelabelselect(l)
l.selected = True
else:
self.selected = False
return
#multi
if self.selectmultibox:
if self.world.mouse_left_up:
self.selecting = False
self.selectmultibox = False
if abs(self.selectbox[2]) > 0 and abs(
self.selectbox[3]) > 0:
self.selected = True
else:
self.selected = False
return
#create the template
if self.selectbox[2] > 0:
x1 = self.selectbox[0] - self.x
x2 = self.selectbox[0] + self.selectbox[2] - self.x
else:
x1 = self.selectbox[0] + self.selectbox[2] - self.x
x2 = self.selectbox[0] - self.x
if self.selectbox[3] > 0:
y1 = self.selectbox[1] - self.y
y2 = self.selectbox[1] + self.selectbox[3] - self.y
else:
y1 = self.selectbox[1] + self.selectbox[3] - self.y
y2 = self.selectbox[1] - self.y
self.template = self.img[y1:y2, x1:x2]
#run template matching, get minimum val
gray = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY)
template = cv2.cvtColor(self.template, cv2.COLOR_BGR2GRAY)
res = cv2.matchTemplate(
gray, template,
cv2.TM_CCOEFF_NORMED) #TM_SQDIFF_NORMED
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# create threshold from min val, find where sqdiff is less than thresh
min_thresh = .9 # (min_val + 1e-6) * 1.5
match_locations = np.where(res >= min_thresh)
match_locations = (np.array(match_locations).T).tolist()
match_locations = group_points(match_locations, 10)
#get the defaults from the actionbar
try:
labeltext = self.controller.actionbar.classes[
self.controller.actionbar.defaultmatch]
labelcolor = hsv_to_rgb(
self.controller.actionbar.colors[
self.controller.actionbar.defaultmatch].
slideValue / 360, 1, 1)
except:
labeltext = "NONE"
labelcolor = (0, 0, 0)
for m in match_locations:
l = label(self.world, self, m[1], m[0],
self.selectbox[2], self.selectbox[3],
labeltext, (0, 255, 0))
#self.labels.append(l)
self.tempautolabels.append(l)
#print(self.match_locations)
self.predict_missing_boxes(labeltext)
def predict_missing_boxes(self, goodlabeltext):
#get the defaults from the actionbar
try:
labeltext = self.controller.actionbar.classes[
self.controller.actionbar.defaultmiss]
labelcolor = hsv_to_rgb(
self.controller.actionbar.colors[
self.controller.actionbar.defaultmiss].slideValue / 360, 1,
1)
except:
labeltext = "NONE"
labelcolor = (0, 0, 0)
for p in self.tempautolabels:
x = p.x
y = p.y
#print(str(x)+","+str(y))
hline = line(self.world, self.x, self.y, [x, x + 10], [y, y])
vline = line(self.world, self.x, self.y, [x, x], [y, y + 10])
self.lines.append(hline)
self.lines.append(vline)
#combine the overlapping lines
slopethresh = .005
interceptthresh = 15
for i in range(len(self.lines)):
for j in range(len(self.lines)):
if not i == j:
try:
#for vertical lines
if (isNaN(self.lines[i].slope)
and isNaN(self.lines[j].slope)):
if np.mean(
self.lines[i].xlist
) - interceptthresh < np.mean(
self.lines[j].xlist) < np.mean(
self.lines[i].xlist) + interceptthresh:
self.lines[i].xlist[0] = (
self.lines[i].xlist[0] +
self.lines[i].xlist[1]) / 2
del self.lines[j]
except Exception as e:
#print(e)
pass
try:
if not (isNaN(self.lines[i].slope)
and isNaN(self.lines[j].slope)):
if self.lines[i].slope - slopethresh < self.lines[
j].slope < self.lines[
i].slope + slopethresh:
if self.lines[
i].intercept - interceptthresh < self.lines[
j].intercept < self.lines[
i].intercept + interceptthresh:
self.lines[i].slope = (
self.lines[i].slope +
self.lines[j].slope) / 2
self.lines[i].intercept = (
self.lines[i].intercept +
self.lines[j].intercept) / 2
del self.lines[j]
except Exception as e:
#print(e)
pass
print("total lines: " + str(len(self.lines)))
#find the intersect of all the lines
'''
1. go through the lines
2. check if all the other lines dont intersect, that is if they have a different slope
'''
colnum = 0
for l1 in self.lines:
for l2 in self.lines:
if l1 != l2:
#get the intersection
x = intersection(l1, l2)
p = []
if not x == None:
if not isNaN(l1.slope):
p = [x, l1.returny(x)]
else:
p = [x, l2.returny(x)]
if p:
if not p in self.intersections:
self.intersections.append(p)
print("total intersections: " + str(len(self.intersections)))
#find the missing points (intersections with no prediction box) with tolerance
tolerance = 5
for p in self.intersections:
#point = [p[1],p[0]]
inMatch = False
#for a in self.match_locations:
for a in self.tempautolabels:
if a.label == goodlabeltext: #"match":
x = a.x
y = a.y
if (p[0] - tolerance < x < p[0] + tolerance
and p[1] - tolerance < y < p[1] + tolerance):
#if not point in self.match_locations:
inMatch = True
if not inMatch:
l = label(self.world, self, p[0], p[1], self.selectbox[2],
self.selectbox[3], labeltext, labelcolor)
self.tempautolabels.append(l)
#self.miss_locations.append(point)
print("total labels: " + str(len(self.tempautolabels)))
#add the temp labels and the labels, delete the temp labels
self.labels = self.labels + self.tempautolabels
return self.tempautolabels
def find(self, labels, img):
self.img = img
#loop through the labels and figure out what labels are in the rows
rows = []
for l1 in self.controller.labels:
if not self.inrow(rows, l1):
temp = [l1]
for l2 in self.controller.labels:
if not l1 == l2:
if not self.inrow(rows, l2):
if ((l1.y <= l2.y <= l1.y + l1.h - 10)
or (l2.y <= l1.y <= l2.y + l2.h - 10)):
temp.append(l2)
rows.append(temp)
#sort the rows
rows = [sorted(r, key=lambda l: l.x) for r in rows]
#filter out all the rows with only one template label in them
temp = []
for r in rows:
template1 = False
template2 = False
for l in r:
if l.template == "square.png":
template1 = True
if l.template == "test.png":
template2 = True
if (template1 and template2):
temp.append(r)
rows = temp
#make the labels for the rows
for r in rows:
#print(["("+str(l.y)+","+str(l.h)+")" for l in r])
for i in range(len(r) - 1):
#seperate
if r[i].template != "square.png":
#underneath the squiggly part
xx = r[i].x
ww = r[i].w
yy = r[i].y + r[i].h
hh = r[i].h * .55
l = label(self.world, self.controller, xx, yy, ww, hh, "1",
(241, 255, 0))
self.tempautolabels.append(l)
#middle section, 3 parts
xx = r[i].x + r[i].w
ww = r[i + 1].x - xx
d = .48
d1 = .3
miny = min(r[i].y, r[i + 1].y) + (
max(r[i].y + r[i].h, r[i + 1].y + r[i + 1].h) -
min(r[i].y, r[i + 1].y)) * .15
totalh = (max(r[i].y + r[i].h, r[i + 1].y + r[i + 1].h) -
miny)
yy1 = miny
hh1 = totalh * d
yy2 = yy1 + hh1
hh2 = hh1 * d1 #hh1*(1-d)
yy3 = yy2 + hh2
hh3 = (totalh * 1.07) - hh2 - hh1
l = label(self.world, self.controller, xx, yy1, ww, hh1,
"2", (241, 51, 255))
self.tempautolabels.append(l)
l = label(self.world, self.controller, xx, yy2, ww, hh2,
"3", (64, 255, 249))
self.tempautolabels.append(l)
l = label(self.world, self.controller, xx, yy3, ww, hh3,
"4", (163, 32, 171))
self.tempautolabels.append(l)
else:
xx = r[i].x + r[i].w + (r[i + 1].x - r[i].x +
r[i].w) * .025
yy = min(r[i].y, r[i + 1].y) + (
max(r[i].y + r[i].h, r[i + 1].y + r[i + 1].h) -
min(r[i].y, r[i + 1].y)) * .15
ww = r[i + 1].x - xx
hh = (max(r[i].y + r[i].h, r[i + 1].y + r[i + 1].h) - yy)
#yy1=yy-hh*.05
#hh1=hh*95
l = label(self.world, self.controller, xx, yy, ww, hh, "5",
(0, 0, 255))
self.tempautolabels.append(l)
#check the labels for defects
for l in self.tempautolabels:
self.defectedAreaCheck(l)