/
calibration.py
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/
calibration.py
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"""
TODO:
-> wykrywanie gestu
-> przepisanie do cythona
-> testy sieciowe
-> dokumentacja
-> proby z innymi metodami + studia literaturowe
"""
import cv2
import os
import numpy as np
from collections import deque
from main_utils import draw_rects
def area(rect):
return rect[2]*rect[3]
def get_roi(img, rect):
x,y,w,h = rect
roi = img[y:y+h, x:x+w]
return roi
class EnviromentInfo(object):
def __init__(self, middle_non_zero, rect_count, biggest_rect_area):
self.middle_non_zero = middle_non_zero
self.rect_count = rect_count
self.biggest_rect_area = biggest_rect_area
def points(self):
if self.rect_count > 400:
r = -30
elif self.rect_count > 300:
r = 0
elif self.rect_count > 200:
r = 8
elif self.rect_count > 100:
r = 20 + (200 - self.rect_count)/3
elif 15 < self.rect_count < 100:
r = 20 + (200 - self.rect_count)/2
else:
r = -30
if self.biggest_rect_area < 2000:
p = -30
elif self.biggest_rect_area < 20000:
p = self.biggest_rect_area/2000
elif self.biggest_rect_area < 60000:
p = self.biggest_rect_area/1300
elif self.biggest_rect_area < 120000:
p = (self.biggest_rect_area - 60000)/2000
else:
p = 0
if self.middle_non_zero < 2000:
m = -30
if self.middle_non_zero < 20000:
m = self.middle_non_zero/2000
elif self.middle_non_zero < 55000:
m = self.middle_non_zero/1300
elif self.middle_non_zero < 110000:
m = (self.middle_non_zero - 55000)/4000
else:
m = 0
#print m, r, p, "#"
return m + r + p
class ThresholdInfo(object):
def __init__(self, threshold, defects_count, prc_defect_area, avg_area, prc_avg_area):
self.threshold = threshold
self.defects_count = defects_count
self.prc_defect_area = prc_defect_area
self.avg_area = avg_area
self.prc_avg_area = prc_avg_area
def points(self):
return 0
class CalibrationOld(object):
"""
Calibrates HSV parameters and cut threashold for binary image
to find best possible match with enviroment.
"""
def __init__(self):
self.img = None
self.element = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
self.choice = 6
self.best_conf = [0, 0, 0, 0]
self.middle_rect = [150, 100, 400, 350]
self.conf_to_check = ([4, 20, 250, 250], [1, 2, 145, 200], [1, 2, 145, 190], [2, 20, 145, 190], [4, 20, 145, 200], [2, 20, 120, 190])
self.conf_match = ["Day", "Night", "Night", "Day", "Day", "Day", "BadDecision"]
self.enviroment_info = []
self.threshold_info = []
self.current_conf = self.conf_to_check[0]
self.counter = 0
self.phase = 0
self.end = 0
self.stabilize = 0
self.threshold_start = 90
self.threshold_end = 125
self.threshold = self.threshold_start
self.final_threshold = 40
self.light = "Day"
self.clear()
def clear(self):
self.current_rect_count = 0
self.current_biggest_rect_area = 0
self.current_middle_non_zero = 0
self.defects_count = 0
self.prc_defect_area = 0
self.avg_area = 0
def show_mode(self, img):
element = self.element
result = np.zeros((img.shape[0], img.shape[1]), np.uint8)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(img_hsv)
color_range = [2, 20, 145, 190]
d = cv2.inRange(h, np.array(color_range[0],np.uint8),
np.array(color_range[1],np.uint8))
d2 = cv2.inRange(h, np.array(color_range[2],np.uint8),
np.array(color_range[3],np.uint8))
d = cv2.bitwise_or(d, d2)
d = cv2.erode(d, element)
d = cv2.dilate(d, element)
d = cv2.dilate(d, element)
d = cv2.dilate(d, element)
d = cv2.dilate(d, element)
result = d
cv2.imshow('Result', result)
def choose_contour(self, contours, threshold=200):
rects = [cv2.boundingRect(cnt) for cnt in contours if cv2.contourArea(cnt) > threshold]
def area(rect):
return 1/float(rect[2]*rect[3])
s_rects = sorted(rects, key=area)
return s_rects[:2]
def biggest_cnt(self, cnts):
biggest = None
biggest_area = 0
for cnt in cnts:
m = cv2.moments(cnt)
if m["m00"] > biggest_area:
biggest = cnt
biggest_area = m["m00"]
return biggest
def smart_filter(self, hch, semi):
h1 = cv2.inRange(hch, np.array([1],np.uint8),
np.array([7],np.uint8))
h2 = cv2.inRange(hch, np.array([145],np.uint8),
np.array([200],np.uint8))
hch = cv2.bitwise_or(h1, h2)
hch = cv2.erode(hch, self.element)
hch = cv2.erode(hch, self.element)
hch = cv2.dilate(hch, self.element)
h,w = hch.shape
hp = 10; wp = 10
for j in range(h/hp):
for i in range(w/wp):
part = hch[j*hp:(j+1)*hp, i*wp:(i+1)*wp]
cnt = cv2.countNonZero(part)
if cnt < 10:
semi[j*hp:(j+1)*hp, i*wp:(i+1)*wp] = 0
return semi
def update(self, img):
if self.end:
return
if self.phase == 0:
self.calibrate_hsv(img)
#print "*",
elif self.phase == 1:
self.feedback()
#print "*",
elif self.phase == 2:
self.feedback()
#print "*",
elif self.phase == 3:
self.calibrate_threshold(img)
print "*",
elif self.phase == 4:
self.feedback()
#print "*"
def calibrate_hsv(self, img):
element = self.element
result = np.zeros((img.shape[0], img.shape[1]), np.uint8)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(img_hsv)
d = cv2.inRange(h, np.array([self.current_conf[0]],np.uint8),
np.array([self.current_conf[1]],np.uint8))
d2 = cv2.inRange(h, np.array([self.current_conf[2]],np.uint8),
np.array([self.current_conf[3]],np.uint8))
d = cv2.bitwise_or(d, d2)
d = cv2.erode(d, element)
d = cv2.dilate(d, element)
d = cv2.dilate(d, element)
d = cv2.dilate(d, element)
d = cv2.dilate(d, element)
result = d
res = result.copy()
contours, hier = cv2.findContours(res, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
self.current_rect_count = len(contours)
self.rects = self.choose_contour(contours)
if len(self.rects) > 0:
self.current_biggest_rect_area = area(self.rects[0])
else:
self.current_biggest_rect_area = 0
middle_roi = get_roi(result, self.middle_rect)
self.current_middle_non_zero = cv2.countNonZero(middle_roi)
self.feedback()
# DEBUG
# draw_rects(result, self.rects, color=(255,255,255))
# draw_rects(result, [self.middle_rect], color=(255,255,255))
# cv2.imshow('Result', result)
def calibrate_threshold(self, img):
hsv1 = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h1,s1,v1 = cv2.split(hsv1)
dummy, v1 = cv2.threshold(v1, self.threshold, 255, cv2.THRESH_BINARY)
v1 = self.smart_filter(h1, v1)
contours, hier = cv2.findContours(v1, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = [cnt for cnt in contours if cv2.contourArea(cnt) > 3000]
c1 = self.biggest_cnt(cnts)
if c1 == None:
self.feedback()
return None
c1 = cv2.approxPolyDP(c1, 5, True)
self.rects = [cv2.boundingRect(c1)]
v1 = np.zeros(v1.shape, np.uint8)
cv2.drawContours(v1,[c1],-1,(255,0,0),-1)
hull = cv2.convexHull(c1, returnPoints = False)
defects = cv2.convexityDefects(c1, hull)
sum_area = 0
for i in range(defects.shape[0]):
s,e,f,d = defects[i][0]
start = tuple(c1[s][0])
end = tuple(c1[e][0])
far = tuple(c1[f][0])
t = np.array([[start], [end], [far]])
sum_area += cv2.contourArea(t)
self.defects_count = defects.shape[0]
self.avg_area = cv2.contourArea(c1)
self.prc_avg_area = self.avg_area/float(area(self.rects[0]))
self.prc_defect_area = (sum_area/defects.shape[0])/self.avg_area
self.feedback()
# DEBUG
# draw_rects(v1, self.rects, color=(255,255,255))
# cv2.imshow('threshold', v1)
def feedback(self):
if self.phase == 0:
if len(self.rects) == 0 or self.stabilize < 4:
self.stabilize += 1
return
self.enviroment_info.append(EnviromentInfo(self.current_middle_non_zero,
self.current_rect_count,
self.current_biggest_rect_area))
self.counter += 1
self.stabilize = 0
if self.counter < len(self.conf_to_check):
self.current_conf = self.conf_to_check[self.counter]
else:
self.phase += 1
elif self.phase == 1:
self.choose_best_configuration()
self.phase += 1
elif self.phase == 2:
self.choose_time_of_day()
if self.light != "Day":
self.phase += 1
else:
self.end = 1
elif self.phase == 3:
if self.stabilize < 4:
self.stabilize += 1
return
self.stabilize = 0
# print "****", self.threshold, self.avg_area, self.prc_defect_area
self.threshold_info.append(ThresholdInfo(self.threshold,
self.defects_count,
self.prc_defect_area,
self.avg_area,
self.prc_avg_area))
self.threshold += 10
if self.threshold > self.threshold_end:
self.phase += 1
elif self.phase == 4:
repeat = self.choose_best_threshold()
if repeat:
self.threshold_start -= 10
self.threshold_end -= 15
self.threshold = self.threshold_start
self.phase -= 1
self.threshold_info = []
if self.threshold_end <= self.threshold_start:
self.end = 1
else:
self.end = 1
def choose_time_of_day(self):
indicator = self.conf_match[self.choice]
if self.enviroment_info[self.choice].rect_count > 100 and indicator == "Day":
indicator = "Night"
self.light = indicator
def choose_best_threshold(self):
if self.threshold_info[0].avg_area < 12000:
#print "Lower boundary, area too small:", self.threshold_info[0].avg_area
return True
for i in range(0, len(self.threshold_info)):
# print self.threshold_info[i].threshold
# print self.threshold_info[i].avg_area
# print self.threshold_info[i].prc_defect_area
# print self.threshold_info[i].prc_avg_area
# print "********"
if 0.008 < self.threshold_info[i].prc_defect_area < 0.025 and self.threshold_info[i].prc_avg_area > 0.71 and \
self.threshold_info > 14000:
self.final_threshold = self.threshold_info[i].threshold
return False
return True
def choose_best_configuration(self):
best_area = -1
tmp_area = 0
best_count = -1
tmp_count = 99999
best_non_zero = -1
tmp_non_zero = 0
for i, e in enumerate(self.enviroment_info):
if e.middle_non_zero > tmp_non_zero:
tmp_non_zero = e.middle_non_zero
best_non_zero = i
if e.rect_count < tmp_count:
tmp_count = e.rect_count
best_count = i
if e.biggest_rect_area > tmp_area:
tmp_area = e.biggest_rect_area
best_area = i
# print i, self.conf_to_check[i], e.middle_non_zero, e.rect_count, e.biggest_rect_area, "*"
l = len(self.enviroment_info)
pts = [(self.enviroment_info[b].points(), b) for b in range(0, l)]
self.choice = sorted(pts)[-1][1]
# print sorted(pts)
self.best_conf = self.conf_to_check[self.choice]
if __name__ == "__main__":
c = cv2.VideoCapture(0)
clbr = Calibration()
while(1):
_,f = c.read()
clbr.update(f)
#clbr.show_mode(f)
k = cv2.waitKey(20)
if clbr.end:
print clbr.best_conf, "final"
print clbr.final_threshold
break
if k == 27:
break
cv2.destroyAllWindows()
c.release()